Reviewerly/exHarmony huggingface dataset:
Personalized recommendation via user preference matching

Graph-based recommendation approaches use a graph model to represent the relationships between users and items, and exploit the graph structure to make recommendations. Recent graph-based recommendation approaches focused on capturing users’ pairwise preferences and utilized a graph model to exploit the relationships between different entities in the graph. In this paper, we focus on the impact of pairwise preferences on the diversity of recommendations. We propose a novel graph-based ranking oriented recommendation algorithm that exploits both explicit and implicit feedback of users. The algorithm utilizes a user-preference-item tripartite graph model and modified resource allocation process to match the target user with users who share similar preferences, and make personalized recommendations. The principle of the additional preference layer is to capture users’ pairwise preferences, provide detailed information of users for further recommendations. Empirical analysis of four benchmark datasets demonstrated that our proposed algorithm performs better in most situations than other graph-based and ranking-oriented benchmark algorithms.
One secure data integrity verification scheme for cloud storage

Cloud computing is a novel kind of information technology that users can enjoy sundry cloud services from the shared configurable computing resources. Compared with traditional local storage, cloud storage is a more economical choice because the remote data center can replace users for data management and maintenance, which can save time and money on the series of work. However, delivering data to an unknown Cloud Service Provider (CSP) makes the integrity of data become a potential vulnerability. To solve this problem, we propose a secure identity based aggregate signatures (SIBAS) as the data integrity checking scheme which resorts Trusted Execution Environment (TEE) as the auditor to check the outsourced data in the local side. SIBAS can not only check the integrity of outsourced data, but also achieve the secure key management in TEE through Shamir’s (t,n) threshold scheme. To prove the security, security analysis in the random oracle model under the computational Diffie–Hellman assumption shows that SIBAS can resist attacks from the adversary that chooses its messages and target identities, experimental results also show that our solution is viable and efficient in practice.
Detecting Malicious URLs via a Keyword-Based Convolutional Gated-Recurrent-Unit Neural Network

With the continuous development of Web attacks, many web applications have been suffering from various forms of security threats and network attacks. The security detection of URLs has always been the focus of Web security. Many web application resources can be accessed by simply entering an URL or clicking a link in the browser. An attacker can construct various web attacks such as SQL, XSS, and information disclosure by embedding executable code or injecting malicious code into the URL. Therefore, it is necessary to improve the reliability and security of web applications by accurately detecting malicious URLs. This paper designs a convolutional gated-recurrent-unit (GRU) neural network for the detection of malicious URLs detection based on characters as text classification features. Considering that malicious keywords are unique to URLs, a feature representation method of URLs based on malicious keywords is proposed, and a GRU is used in place of the original pooling layer to perform feature acquisition on the time dimension, resulting in high-accuracy multicategory results. The experimental results show that our proposed neural network detection model is very suitable for high-precision classification tasks. Compared with other classification models, the model accuracy rate is above 99.6%. The use of deep learning to classify URLs to identify Web visitors' intentions has important theoretical and scientific values for Web security research, providing new ideas for intelligent security detection.
Separability and Compactness Network for Image Recognition and Superresolution

Convolutional neural networks (CNNs) have wide applications in pattern recognition and image processing. Despite recent advances, much remains to be done for CNNs to learn a better representation of image samples. Therefore, constant optimizations should be provided on CNNs. To achieve a good performance on classification, intuitively, samples' interclass separability, or intraclass compactness should be simultaneously maximized. Accordingly, in this paper, we propose a new network, named separability and compactness network (SCNet) to rectify this problem. SCNet minimizes the softmax loss and the distance between features of samples from the same class under a jointly supervised framework, resulting in simultaneous maximization of interclass separability and intraclass compactness of samples. Furthermore, considering the convenience and the efficiency of the cosine similarity in face recognition tasks, we incorporate it into SCNet's distance metric to enable sample features from the same class to line up in the same direction and those from different classes to have a large angle of separation. We apply SCNet to three different tasks: visual classification, face recognition, and image superresolution. Experiments on both public data sets and real-world satellite images validate the effectiveness of our SCNet.
The Socio-Technological Lives of Bitcoin

