Although the Dark web was originally used for maintaining privacy-sensitive communication for business or intelligence services for defence, government and business organizations, fighting against censorship and blocked content, later, the advantage of technologies behind the Dark web were abused by criminals to conduct crimes which involve drug dealing to the contract of assassinations in a widespread manner. Since the communication remains secure and untraceable, criminals can easily use dark web service via The Onion Router (TOR), can hide their illegal motives and can conceal their criminal activities. This makes it very difficult to monitor and detect cybercrimes over the dark web. With the evolution of machine learning, natural language processing techniques, computational big data applications and hardware, there is a growing interest in exploiting dark web data to monitor and detect criminal activities. Due to the anonymity provided by the Dark Web, the rapid disappearance and the change of the uniform resource locators (URLs) of the resources, it is not as easy to crawl the Drak web and get the data as the usual surface web which limits the researchers and law enforcement agencies to analyse the data. Therefore, there is an urgent need to study the technology behind the Dark web, its widespread abuse, its impact on society and the existing systems, to identify the sources of drug deal or terrorism activities. In this research, we analysed the predominant darker sides of the world wide web (WWW), their volumes, their contents and their ratios. We have performed the analysis of the larger malicious or hidden activities that occupy the major portions of the Dark net; tools and techniques used to identify cybercrimes which happen inside the dark web. We applied a systematic literature review (SLR) approach on the resources where the actual dark net data have been used for research purposes in several areas. From this SLR, we identified the approaches (tools and algorithms) which have been applied to analyse the Dark net data, the key gaps as well as the key contributions of the existing works in the literature. In our study, we find the main challenges to crawl the dark web and collect forum data are: scalability of crawler, content selection trade off, and social obligation for TOR crawler and the limitations of techniques used in automatic sentiment analysis to understand criminals’ forums and thereby monitor the forums. From the comprehensive analysis of existing tools, our study summarizes the most tools. However the forum topics rapidly change as their sources changes; criminals inject noises to obfuscate the forum’s main topic and thus remain undetectable. Therefore supervised techniques fail to address the above challenges. Semi-supervised techniques would be an interesting research direction.

Different types of natural dyes are commonly used to fabricate dyesensitized solar cells which are cheaper, simpler, and environmentally friendlier than conventional crystalline silicon solar cells. This paper objectifies the performance of locally grown natural dyes from Dhaka, Bangladesh using a simple fabrication design. Four natural dyes were obtained separately from spinach, turmeric, pomegranate, and beetroot which contain chlorophyll, curcumin, anthocyanin, and betanin pigments respectively. Ultraviolet-visible spectroscopy illustrated the optical properties of these dyes diluted in ethanol. Spinach and turmeric extracts showed sharper absorption peaks which qualify them as better sensitizers than pomegranate and beetroot extracts. Photoanodes (of thickness 15-20 μm) were prepared by employing the doctor blade technique on fluorine-doped tin oxides using TiO2 nanoparticles of anatase form and graphite coated fluorine-doped tin oxides were used as counter electrodes. The photoelectric measurements of the fabricated cells were done under air mass (AM) 1.5 and power density of 100 mW/cm2. The fabricated cell of turmeric extracted dye showed the highest efficiency of 0.031% (Open circuit voltage, VOC = 380 mV, Short circuit current density, JSC = 0.234 mA/cm2) and the cell using beetroot extract gave the highest fill factor (FF) of about 50% among the prepared dyesensitized solar cells. DUJASE Vol. 7(1) 38-44, 2022 (January)

Gambling help websites are an important source of information for gamblers and affected others, but they contain large amounts of information, making it difficult to navigate and access required resources. Usability of such websites can be improved through the implementation of chatbots, which are programmed to respond to user requests in natural language. This study aimed to evaluate the impact of a text-based chatbot on the usability, user satisfaction, and experience of the Australian New South Wales GambleAware website; and explore the usability, user satisfaction, and experience of the chatbot itself. A convenience sample of Australian residents were allocated into website with chatbot access (n = 32) or website only (n = 28). Compared with the website-only group, the website with chatbot access group reported significantly greater ratings of the website's usability and aspects of user satisfaction, but not user experience, and resulted in higher ease of task completion. Compared with available norms, the chatbot was highly rated on usability, usefulness, information quality, and credibility, with the layout, readability of responses, and look and feel identified as areas for improvement. The usability of the NSW GambleAware website could be enhanced by the integration of a text-based chatbot, with potential applications for similar websites.

