Reviewerly/exHarmony huggingface dataset:
DEFAULT: Cipher Level Resistance Against Differential Fault Attack

Differential Fault Analysis (DFA) is a well known cryptanalytic technique that exploits faulty outputs of an encryption device. Despite its popularity and similarity with the classical Differential Analysis (DA), a thorough analysis explaining DFA from a designer's point-of-view is missing in the literature. To the best of our knowledge, no DFA immune block cipher at an algorithmic level has been proposed so far. Furthermore, all known DFA countermeasures somehow depend on the device/protocol or on the implementation such as duplication/comparison. As all of these are outside the scope of the cipher designer, we focus on designing a primitive which can protect from DFA on its own. We present the first concept of cipher level DFA resistance which does not rely on any device/protocol related assumption, nor does it depend on any form of duplication. Our construction is simple, software/hardware friendly and DFA security scales up with the state size. It can be plugged before and/or after (almost) any symmetric key cipher and will ensure a non-trivial search complexity against DFA. One key component in our DFA protection layer is an SBox with linear structures. Such SBoxes have never been used in cipher design as they generally perform poorly against differential attacks. We argue that they in fact represent an interesting trade-off between good cryptographic properties and DFA resistance. As a proof of concept, we construct a DFA protecting layer, named DEFAULT-LAYER, as well as a full-fledged block cipher DEFAULT. Our solutions compare favorably to the state-of-the-art, offering advantages over the sophisticated duplication based solutions like impeccable circuits/CRAFT or infective countermeasures.
Evolutionary self-organizing fuzzy system using fuzzy-classification-based social learning particle swarm optimization

A self-organizing algorithm based on an online cluster and fuzzy sets update algorithm (OCFU) and fuzzy-classification-based social learning particle swarm optimization (FC-SLPSO) is proposed to address the problem of rule initialization and fitness function evaluation. The OCFU algorithm is used to determine the structure of the fuzzy system and build a flexible partition of fuzzy sets in each input variable. The FC-SLPSO algorithm establishes a fuzzy K-Nearest Neighbor (KNN) classifier in each iteration. The fitness evaluation is performed only when the membership degree of the offspring particles belonging to potential particles is greater than that of the parent particles, which effectively reduces the number of fitness function evaluations and accelerates the PSO algorithm. The tracking results of three nonlinear systems show that the tracking accuracy of the proposed method is better. The Pioneer P3DX mobile robot model and simulation environment with a two-dimensional lidar are constructed based on the Coppeliasim robot simulation platform. Using the algorithms proposed in this paper, the tracking task of the pioneer p3dx mobile robot along a wall is achieved. The effectiveness of the proposed algorithm is verified by the cosimulation of Coppeliasim and MATLAB in multiple scenarios.
ReTail: Opting for Learning Simplicity to Enable QoS-Aware Power Management in the Cloud

Many cloud services have Quality-of-Service (QoS) requirements; most requests have to to complete within a given latency constraint. Recently, researchers have begun to investigate whether it is possible to meet QoS while attempting to save power on a per-request basis. Existing work shows that one can indeed hand-tune a request latency predictor offline for a particular cloud application, and consult it at runtime to modulate CPU voltage and frequency, resulting in substantial power savings.In this paper, we propose ReTail, an automated and general solution for request-level power management of latency-critical services with QoS constraints. We present a systematic process to select the features of any given application that best correlate with its request latency. ReTail uses these features to predict latency, and adjust CPU&#x2019;s power consumption. ReTail&#x2019;s predictor is trained fully at runtime. We show that unlike previous findings, simple techniques perform better than complex machine learning models, when using the right input features. For a web search engine, ReTail outperforms prior mechanisms based on complex hand-tuned predictors for that application domain. Furthermore, ReTail&#x2019;s systematic approach also yields superior power savings across a diverse set of cloud applications.
Accelerating Graph Convolutional Networks Using Crossbar-based Processing-In-Memory Architectures

