Reviewerly/exHarmony huggingface dataset:
The use of massive open online courses in teaching the fundamentals of programming to software engineers

The article describes the possibilities of using massive open online courses (MOOC) in teaching the discipline "Fundamentals of Programming" to students majoring in software engineering. The content of studying the discipline "Fundamentals of Programming" and the number of hours allotted for its study in institutions of higher education in Ukraine is analyzed. When studying the discipline "Fundamentals of Programming" at the Zhytomyr Polytechnic State University, the study of the corresponding course on the platform of massive open online courses Sololearn is submitted for independent work. A description of the possibilities of using this MOOC in teaching the discipline "Fundamentals of Programming" to future specialists in software engineering is presented. In addition, using a pedagogical experiment, the effectiveness of using the Sololearn in teaching the discipline "Fundamentals of Programming" to future software engineering specialists was tested. The results of the pedagogical experiment showed the effectiveness of using the MOOC Sololearn in teaching the "Fundamentals of Programming" to future software engineering specialists.
Blockchain for the Healthcare Supply Chain: A Systematic Literature Review

A supply chain (SC) is a network of interests, information, and materials involved in processes that produce value for customers. The implementation of blockchain technology in SC management in healthcare has had results. This review aims to summarize how blockchain technology has been used to address SC challenges in healthcare, specifically for drugs, medical devices (DMDs), and blood, organs, and tissues (BOTs). A systematic review was conducted by following the PRISMA guidelines and searching the PubMed and Proquest databases. English-language studies were included, while non-primary studies, as well as surveys, were excluded. After full-text assessment, 28 articles met the criteria for inclusion. Of these, 15 (54%) were classified as simulation studies, 12 (43%) were classified as theoretical, and only one was classified as a real case study. Most of the articles (n = 23, 82%) included the adoption of smart contracts. The findings of this systematic review indicated a significant but immature interest in the topic, with diverse ideas and methodologies, but without effective real-life applications.
Language-Aware Domain Generalization Network for Cross-Scene Hyperspectral Image Classification

Text information including extensive prior knowledge about land cover classes has been ignored in hyperspectral image classification (HSI) tasks. It is necessary to explore the effectiveness of linguistic mode in assisting HSI classification. In addition, the large-scale pre-training image-text foundation models have demonstrated great performance in a variety of downstream applications, including zero-shot transfer. However, most domain generalization methods have never addressed mining linguistic modal knowledge to improve the generalization performance of model. To compensate for the inadequacies listed above, a Language-aware Domain Generalization Network (LDGnet) is proposed to learn cross-domain invariant representation from cross-domain shared prior knowledge. The proposed method only trains on the source domain (SD) and then transfers the model to the target domain (TD). The dual-stream architecture including image encoder and text encoder is used to extract visual and linguistic features, in which coarse-grained and fine-grained text representations are designed to extract two levels of linguistic features. Furthermore, linguistic features are used as cross-domain shared semantic space, and visual-linguistic alignment is completed by supervised contrastive learning in semantic space. Extensive experiments on three datasets demonstrate the superiority of the proposed method when compared with state-of-the-art techniques.
Few-shot training LLMs for project-specific code-summarization

Very large language models (LLMs), such as GPT-3 and Codex have achieved state-of-the-art performance on several natural-language tasks, and show great promise also for code. A particularly exciting aspect of LLMs is their knack for few-shot and zero-shot learning: they can learn to perform a task with very few examples. Few-shotting has particular synergies in software engineering, where there are a lot of phenomena (identifier names, APIs, terminology, coding patterns) that are known to be highly project-specific. However, project-specific data can be quite limited, especially early in the history of a project; thus the few-shot learning capacity of LLMs might be very relevant. In this paper, we investigate the use few-shot training with the very large GPT (Generative Pre-trained Transformer) Codex model, and find evidence suggesting that one can significantly surpass state-of-the-art models for code-summarization, leveraging project-specific training.
End-to-End Transformer-Based Models in Textual-Based NLP

Transformer architectures are highly expressive because they use self-attention mechanisms to encode long-range dependencies in the input sequences. In this paper, we present a literature review on Transformer-based (TB) models, providing a detailed overview of each model in comparison to the Transformer’s standard architecture. This survey focuses on TB models used in the field of Natural Language Processing (NLP) for textual-based tasks. We begin with an overview of the fundamental concepts at the heart of the success of these models. Then, we classify them based on their architecture and training mode. We compare the advantages and disadvantages of popular techniques in terms of architectural design and experimental value. Finally, we discuss open research, directions, and potential future work to help solve current TB application challenges in NLP.
Effectively Generating Vulnerable Transaction Sequences in Smart Contracts with Reinforcement Learning-guided Fuzzing

