Reviewerly/exHarmony huggingface dataset:
Multi‐task deep neural networks for just‐in‐time software defect prediction on mobile apps

Summary With the development of smartphones, mobile applications play an irreplaceable role in our daily life, which characteristics often commit code changes to meet new requirements. This characteristic can introduce defects into the software. To provide immediate feedback to developers, previous researchers began to focus on just‐in‐time (JIT) software defect prediction techniques. JIT defect prediction aims to determine whether code commits will introduce defects into the software. It contains two scenarios, within‐project JIT defect prediction and cross‐project JIT defect prediction. Regardless of whether within‐project JIT defect prediction or cross‐project JIT defect prediction all need to have enough labeled data (within‐project JIT defect prediction assumes that have plenty of labeled data from the same project, while cross‐project JIT defect prediction assumes that have sufficient labeled data from source projects). However, in practice, both the source and target projects may only have limited labeled data. We propose the MTL‐DNN method based on multi‐task learning to solve this question. This method contains the data preprocessing layer, input layer, shared layers, task‐specific layers, and output layer. Where the common features of multiple related tasks are learned by sharing layers, and the unique features of each task are learned by the task‐specific layers. For verifying the effectiveness of the MTL‐DNN approach, we evaluate our method on 15 Android mobile apps. The experimental results show that our method significantly outperforms the state‐of‐the‐art single‐task deep learning and classical machine learning methods. This result shows that the MTL‐DNN method can effectively solve the problem of insufficient labeled training data for source and target projects.
Multi-Dimensional Information Alignment in Different Modalities for Generalized Zero-Shot and Few-Shot Learning

Generalized zero-shot learning (GZSL) aims to solve the category recognition tasks for unseen categories under the setting that training samples only contain seen classes while unseen classes are not available. This research is vital as there are always existing new categories and large amounts of unlabeled data in realistic scenarios. Previous work for GZSL usually maps the visual information of the visible classes and the semantic description of the invisible classes into the identical embedding space to bridge the gap between the disjointed visible and invisible classes, while ignoring the intrinsic features of visual images, which are sufficiently discriminative to classify themselves. To better use discriminative information from visual classes for GZSL, we propose the n-CADA-VAE. In our approach, we map the visual feature of seen classes to a high-dimensional distribution while mapping the semantic description of unseen classes to a low-dimensional distribution under the same latent embedding space, thus projecting information of different modalities to corresponding space positions more accurately. We conducted extensive experiments on four benchmark datasets (CUB, SUN, AWA1, and AWA2). The results show our model’s superior performance in generalized zero-shot as well as few-shot learning.
Construction of quaternary quantum error-correcting codes via orthogonal arrays

This paper presents a method based on orthogonal arrays of constructing pure quaternary quantum error-correcting codes. As an application of the method, some infinite classes of quantum error-correcting codes with distances 2, 3, and 4 can be obtained. Moreover, the infinite class of quantum codes with distance 2 is optimal. The advantage of our method also lies in the fact that the quantum codes we obtain have less items for a basis quantum state than existing ones.
Detecting Group Shilling Attacks In Recommender Systems Based On User Multi-dimensional Features And Collusive Behaviour Analysis

Abstract Group shilling attacks are more threatening than individual shilling attacks due to the collusive behaviours among group members, which pose a great challenge to the credibility of recommender systems. Detection of group shilling attacks can reduce the risk caused by such attacks and ensure the credibility of recommendations. The existing methods for detecting group shilling attacks mainly extract features from the rating patterns of users at group level to measure the shilling behaviours of groups. However, they may become ineffective with the change of attack strategy, resulting in a decrease in detection performance. Aiming at this problem, a new solution based on user multi-dimensional features and collusive behaviour analysis is presented for detecting group shilling attacks. First, we employ the information entropy and latent semantic analysis to analyse the user behavioural patterns from dimensions of item, rating, time and interest, and propose a suite of indicators to measure the anomaly behaviours of users. Second, we propose a measure based on the multi-dimensional features of users to capture the collusion of group members from the perspective of their synchronized behaviours and abnormal behaviours, and treat the groups with high collusion as candidate groups. Finally, based on the multi-dimensional features of users, we construct the user behaviour similarity matrix using Gaussian radial basis function (Gaussian-RBF) and adopt the spectral clustering algorithm to spot group shilling attackers in the candidate groups. Experiments show that the detection performance (F1-measure) of the proposed method can achieve 0.965, 0.964, 0.991 and 0.868 on the Netflix, CiaoDVD, Epinions and Amazon datasets, respectively, which is better than that of state-of-the-art methods.
Possibilistic space object tracking under epistemic uncertainty

