Reviewerly/exHarmony huggingface dataset:
SpyDark: Surface and Dark Web Crawler

The researchers established that there exists a portion of the hidden web known as the "Dark Web". The nature of the Dark web intrigued researchers to collect information and present inferences about the information spread and communication taking place on this dark side of the internet.The presented work SpyDark is one such attempt to collect information from the surface and dark web. The user is required to enter the search query, the number of web pages to visit, and specify which network to be accessed (surface or dark web). The crawler traverses all the web pages and extracts information on that page, such as text data, images, hyperlinks, etc. The hyperlinks are stored in the database and later visited by the crawler. The text data is fed to a pre-trained NLP (Natural Language Processing) model, and the output is used to quantify the relevance score. The page is categorized as relevant or irrelevant.
Example-feature graph convolutional networks for semi-supervised classification

Graph convolutional networks (GCNs) successfully generalize convolutional neural networks to handle the graphs with high-order arbitrary structures. However, most existing GCNs variants consider only the local geometry of row vectors of high-dimensional data via example graph Laplacian, while neglecting the manifold structure information of column vectors. To address this problem, we propose the example-feature graph convolutional networks (EFGCNs) for semi-supervised classification. Particularly, we introduce the definition of the spectral example-feature graph (EFG) convolution that simultaneously utilizes the example graph Laplacian and feature graph Laplacian to better preserve the local geometry distributions of data. After optimizing the spectral EFG convolution with the first-order approximation, a single-layer EFGCNs is obtained. It is then further extended to build a multi-layer EFGCNs. Extensive experiments on remote sensing and citation networks datasets demonstrate the proposed EFGCNs show superior performance in semi-supervised classification compared with state-of-the-art methods.
Margin-Maximization in Binarized Neural Networks for Optimizing Bit Error Tolerance

To overcome the memory wall in neural network (NN) inference systems, recent studies have proposed to use approximate memory, in which the supply voltage and access latency parameters are tuned, for lower energy consumption and faster access at the cost of reliability. To tolerate the occuring bit errors, the state-of-the-art approaches apply bit flip injections to the NNs during training, which require high overheads and do not scale well for large NNs and high bit error rates. In this work, we focus on binarized NNs (BNNs), whose simpler structure allows better exploration of bit error tolerance metrics based on margins. We provide formal proofs to quantify the maximum number of bit flips that can be tolerated. With the proposed margin-based metrics and the well-known hinge loss for maximum margin classification in support vector machines (SVMs), we are able to construct a modified hinge loss (MHL) to train BNNs for bit error tolerance without any bit flip injections. Our experimental results indicate that the MHL enables the possibility for BNNs to tolerate higher bit error rates than with bit flip training and, therefore, allows to further lower the requirements on approximate memories used for BNNs.
Typical Daily Profiles, a novel approach for photovoltaics performance assessment: Case study on large-scale systems in Chile

A growing photovoltaic industry shows exponential deployment worldwide, and it is expected to largely contribute to the energy transition. Because PV technologies will play a major role in achieving global sustainable development and climate goals, driving more energy-efficient scenarios will require efficient approaches to evaluate systems, especially when dealing with big data of a large region or portfolio. In this work, a novel approach for the PV performance assessment of photovoltaic systems, called “Typical Daily Profiles” (TDP), is presented. This approach is tested on the entire PV fleet operating in Chile from 2014 to 2019. The TDP approach can help to calculate key performance indicators, identify the mounting configuration of PV systems, and to detect major technical issues. A detailed validation carried on the Chilean PV fleet confirms the TDP approach's capabilities to provide accurate PV performance results, neglecting external factors such as failures, grid curtailment, or poor operation activities. Chile was chosen due to the large variety of climate zones in its unique geography, which helps to understand the PV performance under different environmental conditions. Besides, this study reveals the immense potential of PV technologies in Chile compared to mature PV markets worldwide. On an annual basis, their unit capacity factors can reach up to 38%, performance ratios above 90%, and the highest energy yield close to 3350 kWh/kWp. Proving to be an accurate tool, the Typical Daily Profiles approach can be easily applied to other PV portfolios in different regions, and a periodical execution could help to identify and understand long-term performance losses.
Serbian teachers’ perceptions of online assessment during COVID-19 school closure: the role of teachers’ self-efficacy