As computer programs run on top of blockchain, smart contracts have proliferated a myriad of decentralized applications while bringing security vulnerabilities, which may cause huge financial losses. Thus, it is crucial and urgent to detect the vulnerabilities of smart contracts. However, existing fuzzers for smart contracts are still inefficient to detect sophisticated vulnerabilities that require specific vulnerable transaction sequences to trigger. To address this challenge, we propose a novel vulnerability-guided fuzzer based on reinforcement learning, namely RLF, for generating vulnerable transaction sequences to detect such sophisticated vulnerabilities in smart contracts. In particular, we firstly model the process of fuzzing smart contracts as a Markov decision process to construct our reinforcement learning framework. We then creatively design an appropriate reward with consideration of both vulnerability and code coverage so that it can effectively guide our fuzzer to generate specific transaction sequences to reveal vulnerabilities, especially for the vulnerabilities related to multiple functions. We conduct extensive experiments to evaluate RLF's performance. The experimental results demonstrate that our RLF outperforms state-of-the-art vulnerability-detection tools (e.g., detecting 8%-69% more vulnerabilities within 30 minutes).
Audio encryption using two chaotic map based dynamic diffusion and double DNA encoding

A large amount of data in the form of audios are communicated all over the world. However, the attacker can steal this data, and then misuse this information. Hence, it is necessary to create strong audio encryption strategy to protect the data from various types of existing threats. This paper discusses about the implementation of a novel chaos-based strategy for audio encryption that uses the Sine-Cosine (SC) Map, Logistic Sine Cosine (LSC) map, and double DNA operation. The proposed approach is divided into three parts, i.e., Firstly, the permutation is performed using the Sine-Cosine map to generate the permuted audio. The permuted audio samples are scrambled using the double DNA addition encoding in the second phase. Finally, dynamic diffusion is carried out by selecting one of the chaotic maps from SC Chaotic map and the LSC map. The evaluation of the proposed approach has been done using various standard metrics like Peak Signal to Noise Ratio (PSNR), Histogram Analysis, Number of Sample Change Rate (NSCR), Correlation Coefficient (CC).
MDCT: Multi-Kernel Dilated Convolution and Transformer for One-Stage Object Detection of Remote Sensing Images

Deep learning (DL)-based object detection algorithms have gained impressive achievements in natural images and have gradually matured in recent years. However, compared with natural images, remote sensing images are faced with severe challenges due to the complex backgrounds and difficult detection of small objects in dense scenes. To address these problems, a novel one-stage object detection model named MDCT is proposed based on a multi-kernel dilated convolution (MDC) block and transformer block. Firstly, a new feature enhancement module, MDC block, is developed in the one-stage object detection model to enhance small objects’ ontology and adjacent spatial features. Secondly, we integrate a transformer block into the neck network of the one-stage object detection model in order to prevent the loss of object information in complex backgrounds and dense scenes. Finally, a depthwise separable convolution is introduced to each MDC block to reduce the computational cost. We conduct experiments on three datasets: DIOR, DOTA, and NWPU VHR-10. Compared with the YOLOv5, our model improves the object detection accuracy by 2.3%, 0.9%, and 2.9% on the DIOR, DOTA, and NWPU VHR-10 datasets, respectively.
When Privacy, Distrust, and Misinformation Cause Worry About Using COVID-19 Contact-Tracing Apps

Public health authorities worldwide are advocating for contact-tracing apps to help track COVID-19 infections during the pandemic and interrupt virus transmission. However, app users have to share their personally identifiable information, whereabouts, and in some cases, their vaccination records with authorities via mobile Internet. This situation creates grave concerns about how such personal information is transmitted, stored, archived, and disposed. In addition, the apps’ technical design would also impact the adoption, such as whether the apps would drain the battery. Further, citizens’ high distrust of governments also reduces app adoption. This article reviews recent research on contact-tracing apps and examines how privacy concerns, distrust in governments, and misinformation affect people's perceptions of contact-tracing apps. We recommend possible solutions for promoting these apps by analyzing what we learn from recent literature.
Preserving Privacy of Classified Authentic Satellite Lane Imagery Using Proxy Re-Encryption and UAV Technologies