Formalising natural language (NL) requirements is essential to have formal specifications that enable formal checking and improve the quality of requirements. However, the existing formalisation techniques require engineers to (re)write the system requirements using a set of requirements templates with predefined and limited structure and semantics. The main drawback of using such templates, usually with a fixed format, is the inability to capture diverse requirements outside the scope of the template structure. To address this limitation, a comprehensive reference model is needed to enable capturing key requirement properties regardless of their format, order, or structure. NLP technique can then be used to convert unrestricted NL requirements into the reference model. Using a set of transformation rules, the reference model representing the requirements can be transformed into the target formal notation. In this paper, we introduce requirement capturing model (RCM) to represent NL requirements by adapting to their key properties and without imposing constraints on how the requirements are written. We also implemented a requirements formalisation approach that supports transforming RCM into temporal logic (TL). In addition, we developed an automated similarity checking approach to check the correctness of the constructed RCM structures against the source NL requirements. We carried out extensive evaluation of RCM by comparing it against 15 existing requirements representation approaches on a dataset of 162 requirement sentences. The results show that RCM supports a much wider range of requirements formats compared to any of the existing approaches.

Requirements engineering is pivotal to the successful development of any given system. The core artifact for such phase is the requirements specification document. Requirements can be specified in informal, semiformal, and formal notations. The majority of the requirements across many fields and domains are written natural language. However, natural language is inherently ambiguous and imprecise and the requirements cannot be automatically validated. Formal notations on the other hand enable automated testing and validation but is only comprehensible by experts and requires rewriting the requirements. Semi-formal notations strikes a good balance between comprehension and checking for several systems. However, the majority of the existing representation models mandates the requirements to be (re)written to adhere to certain templates. They also do not support automated checking. In this paper, we present SRCM -a semi-formal requirements representation model based on a comprehensive requirements capturing model (RCM) that does not enforce much limitations on how the requirements can be written. We also provide an automated approach to construct SRCM from RCM. In addition to providing a unified visualisation of the system entities and relations between the requirements key components, SRCM also enables automated quality checking on the requirements.

Most existing (semi-)automated requirements formalisation techniques assume requirements to be specified in predefined templates. They also employ template-specific transformation rules to provide the corresponding formal representation. Hence, such techniques have limited expressiveness and more importantly require system engineers to re-write their system requirements following defined templates for maintenance and evolution. In this paper, we introduce an automated requirements extraction technique (RCM-Extractor) to automatically extract the key constructs of a comprehensive and formalisable semi-formal representation model from textual requirements. This avoids the expressiveness issues affecting the existing requirement specification templates, and eliminates the need to rewriting the requirements to match the structure of such templates. We evaluated RCM-Extractor on a dataset of 162 requirements curated from several papers in the literature. RCM-Extractor achieved 87% precision, 98% recall, 92% F-measure, and 86% accuracy. In addition, we evaluated the capabilities of RCM-Extractor to extract requirements on a dataset of 15,000 automatically synthesised requirements that are constructed specifically to evaluate our approach. This dataset has a complete coverage of the possible structures and arrangements of the properties that can exist in system requirements. Our approach achieved 57%, 92% and 100% accuracy for un-corrected, partially-corrected and fully-corrected Stanford typed-dependencies representations of the synthesised requirements, respectively.

It is pivotal to have well-specified requirements to eliminate errors at an early stage of the system development life cycle.Some quality standards recommend the use of formal methods -mandate requirements to be expressed in formal notations -to detect errors.However, formal notations are not suitable for non-experts and may not be understood by all the stakeholder.To fix this, bidirectional transformations among requirement representation levels are required to maintain traceability and facilitate the communication of requirements among all the involved parties.This paper reflects on the different formality levels of requirements specifications including: informal, semi-formal, and formal notations.In addition, an automated multi-layer transformation approach is proposed to enable bi-directional transformation among requirements levels.

Requirements quality checking is a key process in requirements engineering. For complex and large scale systems, it is recommended to use automated requirements quality checking tools because of the size and complexity of requirements. However, such tools are typically evaluated on a small set of manually curated requirements. This limitation affects the comprehensiveness and reliability of the evaluation and leaves several possible quality issues undetected. In this paper, we de-scribe a novel quality-checking-oriented synthesised requirements generator. We provide an input description language so that several quality checking issues and scenarios can be defined. The generator utilises an input dictionary of nouns and verb frames, and generates requirements sentences complying to a user-defined description of a quality affected requirement.