The COVID-19 pandemic has led to the temporary closure of schools around the world. Due to the sudden and complete transition to online instruction, teachers were faced with various challenges. The assessment of student performance is one of the integrative segments of remote teaching. Therefore, the aim of this study was to examine the relationship between teachers' perceptions of online assessment and different facets of self-efficacy. The study involved 147 primary and secondary school teachers who completed an online survey. The results of multiple regression analysis revealed significant effects of teachers' ICT self-efficacy and teachers' self-efficacy concerning student engagement on the perception of some aspects of online assessment. On the other hand, teacher self-efficacy for instructional strategies was not a relevant predictor. In light of the obtained results, suggestions for future research were discussed.
Graph neural networks with multiple kernel ensemble attention

Graph neural networks have recently attracted increasing research attention. Recent research has shown that applying the attention mechanism is helpful for graph learning. The performance of the attention function is critical for graph learning. In this paper, we propose a multiple kernel ensemble attention method for graph learning. Unlike previous work, the attention weights in the proposed method are determined by comparing the similarity between features in the reproducing kernel Hilbert space. Our network can automatically learn the optimal kernel function from a set of predefined candidate kernels which are of the same type. This significantly eases the hyperparameter tuning procedure. We show that our network is effective to improve the graph learning performance. To the best of our knowledge, this is the first work that incorporates automatic multiple kernel learning into graph convolutional networks. We validate our graph network for both transductive learning and inductive learning on four benchmark datasets: Cora, Citesser, Pubmed, and the protein–protein interaction dataset. The experimental results show that we achieve competitive performance when compared with the latest work.
Handling COVID-19 with big data in China: increasing ‘governance capacity’ or ‘function creep’?

Building a national system of social governance (guojia zhili tixi), which is the long-running governance dream of Xi Jinping, has triggered the creation of China's 'smart state' using the tools of new information technologies to advance governance capacity (zhili nengli).These systems were already deployed nationally when the COVID-19 pandemic hit China, but were connected at a lesser capacity, targeting specific domains of security, industry or government administration.In response to the crisis, multiple technologies have been merged, exceeding the scope of their originally intended functions.This is known as function creep, when surveillant technologies remain functional past achievement of their intended purpose, or surveillant assemblages, where multiple surveillant technologies are combined.As more countries turn to digitalisation to increase public security and intensify social and market governance, the expansion of surveillant functions in China and their now-palpable effects on people's lives raises new and pressing questions for scholars and decisionmakers alike.
A Lightweight Residual Networks Framework for DDoS Attack Classification Based on Federated Learning

With the development of network technology, more and more protocols and devices are used in DDoS reflection and exploitation attacks. Different DDoS attacks often require different responses, so in order to protect against DDoS attacks, it requires not only DDoS detection, but also the classification of the detected DDoS traffic. As DDoS attacks have a wide range of potential targets, the existing algorithms for DDoS detection needs global processing of traffic data from different sources, but such data collection is obviously not conducive to the privacy protection of source users. Therefore, we designed our algorithm for DDoS detection and classification based on federated learning. Deferent terminals only need to pass model gradient parameters rather than directly interact the collected data of them, which can not only reduce communication costs, but protect privacy as well. Considering that network structure should not be too complex for high-rated traffic classification, we use a simplified residual network with fewer parameters for detection and classification.
Domain name encryption is not enough: privacy leakage via IP-based website fingerprinting

Abstract Although the security benefits of domain name encryption technologies such as DNS over TLS (DoT), DNS over HTTPS (DoH), and Encrypted Client Hello (ECH) are clear, their positive impact on user privacy is weakened by—the still exposed—IP address information. However, content delivery networks, DNS-based load balancing, co-hosting of different websites on the same server, and IP address churn, all contribute towards making domain–IP mappings unstable, and prevent straightforward IP-based browsing tracking. In this paper, we show that this instability is not a roadblock (assuming a universal DoT/DoH and ECH deployment), by introducing an IP-based website finger-printing technique that allows a network-level observer to identify at scale the website a user visits. Our technique exploits the complex structure of most websites, which load resources from several domains besides their primary one. Using the generated fingerprints of more than 200K websites studied, we could successfully identify 84% of them when observing solely destination IP addresses. The accuracy rate increases to 92% for popular websites, and 95% for popular and sensitive web-sites. We also evaluated the robustness of the generated fingerprints over time, and demonstrate that they are still effective at successfully identifying about 70% of the tested websites after two months. We conclude by discussing strategies for website owners and hosting providers towards hindering IP-based website fingerprinting and maximizing the privacy benefits offered by DoT/DoH and ECH.
Fault-Injection Attacks Against NIST’s Post-Quantum Cryptography Round 3 KEM Candidates

We investigate all NIST PQC Round 3 KEM candidates from the viewpoint of fault-injection attacks: Classic McEliece, Kyber, NTRU, Saber, BIKE, FrodoKEM, HQC, NTRU Prime, and SIKE. All KEM schemes use variants of the Fujisaki-Okamoto transformation, so the equality test with re-encryption in decapsulation is critical.
Rethinking Graph Auto-Encoder Models for Attributed Graph Clustering