Privacy preservation of image data has been a top priority for many applications. The rapid growth of technology has increased the possibility of creating fake images using social media as a platform. However, many people, including researchers, rely on image data for various purposes. In rural areas, lane images have a high level of importance, as this data can be used for analyzing various lane conditions. However, this data is also being forged. To overcome this and to improve the privacy of lane image data, a real-time solution is proposed in this work. The proposed methodology assumes lane images as input, which are further classified as fake or bona fide images with the help of Error Level Analysis (ELA) and artificial neural network (ANN) algorithms. The U-Net model ensures lane detection for bona fide lane images, which helps in the easy identification of lanes in rural areas. The final images obtained are secured by using the proxy re-encryption technique which uses RSA and ECC algorithms. This helps in ensuring the privacy of lane images. The cipher images are maintained using fog computing and processed with integrity. The proposed methodology is necessary for protecting genuine satellite lane images in rural areas, which are further used by forecasters, and researchers for making interpretations and predictions on data.
Fingerprint image encryption using phase retrieval algorithm in gyrator wavelet transform domain using QR decomposition

In this paper, we propose a novel optical asymmetric fingerprint image encryption technique that uses QR decomposition in gyrator wavelet transform domain. A fingerprint image is bonded with a phase mask generated with Gerchberg–Saxton phase retrieval algorithm, which is gyrator wavelet transformed. This output is further applied to QR decomposition scheme and different keys are generated through it. The process is iterated to enhance the level of security. A Haar wavelet has been used in the study. Different phase images, gyrator transform orders and the parameters of the wavelet help achieve imperceptibility and robustness. The asymmetric keys make the system attack free like Brute force attack, known plain text attack, and special attack. Numerical simulations carried out on a MATLAB platform demonstrate the security, privacy, and validity of the proposed encryption scheme.
Balanced knowledge distillation for long-tailed learning

Deep models trained on long-tailed datasets exhibit unsatisfactory performance on tail classes. Existing methods usually modify the classification loss to increase the learning focus on tail classes, which unexpectedly sacrifice the performance on head classes. In fact, this scheme leads to a contradiction between the two goals of long-tailed learning, i.e., learning generalizable representations and facilitating learning for tail classes. In this work, we explore knowledge distillation in long-tailed scenarios and propose a novel distillation framework, named Balanced Knowledge Distillation (BKD), to disentangle the contradiction between the two goals and achieve both simultaneously. Specifically, given a teacher model, we train the student model by minimizing the combination of an instance-balanced classification loss and a class-balanced distillation loss. The former benefits from the sample diversity and learns generalizable representation, while the latter considers the class priors and facilitates learning for tail classes. We conduct extensive experiments on several long-tailed benchmark datasets and demonstrate that the proposed BKD is an effective knowledge distillation framework in long-tailed scenarios, as well as a competitive method for long-tailed learning. Our source code is available: https://github.com/EricZsy/BalancedKnowledgeDistillation.
An outliers detection and elimination framework in classification task of data mining

An outlier is a datum that is far from other data points in which it occurs. It can have a considerable impact on the output. Therefore, removing or resolving it before the analysis is essential to prevent skewing. Outliers in a survey sampling can have a significant outcome on statistical results. The goal of discovering outliers in data mining is to find a pattern in data that does not conform to expected behavior. In this paper, we have proposed a framework in which a popular statistical approach termed Inter-Quartile Range (IQR) is used to detect outliers in data and deal with them by Winsorizing method. A radial basis function network trained by teaching a learning-based optimization model is developed using the preprocessed dataset under this framework. A few standard University of California Irvine (UCI)datasets are employed to measure the framework’s effectiveness. The outcome of the experiments shows that our proposed framework can be a viable alternative tool for the classification task of data mining where prior outliers preprocessing is necessary.
Digital Transformation and Competitive Advantage in the Service Sector: A Moderated-Mediation Model

Digital transformation is important not only in the manufacturing sector but can also help service-based organizations to achieve competitive advantage. Therefore, this study has examined the impact of digital transformation on competitive advantage and the mediating role of entrepreneurial orientation. Innovation capability is considered a moderator between digital transformation and competitive advantage. The data for the research were gathered from service companies in Jordan, and AMOS was utilized for the analysis. The results revealed that digital transformation not only influences competitive advantage but also affects entrepreneurial orientation. Entrepreneurial orientation significantly mediates the relationship between digital transformation and competitive advantage. In addition, innovation capabilities moderate the relationship between digital transformation, competitive advantage, and entrepreneurial orientation. Innovation, production, and operational managers as well as owners of service companies can use the results of this research as a guideline for policy-making in order to develop a competitive advantage.
A Color Image Encryption Algorithm Based on Hash Table, Hilbert Curve and Hyper-Chaotic Synchronization

