Reviewerly/exHarmony huggingface dataset:
Retrieval of individual solutions from encapsulated search with a potentially infinite search space

Constraint-logic object-oriented programming facilitates the development of applications that occasionally solve search problems. To that end it is possible to define constraints imperatively on-the-fly, using conditions over free variables. As a result, the search space is defined dynamically at runtime, based on the evaluation of such conditions in control structures. Consequently, the search space can - intentionally or by accident - become infinitely large if recursion and/or loops are involved. It is therefore desirable that the corresponding encapsulated search can be controlled by application developers. To that end, we discuss mechanisms for encapsulating such a search space and retrieving solutions from it. Our implementation for Muli is based on an appropriately extended symbolic Java virtual machine and it leverages the Java Stream API to provide a control mechanism that integrates well with object-oriented programs. Moreover, we show how our language Muli behaves for some example programs.
Boolean Difference Technique for Detecting All Missing Gate and Stuck-at Faults in Reversible Circuits

Quantum reversible circuit is a new emerging technology attracting the researchers. A reversible circuit is composed of reversible gates. One example of reversible gate is Toffoli gate. A Toffoli gate (also known as [Formula: see text]-CNOT) has two components — the control and the target. Initially, stuck-at fault and other fault models were used for modeling defects in quantum reversible circuits. Later, a new fault model known as missing gate fault model was introduced, which is more effective in capturing the errors in quantum reversible circuit. Boolean Difference is already a known technique to detect stuck-at faults in conventional CMOS circuit. In this paper, Boolean Difference method is applied to derive the test set for detecting each stuck-at fault and missing gate fault in a reversible circuit. Then an optimization algorithm is used to derive an optimal test set, which will detect all possible faults in a circuit. The method is valid also for other fault models.
How sensitive are the term-weighting models of information retrieval to spam Web pages?

Many term-weighting models have been proposed for information retrieval. In this paper we investigate the extent to how retrieval effectiveness of term-weighting models is affected by the presence of spam Web pages. We perform retrieval experiments on the ClueWeb09-English (Category A) dataset – a substantial fraction of which are spam pages that are deliberately designed to manipulate commercial search engines – as well as the ClueWeb12 (Category A) dataset. Ad hoc tasks of TREC Web tracks 2009 through 2012 are completed to examine the spam sensitivity of the state-of-the-art retrieval models using Apache Lucene as the retrieval engine. Moreover, ad hoc tasks of two Web tracks and two Tasks tracks 2013 through 2016 are also included in a part of the experiment where the number of documents that are explicitly judged as spam in the search results returned by each retrieval model is inspected. Our experimental results show that hypergeometric models of information retrieval are more immune than other models to spam content. All the results presented in this article are fully repeatable and reproducible with data and code available online at a public GitHub repository.
Enhancing Traditional Recommender Systems via Social Communities

Collaborative Filtering (CF) has become the most popular approach for developing Recommender Systems in diverse business applications. Unfortunately, problems such as the cold-start problem (i.e., new users or items enter the system and for those no previous preference information is available) and the gray sheep problem (i.e., cases in which a user profile does not match any other profile in the user community) are widely recognized for hindering recommendation effectiveness of traditional CF methods. To alleviate such problems, substantial research has focused on enhancing CF with social information about users (e.g., social relationships and communities). However, despite the crescent interest in social-based approaches, researches and practitioners face the challenge of developing their own Recommender System architecture for appropriately combining social and collaborative filtering methods to improve recommendation results. In this paper, we address this issue by introducing a flexible architecture to support researchers and practitioners in the task of designing real-world Recommender Systems that exploit social network data. We focus on detailing our proposed architecture modules and their interplay, potential algorithms for extracting and combining relevant social information, and candidate technologies for handling diverse and massive data volumes. Additionally, we provide an empirical analysis demonstrating the effectiveness of the proposed architecture on alleviating the cold-start problem over a concrete experimental case.
Platform-Independent Specification and Verification of the Standard Mathematical Square Root Function

