Reviewerly/exHarmony huggingface dataset:
How to Make Latent Factors Interpretable by Feeding Factorization Machines with Knowledge Graphs

Model-based approaches to recommendation can recommend items with a very high level of accuracy. Unfortunately, even when the model embeds content-based information, if we move to a latent space we miss references to the actual semantics of recommended items. Consequently, this makes non-trivial the interpretation of a recommendation process. In this paper, we show how to initialize latent factors in Factorization Machines by using semantic features coming from a knowledge graph in order to train an interpretable model. With our model, semantic features are injected into the learning process to retain the original informativeness of the items available in the dataset. The accuracy and effectiveness of the trained model have been tested using two well-known recommender systems datasets. By relying on the information encoded in the original knowledge graph, we have also evaluated the semantic accuracy and robustness for the knowledge-aware interpretability of the final model.
Digital Space of Leading Universities: The Comparative Analysis of Sites

The article presents the results of a comparative and typological analysis of open data websites of the leading Russian and foreign universities. The analysis has been carried out in order to assess the state of the digital environment of universities in the areas of career guidance of students, the development of students’ competencies and career support of graduates. The authors adduce an empirical evidence of the key problems of digitalization of Russian universities such as the lack of interactive innovative forms of online support of applicants by Russian universities; the lack of digital footprints of the educational process, especially video lessons; depersonalization of digital support of training; non-competitive descriptions of elective courses, the lack of educational case studies as a main technology for the implementation of practice-oriented tasks; the underdevelopment of interaction as a basic principle for providing online support of the educational process. As the prospects of the study, the article makes the case for constructing a model ranking universities according to the level of digital space development.
Solving Vehicle Routing Problem Using Quantum Approximate Optimization Algorithm

In this paper, we describe the usage of the Quantum Approximate Optimization Algorithm (QAOA), which is a quantum-classical heuristic, to solve a combinatorial optimization and integer programming task known as Vehicle Routing Problem (VRP). We outline the Ising formulation for VRP and present a detailed procedure to solve VRP by minimizing its simulated Ising Hamiltonian using the IBM Qiskit platform. Here, we attempt to find solutions for the VRP problems: (4,2), (5,2), and (5,3), where each (n, k) represents a VRP problem with n locations and k vehicles. We find that the performance of QAOA is not just dependent upon the classical optimizer used, the number of steps p in which an adiabatic path is realized, or the way parameters are initialized, but also on the problem instance itself.
Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making

Several strands of research have aimed to bridge the gap between artificial intelligence (AI) and human decision-makers in AI-assisted decision-making, where humans are the consumers of AI model predictions and the ultimate decision-makers in high-stakes applications. However, people's perception and understanding are often distorted by their cognitive biases, such as confirmation bias, anchoring bias, availability bias, to name a few. In this work, we use knowledge from the field of cognitive science to account for cognitive biases in the human-AI collaborative decision-making setting, and mitigate their negative effects on collaborative performance. To this end, we mathematically model cognitive biases and provide a general framework through which researchers and practitioners can understand the interplay between cognitive biases and human-AI accuracy. We then focus specifically on anchoring bias, a bias commonly encountered in human-AI collaboration. We implement a time-based de-anchoring strategy and conduct our first user experiment that validates its effectiveness in human-AI collaborative decision-making. With this result, we design a time allocation strategy for a resource-constrained setting that achieves optimal human-AI collaboration under some assumptions. We, then, conduct a second user experiment which shows that our time allocation strategy with explanation can effectively de-anchor the human and improve collaborative performance when the AI model has low confidence and is incorrect.
Generalized unitary coupled cluster excitations for multireference molecular states optimized by the variational quantum eigensolver

Abstract The variational quantum eigensolver (VQE) algorithm, designed to calculate the energy of molecular ground states on near‐term quantum computers, requires specification of symmetries that describe the system, for example, spin state and number of electrons. This opens the possibility of using VQE to obtain excited states as the lowest‐energy solutions of a given set of symmetries. In this paper, the performances of various unitary coupled cluster (UCC) ansätze applied to VQE calculations on excited states are investigated using quantum circuits designed to represent single reference and multireference wavefunctions to calculate energy curves with respect to variations in the molecular geometry. These ansätze include standard Unitary Coupled Cluster Singles and Doubles (UCCSD), as well as modified versions of Unitary Coupled Cluster Generalized Singles and Doubles (UCCGSD) and k ‐UpCCGSD, which are engineered to tackle excited states without undesired spin symmetry crossover to lower states during VQE optimization. These studies are carried out on a range of systems, including H 2 , H 3 , H 4 , NH, OH + , CH 2 , and , covering examples of spin singlet, doublet, and triplet molecular ground states with single and multireference excited states. In most cases, our calculations are in excellent agreement with results from full‐configuration interaction calculations on classical machines, thus showing that the VQE algorithm is capable of calculating the lowest excited state at a certain symmetry, including multireference closed‐ and open‐shell states, by setting appropriate restrictions on the excitations considered in the cluster operator and appropriate constraints in the qubit register encoding the starting mean field state.
Running Industrial Workflow Applications in a Software-Defined Multicloud Environment Using Green Energy Aware Scheduling Algorithm

