Reviewerly/exHarmony huggingface dataset:
Texture Image Compression Algorithm Based on Self-Organizing Neural Network

With the rapid development of science and technology, human beings have gradually stepped into a brand-new digital era. Virtual reality technology has brought people an immersive experience. In order to enable users to get a better virtual reality experience, the pictures produced by virtual skillfully must be realistic enough and support users' real-time interaction. So interactive real-time photorealistic rendering becomes the focus of research. Texture mapping is a technology proposed to solve the contradiction between real time and reality. It has been widely studied and used since it was proposed. However, due to limited bandwidth and memory storage, it brings challenges to the stain dyeing of many large texture images, so texture compression is introduced. Texture compression can improve the utilization rate of cache but also greatly reduce the pressure on data transmission caused by the system, which largely solves the problem of real-time rendering of realistic graphics. Due to the particularity of texture image compression, it is necessary to consider not only the quality of texture image after compression ratio and decompression but also whether the algorithm is compatible with mainstream graphics cards. On this basis, we put forward the texture image compression method based on self-organizing mapping, the experiment results show that our method has achieved good results, and it is superior to other methods in most performance indexes.
Wheelchair prototype controlled by position, speed and orientation using head movement

A prototype that simulates a wheelchair was built using electronic commercial devices and software implementation with the aim to operate the prototype using head movement and analyzing the system response. The controllers were simulated using MATLAB® toolbox and Python™ libraries. The mean time response of the system with manual control was 37,8 s. The mean orientation control response with constant speed was 36,5 s and the mean orientation control response with variable speed was 44,2 s in a specific route. The variable speed response is slower than constant speed due to head motion error. The system was rated such as" very good" by 10 participants using a System Usability Scale (SUS).
ProSPEC: Proactive Security Policy Enforcement for Containers

By providing lightweight and portable support for cloud native applications, container environments have gained significant momentum lately. A container orchestrator such as Kubernetes can enable the automatic deployment and maintenance of a large number of containerized applications. However, due to its critical role, a container orchestrator also attracts a wide range of security threats exploiting misconfigurations or implementation flaws. Moreover, enforcing security policies at runtime against such security threats becomes far more challenging, as the large scale of container environments implies high complexity, while the high dynamicity demands a short response time. In this paper, we tackle this key security challenge to container environments through a proactive approach, namely, ProSPEC. Our approach leverages learning-based prediction to conduct the computationally intensive steps (e.g., security verification) in advance, while keeping the runtime steps (e.g., policy enforcement) lightweight. Consequently, ProSPEC can ensure a practical response time (e.g., less than 10 ms in contrast to 600 ms with one of the most popular existing approaches) for large container environments (up to 800 Pods).
Perceptions of emergency remote teaching tools used during Covid-19 online teaching by an Israeli English for Academic Purpose (EAP) department

The spring 2020 Covid-19 pandemic caused a rapid transition to emergency remote teaching (ERT) in higher education across the world.This study examined teacher and student perceptions on technological tools used for ERT in an Israeli English for Academic Purposes (EAP) department with the aim to reflect and improve the use of these tools in practice in future semesters.Surveys of EAP teachers and students were conducted to examine the perceived effectiveness of technological teaching and communication tools as well as the support the teachers received during ERT.Teachers and students agreed that learning management system (LMS) quizzes and emails were effective learning and communication tools, but differed on the effectiveness of videoconferencing, recorded videos, personal phone calls and text messaging.Teachers felt constant positive support from each other in the EAP department.To improve effectiveness of these technological tools, higher student buy-in of these tools is needed, especially for videoconferencing.
Feature extractions and selection of bot detection on Twitter A systematic literature review

Abstract Automated or semiautomated computer programs that imitate humans and/or human behavior in online social networks are known as social bots. Users can be attacked by social bots to achieve several hidden aims, such as spreading information or influencing targets. While researchers develop a variety of methods to detect social media bot accounts, attackers adapt their bots to avoid detection. This field necessitates ongoing growth, particularly in the areas of feature selection and extraction. The study's purpose is to provide an overview of bot attacks on Twitter, shedding light on issues in feature extraction and selection that have a significant impact on the accuracy of bot detection algorithms, and highlighting the weaknesses in training time and dimensionality reduction. To the best of our knowledge, this study is the first systematic literature review based on a preset search-strategy that encompasses literature published between 2018 and 2021 which are concerned with Twitter features (attributes). The key findings of this research are threefold. First, the paper provides an improved taxonomy of feature extraction and selection approaches. Second, it includes a comprehensive overview of approaches for detecting bots in the Twitter platform, particularly machine learning techniques. The percentage was calculated using the proposed taxonomy, with metadata, tweet text, and merging (meta and tweet text) accounting for 37%, 31%, and 32%, respectively. Third, some gaps are also highlighted for further research. The first is that public datasets are not precise or suitable in size. Second, the use of integrated systems and real-time detection is uncommon. Third, detecting each bots category identified separately is needed, rather than detecting all categories of bots using one generic model and the same features' values. Finally, extracting influential features that assist machine learning algorithms in detecting Twitter bots with high accuracy is critical, especially if the type of bot is pre-determined.
Communicator-Driven Data Preprocessing Improves Deep Transfer Learning of Histopathological Prediction of Pancreatic Ductal Adenocarcinoma

