Reviewerly/exHarmony huggingface dataset:
Structural Analysis of Large-Scale Socio-Technical Systems Based on the Concept of Influence

The paper is devoted to studying the stability of complex systems, which include diversified and heterogeneous elements. In order to analyze the stability of such systems, it is proposed to develop the process of dynamic determination of the main factors. For this purpose, this paper uses a variant of cognitive modeling based on the concept of “influence”. The proposed approach implies, in a sense, a broader view of the concept of the “fuzzy cognitive map” introduced by B. Kosko. “Influence” does not mean “causality”, allows a broader interpretation range, and is calculated in a special way that makes it possible to prove rigorous theorems. A complex system is represented as a digraph, and the selection of the main factors is proposed to be based on the concept of “influence”, which is introduced as follows. All nodes of the graph are assigned an abstract property “significance”, which allows you to compare heterogeneous factors, and a particular computational procedure is introduced that allows this “significance” to be calculated. The “influence” of factors on each other is defined as a mathematically formalized response in accordance with the introduced computational procedure of the “significance” of the studied factor to the variation in the “significance” of input factors. For example, the analysis of the sustainability of a large-scale model of the Russian economy in terms of the strength of the “influence” is provided.
A Performance–Consumption Balanced Scheme of Multi-Hop Quantum Networks for Teleportation

Teleportation is an important protocol in quantum communication. Realizing teleportation between arbitrary nodes in multi-hop quantum networks is of great value. Most of the existing multi-hop quantum networks are based on Bell states or Greeberger–Horne–Zeilinger (GHZ) states. Bell state is more susceptible to noise than GHZ states after purification, but generating a GHZ state consumes more basic states. In this paper, a new quantum multi-hop network scheme is proposed to improve the interference immunity of the network and avoid large consumption at the same time. Teleportation is realized in a network based on entanglement swapping, fusion, and purification. To ensure the robustness of the system, we also design the purification algorithm. The simulation results show the successful establishment of entanglement with high fidelity. Cirq is used to verify the network on the Noisy Intermediate-Scale Quantum (NISQ) platform. The robustness of the fusion scheme is better than the Bell states scheme, especially with the increasing number of nodes. This paper provides a solution to balance the performance and consumption in a multi-hop quantum network.
Isolating Uncertainty of the Face Expression Recognition with the Meta-Learning Supervisor Neural Network

We investigate whether the well-known poor performance of the head-on usage of the convolutional neural networks for the facial expression recognition task may be improved in terms of reducing the false positive and false negative errors. An uncertainty isolating technique is used that introduces an additional “unknown” class. A self-attention supervisor artificial neural network is used to “learn about learning” of the underlying convolutional neural networks, in particular, to learn patterns of the underlying neural network parameters that accompany wrong or correct verdicts. A novel data set containing artistic makeup and occlusions images is used to aggravate the problem of the training data not representing the test data distribution.
Deep Learning Software Defect Prediction Methods for Cloud Environments Research

This paper provides an in-depth study and analysis of software defect prediction methods in a cloud environment and uses a deep learning approach to justify software prediction. A cost penalty term is added to the supervised part of the deep ladder network; that is, the misclassification cost of different classes is added to the model. A cost-sensitive deep ladder network-based software defect prediction model is proposed, which effectively mitigates the negative impact of the class imbalance problem on defect prediction. To address the problem of lack or insufficiency of historical data from the same project, a flow learning-based geodesic cross-project software defect prediction method is proposed. Drawing on data information from other projects, a migration learning approach was used to embed the source and target datasets into a Gaussian manifold. The kernel encapsulates the incremental changes between the differences and commonalities between the two domains. To this point, the subspace is the space of two distributional approximations formed by the source and target data transformations, with traditional in-project software defect classifiers used to predict labels. It is found that real-time defect prediction is more practical because it has a smaller amount of code to review; only individual changes need to be reviewed rather than entire files or packages while making it easier for developers to assign fixes to defects. More importantly, this paper combines deep belief network techniques with real-time defect prediction at a fine-grained level and TCA techniques to deal with data imbalance and proposes an improved deep belief network approach for real-time defect prediction, while trying to change the machine learning classifier underlying DBN for different experimental studies, and the results not only validate the effectiveness of using TCA techniques to solve the data imbalance problem but also show that the defect prediction model learned by the improved method in this paper has better prediction performance.
Sentiment classification in social media data by combining triplet belief functions