Bitcoin, cryptocurrencies, and blockchains have become buzzwords in the media and are attracting increasing academic interest, mainly from the fields of computer science and financial economics. In this essay, I argue that cryptocurrencies and blockchains are important objects of general social science research and thought, but not for their ‘moneyness’ per se. Through a historical sociology of the antecedents and discourse leading up to Bitcoin, I show that it was never meant to be ‘money’ in the economic sense, but rather a solution to a technical puzzle for preventing opportunistic actors from double-spending digital ‘coins,’ as well as a fervent ideology surrounding online privacy and infringement of individual rights in the digital age. Drawing from themes in science and technology studies, I suggest that Bitcoin and other ‘cryptoassets’ are properly socio-technological assemblages that constitute new and important objects of social inquiry that must be understood beyond the myopic context of crypto-money. I conclude by proposing three alternative ontologies for blockchains relevant to economic, political, and social life: as systems of accounting, as organizational forms, and as institutions in their own right.
Universal Adversarial Attacks on Text Classifiers

Despite the vast success neural networks have achieved in different application domains, they have been proven to be vulnerable to adversarial perturbations (small changes in the input), which lead them to produce the wrong output.In this paper, we propose a novel method, based on gradient projection, for generating universal adversarial perturbations for text; namely sequence of words that can be added to any input in order to fool the classifier with high probability.We observed that text classifiers are quite vulnerable to such perturbations: inserting even a single adversarial word to the beginning of every input sequence can drop the accuracy from 93% to 50%.
Not Alone Here?! Scalability and User Experience of Embodied Ambient Crowds in Distributed Social Virtual Reality

This article investigates performance and user experience in Social Virtual Reality (SVR) targeting distributed, embodied, and immersive, face-to-face encounters. We demonstrate the close relationship between scalability, reproduction accuracy, and the resulting performance characteristics, as well as the impact of these characteristics on users co-located with larger groups of embodied virtual others. System scalability provides a variable number of co-located avatars and Al-controlled agents with a variety of different appearances, including realistic-looking virtual humans generated from photogrammetry scans. The article reports on how to meet the requirements of embodied SVR with today's technical off-the-shelf solutions and what to expect regarding features, performance, and potential limitations. Special care has been taken to achieve low latencies and sufficient frame rates necessary for reliable communication of embodied social signals. We propose a hybrid evaluation approach which coherently relates results from technical benchmarks to subjective ratings and which confirms required performance characteristics for the target scenario of larger distributed groups. A user-study reveals positive effects of an increasing number of co-located social companions on the quality of experience of virtual worlds, i.e., on presence, possibility of interaction, and co-presence. It also shows that variety in avatar/agent appearance might increase eeriness but might also stimulate an increased interest of participants about the environment.
Aggregated topic models for increasing social media topic coherence

This research presents a novel aggregating method for constructing an aggregated topic model that is composed of the topics with greater coherence than individual models. When generating a topic model, a number of parameters have to be specified. The resulting topics can be very general or very specific, which depend on the chosen parameters. In this study we investigate the process of aggregating multiple topic models generated using different parameters with a focus on whether combining the general and specific topics is able to increase topic coherence. We employ cosine similarity and Jensen-Shannon divergence to compute the similarity among topics and combine them into an aggregated model when their similarity scores exceed a predefined threshold. The model is evaluated against the standard topics models generated by the latent Dirichlet allocation and Non-negative Matrix Factorisation. Specifically we use the coherence of topics to compare the individual models that create aggregated models against those of the aggregated model and models generated by Non-negative Matrix Factorisation, respectively. The results demonstrate that the aggregated model outperforms those topic models at a statistically significant level in terms of topic coherence over an external corpus. We also make use of the aggregated topic model on social media data to validate the method in a realistic scenario and find that again it outperforms individual topic models.
Evaluation of Regression Models: Model Assessment, Model Selection and Generalization Error

When performing a regression or classification analysis, one needs to specify a statistical model. This model should avoid the overfitting and underfitting of data, and achieve a low generalization error that characterizes its prediction performance. In order to identify such a model, one needs to decide which model to select from candidate model families based on performance evaluations. In this paper, we review the theoretical framework of model selection and model assessment, including error-complexity curves, the bias-variance tradeoff, and learning curves for evaluating statistical models. We discuss criterion-based, step-wise selection procedures and resampling methods for model selection, whereas cross-validation provides the most simple and generic means for computationally estimating all required entities. To make the theoretical concepts transparent, we present worked examples for linear regression models. However, our conceptual presentation is extensible to more general models, as well as classification problems.
A 3-D Atrous Convolution Neural Network for Hyperspectral Image Denoising