Bayesian filtering is a popular class of estimation algorithms for addressing the space object tracking problem. Bayesian filters assume a random physical system with known statistics of various uncertainty sources. The major challenge is that the exact knowledge of some random process may not be available for analysis, preventing us from performing a probabilistic characterization of the epistemic uncertainty components. In this paper, we explore the use of the Outer Probability Measures (OPMs) to achieve a faithful uncertainty representation derived from all available yet imperfect information in the process of space object tracking. Leveraging the concepts of OPMs, a refined Possibilistic Admissible Region approach is proposed, in which the initial orbital state is modeled using a novel parameter estimation method. The OPM filter is employed to integrate different types of data sources in the presence of assumed ignorance. The efficacy of the developed method is validated by several space object tracking scenarios using real radar measurements and two-line elements data.
A bilateral attention based generative adversarial network for DIBR 3D image watermarking

This paper presents a bilateral attention based generative adversarial network (BAGAN) for depth-image-based rendering (DIBR) 3D image watermarking to protect the image copyright. Convolutional block operations are employed to extract main image features for robust watermarking, but embedding watermark into some features will degrade image quality much. To relieve this kind of image distortion, the bilateral attention module (BAM) is utilized by mining correlations of the center view and the depth map to compute attention of the 3D image for guiding watermark to distribute over different image regions. Since a modality gap exists between the center view and the depth map, a cross-modal feature fusion module (CMFFM) is designed for BAM to bridge the cross-view gap. Because the depth map has lots of flat background information including many redundant features, to prune them, the depth redundancy elimination module (DREM) is used for cross-view feature fusion. In the decoder, two extractors with the same structure are built to recover watermark from the center view and the synthesized view, respectively. In addition, the discriminator is supposed to build a competitive relationship with the encoder to increase the image quality. The noise sub-network is used to train different image attacks for robustness. Extensive experimental results have demonstrated that the proposed BAGAN can obtain higher watermarking invisibility and robustness compared with existing DIBR 3D watermarking methods. Ablation experiments have also proven the effectiveness of DREM, CMFFM and BAM on BAGAN.
Enquiring Electronic Structure Using Quantum Computers: Hands on Qiskit

Abstract Solving the electronic structure for multi-electronic systems is a hard problem. Even for small atoms and molecules, approximations have to be made in order to solve numerically the Schrödinger equation. Although different methods have been developed to take into account electron correlations, their computational cost reduces their feasibility. Quantum simulation provides an alternative to traditional computational methods for enquiring the electronic structure of molecules. Specifically, the Variational Quantum Eigensolver (VQE) algorithm provides a hybrid quantum-classical algorithm for the implementation on current near term quantum devices. In this work, we explore the implementation of VQE on Qiskit for calculating the ground-state energy of diatomic Hydrogen molecule.
Fault-insertion and fault-fixing behavioural patterns in Apache Software Foundation Projects

Developers inevitably make human errors while coding. These errors can lead to faults in code, some of which may result in system failures. It is important to reduce the faults inserted by developers as well as fix any that slip through. To investigate the fault insertion and fault fixing activities of developers. We identify developers who insert and fix faults, ask whether code topic ‘experts’ insert fewer faults, and experts fix more faults and whether patterns of insertion and fixing change over time. We perform a time-based analysis of developer activity on twelve Apache projects using Latent Dirichlet Allocation (LDA), Network Analysis and Topic Modelling. We also build three models (using Petri-net, Markov Chain and Hawkes Processes) which describe and simulate developers’ bug-introduction and fixing behaviour. We show that: the majority of the projects we analysed have developers who dominate in the insertion and fixing of faults; Faults are less likely to be inserted by developers with code topic expertise; Different projects have different patterns of fault inserting and fixing over time. We recommend that projects identify the code topic expertise of developers and use expertise information to inform the assignment of project work.
Sampling of graph signals with successive aggregations based on graph fractional Fourier transform

The sampling defined on the graph nodes is a crucial method for graph signals sampling, especially considering graph structure. The sampling theory of the graph frequency domain for bandlimited graph signals has blossomed in recent years. However, it fails for the graph fractional domain bandlimited signals. In this paper, we first develop the theory of successive aggregations sampling associated with the graph fractional Fourier transform (GFRFT). Then, we propose the optimal node selection scheme in the case of noise. Moreover, we present a general sampling framework and prove that the existing graph signal sampling methods are its special cases. Finally, we explore the sparse reconstruction issue based on the developed successive aggregations sampling. Our proposed sampling method outperforms other sampling schemes in reconstruction error. Several experiments are performed to validate the effectiveness of the proposed sampling method numerically.
A Curriculum of Table-Top Quantum Optics Experiments to Teach Quantum Physics

