Reviewerly/exHarmony huggingface dataset:
A switching multi-level method for the long tail recommendation problem

Recommender systems are decision support systems that play an important part in generating a list of product or service recommendations for users based on the past experiences and interactions. The most popular recommendation method is Collaborative Filtering (CF) that is based on the users' rating history to generate the recommendation. Although, recommender systems have been applied successfully in different areas such as e-Commerce and Social Networks, the popularity bias is still one of the challenges that needs to be further researched. Therefore, we propose a multi-level method that is based on a switching approach which solves the long tail recommendation problem (LTRP) when CF fails to find the target case. We have evaluated our method using two public datasets and the results show that it outperforms a number of bases lines and state-of-the-art alternatives with a further reduce of the recommendation error rates for items found in the long tail.
Trends in Occurrences of Systems Engineering Topics in Literature

Examining past research using content analysis can provide detailed information about the trends a discipline has displayed over years or decades. Disciplines such as software engineering and hospitality management have used methods for analyzing topic frequencies in the past. This paper uses similar methods to study the presence of systems engineering topics in literature to understand the occurrence of approaches and metrics in the community over time. This paper reviews the trends of systems engineering topics over the years 1998–2016 by performing a content analysis of four systems engineering and design engineering journals. A mathematical analysis of the trends is performed using rank ordering and a rank correlation metric. The study shows that model-based approaches have grown significantly since the early 2000s, as advocated in the INOCSE MBSE initiative, while other approaches such as Taguchi’s method and axiomatic design have been seen less frequently in literature. Systems engineering metrics that have been frequently seen in literature include complexity, reliability, and quality. The results from this research provide a portrait of the systems engineering landscape.
Automated and semi-automated map georeferencing

Historical maps contain a wealth of information not generally available, but they must be referenced to well-known coordinate systems for maximum use in spatial analysis. Existing georeferencing tools are essentially manual, requiring considerable data entry, much panning and zooming, and precise on-screen digitizing. Here we present alternative approaches based on pattern-matching and spatial computing intended to overcome the inefficiencies of standard tools. We also describe and make available two computer programs implementing the methods discussed. The first, designed for large-scale quadrangles, locates map boundaries, finds ground control points, and produces georeferenced images without operator assistance. Experiments show that quadrangle georeferencing can be reliably automated (88% success rate in our tests). A second program, developed for general maps at any scale, uses self-learning and other approaches to overcome most of the manual aspects of georeferencing. Both programs find control points with single-pixel accuracy, yield transform errors on the order of map linewidth, and can produce warped or unwarped images as desired.
Explaining Actual Causation via Reasoning About Actions and Change

The study of actual causation concerns reasoning about events that have been instrumental in bringing about a particular outcome. Although the subject has long been studied in a number of fields including artificial intelligence, existing approaches have not yet reached the point where their results can be directly applied to explain causation in certain advanced scenarios, such as pin-pointing causes and responsibilities for the behavior of a complex cyber-physical system. We believe that this is due, at least in part, to a lack of distinction between the laws that govern individual states of the world and events whose occurrence cause state to evolve. In this paper, we present a novel approach to reasoning about actual causation that leverages techniques from Reasoning about Actions and Change to identify detailed causal explanations for how an outcome of interest came to be. We also present an implementation of the approach that leverages Answer Set Programming.
Detecting cognitive bias in a relevance assessment task using an eye tracker

Cognitive biases, such as the bandwagon effect, occur when a participant places a disproportionate emphasis on external information when making decisions under uncertainty. These effects are challenging for humans to overcome - even when they are explicitly made aware of their own biases. One challenge for researchers is to detect if the information is used in decision making and to what degree. One can gain a better understanding of how this external information is used in decision making using an eye tracker. In this paper, we evaluate cognitive biases in the context of assessing the binary relevance of a set of documents in response to a given information need. We show that these cognitive biases can be observed by examining gaze time in Areas of Interest (AOI) that contain this pertinent external information.
A Two-Phase Development of Fuzzy Rule-Based Model and Their Analysis