The aim of the Platform-Independent Approach to Formal Specification and Verification of Standard Mathematical Functions project is the development of an incremental combined approach to specification and verification of standard mathematical functions like sqrt, cos, sin, etc. The term “platform-independence” means that we attempt to design a relatively simple axiomatization of computer arithmetic in terms of real arithmetic (i.e., the arithmetic of the field ℝ of real numbers), but do not specify either the base of the computer arithmetic or the format of the representation of numbers. The incrementality means that we start with the most straightforward specification of the simplest algorithm in real numbers and finish with a realistic specification and a verification of the algorithm in computer arithmetic. We call our approach combined because we start with consideration of a “basic” case, the manual (pen-and-paper) verification of the algorithm in real numbers, then use this verification as proof-outlines for the manual verification of the algorithm in computer arithmetic, and finish with a computer-aided validation of the manual proofs with a proof-assistant system to avoid appeals to “obviousness” that are common in human-carried proofs. In the paper, we apply our platform-independent incremental combined approach to specification and verification of the standard mathematical square root function. By now, the computer-aided validation of the developed algorithms has been carried out only partially to prove, using the ACL2 system, the correctness (consistency) of our fixed-point arithmetic and the existence of a look-up table with the initial approximations of the square roots for fixed-point numbers.
Detecting reliable novel word senses

In this era of Big Data, due to expeditious exchange of information on the web, words are being used to denote newer meanings, causing linguistic shift. With the recent availability of large amounts of digitized texts, an automated analysis of the evolution of language has become possible. Our study mainly focuses on improving the detection of new word senses. This paper presents a unique proposal based on network features to improve the precision of new word sense detection. For a candidate word where a new sense (birth) has been detected by comparing the sense clusters induced at two different time points, we further compare the network properties of the subgraphs induced from novel sense cluster across these two time points. Using the mean fractional change in edge density, structural similarity and average path length as features in an SVM classifier, manual evaluation gives precision values of 0.86 and 0.74 for the task of new sense detection, when tested on 2 distinct time-point pairs, in comparison to the precision values in the range of 0.23-0.32, when the proposed scheme is not used. The outlined method can therefore be used as a new post-hoc step to improve the precision of novel word sense detection in a robust and reliable way where the underlying framework uses a graph structure. Another important observation is that even though our proposal is a post-hoc step, it can be used in isolation and that itself results in a very decent performance achieving a precision of 0.54-0.62. Finally, we show that our method is able to detect the well-known historical shifts in 80% cases.
SEMAX: Multi-Task Learning for Improving Recommendations

Personalization plays an essential role in recommender systems, in which the key task is to predict personalized ratings of users on new items. Recently, a lot of work investigates deep learning-based collaborative filtering techniques to increase the accuracy of rating prediction. However, most exiting works focus on the recommendation task itself. Actually, the multi-task learning exploits an inductive transfer mechanism to enhance the generalization performance of the main task by using the domain information contained in other related tasks. Multi-task learning has shown effectiveness in various real-world problems, including regression and machine translation. To this end, this study proposes a new framework, called SEMAX that extends our previous model SEMA via multi-task learning for improving recommendations, in which the recommendation task gets the domain information from the other task. Specifically, in the recommendation task, SEMAX learns semantic meanings from texts and temporal dynamics from text sequences for both users and items based on our developed hierarchical and symmetrical recurrent neural networks (RNNs) with the long short-term memory. Furthermore, SEMAX exploits the related task that predicts the rating of a text written by a user for an item to reinforce the recommendation task that predicts the rating of the user on the item, because the text can be an important predictor of the rating given by the user to the item. Moreover, SEMAX predicts the rating of a text based on an attention mechanism to choose user-item-specific words so as to generalize the performance of learned word embeddings, user and item representations. Finally, we conduct a comprehensive evaluation for SEMAX using two large-scale real-world review datasets collected from Amazon and Yelp. The experimental results show that the SEMAX achieves significantly superior performance compared to other state-of-the-art recommendation techniques.
A Global Perspective of Classroom Technology Integration and Use

Across the globe, technology has become more commonplace in educational settings. Particularly, educators are utilizing technology to positively impact student academic performance and to engage learners throughout classroom activities. Common trends in educational technology incorporate the inclusion of mobile phones and social media activities. However, distinct differences across the world are found in classroom settings in the availability of educational technology resources and the opportunity for educators to receive professional development training.
Transformed statistics for tests of conditional independence in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll" id="d1e3616" altimg="si56.gif"><mml:mi>J</mml:mi><mml:mo>×</mml:mo><mml:mi>K</mml:mi><mml:mo>×</mml:mo><mml:mi>L</mml:mi></mml:math> contingency tables

Tests of the hypothesis of conditional independence in J×K×L contingency tables are considered. An expression to approximate the null distribution of the test statistics is derived. Using this expression, transformed statistics are obtained which converge to a chi-square limiting distribution faster than the original statistics do. Simulations are used to compare the transformed statistics with the original ones, and transformed statistics are proposed based on a Bartlett-type adjustment. Through this work and earlier ones, we cover testing hierarchical loglinear models in three-way tables except for a model having three interaction terms.
Deep Reader: Information Extraction from Document Images via Relation Extraction and Natural Language