Abstract The rise of quantum information as a viable technology requires appropriate instructional curricula for preparing a future workforce. Key concepts that are the basis of quantum information involve fundamentals of quantum mechanics, such as superposition, entanglement and measurement. To complement modern initiatives to teach quantum physics to the emerging workforce, lab experiences are needed. We have developed a curriculum of quantum optics experiments to teach quantum mechanics fundamentals and quantum algebra. These laboratories provide hands-on experimentation of optical components on a table-top. We have also created curricular materials, manuals, tutorials, parts and price lists for instructors. Automation of the apparatus offers the flexibility of using the apparatus remotely and for giving access to a greater number of students with a single setup.
A domain adaptation approach for resume classification using graph attention networks and natural language processing

With the growth of online recruitment, hiring platforms receive and store an enormous amount of resumes and job posts of various fields. Hence, applying automatic resume classification in database systems can reduce the time and labor required for database management, allowing recruiters to re-update the databases quickly. In past studies on resume classification and machine learning techniques, the shortage of labeled resume datasets used in model training was a problem to be addressed. Therefore, this study applied a domain adaptation approach based on a graph neural network, so that the latent features of job posts can be extracted to classify resumes without requiring labeled resumes and a retraining model. The domain adaptation approach compares the semantic similarity between job posts and resume contents for resume classification. The proposed method was tested in an actual recruitment database and achieved a highly accurate result.
Finding associations between natural and computer languages: A case-study of bilingual LDA applied to the bleeping computer forum posts

In the context of technical support, trails of technical discussions often contain a mixture of natural language (e.g., English) and software log excerpts. Uncovering latent links between certain problems and log excerpts that are often requested during the discussions of those problems enables the construction of a valuable knowledge base. Nevertheless, uncovering such latent links is challenging because English and software logs are two fundamentally different languages. In this paper, we investigate the suitability of multilingual LDA models to address the problem at hand. We study three models, namely: enriched LDA (M+), two-layer LDA (M2L), and off-the-shelf bilingual LDA (Mbi). We use approximately 8K discussion threads from a Bleeping Computer forum as our dataset. We observe that M2L performs the best overall, although it yields a substantially coarser-grained view of the discussed themes in the threads (20 topics, 0.3% of the documents). We also note that M+ outperforms Mbi achieving higher coherence, lower perplexity, and higher cross-lingual coverage ratio. We invite future studies to qualitatively assess the quality of the topics produced by the LDA models, such that the feasibility of employing such models in practice can be better determined.
ORT: Occlusion-robust for multi-object tracking

Although the joint-detection-and-tracking paradigm has promoted the development of multi-object tracking (MOT) significantly, the long-term occlusion problem is still unsolved. After a period of trajectory inactivation due to occlusion, it is difficult to achieve trajectory reconnection with appearance features because they are no longer reliable. Although using motion cues does not suffer from occlusion, the commonly used Kalman Filter is also ineffective in its long-term inertia prediction in cases of no observation updates or wrong updates. Besides, occlusion is prone to cause multiple track-detection pairs to have close similarity scores during the data association phase. The direct use of the Hungarian algorithm to give the global optimal solution may generate the identity switching problem. In this paper, we propose the Long-term Spatio-Temporal Prediction (LSTP) module and the Ordered Association (OA) module to alleviate the occlusion problem in terms of motion prediction and data association, respectively. The LSTP module estimates the states of all tracked objects over time using a combination of spatial and temporal Transformers. The spatial Transformer models crowd interaction and learns the influence of neighbors, while the temporal Transformer models the temporal continuity of historical trajectories. Besides, the LSTP module also predicts the visibilities of the motion prediction boxes, which denote the occlusion attributes of trajectories. Based on the occlusion attribute and active state, the association priority is defined in the OA module to associate trajectories in order, which helps to alleviate the identity switching problem. Comprehensive experiments on the MOT17 and MOT20 benchmarks indicate the superiority of the proposed MOT framework, namely Occlusion-Robust Tracker (ORT). Without using any appearance information, our ORT can achieve competitive performance beyond other state-of-the-art trackers in terms of trajectory accuracy and purity.
Impact of Genetic Algorithm for the Diagnosis of Breast Cancer: Literature Review