Fuzzy rule-based models form a commonly encountered category of fuzzy models. As such they have enjoyed a great deal of conceptual and algorithmic developments followed by numerous case studies. This paper contributes to this area by bringing forward a two-phase design of fuzzy rules completed on the basis of experimental data. This design directly reflects upon the nature of the rules vis-à-vis the data used in their construction. First, information granules (fuzzy sets) standing in condition and conclusion parts of the individual rules are formed following a commonly used clustering technique of Fuzzy C-Means (FCM). The results of fuzzy clustering are directly used to build a collection of fuzzy sets of conditions and conclusions forming the individual rules. Some optimization aspects are raised in this context by expressing the performance of the condition and conclusion fuzzy sets in terms of the reconstruction abilities of the data captured by the rules. Second, fuzzy sets present in the rules (which are typically described by membership functions having infinite support) are transformed into interval-valued information granules of finite support that capture the essential (core) relationships between the regions in the input and output spaces strongly supported by the experimental data. In this way, the proposed rule-based model exhibits a two-tier architecture built in two successive phases. Subsequently, the proposed architecture invokes two fundamentally different modes of reasoning: 1) a recall mode in case when a new datum is positioned within the interval-valued information granules and 2) approximation mode where we invoke an aggregation of the individual rules given their activation levels in case a new datum does not belong to the core structure of the rules. These two modes produce granular results (represented as intervals). A way of assessing the quality of the obtained results is provided. Along with these two modes, we offer a characterization of the quality of results as well as the quality of the rules (expressed in terms of coverage, specificity of condition space and specificity of conclusion space). Experimental results are reported to illustrate the design process and the performance of the constructed model.
Citizen sociolinguistics: A new method to understand fat talk

Fat talk is a spontaneous verbal interaction in which interlocutors make self-disparaging comments about the body, usually as a request for assessment. Fat talk often reflects concerns about the self that stem from broader sociocultural factors. It is therefore an important target for sociocultural linguistics. However, real-time studies of fat talk are uncommon due to the resource and time burdens required to capture these fleeting utterances. This limits the scope of data produced using standard sociolinguistic methods. Citizen science may alleviate these burdens by producing a scale of social observation not afforded via traditional methods. Here we present a proof-of-concept for a novel methodology, citizen sociolinguistics. This research approach involves collaborations with citizen researchers to capture forms of conversational data that are typically inaccessible, including fat talk.This study had two primary aims. Aim 1 focused on scientific output, testing a novel research strategy wherein citizen sociolinguists captured fat talk data in a diverse metropolitan region (Southwestern United States). Results confirm that citizen sociolinguistic research teams captured forms of fat talk that mirrored the scripted responses previously reported. However, they also capture unique forms of fat talk, likely due to greater diversity in sample and sampling environments. Aim 2 focused on the method itself via reflective exercises shared by the citizen sociolinguists throughout the project. In addition to confirming that the citizen sociolinguistic method produces reliable, scientifically valid data, we contend that citizen sociolinguist inclusion has broader scientific benefits which include applied scientific training, fostering sustained relationships between professional researchers and the public, and producing novel, meaningful scientific output that advances professional discourse.
Color Face Recognition by Using Quaternion and Deep Neural Networks

In this paper we propose a new model for color face recognition based on quaternion number and deep neural networks (DNN), to enhance the classification accuracy of color face recognition. The proposed model is derived by introducing image represented in quaternion domain as an input vector in DNN model, frequently, utilized in many vision tasks of pattern recognition. The utilization of quaternion representation (QR) aims to reduce the size of input vector and consequently the computational complexity. On the other hand, color information from the image while obtaining best classification results. The simulations results are performed on color face databases to demonstrate the effectiveness of the proposed model. The obtained results show that they outperform other existing algorithms.
Detecting Collaborative Learning Through Emotions: An Investigation Using Facial Expression Recognition

Providing adaptive feedback to learners engaging in collaborative learning activities is one research topic in the development of intelligent tutoring systems. However, there is a need to investigate how to systematically evaluate a learner's activities and provide feedback on them. The present study investigates how emotional states, detected through facial recognition, can be utilized to capture the learning process in a simple jigsaw-type collaborative task. It was predicted that when learners argue with each other and reason deeply, they may experience several emotional states such as positive and negative states. The results show that when learners work harder on developing a mutual understanding through conflictive interaction, negative emotions can be used to predict this process. This study contributes to the knowledge of how emotional states detected by facial recognition technology can be applied to predict learning process in conflictive tasks. Moreover, these empirical results will impact the development of adaptive feedback mechanisms for intelligent tutoring systems for collaborative learning.
Factors influencing dwell time during source code reading