Industry 4.0 have automated the entire manufacturing sector (including technologies and processes) by adopting Internet of Things and cloud computing. To handle the workflows from Industrial Cyber-Physical systems, more and more data centers have been built across the globe to serve the growing needs of computing and storage. This has led to an enormous increase in energy usage by cloud data centers, which is not only a financial burden but also increases their carbon footprint. The private software defined wide area network (SDWAN) connects a cloud provider's data centers across the planet. This gives the opportunity to develop new scheduling strategies to manage cloud providers workload in a more energy-efficient manner. In this context, this article addresses the problem of scheduling data-driven industrial workflow applications over a set of private SDWAN connected data centers in an energy-efficient manner while managing tradeoff of a cloud provider' revenue. Our proposed algorithm aims to minimize the cloud provider's revenue and the usage of nonrenewable energy by utilizing the real-world electricity prices with the availability of green energy on different cloud data centers, where the energy consumption consists of the usage of running application over multiple data centers and transferring the data among them through SDWAN. The evaluation shows that our proposed method can increase usage of green energy for the execution of industrial workflow up to 3× times with a slight increase in the cost when compared to cost-based workflow scheduling methods.
On the Security of Time-Lock Puzzles and Timed Commitments

Time-lock puzzles—problems whose solution requires some amount of sequential effort—have recently received increased interest (e.g., in the context of verifiable delay functions). Most constructions rely on the sequential-squaring conjecture that computing $$g^{2^T} \bmod N$$ for a uniform g requires at least T (sequential) steps. We study the security of time-lock primitives from two perspectives:
Semantic Representation for Dialogue Modeling

Although neural models have achieved competitive results in dialogue systems, they have shown limited ability in representing core semantics, such as ignoring important entities.To this end, we exploit Abstract Meaning Representation (AMR) to help dialogue modeling.Compared with the textual input, AMR explicitly provides core semantic knowledge and reduces data sparsity.We develop an algorithm to construct dialogue-level AMR graphs from sentence-level AMRs and explore two ways to incorporate AMRs into dialogue systems.Experimental results on both dialogue understanding and response generation tasks show the superiority of our model.To our knowledge, we are the first to leverage a formal semantic representation into neural dialogue modeling.
SemEval-2021 Task 1: Lexical Complexity Prediction

This paper presents the results and main findings of SemEval-2021 Task 1 -Lexical Complexity Prediction.We provided participants with an augmented version of the CompLex Corpus (Shardlow et al., 2020).CompLex is an English multi-domain corpus in which words and multi-word expressions (MWEs) were annotated with respect to their complexity using a five point Likert scale.SemEval-2021 Task 1 featured two Sub-tasks: Sub-task 1 focused on single words and Sub-task 2 focused on MWEs.The competition attracted 198 teams in total, of which 54 teams submitted official runs on the test data to Sub-task 1 and 37 to Sub-task 2.
Efficient Steganography in JPEG Images by Minimizing Performance of Optimal Detector

Since the introduction of adaptive steganography, most of the recent research works seek at designing cost functions that are evaluated against steganalysis methods.While those approaches have been successful, they rely on intuitive principles and ad-hoc costs associated with each pixel or Discrete Cosine Transform (DCT) coefficient.Beyond the empirical assessments, the insights one can get from such approaches are very limited.On the opposite, this paper presents an original method for steganography in JPEG images that exploits a statistical model of the DCT coefficients.Within the framework of hypothesis testing theory, we use a statistical model of covers to derive the analytical expression of the most powerful detector.The objective of the steganographer is to minimize the statistical performance of this "omniscient detector" which represents a "worst-case" scenario for security.This paper shows how this method allows designing effective steganography, in terms of both security and computational complexity, in the two main use cases: when having only one single JPEG image and when the uncompressed image is available, case also known as Side-Informed (SI).A wide range of numerical comparisons shows that the proposed method outperforms the current state-of-theart especially against the latest and most accurate steganalysis approaches based on Deep Learning.
Opinion Mining for Software Development: A Systematic Literature Review