Graph convolutional networks (GCNs) are promising to enable machine learning on graphs. GCNs exhibit mixed computational kernels, involving regular neural-network-like computing and irregular graph-analytics-like processing. Existing GCN accelerators obey a divide-and-conquer philosophy to architect two separate types of hardware to accelerate these two types of GCN kernels, respectively. This hybrid architecture improves intra-kernel efficiency but considers little inter-kernel interactions in a holistic view for improving overall efficiency.In this paper, we present a new GCN accelerator, RE-FLIP, with three key innovations in terms of architecture design, algorithm mappings, and practical implementations. First, ReFlip leverages PIM-featured crossbar architectures to build a unified architecture for supporting the two types of GCN kernels simultaneously. Second, ReFlip adopts novel algorithm mappings that can maximize potential performance gains reaped from the unified architecture by exploiting the massive crossbar-structured parallelism. Third, ReFlip assembles software/hardware co-optimizations to process real-world graphs efficiently. Compared to the state-of-the-art software frameworks running on Intel Xeon E5-2680v4 CPU and NVIDIA Tesla V100 GPU, ReFlip achieves the average speedups of 6,432&#x00D7; and 86.32&#x00D7; and the average energy savings of 9,817&#x00D7; and 302.44&#x00D7;, respectively. In addition, ReFlip also outperforms a state-of-the-art GCN hardware accelerator, AWB-GCN, by achieving an average speedup of 5.06&#x00D7; and an average energy saving of 15.63&#x00D7;.
Early detection and classification of abnormality in prior mammograms using image-to-image translation and YOLO techniques

Computer-aided-detection (CAD) systems have been developed to assist radiologists on finding suspicious lesions in mammogram. Deep Learning technology have recently succeeded to increase the chance of recognizing abnormality at an early stage in order to avoid unnecessary biopsies and decrease the mortality rate. In this study, we investigated the effectiveness of an end-to-end fusion model based on You-Only-Look-Once (YOLO) architecture, to simultaneously detect and classify suspicious breast lesions on digital mammograms. Four categories of cases were included: Mass, Calcification, Architectural Distortions, and Normal from a private digital mammographic database including 413 cases. For all cases, Prior mammograms (typically scanned 1 year before) were all reported as Normal, while Current mammograms were diagnosed as cancerous (confirmed by biopsies) or healthy.We propose to apply the YOLO-based fusion model to the Current mammograms for breast lesions detection and classification. Then apply the same model retrospectively to synthetic mammograms for an early cancer prediction, where the synthetic mammograms were generated from the Prior mammograms by using the image-to-image translation models, CycleGAN and Pix2Pix.Evaluation results showed that our methodology could significantly detect and classify breast lesions on Current mammograms with a highest rate of 93% ± 0.118 for Mass lesions, 88% ± 0.09 for Calcification lesions, and 95% ± 0.06 for Architectural Distortion lesions. In addition, we reported evaluation results on Prior mammograms with a highest rate of 36% ± 0.01 for Mass lesions, 14% ± 0.01 for Calcification lesions, and 50% ± 0.02 for Architectural Distortion lesions. Normal mammograms were accordingly classified with an accuracy rate of 92% ± 0.09 and 90% ± 0.06 respectively on Current and Prior exams.Our proposed framework was first developed to help detecting and identifying suspicious breast lesions in X-ray mammograms on their Current screening. The work was also suggested to reduce the temporal changes between pairs of Prior and follow-up screenings for early predicting the location and type of abnormalities in Prior mammogram screening. The paper presented a CAD method to assist doctors and experts to identify the risk of breast cancer presence. Overall, the proposed CAD method incorporates the advances of image processing, deep learning and image-to-image translation for a biomedical application.
Contrastive and attentive graph learning for multi-view clustering

Graph-based multi-view clustering aims to take advantage of multiple view graph information to provide clustering solutions. The consistency constraint of multiple views is the key of multi-view graph clustering. Most existing studies generate fusion graphs and constrain multi-view consistency by clustering loss. We argue that local pair-view consistency can achieve fine-modeling of consensus information in multiple views. Towards this end, we propose a novel Contrastive and Attentive Graph Learning framework for multi-view clustering (CAGL). Specifically, we design a contrastive fine-modeling in multi-view graph learning using maximizing the similarity of pair-view to guarantee the consistency of multiple views. Meanwhile, an Att-weighted refined fusion graph module based on attention networks to capture the capacity difference of different views dynamically and further facilitate the mutual reinforcement of single view and fusion view. Besides, our CAGL can learn a specialized representation for clustering via a self-training clustering module. Finally, we develop a joint optimization objective to balance every module and iteratively optimize the proposed CAGL in the framework of graph encoder–decoder. Experimental results on six benchmarks across different modalities and sizes demonstrate that our CAGL outperforms state-of-the-art baselines.
Knowledge guided distance supervision for biomedical relation extraction in Chinese electronic medical records

