In this study, we consider the challenge of adapting the difficulty level of the tasks suggested to a student using an educational software system. We investigate the effectiveness of different learning algorithms for the challenge of adapting the difficulty of the tasks to a student's level, and compare their efficiency by means of simulation with virtual students. Our results demonstrate that the methods based on Bayesian inference outperformed most of the other methods, while in dynamic improvement domains, the item response theory method reached the best results. Given the fact that correctly adapting the tasks to the individual learners' abilities can help them increase their improvement and satisfaction, our study can assist the designers of intelligent tutoring systems in selecting an appropriate adaptation method, given the needs and goals of the educational system, and given the characteristics of the learners.

This paper offers a Talmudic norms solution to the paradox of the heap. The claim is that the paradox arises because philosophers use the wrong language to discuss it. We need a language about objects which is capable of expressing not only the declarative properties of the object (such as being a heap) but also how the object/heap was constructed. Such a view of objects comes from the Talmudic theory of mixtures.To this we add a first attempt at modelling the Talmudic normative theory of mixing (Talmudic calculus of Sorites). We seek a correlation between Talmudic positions on mixtures and philosophical positions on Sorites. The Talmud is very practical and cannot allow for any theoretically unresolved paradox to get in the way, and so it has a lot to offer to philosophy in general and to the heap paradox in particular.

One of the challenges of an intelligent tutoring system (ITS) is adapting the difficulty level of the questions posed to the student to suit the student’s academic level. Our study examines the task of adjusting the system’s level of challenges to the level of the learner and addresses the questions of how best to do so and whether there is any benefit from such adjustment. To answer these questions, we developed reading comprehension courseware that includes three adaptive algorithms for adjusting the level of the questions presented to the students: the random selection algorithm, the Q-learning based algorithm, and the Bayesian inference algorithm. We conduct a real-world experiment in which real high school students used the courseware to improve their reading comprehension skills. In order to compare and evaluate the performance of the algorithms, the courseware used by each student utilized one of the three adaptive algorithm alternatives. Our results demonstrate that when considering all of the students, there was significant improvement (learning gain) using each of the methods.

Dielectric spectroscopy and DC conduction current of Polyesterimide (PEI) -based nanocomposite filled with low concentration, 1.5wt.%, of SiO2 nanoparticles of about 10nm mean size have been performed and analysis in comparison with the unfilled PEI. The permittivity and dielectric loss of the nanocomposite were found lower than those of neat PEI respectively, particularly at high temperature and low frequencies. A distinct additional relaxation process was observed in the case of the nanocomposite due to the interfacial relaxation occurring at the phases’ boundaries. Both the low field and the high field conductivity was found to be lower in the case of the nanocomposites. Therefore, in view of these result, the incorporation of small amount of SiO2 nanoparticles in PEI resins, extend the critical limits of use and make them suitable for higher voltage and temperature for dielectric applications.

We describe the state of the Talmudic Logic project as of end of 2019. The Talmud is the most comprehensive and fundamental work of Jewish religious law, employing a large number of logical components centuries ahead of their time. In many cases the basic principles are not explicitly formulated, which makes it difficult to formalize and make available to the modern student of Logic. This project on Talmudic Logic, aims to present logical analysis of Talmudic reasoning using modern logical tools. We investigate principles of Talmudic Logic and publish a series of books, one book or more for each principle. http://www.collegepublications.co.uk/stl/ The series begins with the systematic analysis of Talmudic inference rules. The first book shows that we can present Talmudic reasoning intuitions as a systematic logical system basic to modern non-deductive reasoning, such as Argumentum A Fortiori, Abduction and Analogy. The second book offers a systematic common sense method for intuitively defining sets and claims that this method adequately models the Talmudic use of the rules Klal uPrat. These books also criticize modern Talmudic research methodology. Later books deal with additional topics like Deontic logic, and Temporal logic, Agency and processes in the Talmud and more. The aims of the project are two fold: To import into the Talmudic study modern logical methods with a view to help understand complicated Talmudic passages, which otherwise cannot be addressed. To export from the Talmud new logical principles which are innovative and useful to modern contemporary logic.

The Internet of Things (IoT) is composed of a vast number of connected devices, interacting among them in real-time and high messaging volume. Such setting is in high probability to be targeted by malicious attackers. Therefore, robust security measures are required. Encryption is one of the ways to prevent the exposure of the transmitted messages and authenticate it. The main challenge of implementing encryption, is the need to frequently and securely change the encryption keys, which require constant key construction and key distribution. IoT devices have poor memory, storage, and processing bandwidth. Most of the existing security solutions cannot be implemented on them, and so leading to lack of adequate security. Allowing safe interaction between any two IoT-devices, means having a unique encryption key per conversation. This requires frequent changes of the encryption keys. To increase the availability of keys at each IoT-device, we propose an ongoing key construction process that loads the network with a common key-pool. The protocol is scalable to ensure long term security sustainability and encryption availability. The proposed protocol is based on a probability analysis that ensures the existence of a common key between any pair of IoT devices in a predefine probability which is set by the system designer. The implementation proves the feasibility of our proposed security protocol for IoT networks.