Opinion mining, sometimes referred to as sentiment analysis, has gained increasing attention in software engineering (SE) studies. SE researchers have applied opinion mining techniques in various contexts, such as identifying developers’ emotions expressed in code comments and extracting users’ critics toward mobile apps. Given the large amount of relevant studies available, it can take considerable time for researchers and developers to figure out which approaches they can adopt in their own studies and what perils these approaches entail. We conducted a systematic literature review involving 185 papers. More specifically, we present (1) well-defined categories of opinion mining-related software development activities, (2) available opinion mining approaches, whether they are evaluated when adopted in other studies, and how their performance is compared, (3) available datasets for performance evaluation and tool customization, and (4) concerns or limitations SE researchers might need to take into account when applying/customizing these opinion mining techniques. The results of our study serve as references to choose suitable opinion mining tools for software development activities and provide critical insights for the further development of opinion mining techniques in the SE domain.
EVALUATION OF DISTANCE LEARNING SYSTEM (E-LEARNING): A SYSTEMATIC LITERATURE REVIEW

Research in the field of e-learning is currently experiencing rapid development, especially due to the covid 19 pandemic. The application of e-learning in the world of education is currently the main thing and requires evaluation of its use. This study aims to determine the evaluation of models and trends in the development of e-learning (Learning Management System). Applying the Kitchenham approach, this System Literature Review (SLR) uses three main databases including Science Direct, ACM, SCOPUS. The final result obtained 38 articles published between 2016 and 2021. From this SLR, it was found that there were 7 criteria, namely Platform, Evaluation Model, Evaluation, Model, Approach, Problem, Trend and Challenge. These 7 criteria can be used for further research on e-learning. Thus this research provides knowledge about criteria that can be used further in research on E-learning and provides insight into its state-of-the-art.
The mediating role of organizational culture on the relationship between information technology and internal audit effectiveness

Internal audit effectiveness has become critical in helping businesses achieve their objectives. In today’s business world, information technology (IT) is frequently used in internal audit activities. Internal auditing is an important part of an organization’s control. Because internal audit and information technology play such important roles in each firm, this study looks at the function of organizational culture (OC) in mediating the link between the efficacy of information technology and internal audit in the Jordanian public sector. The study looks at this issue by gathering information from 153 internal auditors in Jordanian public enterprises. According to the findings of the study, information technology use and OC support are critical to increasing the efficacy of internal audits. The findings indicate that there is a considerable association between information technology and the efficacy of internal audits. Additionally, organizational culture has a key role in mediating the link between information technology and the success of internal audits. Future studies may examine managerial support and internal audit compliance
Tracker Meets Night: A Transformer Enhancer for UAV Tracking

Most previous progress in object tracking is realized in daytime scenes with favorable illumination. State-of-the-arts can hardly carry on their superiority at night so far, thereby considerably blocking the broadening of visual tracking-related unmanned aerial vehicle (UAV) applications. To realize reliable UAV tracking at night, a spatial-channel Transformer-based low-light enhancer (namely SCT), which is trained in a novel task-inspired manner, is proposed and plugged prior to tracking approaches. To achieve semantic-level low-light enhancement targeting the high-level task, the novel spatial-channel attention module is proposed to model global information while preserving local context. In the enhancement process, SCT denoises and illuminates nighttime images simultaneously through a robust non-linear curve projection. Moreover, to provide a comprehensive evaluation, we construct a challenging nighttime tracking benchmark, namely DarkTrack2021, which contains 110 challenging sequences with over 100 K frames in total. Evaluations on both the public UAVDark135 benchmark and the newly constructed DarkTrack2021 benchmark show that the task-inspired design enables SCT with significant performance gains for nighttime UAV tracking compared with other top-ranked low-light enhancers. Real-world tests on a typical UAV platform further verify the practicability of the proposed approach. The DarkTrack2021 benchmark and the code of the proposed approach are publicly available at https://github.com/vision4robotics/SCT.
Securing Drug Distribution Systems from Tampering Using Blockchain