Most recent graph clustering methods have resorted to Graph Auto-Encoders (GAEs) to perform joint clustering and embedding learning. However, two critical issues have been overlooked. First, the accumulative error, inflicted by learning from noisy clustering assignments, degrades the effectiveness of the clustering model. This problem is called Feature Randomness. Second, reconstructing the adjacency matrix sets the model to learn irrelevant similarities for the clustering task. This problem is called Feature Drift. Furthermore, the theoretical relation between the aforementioned problems has not yet been investigated. We study these issues from two aspects: (1) there is a trade-off between Feature Randomness and Feature Drift when clustering and reconstruction are performed at the same level, and (2) the problem of Feature Drift is more pronounced for GAE models, compared with vanilla auto-encoder models. Thus, we reformulate the GAE-based clustering methodology. Our solution is two-fold. First, we propose a sampling operator <inline-formula><tex-math notation="LaTeX">$\Xi$</tex-math></inline-formula> that triggers a protection mechanism against Feature Randomness. Second, we propose an operator <inline-formula><tex-math notation="LaTeX">$\Upsilon$</tex-math></inline-formula> that triggers a correction mechanism against Feature Drift by gradually transforming the reconstructed graph into a clustering-oriented one. As principal advantages, our solution grants a considerable improvement in clustering effectiveness and can be easily tailored to GAE models.
On the Influence of Optimizers in Deep Learning-Based Side-Channel Analysis

The deep learning-based side-channel analysis represents a powerful and easy to deploy option for profiling side-channel attacks. A detailed tuning phase is often required to reach a good performance where one first needs to select relevant hyperparameters and then tune them. A common selection for the tuning phase are hyperparameters connected with the neural network architecture, while those influencing the training process are less explored. In this work, we concentrate on the optimizer hyperparameter, and we show that this hyperparameter has a significant role in the attack performance. Our results show that common choices of optimizers (Adam and RMSprop) indeed work well, but they easily overfit, which means that we must use short training phases, small profiling models, and explicit regularization. On the other hand, SGD type of optimizers works well on average (slower convergence and less overfit), but only if momentum is used. Finally, our results show that Adagrad represents a strong option to use in scenarios with longer training phases or larger profiling models.
A density-based evolutionary clustering algorithm for intelligent development

Inspired by the clustering mechanism of human cognitive development, this paper proposes a density-based evolutionary clustering algorithm based on incremental data (DBEC). The DBEC algorithm is developed from a zero sample state without prior knowledge, and the control parameter can evolve as the number of samples increases, which simulates the human cognitive development process structurally. Furthermore, we propose conservative, robust, and radical DBEC algorithms based on different combinations of strategies. These three types of DBEC algorithms have different characteristics, and can find various clustering attributes of the samples to be clustered. Finally, we analyse the performance of three types of DBEC algorithms and compare their result with those of other popular clustering algorithms on the datasets in the experiment. The results show the effectiveness of DBEC algorithms.
Event-based distributed bias compensation pseudomeasurement information filter for 3D bearing-only target tracking

This paper investigates the distributed 3D bearing-only target tracking problem in UAV network. Because of the limited computation capability and constrained power supply of UAV, traditional time-triggered nonlinear filters would inevitably cause serious network congestions. Thus, we proposed an efficient yet effective algorithm named event-based distributed pseudomeasurement information filter (EB-PMIF) by combining the deterministic Send-on-Delta triggering event-based mechanism and pseudomeasurement information filter in order to reduce the computation load and communication requirements. Since the correlated pseudomeasurement matrix and pseudomeasurement noise in EB-PMIF cause bias estimation, we analyzed the root cause of the bias and proposed a bias compensation filter named event-based distributed bias compensation pseudomeasurement information filter (EB-PMIF-BC) to eliminate the bias and improve the estimation accuracy. The proposed EB-PMIF and EB-PMIF-BC are demonstrated with an illustrative 3D bearing-only target tracking example. Simulation results verify the efficiency and effectiveness of the proposed methods.
Few-Shot and Continual Learning with Attentive Independent Mechanisms