Recent advancements in the area of Computer Vision with state-of-art Neural Networks has given a boost to Optical Character Recognition (OCR) accuracies. However, extracting characters/text alone is often insufficient for relevant information extraction as documents also have a visual structure that is not captured by OCR. Extracting information from tables, charts, footnotes, boxes, headings and retrieving the corresponding structured representation for the document remains a challenge and finds application in a large number of real-world use cases. In this paper, we propose a novel enterprise based end-to-end framework called DeepReader which facilitates information extraction from document images via identification of visual entities and populating a meta relational model across different entities in the document image. The model schema allows for an easy to understand abstraction of the entities detected by the deep vision models and the relationships between them. DeepReader has a suite of state-of-the-art vision algorithms which are applied to recognize handwritten and printed text, eliminate noisy effects, identify the type of documents and detect visual entities like tables, lines and boxes. Deep Reader maps the extracted entities into a rich relational schema so as to capture all the relevant relationships between entities (words, textboxes, lines etc.) detected in the document. Relevant information and fields can then be extracted from the document by writing SQL queries on top of the relationship tables. A natural language based interface is added on top of the relationship schema so that a non-technical user, specifying the queries in natural language, can fetch the information with minimal effort. In this paper, we also demonstrate many different capabilities of Deep Reader and report results on a real-world use case.
Telemonitoring Healthcare System-Based Mobile Agent Technology

Generally, distributed computing through a handheld/mobile device has to be considered with care because of the limited capabilities on these devices. Especially in ubiquitous telemonitoring healthcare, which refers to the disposition of any type of health services, such that medical staff members (physicians, emergency workers, other healthcare providers, etc.) through mobile computing devices can access them and expect data to be made available. In this chapter, the authors present a new system based on ubiquitous agents to assist telemonitoring employees, not only anytime and anywhere but also on any device.
Detection of Human Facial Parts Using Viola-Jones Algorithm in Group of Faces

Face detection is an image processing technique used in computer system to detect face in digital image. This article proposes an approach to detect faces and facial parts from an image of a group of people using the Viola Jones algorithm. Face detection is used in face recognition and identification systems. Automatic face detection and recognition is most challenging and a fast-growing research area in real-time applications like CC TV surveillance, video tracking, facial expression recognition, gesture recognition, human computer interaction, computer vision, and gender recognition. For face detection purposes various techniques and methods are applied in a computer system. In proposed system, a Viola Jones algorithm is implemented for multiple faces and facial parts and detected with a high rate of accuracy.
Group affinity guided deep hypergraph model for person re‐identification

Person re-identification aims at searching in a large gallery image database for images of the same identity as probe image, which can also be treated as a retrieval task in which the pairwise affinity is often used to rank the retrieved images. However, most existing methods of person re-identification only consider pairwise affinity but ignore the group affinity information. Some frameworks incorporate group affinity into the testing phase, which is not end-to-end trainable for deep neural networks. In this Letter, a powerful deep learning based framework for person re-identification is presented, which aims at fully utilising group affinity information for more discriminative features. Specifically, hypergraph model for group information mining is novelly proposed in the training phase, which significantly improves the performance of features acquired by the baseline networks. Extensive experiments on two popular benchmarks demonstrate the effectiveness of the authors' model and applicability on different baseline networks.
Protected pointers to specify access privileges in distributed systems

With reference to a distributed environment consisting of nodes connected in an arbitrary network topology, we propose the organization of a protection system in which a set of subjects, e.g. processes, generates access attempts to memory segments. One or more primary passwords are associated with each node. An access to a given segment can be successfully accomplished only if the subject attempting the access holds an access privilege, certified by possession of a valid protected pointer (p-pointer) referencing that segment. Each p-pointer includes a local password; the p-pointer is valid if the local password descends from a primary password by application of a universally known, parametric one-way generation function. A set of protection primitives makes it possible to manage the primary passwords, to reduce p-pointers to include less access rights, to allocate new segments, to delete existing segments, to read the segment contents and to overwrite these contents. The resulting protection environment is evaluated from a number of viewpoints, which include p-pointer forging and revocation, the network traffic generated by the execution of the protection primitives, the memory requirements for p-pointer storage, security, and the relation of our work to previous work. An indication of the flexibility of the p-pointer concept is given by applying p-pointers to the solution of a variety of protection problems.
Optimal Pricing for Service Provision in Heterogeneous Cloud Market