The goal of biomedical relation extraction is to obtain structured information from electronic medical records by identifying relations among clinical entities. By integrating the advantages of unsupervised and semi-supervised learning, the distant supervision approach has achieved significant success for a relation extraction task without a large amount of labeled corpora. However, in many cases, the recognized entities from the Chinese clinical text are not defined in semantic knowledge base, which limits the application of distant supervision for biomedical relation extraction. This work proposes a Knowledge Guided Distance Supervision (KGDS) model for handling the biomedical relation extraction task in Chinese electronic medical records. To handle the unknown entities, entity-type alignment (instead of entity alignment in traditional distant supervision) is employed for extracting coarse-grained relations. Then, by learning the relation embeddings both from semantic knowledge base and electronic medical record dataset as knowledge-enhanced features, this work presents a knowledge-enhanced bootstrapping learning process for fine-grained relation disambiguation. The empirical experiments on the real-world dataset of electronic medical records illustrate that our KGDS model achieves the best performance comparing to other state-of-the-art models, thereby advancing the field of biomedical relation extraction from Chinese electronic medical records.
Digital Marketing Enhancement of Cryptocurrency Websites through Customer Innovative Data Process

Today, more than ever, the popularity of decentralized payment systems has risen, creating an outbreak of new cryptocurrencies hitting the market. Unique websites have been staged for each cryptocurrency, where information and means for mining cryptocurrencies are available daily. People visit those cryptocurrency websites either from desktop or mobile devices. Thus, the impulsion for appropriate promotion of cryptocurrency websites and customer factors affecting it rises. The above process increases cryptocurrency organizations’ website visibility, raising the need for customer relationships and satisfaction optimization concerning organizations’ supply chain strategy. Research data were collected from 10 well-known cryptocurrency websites, regarding mobile and desktop devices, in 180 days, regarding on-site web analytics. Therefore, a model consisting of three stages was applied. Starting phase of the model pertains to statistical and regression analysis of cryptocurrency web analytics, followed by Fuzzy Cognitive Mapping and Agent-Based Model deployment. Throughout this study, methods for promoting cryptocurrency websites can be deduced from assessing specific website metrics and device preferences. Research results indicate that web analytics give a clearer image of customer behavior in cryptocurrency websites and, therefore, provide opportunities for further website optimization through increased web traffic and digital reputation.
Influence of COVID-19 Pandemic on Dissemination of Innovative E-Learning Tools in Higher Education in Poland

The paper presents the results of the research on the influence of the COVID-19 pandemic on the dissemination of innovative e-learning tools in higher education. Research was carried out in Poland in December 2021 on a sample of 621 students. The main issue that was the subject of the author's analysis was the influence of the COVID-19 pandemic on the change in the use of innovative e-learning tools in university education. After conducting the research and discussing this and related research about e-learning during the pandemic, it was concluded that the percentage of students familiar with the analyzed e-learning tools has increased significantly during the pandemic. There has been a visible rise, especially in the usage of the following tools: MS Teams, Zoom, and Google Classroom. The most frequently used e-learning tools during the COVID-19 pandemic have been mainly videoconferencing tools such as MS Teams and Zoom. However, students also have used e-learning platforms and e-mails. The author's research identified three hidden factors (categories) of the used e-learning tools. They include the following categories: popular services and applications adapted to e-learning; popular applications for synchronous meetings adapted to e-learning; and other synchronous and asynchronous e-learning methods. The familiarity with information technology, as well as an interest in innovative e-learning tools, have positive influence on the ease of acquiring content in e-learning. Having the proper resources also positively influences the absorption of e-learning content. On the basis of the achieved results, the authors prepared a model of relations between students' interest in innovative e-learning technology and the resources they possess to participate in e-learning classes. This model enables us to assess which method—e-learning, traditional or hybrid—should be used in the given situation. The developed model can be useful for universities. They can assess the students' interest in innovative e-learning technologies and their level of technical resources using questionnaires and on this basis divide students into groups to prepare the optimal learning way—e-learning, traditional or hybrid.
A digital twins enabled underwater intelligent internet vehicle path planning system via reinforcement learning and edge computing