The paper partially replicates and extends a previous study by Busjahn et al. [4] on the factors influencing dwell time during source code reading, where source code element type and frequency of gaze visits are studied as factors. Unlike the previous study, this study focuses on analyzing eye movement data in large open source Java projects. Five experts and thirteen novices participated in the study where the main task is to summarize methods. The results examine semantic line-level information that developers view during summarization. We find no correlation between the line length and the total duration of time spent looking on the line even though it exists between a token's length and the total fixation time on the token reported in prior work. The first fixations inside a method are more likely to be on a method's signature, a variable declaration, or an assignment compared to the other fixations inside a method. In addition, it is found that smaller methods tend to have shorter overall fixation duration for the entire method, but have significantly longer duration per line in the method. The analysis provides insights into how source code's unique characteristics can help in building more robust methods for analyzing eye movements in source code and overall in building theories to support program comprehension on realistic tasks.
Unsupervised emotion recognition algorithm based on improved deep belief model in combination with probabilistic linear discriminant analysis

Since the initial weight matrix between the last hidden layer of the network and the classification layer is usually generated randomly, the weight matrix does not have the discrimination ability to accurately classify the facial expression recognition, which results in that the features obtained by the weight matrix mapping cannot be guaranteed to be suitable for classification tasks. To solve this problem, a novel linear discriminant deep belief network is proposed in this paper. Firstly, the traditional linear discriminant analysis method is improved, and a new type of inter-class dispersion matrix is designed to solve the rank limitation problem in the traditional Linear Discriminant Analysis Method (LDA). Then, the weight matrix between the last hidden layer and the classification layer of the deep belief network is initialized by the improved linear discriminant analysis method, so that the network is more suitable for the classification task. In the experiments, our proposed deep network obtains respectively the recognition rates of 78.26% and 94.48% on the JAFFE database and the Extended Cohn-Kanade database. In addition, using our proposed algorithm for aggregating linear discriminant analysis into a deep belief network, we were able to produce an accuracy of 81.03% on the challenge test set.
Directional pixel value ordering based secret sharing using sub-sampled image exploiting Lagrange polynomial

In this paper, we proposed secret sharing within the sub-sampled image using Directional Pixel Value Ordering (DPVO) for Reversible Data Hiding scheme. The original cover is partitioned into sub-sampled images which extend its size through image interpolation technique. The secret information has been converted through Lagrange interpolation polynomial techniques. These new secret data is embedded within pixels of each interpolated sub-sampled images using DPVO. In the decoder side, the secret data is taken out from the pixels of every stego image using reverse DPVO. After that, Lagrange's interpolation is applied to generate the original secret message. The proposed scheme enhanced the security for the sharable characteristics of secrets amongst several images. It raises the data embedding capacity and improves the visual quality that is determined by peak signal to noise ratio (PSNR). The average PSNR value is over 60 dB. It shows the superiority of the new algorithm over the other existing data hiding methods regarding payload of data, quality, and security of the image. Also, the stego images have evaluated through standard deviation, regular singular analysis, correlation coefficient, normalized cross-correlation and structural similarity index in between original cover and marked (stego) image to display the robustness of our result among the several steganographic strikes.
Authorship Verification using a Graph Knowledge Discovery Approach

This paper presents an approach to solve authorship verification, a forensic text problem which consists in determining whether or not an unknown document was written by a particular author, from some samples of the author’s writing style. The core of the approach is the use of a graph representati on to extract relevant linguistic features based on network analysis techniques. The use of graphs provides rich data structures for representing lexical and syntactic aspects of texts, allowing the reinterpretation of centrality measures to extract linguistic features that do not depend entirely of stylistic elements of text documents. The proposed method is applied on the English language partitions of the clef PAN 2014 and 2015 author verification datasets, producing competitive results that outperform the state of the art baselines and are near (or surpass in one of the cases) to the best results reported so far, given the same training and test corpora. These experimental results showed that our interpretation of the four centrality measures: closeness, betweenness, degree and eigenvector allow to detect relevant patterns of an author’s writing style. In particular, words with high closeness which are part of some chunk phrases and words with high betweenness that are included in bigrams and trigrams, contribute in a more effective way to verify document authorship.
EduApp: A Collaborative Application for Mobile Devices to Support the Educational Process in Greek Secondary Education

