Reviewerly/exHarmony huggingface dataset:
Improving News Recommendation with Channel-Wise Dynamic Representations and Contrastive User Modeling

News modeling and user modeling are the two core tasks of news recommendation. Accurate user representation and news representation can enable the recommendation system to provide users with precise recommendation services. Most existing methods use deep learning models such as CNN and Self-Attention to extract text features from news titles and abstracts to generate specific news vectors. However, the CNN-based methods have fixed parameters and cannot extract specific features for different input words, while the Self-Attention-based methods have high computational costs and are difficult to capture local features effectively. In our proposed method, we build a category-based dynamic component to generate suitable parameters for different inputs and extract local features from multiple perspectives. Meanwhile, users will mistakenly click on some news terms they are not interested in, so there will be some interaction noises in the datasets. In order to explore the critical user behaviors in user data and reduce the impact of noise data on user modeling, we adopt a frequency-aware contrastive learning method in user modeling. Experiments on real-world datasets verify the effectiveness of our proposed method.
How Private Blockchain Technology Secure IoT Data Record

Scalability, security, and communication delays in the Internet of Things can be resolved using blockchain technology, which is the final piece of the puzzle. Blockchain technology may provide the IoT sector with the panacea it needs. Blockchain technology can record massive amounts of access points, allowing for the recording of data and collaboration between equipment, and providing manufacturers in the IoT sector with significant cost reductions. By removing individual points of failure, this decentralized strategy would build a more robust environment for the operation of equipment. Wondering in the era of information technology. Blockchain has attracted a lot of interest in recent decades for its ability to provide decentralized, definitive, and auditable usage in the Internet of Things (IoT). The majority of Internet of Things (IoT) devices are facing significant adaptability and privacy difficulties. This study proposes an understanding of How blockchain works with IoT for security data records.
Deep-learning-based cross-talk free and high-security compressive encryption with spatially incoherent illumination

Incoherent optical cryptosystem is promising for its immunity against coherent noise and insensitivity to misalignment, and compressive encryption is desirable considering the increasingly demand on the exchange of encrypted data via Internet. In this paper, we propose a novel optical compressive encryption approach with spatially incoherent illumination based on deep learning (DL) and space multiplexing. For encryption, the plaintexts are individually sent to the scattering-imaging-based encryption (SIBE) scheme where they are transformed to scattering images with noise appearances. Afterwards, these images are randomly sampled and then integrated into a single package (i.e., ciphertext) by space multiplexing. The decryption is basically the inverse of the encryption, while it involves an ill-posed problem (i.e., recovering the noise-like scattering image from its randomly sampled version). We demonstrated that such a problem can be well resolved by DL. The proposal is radically free from the cross-talk noise existing in many current multiple-image encryption schemes. Also, it gets rid of the linearity bothering the SIBE and is hence robust against the ciphertext-only attack based on phase retrieval algorithm. We present a series of experimental results to confirm the effectiveness and feasibility of the proposal.
Open science: Scientists are in favor, what about the librarians?

The author reviews the results of the survey of the researchers of the Siberian Branch of the Russian Academy of Sciences (SB RAS), responses to The State of Open Data survey of researchers in 192 countries (2016–2022), and interviews of the librarians of SB RAS scientific organizations on their attitude to open science and its information support. The study was accomplished within the research project of SB RAS State Public Scientific and Technological Library “Development of the functional model of scientific library in the open science information ecosystem”. The author demonstrates that the researchers have been increasingly publishing their results in the open science (OS) journals, though still are unaware of OS types, doubt OS publications quality, and beware data exchange. The scientists are positive about open science and an open data (OD) system, OD and open-source software, open cooperation between researchers, as well as open peer reviewing. The study also proves that the librarians have been providing traditional services, not very eager about open science support services, doubt their necessity and relevance. The author argues that the librarians have to reconsider their attitude toward open science support, to explore new resources, to expand their competences, and to build the system of integrated information support of scientific research, which makes the libraries active actors in advancing open science practices.
Evaluation of Deep Learning CNN Model for Recognition of Devanagari Digit

Devanagari character and digit recognition are a difficult undertaking because writing style depends on a person’s traits and differs from person to person. We get more precise results in digit recognition, thanks to deep learning convolutional neural networks (CNNs), which function similarly to the human brain. In this study, the CNN method was put into practice and contrasted with the feed-forward neural network and random forest approaches. In comparison to previous methods, CNN has reportedly provided an accuracy rating of up to 99.2%. CNN is effective with both organized and unstructured data, including pictures, video, and audio.
A Personalized Neighborhood-based Model for Within-basket Recommendation in Grocery Shopping