Formal verification requires system requirements to be specified in formal notations. Formalisation of system requirements manually is a time-consuming and error-prone process, and requires engineers to have strong mathematical and domain expertise. Most existing requirements formalisation techniques assume requirements to be specified in pre-defined templates and these techniques employ pre-defined transformation rules to transform requirements specified in the predefined templates to formal notations. These techniques tend to have limited expressiveness and more importantly require system engineers to re-write their system requirements following these templates. In this paper, we introduces an automated extraction technique (RCMExtractor) to extract the key constructs of a comprehensive and formalisable semi-formal representation model from textual requirements. We have evaluated our RCM-Extractor on a dataset of 162 requirements curated from the literature. RCM-Extractor achieved 95% precision, 79% recall, 86% F-measure and 75% accuracy.

Most existing automated requirements formalisation techniques require system engineers to (re)write their requirements using a set of predefined requirement templates with a fixed structure and known semantics to simplify the formalisation process. However, these techniques require understanding and memorising requirement templates, which are usually fixed format, limit requirements captured, and do not allow capture of more diverse requirements. To address these limitations, we need a reference model that captures key requirement details regardless of their structure, format or order. Then, using NLP techniques we can transform textual requirements into the reference model. Finally, using a suite of transformation rules we can then convert these requirements into formal notations. In this paper, we introduce the first and key step in this process, a Requirement Capturing Model (RCM) - as a reference model - to model the key elements of a system requirement regardless of their format, or order. We evaluated the robustness of the RCM model compared to 15 existing requirements representation approaches and a benchmark of 162 requirements. Our evaluation shows that RCM breakdowns support a wider range of requirements formats compared to the existing approaches. We also implemented a suite of transformation rules that transforms RCM-based requirements into temporal logic(s). In the future, we will develop NLP-based RCM extraction technique to provide end-to-end solution.

The majority of requirements formalisation techniques operate on textual requirements as input. To establish and verify the reliability and coverage of such techniques, a large set of textual requirements with diverse structures and formats is required. However, such techniques are typically evaluated on only a few manually curated requirements that do not provide enough coverage of the targeted structures. Motivated by this problem, we introduce a Component-oriented synthetic textual requirements generator (CORG) that can generate large numbers of synthesised diverse-structure textual requirements, along with key components breakdowns. CORG utilises a controlled random-selection (CRS) strategy throughout the backtracking-based generation. We evaluate the coverage, diversity, performance and correctness of CORG. The evaluation results show that CORG can generate comprehensive diverse-structure combinations in reasonable time without being affected by the size of the produced requirements.

The formalisation of natural language (NL) requirements is a challenging problem because NL is inherently vague and imprecise. Existing formalisation approaches only support requirements adhering to specific boilerplates or templates, and are affected by the requirements quality issues. Several quality models are developed to assess the quality of NL requirements. However, they do not focus on the quality issues affecting the formalisability of requirements. Such issues can greatly compromise the operation of complex systems and even lead to catastrophic consequences or loss of life (in case of critical systems). In this paper, we propose a requirements quality checking approach utilising natural language processing (NLP) analysis. The approach assesses the quality of the requirements against a quality model that we developed to enhance the formalisability of NL requirements. We evaluate the effectiveness of our approach by comparing the formalisation efficiency of a recent automatic formalisation technique before and after utilising our approach. The results show an increase of approximately 15% in the F-measure (from 83.8% to 98%).

Formal verification techniques enable the detection of complex quality issues within system specifications. However, the majority of system requirements are usually specified in natural language (NL). Manual formalisation of NL requirements is an error-prone and labour-intensive process requiring strong mathematical expertise, and can be infeasible for large numbers of requirements. Existing automatic formalisation techniques usually support heavily constrained natural language relying on requirement boilerplates or templates. In this paper, we introduce ARF: Automatic Requirements Formalisation Tool. ARF can automatically transform free-format natural language requirements into temporal logic based formal notations. This is achieved through two steps: 1) extraction of key requirement attributes into an intermediate representation (RCM: Requirement Capturing Model), and 2) transformation rules that convert requirements from the RCM format to formal notations.

Despite the significant increase in cybersecurity solutions investment, organizations are still plagued by security breaches, especially data breaches. As more organizations experience crippling security breaches, the wave of compromised data is growing significantly. The financial consequences of a data breach are set on the rise, but the cost goes beyond potential fines. Data breaches could have a catastrophic impact not only in loss of company's reputation and stock price, but also in economic terms. Threat Intelligence has been recently introduced to enable greater visibility of cyber threats, in order to better protect organizations' digital assets and prevent data breaches. Threat intelligence is the practice of integrating and analyzing disjointed cyber data to extract evidence-based insights regarding an organization's unique threat landscape. This helps explain who the adversary is, how and why they are comprising the organization's digital assets, what consequences could happen following the attack, what assets actually could be compromised, and how to detect or respond to the threat. Every organization is different and threat intelligence frameworks are custom-tailored to the business process itself and the organization's risks, as there is no "one-size-fits-all " in cyber. In this paper, we review the problem of data breaches and discuss the challenges of implementing threat intelligence that scales in today's complex threat landscape and digital infrastructure. This is followed by an illustration of how the future of effective threat intelligence is closely linked to efficiently applying Artificial Intelligence and Machine Learning approaches, and we conclude by outlining future research directions in this area.