Pancreatic cancer is a fatal malignancy with poor prognosis and limited treatment options. Early detection in primary and secondary locations is critical, but fraught with challenges. While digital pathology can assist with the classification of histopathological images, the training of such networks always relies on a ground truth, which is frequently compromised as tissue sections contain several types of tissue entities. Here we show that pancreatic cancer can be detected on hematoxylin and eosin (H&E) sections by convolutional neural networks using deep transfer learning. To improve the ground truth, we describe a preprocessing data clean-up process using two communicators that were generated through existing and new datasets. Specifically, the communicators moved image tiles containing adipose tissue and background to a new data class. Hence, the original dataset exhibited improved labeling and, consequently, a higher ground truth accuracy. Deep transfer learning of a ResNet18 network resulted in a five-class accuracy of about 94% on test data images. The network was validated with independent tissue sections composed of healthy pancreatic tissue, pancreatic ductal adenocarcinoma, and pancreatic cancer lymph node metastases. The screening of different models and hyperparameter fine tuning were performed to optimize the performance with the independent tissue sections. Taken together, we introduce a step of data preprocessing via communicators as a means of improving the ground truth during deep transfer learning and hyperparameter tuning to identify pancreatic ductal adenocarcinoma primary tumors and metastases in histological tissue sections.
Application of Transformer-based Language Models to Detect Hate Speech in Social Media

Detecting and removing hateful speech in various online social media is a challenging task. Researchers tried to solve this problem by using both classical and deep learning methods, which are found to have limitations in terms of the requirement of extensive hand-crafted features, model architecture design, and pretrained embeddings, that are not very proficient in capturing semantic relations between words. Therefore, in this paper, we tackle the problem using Transformer-based pretrained language models which are specially designed to produce contextual embeddings of text sequences. We have evaluated two such models—RoBERTa and XLNet—using four publicly available datasets from different social media platforms and compared them to the existing baselines. Our investigation shows that the Transformer-based models either surpass or match all of the existing baseline scores by significant margins obtained by previously used models such as 1-dimensional convolutional neural network (1D-CNN) and long short-term memory (LSTM). The Transformer-based models proved to be more robust by achieving native performance when trained and tested on two different datasets. Our investigation also revealed that variations in the characteristics of the data produce significantly different results with the same model. From the experimental observations, we are able to establish that Transformer-based language models exhibit superior performance than their conventional counterparts at a fraction of the computation cost and minimal need for complex model engineering.
Optimizing Performance of Federated Person Re-identification: Benchmarking and Analysis

The increasingly stringent data privacy regulations limit the development of person re-identification (ReID) because person ReID training requires centralizing an enormous amount of data that contains sensitive personal information. To address this problem, we introduce federated person re-identification (FedReID) -- implementing federated learning, an emerging distributed training method, to person ReID. FedReID preserves data privacy by aggregating model updates, instead of raw data, from clients to a central server. Furthermore, we optimize the performance of FedReID under statistical heterogeneity via benchmark analysis. We first construct a benchmark with an enhanced algorithm, two architectures, and nine person ReID datasets with large variances to simulate the real-world statistical heterogeneity. The benchmark results present insights and bottlenecks of FedReID under statistical heterogeneity, including challenges in convergence and poor performance on datasets with large volumes. Based on these insights, we propose three optimization approaches: (1) We adopt knowledge distillation to facilitate the convergence of FedReID by better transferring knowledge from clients to the server; (2) We introduce client clustering to improve the performance of large datasets by aggregating clients with similar data distributions; (3) We propose cosine distance weight to elevate performance by dynamically updating the weights for aggregation depending on how well models are trained in clients. Extensive experiments demonstrate that these approaches achieve satisfying convergence with much better performance on all datasets. We believe that FedReID will shed light on implementing and optimizing federated learning on more computer vision applications.
A high-efficiency gold precipitation model associated with Fe carbonates: Example from the Jiudian deposit of the world-class Jiaodong gold province