Users of online shopping platforms typically purchase multiple items at a time in the form of a shopping basket. Personalized within-basket recommendation is the task of recommending items to complete an incomplete basket during a shopping session. In contrast to the related task of session-based recommendation, where the goal is to complete an ongoing anonymous session, we have access to the shopping history of the user in within-basket recommendation. Previous studies have shown the superiority of neighborhood-based models for session-based recommendation and the importance of personal history in the grocery shopping domain. But their applicability in within-basket recommendation remains unexplored.
Zero-Shot Information Extraction via Chatting with ChatGPT

Zero-shot information extraction (IE) aims to build IE systems from the unannotated text. It is challenging due to involving little human intervention. Challenging but worthwhile, zero-shot IE reduces the time and effort that data labeling takes. Recent efforts on large language models (LLMs, e.g., GPT-3, ChatGPT) show promising performance on zero-shot settings, thus inspiring us to explore prompt-based methods. In this work, we ask whether strong IE models can be constructed by directly prompting LLMs. Specifically, we transform the zero-shot IE task into a multi-turn question-answering problem with a two-stage framework (ChatIE). With the power of ChatGPT, we extensively evaluate our framework on three IE tasks: entity-relation triple extract, named entity recognition, and event extraction. Empirical results on six datasets across two languages show that ChatIE achieves impressive performance and even surpasses some full-shot models on several datasets (e.g., NYT11-HRL). We believe that our work could shed light on building IE models with limited resources.
Global Central Bank Digital Currency Research and Developments: Implication for Cryptocurrency

Central bank digital currency (CBDC) is part of the ongoing change and disruptive financial innovation occurring in the modern financial system. Historically, financial innovation lies within the realm of private sector agents such as regulated financial institutions and financial technology companies. They develop innovative ideas and create innovative financial products which the central bank or the regulator may approve or disallow. This means that the originator of disruptive financial innovations are primarily private sector agents, not the central bank. This historical understanding is important because it helps to understand why issuing a CBDC is a significant venture for any central bank because issuing a CBDC presents an opportunity for central banks to innovate, even though central banks are not innovative by nature.
Highly Secure and Easy to Remember Password-Based Authentication Approach

Everyone connected and using the Internet is concerned regarding the security and also the privacy of their sensitive information available on the Internet. As authentication is the fundamental part of security, there are different authentication mechanisms through which the systems can be secured. The password-based authentication mechanism is a cheap and easy method for enforcing authentication in the systems for many years. The weakest aspect in password security is human, as they choose weak and easy to guess passwords or a highly secure and complex password which might be difficult to remember and recover the password. On the other hand, Dictionary and Brute force attacks are widely used to compromise the passwords of the users over the Internet. In this paper, a password generation system is proposed which generates a password based on the user’s input like, time and location data. The system generates a password that is highly secure, easy to remember, easy to recover, and can effectively defend against Brute force and dictionary attacks. The generated passwords have been checked in three online password checkers, which verifies that the system is generating highly secure and crack resistant passwords. The system is implemented using PHP scripting language with a user-friendly environment.
Novel Convolutional Neural Networks based Jaya algorithm Approach for Accurate Deepfake Video Detection

Deepfake videos are becoming an increasing concern due to their potential to spread misinformation and cause harm. In this paper, we propose a novel approach for accurately detecting deepfake videos using the combination of Convolutional Neural Networks (CNNs) with the Jaya algorithm optimization. The approach is evaluated on two publicly available datasets, the DeepFake Detection Challenge (DFDC) dataset and the Celeb-DF dataset, and achieves state-of-the-art performance on both datasets. Our approach achieves an accuracy of 99.3% on the DFDC dataset and 97.6% on the Celeb-DF dataset, with high F1 scores indicating a high precision and recall for detecting deepfake videos. Furthermore, our approach is more robust against adversarial attacks than existing state-of-the-art methods. The combination of CNNs with the Jaya algorithm optimization enables effective capture of the temporal information in the video sequence, while the use of robust evaluation metrics ensures objective measurement and comparison with existing methods. Our proposed approach offers a highly effective solution for detecting deepfake videos, which has the potential to be a valuable tool for media forensics, content moderation, and cyber security.
Economics of Open-Source Solar Photovoltaic Powered Cryptocurrency Mining