In this paper we propose an auction-based simultaneous ad-placement mechanism for multiple public electronic displays, that reflects the preferences of the public display owners and is run by a mediator. The mechanism assumes that the private owners of the public displays incur some cost for showing ads which is in accordance to the relative matching between the display owner’s preferences and the ads’ characteristics. The mechanism aims to maximize the exposure of a variety of ads to as wide an audience as possible, while maximizing the general welfare by applying a charging scheme that satisfies multiple desirable economic properties as truthful bidding on the part of both sides, i.e., the display owners and the advertisers, which in turn results in a balanced budget over time. We demonstrate, by means of simulations, that the performance of this auctioned-based mechanism efficiently selects the best adverts in response to the audience presents and achieves up to 99% of the optimal efficiency, if full information about the public owners and advertisers preferences is available to the mediator.

In this paper we concentrate on ad exchange mechanisms that take into consideration the publishers’ preferences concerning the attributes of ads published in their ad space, in addition to their desire to earn money from the ads. We suggest allocation (ad placement) and pricing protocols which take into account preferences of both the publishers and the advertisers. The most promising protocol, the Weighted Bipartite Hungarian VCG protocol, collects the preferences of the auction’s participants and uses the Hungarian algorithm to maximize a weighted function of their preferences. Simulations show that this advantageous protocol can maintain a balanced budget over time while preserving most of the desired economic properties (e.g., individual rational, and truth telling), and reaches near optimal solutions.

This paper presents methods for improving the attention span of workers in tasks that heavily rely on their attention to the occurrence of rare events. The underlying idea in our approach is to dynamically augment the task with some dummy (artifi-cial) events at different times throughout the task, rewarding the worker upon identifying and report-ing them. The proposed approach is an alternative to the traditional approach of exclusively relying on rewarding the worker for successfully identifying the event of interest itself. We propose three methods for timing the dummy events throughout the task. Two of these methods are static and determine the timing of the dummy events at random or uniformly throughout the task. The third method is dynamic and uses the identification (or misidentifica-tion) of dummy events as a signal for the worker’s attention to the task, adjusting the rate of dummy events generation accordingly.

This paper presents methods for improving the attention span of workers in tasks that heavily rely on their attention to the occurrence of rare events. The underlying idea in our approach is to dynamically augment the task with some dummy (artificial) events at different times throughout the task, rewarding the worker upon identifying and reporting them. The proposed approach is an alternative to the traditional approach of exclusively relying on rewarding the worker for successfully identifying the event of interest itself. We propose three methods for timing the dummy events throughout the task. Two of these methods are static and determine the timing of the dummy events at random or uniformly throughout the task. The third method is dynamic and uses the identification (or misidentification) of dummy events as a signal for the worker's attention to the task, adjusting the rate of dummy events generation accordingly.

An anomaly, or outlier, is an object exhibiting differences that suggest it belongs to an as-yet undefined class or category. Early detection of anomalies often proves of great importance because they may correspond to events such as fraud, spam, or device malfunctions. By automating the creation of a ranking or list of deviations, we can save time and decrease the cognitive overload of the individuals or groups responsible for responding to such events.Over the years many anomaly and outlier metrics have been developed. In this paper we propose a clustering-based score ensembling method for outlier detection. Using benchmark datasets we evaluate quantitatively the robustness and accuracy of different ensemble strategies. We find that ensembling strategies offer only limited value for increasing overall performance, but provide robustness by negating the influence of severely underperforming models. (C) 2017 Elsevier B.V. All rights reserved.

IoT systems collect vast amounts of data which can be used in order to track and analyze the structure of future recorded data. However, due to limited computational power, bandwith, and storage capabilities, this data cannot be stored as is, but rather must be reduced in such a way so that the abilities to analyze future data, based on past data, will not be compromised. We propose a parameterized method of sampling the data in an optimal way. Our method has three parameters — an averaging method for constructing an average data cycle from past observations, an envelope method for defining an interval around the average data cycle, and an entropy method for comparing new data cycles to the constructed envelope. These parameters can be adjusted according to the nature of the data, in order to find the optimal representation for classifying new cycles as well as for identifying anomalies and predicting future cycle behavior. In this work we concentrate on finding the optimal envelope, given an averaging method and an entropy method. We demonstrate with a case study of meteorological data regarding El Ninio years.

This paper studies a new paradigm for improving the attention span of workers in tasks that heavily rely on user's attention to the occurrence of rare events. Such tasks are highly common, ranging from crime monitoring to controlling autonomous complex machines, and many of them are ideal for crowdsourcing. The underlying idea in our approach is to dynamically augment the task with some dummy (artificial) events at different times throughout the task, rewarding the worker upon identifying and reporting them. This, as an alternative to the traditional approach of exclusively relying on rewarding the worker for successfully identifying the event of interest itself. We propose three methods for timing the dummy events throughout the task. Two of these methods are static and determine the timing of the dummy events at random or uniformly throughout the task. The third method is dynamic and uses the identification (or misidentification) of dummy events as a signal for the worker's attention to the task, adjusting the rate of dummy events generation accordingly. We use extensive experimentation to compare the methods with the traditional approach of inducing attention through rewarding the identification of the event of interest and within the three. The analysis of the results indicates that with the use of dummy events a substantially more favorable tradeoff between the detection (of the event of interest) probability and the expected expense can be achieved, and that among the three proposed method the one that decides on dummy events on the fly is (by far) the best.