Pyrite (FeS2) is a common host for gold in various hydrothermal gold deposits, but the close relationship between siderite (FeCO3) and gold is rarely reported. Furthermore, the gold precipitation mechanism is also poorly understood. In the Jiudian gold deposit of the world-class Jiaodong gold province (eastern China), two gold ore stages were identified: pyrite-smoky grey quartz (the first ore stage), quartz-siderite-polymetallic sulfide (the second ore stage). Gold occurs as visible electrum with minor native gold in both ore stages. The latter stage ores have higher gold grade, indicating gold precipitation associated with second-stage siderite is more efficient than that associated with first-stage pyrite. An integration of mineral texture and composition and fluid inclusion evidence demonstrates that gold deposition in the first ore stage was formed by fluid immiscibility, which destabilized the Au (HS)2− complex in the ore-forming fluid. Fluid immiscibility (boiling) also occurred in the second ore stage, resulted from the abrupt drop of pressure and temperature due to the fracturing of the ore-controlling faults during seismic activity and mixing with cold oxidized meteoric water, and then the polymetallic sulfide precipitation was triggered. The presence of CH4 in the hydrothermal fluid of the second ore stage may have also promoted the fluid unmixing. Subsequently, the metasomatic reactions of pre-existing pyrite into marcasite and siderite occurred successively, and further facilitated with fluid immiscibility mutually by building a positive chain circuit. These complex processes may have formed a highly drastic and disequilibrated fluid system, in which the crystallized pyrite is featured by the decoupling of As and Au, which is different from the common early-ore Au-As association. The abnormal hydrothermal physicochemical perturbance may have triggered high-efficient gold precipitation associated with siderite.
Adversarial Framework with Certified Robustness for Time-Series Domain via Statistical Features

Time-series data arises in many real-world applications (e.g., mobile health) and deep neural networks (DNNs) have shown great success in solving them. Despite their success, little is known about their robustness to adversarial attacks. In this paper, we propose a novel adversarial framework referred to as Time-Series Attacks via STATistical Features (TSA-STAT)}. To address the unique challenges of time-series domain, TSA-STAT employs constraints on statistical features of the time-series data to construct adversarial examples. Optimized polynomial transformations are used to create attacks that are more effective (in terms of successfully fooling DNNs) than those based on additive perturbations. We also provide certified bounds on the norm of the statistical features for constructing adversarial examples. Our experiments on diverse real-world benchmark datasets show the effectiveness of TSA-STAT in fooling DNNs for time-series domain and in improving their robustness. The source code of TSA-STAT algorithms is available at https://github.com/tahabelkhouja/Time-Series-Attacks-via-STATistical-Features
Virtual Digital Communication Feature Fusion Based on Virtual Augmented Reality

In order to have a deeper understanding of the integration of virtual digital communication characteristics, it is studied and combined with virtual augmented reality technology. The progress of science and technology has promoted the development and progress of virtual reality technology. In recent years, virtual reality technology has been more and more widely used in digital media art creation and plays an important role in improving the quality of digital media art. This study introduces digital media art and virtual reality technology and analyzes the specific measures and practical significance of the application of virtual reality technology in digital media art creation. As an emerging technology, virtual reality technology has certain advantages. Its application in digital media art creation can effectively make up for the shortcomings of traditional creation and realize the innovation and reform of art creation.
Solar Radiation Nowcasting Using a Markov Chain Multi-Model Approach

Solar energy has found increasing applications in recent years, and the demand will continue to grow as society redirects to a more renewable development path. However, the required high-frequency solar irradiance data are not yet readily available everywhere. There have been endeavors to improve its forecasting in order to facilitate grid integration, such as with photovoltaic power planning. The objective of this study is to develop a hybrid approach to improve the accuracy of solar nowcasting with a lead time of up to one hour. The proposed method utilizes irradiance data from the Copernicus Atmospheric Monitoring Service for four European cities with various cloud conditions. The approach effectively improves the prediction accuracy in all four cities. In the prediction of global horizontal irradiance for Berlin, the reduction in the mean daily error amounts to 2.5 Wh m−2 over the period of a month, and the relative monthly improvement reaches nearly 5% compared with the traditional persistence method. Accuracy improvements can also be observed in the other three cities. Furthermore, since the required model inputs of the proposed approach are solar radiation data, which can be conveniently obtained from CAMS, this approach possesses the potential for upscaling at a regional level in response to the needs of the pan-EU energy transition.
ShapeNet: Shape constraint for galaxy image deconvolution