Solar photovoltaic (PV) technology offers a promising means to alleviate environmental and electricity costs challenges for cryptocurrency miners. To analyze this promise, this study investigated the feasibility of using electricity from individually optimized PV systems to power: 1) an individual Bitcoin miner, 2) a DIY intermodal shipping container holding 50 miners, and 3) a commercial mining farm container holding 408 miners. In a controlled lab environment, miners were monitored for electricity use. Then using these values, numerical simulations of both the PV system yield and sensitivity ranges based on the Bitcoin price, Bitcoin halving events, and miner hardware were investigated for informed financial planning. In addition, sensitivity for geographic locations in North America, utility electric rates and PV capital costs were analyzed. The profitability and return on investment (ROI) varied by location primarily because of the geographic distribution of solar flux and utility rates. The ROI for using PV with Bitcoin mining was found to be negative for Toronto and Montreal because of low-cost electricity, while it was 8% for Calgary. In the U.S. cities evaluated, the ROIs were substantial and ranged from 34% in New York, to 64% in Boulder, and up to 104% in Los Angeles. Although the study is based in North America regarding energy rates, climate, and energy laws, the analysis methodology is generalizable globally and grants the average cryptocurrency business the knowledge to make an informed decision on whether to pursue this venture from a financial and environmental perspective. This study contributes to the body of knowledge in cryptocurrency mining by providing an economic means of environmental preservation by powering cryptocurrency miners with renewable solar energy.
Mammographically detected asymmetries in the era of artificial intelligence

Abstract Background Proper assessment of mammographically detected asymmetries is essential to avoid unnecessary biopsies and missed cancers as they may be of a benign or malignant cause. According to ACR BIRADS atlas 2013, mammographically detected asymmetries are classified into asymmetry, focal asymmetry, global asymmetry, and developing asymmetry. We aimed to assess the diagnostic performance of artificial intelligence in mammographically detected asymmetries compared to breast ultrasound as well as combined mammography and ultrasound. Results This study was a prospective study that comprised 51 women with breast asymmetry found on screening as well as diagnostic mammography. All participants conducted full-field digital mammography and ultrasound. Then the obtained mammographic images were processed by the artificial intelligence software system. Mammography had a sensitivity of 100%, specificity of 73%, a positive predictive value of 56.52%, a negative predictive value of 100%, and diagnostic accuracy of 80%. The results of Ultrasound revealed a sensitivity of 100.00%, a specificity of 89.47%, a positive predictive value of 76.47%, a negative predictive value of 100.00%, and an accuracy of 92.16%. Combined mammography and breast ultrasound showed a sensitivity of 100.00%, a specificity of 86.84%, a positive predictive value of 72.22%, a negative predictive value of 100.00%, and an accuracy of 90.20%. Artificial intelligence results demonstrated a sensitivity of 84.62%, a specificity of 94.74%, a positive predictive value of 48.26%, a negative predictive value of 94.47%, and an accuracy of 92.16%. Conclusions Adding breast ultrasound in the assessment of mammographically detected asymmetries led to better characterization, so it reduced the false-positive results and improved the specificity. Also, Artificial intelligence showed better specificity compared to mammography, breast ultrasound, and combined Mammography and ultrasound, so AI can be used to decrease unnecessary biopsies as it increases confidence in diagnosis, especially in cases with no definite ultrasound suspicious abnormality.
A Survey on Zero-Knowledge Authentication for Internet of Things

The Internet of Things (IoT) is ubiquitous in our lives. However, the inherent vulnerability of IoT smart devices can lead to the destruction of networks in untrustworthy environments. Therefore, authentication is a necessary tool to ensure the legitimacy of nodes and protect data security. Naturally, the authentication factors always include various sensitive users’ information, such as passwords, ID cards, even biological information, etc. How to prevent privacy leakage has always been a problem faced by the IoT. Zero-knowledge authentication is a crucial cryptographic technology that uses authenticates nodes on the networks without revealing identity or any other data entered by users. However, zero-knowledge proof (ZKP) requires more complex data exchange protocols and more data transmission compared to traditional cryptography technologies. To understand how zero-knowledge authentication works in IoT, we produce a survey on zero-knowledge authentication in privacy-preserving IoT in the paper. First, we overview the IoT architecture and privacy, including security challenges and open question in different IoT layers. Next, we overview zero-knowledge authentication and provide a comprehensive analysis of designing zero-knowledge authentication protocols in various IoT networks. We summarize the advantages of ZKP-based authentication in IoT. Finally, it summarizes the potential problems and future directions of ZKP in IoT.
Tighter Monogamy Relations for Concurrence and Negativity in Multiqubit Systems

The entanglement in multipartite quantum system is hard to characterize and quantify, although it has been intensively studied in bipartite systems. The monogamy of entanglement, as a special property of multipartite systems, shows the distribution of entanglement in the system. In this paper, we investigate the monogamy relations for multi-qubit systems. By using two entangled measures, namely the concurrence C and the negativity Nc, we establish tighter monogamy inequalities for their α-th power than those in all the existing ones. We also illustrate the tightness of our results for some classes of quantum states.
A Lightweight Automatic Wildlife Recognition Model Design Method Mitigating Shortcut Learning