Abstract Sentiment analysis is an emerging technique that caters for semantic orientation and opinion mining. It is increasingly used to analyze online reviews and posts for identifying people's opinions and attitudes to products and events in order to improve business performance of companies and aid to make better organizing strategies of events. This paper presents an innovative approach to combining the outputs of sentiment classifiers under the framework of belief functions. It consists of the formulation of sentiment classifier outputs in the triplet evidence structure and the development of general formulas for combining triplet functions derived from sentiment classification results via three evidential combination rules along with comparative analyses. The empirical studies have been conducted on examining the effectiveness of our method for sentiment classification individually and in combination, and the results demonstrate that the best combined classifiers by our method outperforms the best individual classifiers over five review datasets.
Service Composition Considering QoS Fluctuations and Anchoring Cost

Traditional service composition focuses on how to construct optimal composite solutions that satisfy end-to-end QoS constraints raised by users. Although there have been rich well-recognized service composition approaches, most of them ignore an obvious phenomenon: users have to go through a cognition process on unacquainted services to gradually get familiar with these services, and directly using unacquainted services in composite solutions might lead to potential risk on user experiences. QoS fluctuations of services might affect the cognition process to some extent, too. In this paper, we define a new concept anchoring cost to measure such degree of unfamiliarity and give its measurement in terms of historical service usage records and QoS fluctuations. A multi-objective optimization model considering QoS fluctuations and anchoring cost is introduced: besides pursuing the optimality of QoS attributes, to reduce the risk that is caused by importing services with higher anchoring cost into composite solutions is another optimization objective. A set of Pareto-optimal non-dominated composite solutions are obtained by the NSGA-II algorithm and different types of users in terms of risk aversion (e.g., balanced, conservative, risk-taking) may choose their preferred solutions from them. Experiments are conducted on real QoS datasets in three scenarios to demonstrate the rationality and significance of the proposed approach. To the best of our knowledge, this is the first time that cognition cost is incorporated into the service composition problem.
Guest Column

Quantum computing is a prolific research area, halfway between physics and computer science [27, 29, 52]. Most likely, its origins may be dated back to 70's, when some works on quantum information began to appear (see, e.g., [34, 37]). In early 80's, R.P. Feynman suggested that the computational power of quantum mechanical processes might be beyond that of traditional computation models [25]. Almost at the same time, P. Benioff already proved that such processes are at least as powerful as Turing machines [9]. In 1985, D. Deutsch [22] proposed the notion of a quantum Turing machine as a physically realizable model for a quantum computer. From the point of view of structural complexity, E. Bernstein and U. Vazirani introduced in [20] the class BQP of problems solvable in polynomial time on quantum Turing machines, focusing attention on relations with the corresponding deterministic and probabilistic classes P and BPP, respectively. Several works in the literature explored classical issues in complexity theory from the quantum paradigm perspective (see, e.g., [7, 60, 61]).
Sentiment analysis through word embedding using AraBERT: Moroccan dialect use case

Nowadays, Sentiment Analysis (SA) represents a big chunk of Natural Language Processing (NLP) problems. The latter makes it possible to assign feelings and polarity to portions of text, which comes handy in multiple areas of social conduct such as product reviewing in business, determining political opinions of the masses and other uses. Nevertheless, sentiment analysis can be tricky when dealing with unstructured languages due to the lack of conventional syntactic and morphological structures. In this paper, we discuss several attempts of the literature at solving the challenge of Sentiment analysis of regional dialects, and we propose an approach based on AraBERT word embedding for Moroccan dialect (MD) sentiment analysis. The method goes through a pipeline of steps starting with preprocessing, lexicon-based translation and feature extraction. Afterwards we conduct a comparative study, in 2-way classification, of machine learning algorithms as SVM, DT, LR, RF, NB and deep learning algorithms such as LSTM, BiLSTM and LSTM-CNN from state of art. On the other hand, we managed to train our model with four different outputs in 4 way classification. As a result, BiLSTM proved to be the best in both 2-way classification scoring 83% accuracy, and in 4-way classification achieving scores ranging between 62% and 92% of accuracy for each of the 4 classes.
A novel approach for usability evaluation of mobile applications