The quality of a delivered product relies heavily upon the quality of its requirements. Across many disciplines and domains, system and software requirements are mostly specified in natural language (NL). However, natural language is inherently ambiguous and inconsistent. Such intrinsic challenges can lead to misinterpretations and errors that propagate to the subsequent phases of the system development. Pattern-based natural language processing (NLP) techniques have been proposed to detect the ambiguity in requirements specifications. However, such approaches typically address specific cases or patterns and lack the versatility essential to detecting different cases and forms of ambiguity. In this paper, we propose an efficient and versatile automatic syntactic ambiguity detection technique for NL requirements. The proposed technique relies on filtering the possible scored interpretations of a given sentence obtained via Stanford CoreNLP library. In addition, it provides feedback to the user with the possible correct interpretations to resolve the ambiguity. Our approach incorporates four filtering pipelines on the input NL-requirements working in conjunction with the CoreNLP library to provide the most likely possible correct interpretations of a requirement. We evaluated our approach on a suite of datasets of 126 requirements and achieved 65% precision and 99% recall on average.

We describe a new variability-intensive system idea, the "self-securing software system." We describe how such a system works using a multi-tenant cloud application as a motivating example. This supports runtime composition, detects emergent attacks and vulnerabilities, and supports runtime updating to mitigate problems. We describe recent work we have done in architecting and proof-of-concept prototypes for aspects of such systems. We then describe current limitations and future work plans to address these.

With the proliferation of Internet of Things (IoT) devices, there is a demand for technologies to support high-velocity, dynamic resource provisioning to provide secure, cost-efficient, and real-time IoT services in resource-constrained environments. Conventional fog computing by itself cannot address such requirements and needs to be complemented with opportunistic fog computing, by providing mobile fog resources on-demand. In this paper, we discuss key issues in this area, and investigate potential solutions from existing work. We conclude this paper with a summary of gaps, and propose an opportunistic architecture for future work.

In this work, a dual-axis automated solar tracker is developed by using two linear motors, four light dependent resistors (LDRs) and two mono crystalline solar panels. The LDRs are placed on the rotating frame where the solar panels are placed to detect the position of the sun and the controller circuit drives the motors to place the frame towards the sun. The controlling unit has been developed using PLC microcontroller. The motor driver circuit has been designed using a code to align the solar panels to a suitable position so that it is exposed to the maximum amount of solar irradiance. The driver circuit receives data from the LDRs and the microcontroller controls the motors to move the panel along its horizontal and vertical axis. To evaluate the performance of the solar tracker, output power of the solar tracker and an identical set of static solar panels set in an optimum fixed orientation are measured from the open-circuit voltage and the short circuit current for two consecutive days. The output power produced by the automatic solar tracker was consistently higher than that by the static solar panel. The energy gain due to using the automatic solar tracker is at highest in the morning and in the afternoon at almost 40%. The lowest value of energy gain is observed during noon at as low as 1%. The average increase in output throughout the day is 24.09%.

Software systems are now ubiquitous and are used every day for automation purposes in personal and enterprise applications; they are also essential to many safety-critical and mission-critical systems, e.g., air traffic control systems, autonomous cars, and SCADA systems. With the availability of massive storage capabilities, high speed Internet, and the advent of Internet of Things devices, modern software systems are growing in both size and complexity. Maintaining a high quality of such complex systems while manually keeping the error rate at a minimum is a challenge. Therefore, automated detection of faulty components in a software system is important during software development and also post-delivery. Fault detection models usually needs to be trained on a labeled-balanced dataset with both faulty and nonfaulty samples. Earlier work, e.g. Mohsin et al. (2016), showed that most real fault detection training dataset are imbalanced. Thereby, the trained model gets over-fitted and classifies faulty components as non-faulty components. The consequence of a high false negative rate is cumulative and results in generating more errors when using the model in other software systems -never seen before, which is very expensive. In this paper, we propose a software defect prediction ensemble model which considers the class imbalance problem in real software datasets. We use different oversampling techniques to build an ensemble classifier that can reduce the effect of low minority samples in the defective data. The proposed approach is verified using PROMISE software engineering datasets. The results show that our ensemble oversampling technique can more greatly reduce the false negative rate compared to the standard classification techniques and identify the faulty components more accurately resulting in a less expensive detection system (lowering the rate of non-faulty predictions of faulty modules).