Purpose Social network sites have been widely adopted by politicians in the last election campaigns. To increase the effectiveness of these campaigns the potential electorate is to be identified, as targeted ads are much more effective than non-targeted ads. Therefore, the purpose of this paper is to propose and implement a new methodology for automatic prediction of political orientation of users on social network sites by comparison to texts from the overtly political parties’ pages. Design/methodology/approach To this end, textual information on personal users’ pages is used as a source of statistical features. The authors apply automatic text categorization algorithms to distinguish between texts of users from different political wings. However, these algorithms require a set of manually labeled texts for training, which is typically unavailable in real life situations. To overcome this limitation the authors propose to use texts available on various political parties’ pages on a social network site to train the classifier. The political leaning of these texts is determined by the political affiliation of the corresponding parties. The classifier learned on such overtly political texts is then applied on the personal user pages to predict their political orientation. To assess the validity and effectiveness of the proposed methodology two corpora were constructed: personal Facebook pages of 450 Israeli citizens, and political parties Facebook pages of the nine prominent Israeli parties. Findings The authors found that when a political tendency classifier is trained and tested on data in the same corpus, accuracy is very high. More significantly, training on manifestly political texts (political party Facebook pages) yields classifiers which can be used to classify non-political personal Facebook pages with fair accuracy. Social implications Previous studies have shown that targeted ads are more effective than non-targeted ads leading to substantial saving in the advertising budget. Therefore, the approach for automatic determining the political orientation of users on social network sites might be adopted for targeting political messages, especially during election campaigns. Originality/value This paper proposes and implements a new approach for automatic cross-corpora identification of political bias of user profiles on social network. This suggests that individuals’ political tendencies can be identified without recourse to any tagged personal data. In addition, the authors use learned classifiers to determine which self-identified centrists lean left or right and which voters are likely to switch allegiance in subsequent elections.

Purpose – Reliability and political bias of mass media has been a controversial topic in the literature. The purpose of this paper is to propose and implement a methodology for fully automatic evaluation of the political tendency of the written media on the web, which does not rely on subjective human judgments. Design/methodology/approach – The underlying idea is to base the evaluation on fully automatic comparison of the texts of articles on different news websites to the overtly political texts with known political orientation. The authors also apply an alternative approach for evaluation of political tendency based on wisdom of the crowds. Findings – The authors found that the learnt classifier can accurately distinguish between self-declared left and right news sites. Furthermore, news sites’ political tendencies can be identified by automatic classifier learnt from manifestly political texts without recourse to any manually tagged data. The authors also show a high correlation between readers’ perception (as a “wisdom of crowds” evaluation) of the bias and the classifier results for different news sites. Social implications – The results are quite promising and can put an end to the never ending dispute on the reliability and bias of the press. Originality/value – This paper proposes and implements a new approach for fully automatic (independent of human opinion/assessment) identification of political bias of news sites by their texts.

There are special markets where not all buyers are symmetric from the seller's perspective and similarly, there are cases where not all sellers are symmetric from the buyer's perspective. For example when a person attempts to acquire some information it most definitely matters who the information provider/seller is. The higher the reputation an information provider has the more valuable his information is from the advertisers' perspective. The main challenges in such scenarios are the ability to (i) elicit true information from the participants, and (ii) find the most efficient allocation. The VCG could have been a good mechanism for this purpose, however, it is not budget balanced, making it impractical. In this paper we propose the weighted bilateral VCG mechanism which comprises most of the desired economic properties for being strategy proof, and individually rational. Moreover, our mechanism has been shown to be (i) budget balanced for the long term, (ii) does not add complexity overhead to the optimization problem complexity, (iii) may be tuned by the auctioneer using the weight parameter to decide about the level of profit it decides on, and (iv) produces suboptimal allocations which are very close to the optimal ones.

This paper concerns a scenario where sellers (i.e., publishers), who are willing to dedicate space on their Website to ads, and buyers (i.e., advertisers), are brought to a common, automatic marketplace. State of the art mechanisms exist for this ad-exchange scenario, however none of the previously proposed solutions fully take into account the preferences of the publishers. In this paper, we developed solutions for the case of multiple advertisers and multiple publishers, while considering the publishers' preferences. We propose three truthful mechanisms: (i) the Hungarian VCG, (ii) the Simultaneous English Auction, and (iii) the Distributed Relocation Protocol. Each mechanism includes an allocation rule and a payment scheme. The Hungarian VCG achieves the optimal allocation, but it is not budget balanced, and it causes a deficit on the part of the ad exchange auctioneer. The other two mechanisms are heuristics, budget balanced, decentralized, avoid manipulations on the advertisers' side, and simulations show that they reach near optimal solutions.