Deep learning, especially a discriminative model for image denoising, has shown great potential in removing complex spectral-spatial noise in hyperspectral images (HSI). For HSI denoising, it is crucial to extract more context information around each pixel and to predict each pixel according to the surrounding context. Therefore, the effective receptive field plays an important role when performing denoising task. Generally, an HSI denoising model can achieve better performance by reserving the correlation of adjacent spectral bands and extracting more pixel features in the spatial domain. In this paper, 3-D atrous denoising convolution neural network (3DADCNN) is proposed for HSI. The model extracts feature maps along both spatial and spectral dimensions and enlarges the receptive field without significantly increasing the number of network parameters. Simultaneously, the multibranch and multiscale structure is utilized to reduce training difficulty, lessen overfitting risk, and preserve details in texture. The proposed model can be applied to the corrupted image with a mixed type of photon and thermal noise. Experimental results of the quantitative and qualitative evaluation show that 3DADCNN outperforms state-of-the-art HSI denoising methods.
Encouraging users to improve password security and memorability

Security issues in text-based password authentication are rarely caused by technical issues, but rather by the limitations of human memory, and human perceptions together with their consequential responses. This study introduces a new user-friendly guideline approach to password creation, including persuasive messages that motivate and influence users to select more secure and memorable text passwords without overburdening their memory. From a broad understanding of human factors-caused security problems, we offer a reliable solution by encouraging users to create their own formula to compose passwords. A study has been conducted to evaluate the efficiency of the proposed password guidelines. Its results suggest that the password creation methods and persuasive message provided to users convinced them to create cryptographically strong and memorable passwords. Participants were divided into two groups in the study. The participants in the experimental group who were given several password creation methods along with a persuasive message created more secure and memorable passwords than the participants in the control group who were asked to comply with the usual strict password creation rules. The study also suggests that our password creation methods are much more efficient than strict password policy rules. The security and usability evaluation of the proposed password guideline showed that simple improvements such as adding persuasive text to the usual password guidelines consisting of several password restriction rules make significant changes to the strength and memorability of passwords. The proposed password guidelines are a low-cost solution to the problem of improving the security and usability of text-based passwords.
Food Safety Supervision System Based on Hierarchical Multi-Domain Blockchain Network

The traditional food supervision system has problems such as lack of industry chain and data fragmentation, which has caused the phenomenon of field regulation to be uncomprehensive in the existing regulatory system and the disposal of response lag. Combining the characteristics of blockchain such as distribution, transparent, and collegiality with the actual needs of regional autonomy, we propose the hierarchical multi-domain blockchain (HMDBC) network structure and the secondary-check mechanism, which can support timely correction and replacement of the malicious supervision nodes by regional nodes co-governance, auxiliary verification of supervision nodes, and arbitration of superior regions. In order to optimize the supervision nodes election, we proposed fuzzy comprehensive evaluation model of credibility, which can be used to objectively and fairly evaluate the comprehensive reputation of each node in that region by considering various influencing factors of node performance indicators. Furthermore, we designed the data block structure model, which can support the supervisor node replacement. Our system can perform automatic food quality detection and warning of substandard food in the entire industrial chain, with the use of smart contracts combined with the food industry standards. Finally, comparing the complexity of the traditional blockchain system with PBFT consortium, our system is more secure, with lower broadcast complexity.
Protection, Productivity and Pleasure in the Smart Home

Interest and uptake of smart home technologies has been lower than anticipated, particularly among women. Reporting on an academic-industry partnership, we present findings from an ethnographic study with 31 Australian smart home early adopters. The paper analyses these households' experiences in relation to three concepts central to Intel's ambient computing vision for the home: protection, productivity and pleasure, or 'the 3Ps'. We find that protection is a form of caregiving; productivity provides 'small conveniences', energy savings and multi-tasking possibilities; and pleasure is derived from ambient and aesthetic features, and the joy of 'playing around' with tech. Our analysis identifies three design challenges and opportunities for the smart home: internal threats to household protection; feminine desires for the smart home; and increased 'digital housekeeping'. We conclude by suggesting how HCI designers can and should respond to these gendered challenges.
Witchcraft and HCI