Deep Learning (DL) has shown remarkable results in solving inverse problems in various domains. In particular, the Tikhonet approach is very powerful to deconvolve optical astronomical images (Sureau et al. 2020). Yet, this approach only uses the $\ell_2$ loss, which does not guarantee the preservation of physical information (e.g. flux and shape) of the object reconstructed in the image. In Nammour et al. (2021), a new loss function was proposed in the framework of sparse deconvolution, which better preserves the shape of galaxies and reduces the pixel error. In this paper, we extend Tikhonet to take into account this shape constraint, and apply our new DL method, called ShapeNet, to optical and radio-interferometry simulated data set. The originality of the paper relies on i) the shape constraint we use in the neural network framework, ii) the application of deep learning to radio-interferometry image deconvolution for the first time, and iii) the generation of a simulated radio data set that we make available for the community. A range of examples illustrates the results.
Low-latency readout electronics for dynamic superconducting quantum computing

Dynamic quantum computing can support quantum error correction circuits to build a large general-purpose quantum computer, which requires electronic instruments to perform the closed-loop operation of readout, processing, and control within 1% of the qubit coherence time. In this paper, we present low-latency readout electronics for dynamic superconducting quantum computing. The readout electronics use a low-latency analog-to-digital converter to capture analog signals, a field-programmable gate array (FPGA) to process digital signals, and the general I/O resources of the FPGA to forward the readout results. Running an algorithm based on the design of multichannel parallelism and single instruction multiple data on an FPGA, the readout electronics achieve a readout latency of 40 ns from the last sample input to the readout valid output. The feedback data link for cross-instrument communication shows a communication latency of 48 ns when 16 bits of data are transmitted over a 2 m-length cable using a homologous clock to drive the transceiver. With codeword-based triggering mechanisms, readout electronics can be used in dynamic superconducting quantum computing.
Reducing response latency of composite functions-as-a-service through scheduling

• Function composition is a recent addition to Function-as-a-Service model. • Scheduling algorithms can be adjusted to the FaaS composition specificity. • Considering the FaaS composition structure can lead to lower response latencies. In Function-as-a-Service (FaaS) clouds, customers deploy to cloud individual functions, in contrast to complete virtual machines (IaaS) or Linux containers (PaaS). FaaS offerings are available in the largest public clouds (Amazon Lambda, Google Cloud Functions, Azure Serverless); there are also popular open-source implementations (Apache OpenWhisk) with commercial offerings (Adobe I/O Runtime, IBM Cloud Functions). A recent addition to FaaS is the ability to compose functions: a function may call another functions, which, in turn, may call yet another function — forming a directed acyclic graph (DAG) of invocations. From the perspective of the infrastructure, a composed function is less opaque than a virtual machine or a container. We show that this additional information about the internal structure of the function enables the infrastructure provider to reduce the response latency. In particular, knowing the successors of a function in a DAG, the infrastructure can schedule these future invocations along with necessary preparation of environments. We model resource management in FaaS as a scheduling problem combining (1) sequencing of invocations; (2) deploying execution environments on machines; and (3) allocating invocations to deployed environments. For each aspect, we propose heuristics that employ FaaS-specific features. We explore their performance by simulation on a range of synthetic workloads and on workloads inspired by trace from existing system. Our results show that if the setup times are long compared to invocation times, algorithms that use information about the composition of functions consistently outperform greedy, myopic algorithms, leading to significant decrease in response latency.
Direct Spread Spectrum Technology for Data Hiding in Audio

Direct spread spectrum technology is traditionally used in radio communication systems with multiple access, for example, in CDMA standards, in global satellite navigation systems, in Wi-Fi network wireless protocols, etc. It ensures high security and reliability of information transfer. In addition, spread spectrum technology provides the transmitted signals with a noise-like appearance, thus hiding the semantic content of the messages. We researched this technology for other implementations. The purpose of our study was to investigate new technologies for hiding data in multimedia files. In particular, we investigated the direct spread spectrum in the context of the development of methods for hiding data in audio containers. We considered various spreading sequences (chip codes) and also explored their use for hiding information in audio files. We conducted experimental studies and estimated the bit error rate (BER) in the recovered data. The article also evaluates the distortion of an audio container by the peak signal-to-noise ratio (PSNR). The results of our research enable us to find out which method of forming chip codes gives a lower BER with equal PSNR. We provide recommendations on the formation of spreading sequences to reliably and safely hide informational messages in audio files.