Mobile applications (apps) have been massively developed and released in recent years. In this competitive market, the users’ adoption of the app or satisfaction greatly depends on its usability. Therefore, tech companies and app developers are looking for quick and easy approaches to evaluate the usability of their products early in the design cycle. The objective of this study was to introduce a novel approach combining the capabilities of the gesture-level model (GLM) and heuristic evaluation to assess the usability of mobile apps in early stages of the design cycle and without the need for costly and time-consuming user testing. The approach can be run on a graphical user interface and includes uncertainties regarding user skill level and distraction. Preliminary results suggested that this approach can be used for usability evaluation of mobile apps; however, its validity and generalizability to other domains need to be validated in the future with human-subject experiments.
Breaking real-world COTS USIM cards with unknown side-channel countermeasures

In this paper, we examine the question of how to perform an efficient side-channel analysis (SCA) of real-world crypto products with proprietary side-channel countermeasures. Concretely, answering this question requires a suitable selection of side-channel analysis methods and various steps to instantiate it to break various side-channel countermeasures. We take the analysis of a protected prototype system as an example, to discuss how to efficiently use traditional SCA methods and Deep-Learning based SCA methods in the context of analyzing current side-channel countermeasures. Then, we extend the analysis research on a real-world commercial off-the-shelf (COTS) 4G Universal Subscriber Identity Module (USIM) card, showing how to reverse-engineer the embedded side-channel countermeasures by using side-channel information only. The experiment results show that the secret key and other operator-related parameters can be fully recovered without prior knowledge of side-channel countermeasures. This research allows us to provide a living case study of the physical security analysis of real-world crypto products, which might be worthy of more in-depth investigations.
Game Engine Architecture and Comparative Study of Different Game Engines

Nowadays Game Engines have become an integral component of the game development environment. Not only do they accelerate the game development process but also facilitate the integration of gaming modules like animations, graphics, artificial intelligence, and physics using their in-built functionalities. Game Engines also provide a major advantage of reusability of their components making them highly scalable and modifiable. Game engines can be used by the developers to construct and develop games for consoles and different types of platforms such as Android, IOS, Desktop, and many more. This paper eludes the game engine architecture and its constituents and illustrates the features and comparative analysis between four popular game engines namely, Unity, GameMaker, Unreal, and CryEngine. The parameters of this comparison are based on the game engines' technical and non-technical aspects. In conclusion, users will be assisted by the extensive overview provided by this paper in choosing the most preferable engine for their game according to the requirements.
On the impact of the performance metric on efficient algorithm configuration

Algorithm configurators are automated methods to optimise the parameters of an algorithm for a class of problems. We analyse the impact of the cutoff time κ (the time spent evaluating a configuration for a problem instance) on the expected number of configuration comparisons required to find the optimal parameter value for the performance metrics (the measure used to judge the performance of a configuration) that compare configurations using either the best-found fitness values or optimisation times. We first prove that the configurators that use optimisation time as performance metric are not able to tune any unary unbiased algorithm for any function with up to an exponential number of optima using κ≤(nln⁡n)/2. Afterwards, we show that for simple algorithm configuration scenarios the required cutoff time for the optimisation time metric may be considerably larger while using the best fitness metric allows the tuners to configure the target algorithm in linear time in the number of parameters.
Immersive vs. non-immersive virtual reality learning environments

This paper presents the educational experience of using virtual reality learning environments (VRLEs) for teaching crystal lattices. For this purpose, two environments were used: (i) non-immersive and (ii) immersive. As the only difference of both VRLEs is the level of immersion, the results of this study confirm the influence of immersion on some important aspects in the design of VRLEs, such as: usability, ease of use, motivation and interactivity.
Adaptive Virtual Reality Exergame: Promoting Physical Activity Among Workers

Abstract This work presents a virtual reality (VR) Exergame application designed to prevent work-related musculoskeletal disorders (WMSDs). Moreover, to help adapt the tasks of the exergame, a machine learning model that predicts users’ exercise intensity level is presented. WMSDs are an important issue that can have a direct economic impact to an organization. Exercise and stretching is one method that can benefit workers and help prevent WMSDs. While several applications have been developed to prevent WMSDs, most of them suffer from a lack of immersivity or they just focus on education and not necessarily on helping workers warm-up or stretch. In light of this, an Exergame application that leverages VR and Depth-sensor technology to help provide users with an immersive first-person experience that engages them in physical activities is introduced in this work. The objective of the Exergame is to motivate users to perform full-body movements in order to pass through a series of obstacles. While in the game, users can visualize their motions by controlling the virtual avatar with their body movements. It is expected that this immersivity will motivate and encourage the users. Initial findings show the positive effects that the base exergame has on individuals’ motivation and physical activity. The results indicate that the application was able to engage individuals in low-intensity exercises that produced significant and consistent increases in their heart rate. Lastly, the results show that the machine learning model predicted users’ exercise activity level with an accuracy of 76.67%.
On Communication-Efficient Asynchronous MPC with Adaptive Security