While Human-Computer Interaction (HCI) research on health and well-being is increasingly becoming more aware and inclusive of its social and political dimensions, spiritual practices are still largely overlooked there. For a large number of people around the world, especially in the global south, witchcraft, sorcery, and other occult practices are the primary means of achieving health, wealth, satisfaction, and happiness. Building on an eight-month long ethnography in six villages in Jessore, Bangladesh, this paper explores the knowledge, materials, and politics involved in the local witchcraft practices there. By drawing from a rich body of anthropological work on witchcraft, this paper discusses how those findings contribute to the broader issues in HCI around morality, modernity, and postcolonial computing. This paper concludes by recommending ways for smooth integration of traditional occult practices with HCI through design and policy. We argue for occult practices as an under-appreciated site for HCI to learn how to combat ideological hegemony.
Differential Privacy Preservation in Deep Learning: Challenges, Opportunities and Solutions

Nowadays, deep learning has been increasingly applied in real-world scenarios involving the collection and analysis of sensitive data, which often causes privacy leakage. Differential privacy is widely recognized in the majority of traditional scenarios for its rigorous mathematical guarantee. However, it is uncertain to work effectively in the deep learning model. In this paper, we introduce the privacy attacks facing the deep learning model and present them from three aspects: membership inference, training data extraction, and model extracting. Then we recall some basic theory about differential privacy and its extended concepts in deep learning scenarios. Second, in order to analyze the existing works that combine differential privacy and deep learning, we classify them by the layers differential privacy mechanism deployed, such as input layer, hidden layer, and output layer, and discuss their advantages and disadvantages. Finally, we point out several key issues to be solved and provide a broader outlook of this research direction.
Speckle Noise Reduction Technique for SAR Images Using Statistical Characteristics of Speckle Noise and Discrete Wavelet Transform

Synthetic aperture radar (SAR) images map Earth’s surface at high resolution, regardless of the weather conditions or sunshine phenomena. Therefore, SAR images have applications in various fields. Speckle noise, which has the characteristic of multiplicative noise, degrades the image quality of SAR images, which causes information loss. This study proposes a speckle noise reduction algorithm while using the speckle reducing anisotropic diffusion (SRAD) filter, discrete wavelet transform (DWT), soft threshold, improved guided filter (IGF), and guided filter (GF), with the aim of removing speckle noise. First, the SRAD filter is applied to the SAR images, and a logarithmic transform is used to convert multiplicative noise in the resulting SRAD image into additive noise. A two-level DWT is used to divide the resulting SRAD image into one low-frequency and six high-frequency sub-band images. To remove the additive noise and preserve edge information, horizontal and vertical sub-band images employ the soft threshold; the diagonal sub-band images employ the IGF; while, the low- frequency sub-band image removes additive noise using the GF. The experiments used both standard and real SAR images. The experimental results reveal that the proposed method, in comparison to state-of-the art methods, obtains excellent speckle noise removal, while preserving the edges and maintaining low computational complexity.
QURE

Concerted efforts by the academia and the industries e.g., IBM, Google and Intel have brought us to the era of Noisy Intermediate-Scale Quantum (NISQ) computers. Qubits, the basic elements of quantum computer, have been proven extremely susceptible to different noises. Recent experiments have exhibited spatial variations among the qubits in NISQ hardware. Therefore, conventional mapping of qubit done without quality awareness results in significant loss of fidelity for a given workload. In this paper, we have analyzed the effects of various noise sources on the overall fidelity of the given workload for a real NISQ hardware. We have also presented novel optimization technique namely, Qubit Re-allocation (QURE) to maximize the sequence fidelity of a given workload. QURE is scalable and can be applied to future large scale quantum computers. QURE can improve the fidelity of a quantum workload up to 1.54X (1.39X on average) in simulation and up to 1.7X in real device compared to variation oblivious qubit allocation without incurring any physical overhead.
Silicon Photonic Circuit Design Using Rapid Prototyping Foundry Process Design Kits

A successful design, fabrication and test of silicon photonic circuits requires design tools, process design kits (PDKs), foundries for fabrication, and test facilities. This paper describes the complete design flow of photonic circuits using rapid-prototyping multiproject wafer foundry processes available in the SiEPIC program. The focus of this paper is on rapid prototyping based on electron beam lithography as an alternative and complementary to what is available via deep-UV lithography-based foundries. We describe in detail the PDK and the use of open-source and commercial tools for the design of optical filters, sensors, neuromorphic photonic processors, optical switches, and discuss test and packaging approaches for these designs. We demonstrate that a “germanium less” process can be used to build small systems featuring photoconductive detectors, electronics, and phase shifters.