The Autonomous Underwater Glider (AUG) is a kind of prevailing underwater intelligent internet vehicle and occupies a dominant position in industrial applications, in which path planning is an essential problem. Due to the complexity and variability of the ocean, accurate environment modeling and flexible path planning algorithms are pivotal challenges. The traditional models mainly utilize mathematical functions, which are not complete and reliable. Most existing path planning algorithms depend on the environment and lack flexibility. To overcome these challenges, we propose a path planning system for underwater intelligent internet vehicles. It applies digital twins and sensor data to map the real ocean environment to a virtual digital space, which provides a comprehensive and reliable environment for path simulation. We design a value-based reinforcement learning path planning algorithm and explore the optimal network structure parameters. The path simulation is controlled by a closed-loop model integrated into the terminal vehicle through edge computing. The integration of state input enriches the learning of neural networks and helps to improve generalization and flexibility. The task-related reward function promotes the rapid convergence of the training. The experimental results prove that our reinforcement learning based path planning algorithm has great flexibility and can effectively adapt to a variety of different ocean conditions.
Explainable artificial intelligence for crypto asset allocation

Many investors have been attracted by Crypto assets in the last few years. However, despite the possibility of gaining high returns, investors bear high risks in crypto markets. To help investors and make the markets more reliable, Robot advisory services are rapidly expanding in the field of crypto asset allocation. Robot advisors not only reduce costs but also improve the quality of the service by involving investors and make the market more transparent. However, the reason behind the given solutions is not clear and users face a black-box model that is complex. The aim of this paper is to improve trustworthiness of robot advisors, to facilitate their adoption. For this purpose, we apply Shapley values to the predictions generated by a machine learning model based on the results of a dynamic Markowitz portfolio optimization model and provide explanations for what is behind the selected portfolio weights.
Effect of Online and Offline Blended Teaching of College English Based on Data Mining Algorithm

Blended teaching is a kind of teaching that combines online teaching with traditional teaching, which is defined as “online and offline”. Through the organic combination of these two teaching forms, students’ learning can be from shallow to deep. Therefore, based on the data mining algorithm, this paper designs the method of College English online and offline blended teaching effect. First, it collects the College English blended teaching resources, then builds the College English online and offline teaching support, debugs the College English teaching environment, and finally designs the College English blended teaching model based on the data mining algorithm, so as to realize the College English online and offline blended teaching, The experiment shows that the method designed in this paper can effectively improve the reading ability of College English, and has certain application value.
Region-of-interest and channel attention-based joint optimization of image compression and computer vision

Deep neural networks (DNN) have been widely applied in many computer vision problems. These tasks are often conducted on input images with high quality without consideration of storage and transmission costs, making it necessary to compress the images for bandwidth-constrained networks. Recently, researches on deep learning based image compression show promising performance compared with traditional image compression codecs. However, these image compression approaches focus on improving user-perceived visual quality, rather achieving high DNN inference accuracy for computer vision. In this work, we design a concrete system with the goal of maximizing the computer vision performance metric, subject to a compression ratio constraint. The entire framework is efficiently optimized in an end-to-end manner without multiple training phases. We find that the conventional distortion metric Mean Squared Error (MSE) in compression does not suffice to get desirable computer vision performance in our system. It is essential to exploit machine-centric evaluation metrics for high inference accuracy. We also propose to apply class-agnostic object masks combined with channel attention mechanism to dynamically allocate bits in regions of interest (ROI) and the background regions (BG) so as to optimize computer vision performance over a range of bitrates. We experiment on three diverse applications separately: image classification, human pose estimation and semantic segmentation. Extensive experiments show that our approach not only outperforms many traditional compression codecs in image compression, but also achieves superior computer vision performance than all other counterparts.
Overview on the Development of Intelligent Methods for Mineral Resource Prediction under the Background of Geological Big Data

In the age of big data, the prediction and evaluation of geological mineral resources have gradually entered a new stage, intelligent prospecting. This review briefly summarizes the research development of textual data mining and spatial data mining. It is considered that the current research on mineral resource prediction has integrated logical reasoning, theoretical models, computational simulations, and other scientific research models, and has gradually advanced toward a new model. This type of new model has tried to mine unknown and effective knowledge from big data by intelligent analysis methods. However, many challenges have come forward, including four aspects: (i) discovery of prospecting big data based on geological knowledge system; (ii) construction of the conceptual prospecting model by intelligent text mining; (iii) mineral prediction by intelligent spatial big data mining; (iv) sharing and visualization of the mineral prediction data. By extending the geological analysis in the process of prospecting prediction to the logical rules associated with expert knowledge points, the theory and methods of intelligent mineral prediction were preliminarily established based on geological big data. The core of the theory is to promote the flow, invocation, circulation, and optimization of the three key factors of “knowledge”, “model”, and “data”, and to preliminarily constitute the prototype of intelligent linkage mechanisms. It could be divided into four parts: intelligent datamation, intelligent informatization, intelligent knowledgeization, and intelligent servitization.