In recent research from the total number of new cancer cases in Africa about 29.46% and in Ethiopia 31.85% are breast cancer cases.25.84% of all cancer related death is from breast cancer.One of the challenges in the treatment of breast cancer is early detection.Researchers agreed that, improving the preventive mechanism of breast cancer is an early predicting and detecting model.Research efforts are continuing to present different solution approaches using advanced techniques of Artificial intelligence (AI), Machine learning (ML), Deep Learning (DL), and Computational Intelligence as well.A genetic algorithm is a hyper-parameter optimization algorithm that belongs to the class of evolutionary algorithms.Genetic Algorithm (GA) is used for complex search spaces for search and optimization.This reviewed literature paper shows the positive effect of GA in the diagnosis of breast cancer on AI algorithms.
Digitalization of Education and Distance Learning Technologies: Development Trends

The article analyzes data on the use of digital technologies in education, on the difficulties and prospects of its digital transformation, which meets the challenges of the emerging digital economy. They discuss changes in the field of digital technologies and the educational process that will become dominant in the development of education in the next decade. The article considers specific measures that allow in the developing digital educational environment to move from the traditional to the personalized and result-oriented organization of the educational process or the training system that helps better solve the problems of improving the quality of education. The article deals with distance learning prospects, as well as with its advantages and disadvantages. The flexibility and accessibility of online education, individual approach and cost-effectiveness, which are the main advantages of distance learning, are reviewed. It is pointed out that the incorrect use of information technologies, the lack of social interaction and the lack of proper qualifications of the user make distance learning an imperfect educational system. The factors of practical implementation of distance learning in the higher education system are described.
Comparative Analysis of Scheduling Algorithms in Cloud Computing using CloudSim

Cloud computing restructured the entire world of IT by providing shared scalable resources to the organization. Cloud establishes a huge path to find the solution for many major problems found in the industry. Cloud offers virtualized resources such as applications, software, networks, servers, and storage services. Cloud enables all the virtualized resources to clients on a pay-per-use basis. Because of handling the vast request sent by multiple clients and providing more versatile services to the organization, the cloud faces many critical problems such as security issues, dissatisfied Quality-Of-Services, and sometimes an unbalanced load arises. Among those, the most serious problem is balancing the load across the network. Load balancing issues can be handled by scheduling the work eventually to all nodes without overloading any single node. This paper gives an overview idea of different load balancing algorithms and provides comparative results on its quality metrics.
Image-Based Attendance System using Facial Recognition

In today's digital age, attendance systems utilizing facial recognition are essential in schools, universities, companies, etc. One feature of the human body that might help identify a person is the face. Using a camera, we initially built a database of various people's faces for this project, and the recognizer algorithm would use this information. The system will be prepared to take attendance on its own after the database has been established. A cross-check will be performed between the recently acquired face image and the database. The attendance will be recorded and the data immediately saved into the excel sheet if the same face's image is found in the database. This attendance-based system using Face Recognition will be developed using PCA (Principal Component Analysis) and CNN (Convolutional Neural Network).
Blockchain in Cloud Computing: Design Challenges

Blockchains is an immutable network comprising data records that are shared from across show's nodes and include all the specifics of the transactions that have transpired. Consent techniques are used to validate every transaction made in the computer, and once data has been recorded, it cannot be changed. Bitcoin, a well-known virtual currency, is supported by blockchains, which is a prerequisite new tech. The term “cloud technology” refers to the usage of a network of remote servers located on the internet to store, process, and analyse data instead of a server or even a personal computer. Numerous difficulties still exist, including those associated with data management, safety, and dependability. In this post, we've discussed some of the major problems that the clouds are now facing and suggested remedies by fusing the internet with block chain. To demonstrate their superiority, we prefer to go at a quick review of prior research that were centre on blockchain integration with the cloud. In this study, we have created an infrastructure that integrates cryptocurrency and cloud, exposing how they communicate.
Blockchain-Based Secure Mengers Authentication for Industrial IoT

The Industrial Internet of Things (I-IoT) is positioned as one of the most evolutionarily potential techniques for enhancing the production environment, logistics, and production in industrial settings. While a production method is more complicated, it is necessary to give a full operational IoT where equipment from various environments, such as mobility, plan to operate on the same job, ensuring more security and confidentiality for equipment when communicating. Some popular something which result in third-party secure and address the main management challenges. As a result, security and confidentiality need more attention since they are still a problem. In this study, Menger's identification with block chain assistance is a brand-new and effective security method that is expected to build trust across diverse domains. Security is implemented here via the authorization process. Gadget privacy is maintained by the mengers verification system, which also achieves unit identification across linked devices. The distribution of keys among the linked parties is still subject to negotiation via the appropriate channels for communication. Here, MATLAB is used for the simulation, and the results demonstrate superior results during the authorization process.