Chaotic systems, especially hyper-chaotic systems are suitable for digital image encryption because of their complex properties such as pseudo randomness and extreme sensitivity. This paper proposes a new color image encryption algorithm based on a hyper-chaotic system constructed by a tri-valued memristor. The encryption process is based on the structure of permutation-diffusion, and the transmission of key information is realized through hyper-chaotic synchronization technology. In this design, the hash value of the plaintext image is used to generate the initial key the permutation sequence with the Hash table structure based on the hyper-chaotic sequence is used to implement pixel-level and bit-level permutation operations. Hilbert curves combining with the ciphertext feedback mechanism are applied to complete the diffusion operation. A series of experimental analyses have been applied to measure the novel algorithm, and the results show that the scheme has excellent encryption performance and can resist a variety of attacks. This method can be applied in secure image communication fields.
Reservoir computing based on electric-double-layer coupled InGaZnO artificial synapse

Physical reservoir computing (PRC) is thought to be a potential low training-cost temporal processing platform, which has been explored by the nonlinear and volatile dynamics of materials. An electric-double-layer (EDL) formed at the interface between a semiconductor and an electrolyte provided a great potential for building high energy-efficiency PRC. In this Letter, EDL coupled indium-gallium-zinc-oxide (IGZO) artificial synapses are used to implement reservoir computing (RC). Rich reservoir states can be obtained based the ionic relaxation-based time multiplexing mask process. Such an IGZO-based RC device exhibits nonlinearity, fade memory properties, and a low average power of ∼9.3 nW, well matching the requirement of a high energy-efficiency RC system. Recognition of handwritten digit and spoken-digit signals is simulated with an energy consumption per reservoir state of ∼1.9 nJ, and maximum accuracy of 90.86% and 100% can be achieved, respectively. Our results show a great potential of exploiting such EDL coupling for realizing a physical reservoir that would underlie a next-generation machine learning platform with a lightweight hardware structure.
Dynamic knowledge modeling and fusion method for custom apparel production process based on knowledge graph

Compared with ordinary mass-produced apparel products, custom apparel products will generate more data at each stage of their life cycle. Such data is in a highly dynamic state, while the relationship between the data is more complex. However, the current use of traditional relational data to store the whole life cycle data of custom apparel products has several problems of high redundancy, weak correlation, discrete distribution, and certain limitation of storage capacity. Therefore, based on knowledge graph, a dynamic knowledge modeling and fusion method is proposed for the production process of custom apparel. Firstly, an ontology-based knowledge modeling method is designed for custom apparel, which defined three types of ontology modeling methods for the process, resources, and features. On this basis, a knowledge graph construction method based on bi-directional fusion for the custom apparel production system is proposed. With one order as a unit, a knowledge graph facet (KGF) model, as well as the derived knowledge representation, generation and fusion method, is established to realize dynamic knowledge fusion of the custom apparel production process. Finally, taking the suit production process of a custom apparel factory as an example, the corresponding knowledge graph is constructed based on the ontology knowledge model, and the effectiveness of the proposed knowledge fusion method is verified.
What Does DALL-E 2 Know About Radiology?

Generative models, such as DALL-E 2 (OpenAI), could represent promising future tools for image generation, augmentation, and manipulation for artificial intelligence research in radiology, provided that these models have sufficient medical domain knowledge. Herein, we show that DALL-E 2 has learned relevant representations of x-ray images, with promising capabilities in terms of zero-shot text-to-image generation of new images, the continuation of an image beyond its original boundaries, and the removal of elements; however, its capabilities for the generation of images with pathological abnormalities (eg, tumors, fractures, and inflammation) or computed tomography, magnetic resonance imaging, or ultrasound images are still limited. The use of generative models for augmenting and generating radiological data thus seems feasible, even if the further fine-tuning and adaptation of these models to their respective domains are required first.
Talking to a bot or a wall? How chatbots vs. human agents affect anticipated communication quality

Chatbots have been applied to computer-mediated communication to replace human agents due to their high efficiency and cost-effectiveness. However, their outcomes are not always desirable, and limited guidance exists on how chatbots impact users' perceptions of the communication process. Drawing on cue-filtered-out theories and multiple resource theory, this study investigated the impacts of the communicating agent (chatbot vs. human agent) on anticipated communication quality and the underlying mechanism. Two experimental studies revealed that users' anticipated communication quality was lower with chatbots than with human agents due to the serial mediating role of self-focused attention and user empathy. Moreover, moderation analyses found that a multiple-choice communication strategy for chatbots can enhance users' anticipated communication quality by impacting self-focused attention compared to an open-ended strategy. The findings contribute to the knowledge of the processes driving users’ anticipated communication quality toward chatbot applications and offer managerial implications for chatbots and communication strategies.