Deep neural networks (DNNs) are known to perform well when deployed to test distributions that shares high similarity with the training distribution. Feeding DNNs with new data sequentially that were unseen in the training distribution has two major challenges — fast adaptation to new tasks and catastrophic forgetting of old tasks. Such difficulties paved way for the on-going research on few-shot learning and continual learning. To tackle these problems, we introduce Attentive Independent Mechanisms (AIM). We incorporate the idea of learning using fast and slow weights in conjunction with the decoupling of the feature extraction and higher-order conceptual learning of a DNN. AIM is designed for higher-order conceptual learning, modeled by a mixture of experts that compete to learn independent concepts to solve a new task. AIM is a modular component that can be inserted into existing deep learning frameworks. We demonstrate its capability for few-shot learning by adding it to SIB and trained on MiniImageNet and CIFAR-FS, showing significant improvement. AIM is also applied to ANML and OML trained on Omniglot, CIFAR-100 and MiniImageNet to demonstrate its capability in continual learning. Code made publicly available at https://github.com/huang50213/AIM-Fewshot-Continual.
Hitboxes: A Survey About Collision Detection in Video Games

Over the past decades, video games have become a mainstream form of entertainment and are increasingly used for other purposes such as education or health. This paper surveys recent research and practice in Collision detection in computer gaming that is the detection when two or more objects collide with each other and it plays a crucial role in almost every game as the majority of them are about one thing hitting another. Ground for your feet; a sword and a warrior’s body; a golf ball and a golf club. Hence we have a lot of hitboxes, an invisible geometry around objects that inform them when a collision takes place. There are several methods which solve this kind of problems but what is proven so far from the research is that computers are unexpectedly bad at dealing with collisions. As a result, it is sensible different games take dissimilar approaches on how the way hitboxes work. In this paper we will be focused on Bounding Volumes Hierarchy (BVH) class analyzing three basic methods: OBB, AABB and k-DOP along with some improvements that have been done throughout the years and how these are applied in modern video games.
Self-Conditioned Probabilistic Learning of Video Rescaling

Bicubic downscaling is a prevalent technique used to reduce the video storage burden or to accelerate the downstream processing speed. However, the inverse upscaling step is non-trivial, and the downscaled video may also deteriorate the performance of downstream tasks. In this paper, we propose a self-conditioned probabilistic framework for video rescaling to learn the paired downscaling and upscaling procedures simultaneously. During the training, we decrease the entropy of the information lost in the downscaling by maximizing its probability conditioned on the strong spatial-temporal prior information within the downscaled video. After optimization, the downscaled video by our framework preserves more meaningful information, which is beneficial for both the upscaling step and the downstream tasks, e.g., video action recognition task. We further extend the framework to a lossy video compression system, in which a gradient estimator for non-differential industrial lossy codecs is proposed for the end-to-end training of the whole system. Extensive experimental results demonstrate the superiority of our approach on video rescaling, video compression, and efficient action recognition tasks.
InfoScrub: Towards Attribute Privacy by Targeted Obfuscation

Personal photos of individuals when shared online, apart from exhibiting a myriad of memorable details, also reveals a wide range of private information and potentially entails privacy risks (e.g., online harassment, tracking). To mitigate such risks, it is crucial to study techniques that allow individuals to limit the private information leaked in visual data. We tackle this problem in a novel image obfuscation framework: to maximize entropy on inferences over targeted privacy attributes, while retaining image fidelity. We approach the problem based on an encoder-decoder style architecture, with two key novelties: (a) introducing a discriminator to perform bi-directional translation simultaneously from multiple unpaired domains; (b) predicting an image interpolation which maximizes uncertainty over a target set of attributes. We find our approach generates obfuscated images faithful to the original input images and additionally increases uncertainty by 6.2× (or up to 0.85 bits) over the non-obfuscated counterparts.
Tunisian Dialectal End-to-end Speech Recognition based on DeepSpeech

Recognize automatically the spontaneous Human speech and transcribe it into text is becoming an important task. However, freely available models are rare especially for under-resourced languages and dialects since they require large amounts of data in order to achieve high performances. This paper describes an approach to build an end-to-end Tunisian dialect speech system based on deep learning. For this propose, a Tunisian dialect paired text-speech dataset called "TunSpeech" was created. Existing Modern Standard Arabic (MSA) speech data was also combined with dialectal Tunisian data and decreased the Out-Of-Vocabulary rate and improve perplexity. On the other hand, synthetic dialectal data from a text to speech increased the Word Error Rate.
A Systematic Review on Extreme Programming

Abstract Since the advent of start-ups and old companies starting to shift to e-business, it has become troublesome to choose the right methods for projects that’s where extreme programming came to use. In this paper, we tried to compile different applications of the same. Extreme programming is a well-known agile method for software development. Extreme programming ensures customer satisfaction, better software quality and efficient project management. The team size is usually small but the group is team-oriented. It is a dynamic software development model, i.e., continuous discussion and integration of new features and ideas is the cornerstone of this model.