The purpose of this study is to overcome coordination flaws and enhance end-to-end security in the drug distribution market (DDM). One of the major issues in drug market coordination management is the absence of a centralized monitoring system to provide adequate market control and offer real-time prices, availability, and authentication data. Further, tampering is another serious issue affecting the DDM, and as a consequence, there is a significant global market for counterfeit drugs. This vast counterfeit drug business presents a security risk to the distribution system. This study presents a blockchain-based solution to challenges such as coordination failure, secure drug delivery, and pharmaceutical authenticity. To optimize the drug distribution process (DDP), a framework for drug distribution is presented. The proposed framework is evaluated using mathematical modeling and a real-life case study. According to our results, the proposed technique helps to maintain market equilibrium by guaranteeing that there is adequate demand while maintaining supply. Using the suggested framework, massive data created by the medication supply chain would be appropriately handled, allowing market forces to be better regulated and no manufactured shortages to inflate medicine prices. The proposed framework calls for the Drug Regulatory Authority (DRA) to authenticate users on blockchain and to monitor end-to-end DDP. Using the proposed framework, big data generated through drug supply chain will be properly managed; thus, market forces will be better controlled, and no artificial shortages will be generated to raise drug costs.
Analysis of deep learning frameworks for object detection in motion

Object detection and recognition is a computer vision technology and is considered as one of the challenging tasks in the field of computer vision. Many approaches for detection have been proposed in the past. AIM: This paper is mainly aiming to discuss the existing detection and classification techniques of Deep Convolutional Neural Networks (CNN) with an importance placed on highlighting the training and accuracy of the different CNN models. METHODS: In the proposed work, Faster RCNN, YOLO and SSD are used to detect helmets. OUTCOME: The survey says MobileNets has higher accuracy when compared to VGG16, VGG19 and Inception V3 and is therefore chosen to be used with SSD. The impact of the differences in the amount of training of each algorithm is highlighted which helps understand the advantages and disadvantages of each algorithm and deduce the most suitable.
DisenCDR

Data sparsity is a long-standing problem in recommender systems. To alleviate it, Cross-Domain Recommendation (CDR) has attracted a surge of interests, which utilizes the rich user-item interaction information from the related source domain to improve the performance on the sparse target domain. Recent CDR approaches pay attention to aggregating the source domain information to generate better user representations for the target domain. However, they focus on designing more powerful interaction encoders to learn both domains simultaneously, but fail to model different user preferences of different domains. Particularly, domain-specific preferences of the source domain usually provide useless information to enhance the performance in the target domain, and directly aggregating the domain-shared and domain-specific information together maybe hurts target domain performance. This work considers a key challenge of CDR: How do we transfer shared information across domains? Grounded in the information theory, we propose DisenCDR, a novel model to disentangle the domain-shared and domain-specific information. To reach our goal, we propose two mutual-information-based disentanglement regularizers. Specifically, an exclusive regularizer aims to enforce the user domain-shared representations and domain-specific representations encoding exclusive information. An information regularizer is to encourage the user domain-shared representations encoding predictive information for both domains. Based on them, we further derive a tractable bound of our disentanglement objective to learn desirable disentangled representations. Extensive experiments show that DisenCDR achieves significant improvements over state-of-the-art baselines on four real-world datasets.
MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER

Data augmentation is an effective solution to data scarcity in low-resource scenarios.However, when applied to token-level tasks such as NER, data augmentation methods often suffer from token-label misalignment, which leads to unsatsifactory performance.In this work, we propose Masked Entity Language Modeling (MELM) as a novel data augmentation framework for low-resource NER.To alleviate the token-label misalignment issue, we explicitly inject NER labels into sentence context, and thus the fine-tuned MELM is able to predict masked entity tokens by explicitly conditioning on their labels.Thereby, MELM generates high-quality augmented data with novel entities, which provides rich entity regularity knowledge and boosts NER performance.When training data from multiple languages are available, we also integrate MELM with codemixing for further improvement.We demonstrate the effectiveness of MELM on monolingual, cross-lingual and multilingual NER across various low-resource levels.Experimental results show that our MELM presents substantial improvement over the baseline methods. 1
Video Hand Gestures Recognition Using Depth Camera and Lightweight CNN

Hand gestures are a well-known and intuitive method of human-computer interaction. The majority of the research has concentrated on hand gesture recognition from the RGB images, however, little work has been done on recognition from videos. In addition, RGB cameras are not robust in varying lighting conditions. Motivated by this, we present the video based hand gestures recognition using the depth camera and a light weight convolutional neural network (CNN) model. We constructed a dataset and then used a light weight CNN model to detect and classify hand movements efficiently. We also examined the classification accuracy with a limited number of frames in a video gesture. We compare the depth camera’s video gesture recognition performance to that of the RGB camera. We evaluate the proposed model’s performance on edge computing devices and compare to benchmark models in terms of accuracy and inference time. The proposed model results in an accuracy of 99.48% on the RGB version of test dataset and 99.18% on the depth version of test dataset. Finally, we compare the accuracy of the proposed light weight CNN model with the state-of-the hand gesture classification models.