In recent years, the adoption of Software as a Service (SaaS) cloud services has surpassed that of Infrastructure as a Service (IaaS) cloud service and is now the focus of attention in cloud computing. The cloud market is becoming highly competitive owing to the increasing number of cloud service providers (CSPs), who are likely to exhibit different cloud capacities, i.e., the cloud market is heterogeneous. Moreover, as different users generally exhibit different Quality of Service (QoS) preferences, it is challenging to set prices for cloud services of good QoS. In this study, we investigate the price competition in the heterogeneous cloud market where two SaaS providers, denoted by CSP1 and CSP2, lease virtual machine (VM) instances from IaaS providers to offer cloud-based application services to users. We assume that CSP1 only has M/M/1 queue of VM instances owing to its limited cloud resources, whereas CSP2 has M/M/∞ queue of VM instances reflecting its adequate resources. We consider two price competition scenarios in which two CSPs engage in two games: one is a noncooperative strategic game (NSG) where the two CSPs set prices simultaneously and the other is a Stackelberg game (SG) where CSP2 sets the price first as the leader and is followed by CSP1, who sets the price in response to CSP2. Each user decides which cloud services to purchase (if purchases are to be made) based on the prices and QoS. The NSG scenario corresponds to the practical cloud market, where two CSPs with different cloud capacities begin to offer cloud services simultaneously; meanwhile, the SG scenario covers the instance where a more recent CSP plans to enter a cloud market whose incumbent CSP has larger cloud resources. Equilibrium is achieved in each of the scenarios. Numerical results are presented to verify our theoretical analysis.
Government accounting optimization based on computational linguistics

The level of moral development and moral intensity in cognitive psychology will not only affect the ethical behavior of accountants, but also have a direct impact on the quality and level of accounting work. Therefore, in this paper, the ethical behavior of accountants was analyzed from the perspective of cognitive psychology. Computer-aided data mining techniques were introduced, and government accounting risk assessment management of financial accountants was studied. In this paper, the principle of cognitive psychology to measure the ethical level of accountants was first described. The predicament of moral judgments was analyzed and an optimization plan to improve the ethical intention of accountants was proposed. Support Vector Machine classification technology in data mining was studied to explore how to conduct effective and reliable evaluation, so as to provide a scientific basis for decision-making in improving accounting management. After the simulation experiment, it is proved that continuously improving the ethical standards of accountants and strengthening the forecast of accounting risks can continue to optimize the accounting office management.
Cyclotomy and arithmetic properties of some families in Z[x]/〈xpq − 1〉

Cyclotomic classes of order 2 with respect to a product of two distinct odd primes [Formula: see text] and [Formula: see text] are represented in some specific forms and using these forms an alternate proof of Theorem 3 of [C. Ding and T. Helleseth, New generalized cyclotomy and its applications, Finite Fields Appl. 4 (1998) 140–166] is given, when [Formula: see text]. Further, it is observed that these classes are related to [Formula: see text]-cyclotomic cosets, where [Formula: see text] and [Formula: see text] such that gcd([Formula: see text]. Finally, arithmetic properties of some families in [Formula: see text] and hence in [Formula: see text] are studied.
A dynamic epistemic framework for reasoning about conformant probabilistic plans

In this paper, we introduce a probabilistic dynamic epistemic logical framework that can be applied for reasoning and verifying conformant probabilistic plans in a single agent setting. In conformant probabilistic planning (CPP), we are looking for a linear plan such that the probability of achieving the goal after executing the plan is no less than a given threshold probability δ. Our logical framework can trace the change of the belief state of the agent during the execution of the plan and verify the conformant plans. Moreover, with this logic, we can enrich the CPP framework by formulating the goal as a formula in our language with action modalities and probabilistic beliefs. As for the main technical results, we provide a complete axiomatization of the logic and show the decidability of its validity problem.
Visualization-Driven Approach to Fraud Detection in the Mobile Money Transfer Services

Mobile money transfer services (MMTS) are widely spread in the countries lacking conventional financial institutions. Like traditional financial systems they can be used to implement financial frauds. The chapter presents a novel visualization-driven approach to detection of the fraudulent activity in the MMTS. It consists in usage of a set of interactive visualization models supported by outlier detection techniques allowing to construct comprehensive view on the MMTS subscriber behavior according to his/her transaction activity. The key element of the approach is the RadViz visualization that helps to identify groups with similar behavior and outliers. The scatter plot visualization of the time intervals with transaction activity supported by the heat map visualization of the historical activity of the MMTS subscriber is used to conduct analysis of how the MMTS users' transaction activity changes over time and detect sudden changes in it. The results of the efficiency evaluation of the developed visualization-driven approach are discussed.