Recognizing wildlife based on camera trap images is challenging due to the complexity of the wild environment. Deep learning is an optional approach to solve this problem. However, the backgrounds of images captured from the same infrared camera trap are rather similar, and shortcut learning of recognition models occurs, resulting in reduced generality and poor recognition model performance. Therefore, this paper proposes a data augmentation strategy that integrates image synthesis (IS) and regional background suppression (RBS) to enrich the background scene and suppress the existing background information. This strategy alleviates the model's focus on the background, guiding it to focus on the wildlife in order to improve the model's generality, resulting in better recognition performance. Furthermore, to offer a lightweight recognition model for deep learning-based real-time wildlife monitoring on edge devices, we develop a model compression strategy that combines adaptive pruning and knowledge distillation. Specifically, a student model is built using a genetic algorithm-based pruning technique and adaptive batch normalization (GA-ABN). A mean square error (MSE) loss-based knowledge distillation method is then used to fine-tune the student model so as to generate a lightweight recognition model. The produced lightweight model can reduce the computational effort of wildlife recognition with only a 4.73% loss in accuracy. Extensive experiments have demonstrated the advantages of our method, which is beneficial for real-time wildlife monitoring with edge intelligence.
<i>Trust–SIoT</i>: Toward Trustworthy Object Classification in the Social Internet of Things

The recent emergence of the promising paradigm of the Social Internet of Things (SIoT) is a result of an intelligent amalgamation of the social networking concepts with the Internet of Things (IoT) objects (also referred to as “things”) in an attempt to unravel the challenges of network discovery, navigability, and service composition. This is realized by facilitating the IoT objects to socialize with one another, i.e., similar to the social interactions amongst human beings. A fundamental issue that mandates careful attention is to thus establish, and over time, maintain trustworthy relationships amongst these IoT objects. Therefore, a trust framework for SIoT must include object-object interactions, the aspects of social relationships, credible recommendations, etc., however, the existing literature has only focused on some aspects of trust by primarily relying on the conventional approaches that govern linear relationships between input and output. In this paper, an artificial neural network-based trust framework, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Trust–SIoT</i> , has been envisaged for identifying the complex non-linear relationships between input and output in a bid to classify trustworthy objects. Moreover, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Trust–SIoT</i> has been designed for capturing a number of key trust metrics as input, i.e., direct trust by integrating both current and past interactions, reliability and benevolence of an object, credible recommendations, and the degree of relationship by employing knowledge graph embedding. Finally, we have performed extensive experiments to evaluate the performance of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Trust–SIoT</i> vis-á-vis state-of-the-art heuristics on two real-world datasets. The results demonstrate that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Trust–SIoT</i> achieves a higher F1-score and lower MAE and MSE scores.
The search for Minimal Search: A graph-theoretic approach

<title xmlns="http://www.ncbi.nlm.nih.gov/JATS1" /><p xmlns="http://www.ncbi.nlm.nih.gov/JATS1">This paper examines Minimal Search, an operation that is at the core of current Minimalist inquiry. We argue that, given Minimalist assumptions about structure building consisting of unordered set-formation, there are serious difficulties in defining Minimal Search as a search algorithm. Furthermore, some problematic configurations for Minimal Search (namely, {XP, YP} and {X, Y}) are argued to be an artefact of these set-theoretic commitments. However, if unordered sets are given up as the format of structural descriptions in favour of directed graphs such that Merge(X, Y) creates an arc from X to Y, Minimal Search can be straightforwardly characterised as a sequential deterministic search algorithm: the total order required to define MS as a sequential search algorithm is provided by structure building.
Semi-Sparsity for Smoothing Filters

In this paper, we propose a semi-sparsity smoothing method based on a new sparsity-induced minimization scheme. The model is derived from the observations that semi-sparsity prior knowledge is universally applicable in situations where sparsity is not fully admitted such as in the polynomial-smoothing surfaces. We illustrate that such priors can be identified into a generalized L0-norm minimization problem in higher-order gradient domains, giving rise to a new "feature-aware" filter with a powerful simultaneous-fitting ability in both sparse singularities (corners and salient edges) and polynomial-smoothing surfaces. Notice that a direct solver to the proposed model is not available due to the non-convexity and combinatorial nature of L0-norm minimization. Instead, we propose to solve it approximately based on an efficient half-quadratic splitting technique. We demonstrate its versatility and many benefits to a series of signal/image processing and computer vision applications.