The use of mobile devices in secondary education is an emerging trend which introduces new ways of teaching and learning when applied according to the respective educational and legal regulations. In this study, we focus on the design, development, presentation and evaluation of an application, named “EduApp”, which uses smartphones and tablets and which is addressed to secondary education teachers and students, during or outside the classroom settings. The ulitmate goal of EduApp is to support any lesson by providing interaction and collaboration between all the involved in the education process. In this paper, we present the technical and usability evaluation by experts that we performed before the implementation of the first version of EduApp in real classroom conditions. The evaluation results indicate, among others, a stable and easy to use application that does not increase the users' cognitive load.
Determining the Eps Parameter of the DBSCAN Algorithm

Clustering is an attractive technique used in many fields and lots of clustering algorithms have been proposed so far. The Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is one of the most popular algorithms, which has been widely applied in many different applications. This algorithm can discover clusters of arbitrary shapes in large datasets. However, the fundamental issue is the right choice of two input parameters, i.e. radius eps and density threshold MinPts. In this paper, a new method is proposed to determine the value of eps. The suggested approach is based on an analysis of the sorted values of the distance function. The performance of the new approach has been demonstrated for several different datasets.
Social Engineering and Organisational Dependencies in Phishing Attacks

Phishing emails are a widespread cybersecurity attack method. Their breadth and depth have been on the rise as they target individuals and organisations with increased sophistication. In particular, social engineering in phishing focuses on human vulnerabilities by exploiting established psychological and behavioural cues to increase the credibility of phishing emails. This work presents the results of a 56,000-participant phishing attack simulation carried out within a multi-national financial organisation. The overarching hypothesis was that strong cultural and contextual factors impact employee vulnerability. Thus, five phishing emails were crafted, based on three of Cialdini's persuasion principles used in isolation and in combination. Our results showed that Social proof was the most effective attack vector, followed by Authority and Scarcity. Furthermore, we examined these results in the light of a set of demographic and organisational features. Finally, both click-through rates and reporting rates were examined, to provide rich insights to developers of cybersecurity educational solutions.
When finding nothing may be evidence of something: Anti-forensics and digital tool marks

There are an abundance of measures available to the standard digital device users which provide the opportunity to act in an anti-forensic manner and conceal any potential digital evidence denoting a criminal act. Whilst there is a lack of empirical evidence which evaluates the scale of this threat to digital forensic investigations leaving the true extent of engagement with such tools unknown, arguably the field should take proactive steps to examine and record the capabilities of these measures. Whilst forensic science has long accepted the concept of toolmark analysis as part of criminal investigations, 'digital tool marks' (DTMs) are a notion rarely acknowledged and considered in digital investigations. DTMs are the traces left behind by a tool or process on a suspect system which can help to determine what malicious behaviour has occurred on a device. This article discusses and champions the need for DTM research in digital forensics highlighting the benefits of doing so.
Comprehensive structured knowledge base system construction with natural language presentation

Abstract Constructing an ontology-based machine-readable knowledge base system from different sources with minimum human intervention, also known as ontology-based machine-readable knowledge base construction (OMRKBC), has been a long-term outstanding problem. One of the issues is how to build a large-scale OMRKBC process with appropriate structural information. To address this issue, we propose Natural Language Independent Knowledge Representation (NLIKR), a method which regards each word as a concept which should be defined by its relations with other concepts. Using NLIKR, we propose a framework for the OMRKBC process to automatically develop a comprehensive ontology-based machine-readable knowledge base system (OMRKBS) using well-built structural information. Firstly, as part of this framework, we propose formulas to discover concepts and their relations in the OMRKBS. Secondly, the challenges in obtaining rich structured information are resolved through the development of algorithms and rules. Finally, rich structured information is built in the OMRKBS. OMRKBC allows the efficient search of words and supports word queries with a specific attribute. We conduct experiments and analyze the results of relational information extraction, with the results showing that OMRKBS had an accuracy of 84% which was higher than the other knowledge base systems, namely ConceptNet, DBpedia and WordNet.