Secure multi-party computation (MPC) allows a set of n parties to jointly compute an arbitrary computation over their private inputs. Two main variants have been considered in the literature according to the underlying communication model. Synchronous MPC protocols proceed in rounds, and rely on the fact that the communication network provides strong delivery guarantees within each round. Asynchronous MPC protocols achieve security guarantees even when the network delay is arbitrary.
An Improved PIN Input Method for the Visually Impaired

Despite the recent introduction of biometric identification technology, Personal Identification Numbers (PIN) are the standard for granting access to restricted areas and for authorizing operations on most systems, including mobile phones, payment devices, smart locks. Unfortunately, PINs have several inherent vulnerabilities and expose users to different types of social engineering attacks. Specifically, the risk of shoulder surfing in PIN-based authentication is especially high for individuals who are blind. In this paper, we introduce a new method for improving the trade-off between security and accessibility in PIN-based authentication systems. Our proposed solution aims at minimizing the threats posed by malicious agents while maintaining a low level of complexity for the user. We present the method and discuss the results of an evaluation study that demonstrates the advantages of our solution compared to state-of-the-art systems.
Verifying Secure Speculation in Isabelle/HOL

Secure speculation is an information flow security hyperproperty that prevents transient execution attacks such as Spectre, Meltdown and Foreshadow. Generic compiler mitigations for secure speculation are known to be insufficient for eliminating vulnerabilities. Moreover, these mitigation techniques often overprescribe speculative fences, causing the performance of the programs to suffer. Recently Cheang et al. have developed an operational semantics of program execution capable of characterising speculative executions as well as a new class of information flow hyperproperties named TPOD that ensure secure speculation. This paper presents a framework for verifying TPOD using the Isabelle/HOL proof assistant by encoding the operational semantics of Cheang et al. We provide translation tools for automatically generating the required Isabelle/HOL theory templates from a C-like program syntax, which speeds up verification. Our framework is capable of proving the existence of vulnerabilities and correctness of secure speculation. We exemplify our framework by proving the existence of secure speculation bugs in 15 victim functions for the MSVC compiler as well as correctness of some proposed fixes.
A Combination between Deep learning for feature extraction and Machine Learning for Recognition

Feature extraction is a technique that analyzes faces in pictures or videos which gives the features of the face, and then it determines the specific target. Deep learning has made a real leap in the field of feature extraction. The developers have developed facial feature techniques by relying on deep learning technology to improve the accuracy and efficiency of face analysis and recognition. As a result of the above, developers and programmers began to pay great attention to face recognition technology and started to use it in so many applications. Therefore, this paper provides a comparison between the results of classification methods such as kNN, SVM, Random Forest, and Logistic Regression, based on the deep learning methods for feature extraction such as Squeeze Net and Inception model on the Pins Face Recognition dataset. Logistic Regression with Ridge regularization based on the results of the Inception model was the fastest model and it achieved the best accuracy 94.87% in 68 minutes.
E-commerce Product Recommendation Based on Product Specification and Similarity

Recommender systems play the role of leading users to customized suggestions in the broad universe of available possibilities. While producers use it for cross-selling, which suggests additional products or services to customers, consumers use recommender systems to seek items that match their interests and preferences. By establishing a value-added relationship between the system and the customer, recommender systems boost loyalty. In present e-commerce systems, user pattern search, item, and historical analysis is a substantial component of a recommendation system. A better recommendation system based on product specifications and product similarity measures rather than historical data could lead to a progressive change in e-commerce recommendation technologies. This paper proposes a model that uses product specifications and various similarity measures to compute the user recommendations. The model considers product description and specifications to calculate a similarity measure and then uses these similarity values to form clusters of products. Based on the generated cluster of products, relevant products are recommended to the user. The paper presents method analysis of the various measures and matrices using a sample data set. It also compares the results of our model with the traditionally followed model. The proposed methodology promises to build a user-friendly recommendation system.