Reviewerly/exHarmony huggingface dataset:
Evaluation of the Effectiveness Computer-Assisted Language Teaching by Big Data Analysis

This paper presents an in-depth study and evaluation analysis of the effects of the computer-assisted language teaching method on foreign language learning through its application. Using an empirical research approach, a practical study of computer-assisted English language teaching was conducted to verify the effects of CALL on oral language learning. In exploring the effects of CALL on students’ oral learning, including the effects on fluency, accuracy, and complexity of oral expressions, as well as the effects on learning attitudes, CALL is conducive to improving the fluency of oral expressions, reducing the number of pauses and repetitions in oral expressions, and enabling students to consciously use articulation words to facilitate smooth expressions. CALL is good for improving the accuracy of students’ speech, but it does not play a significant role in grammatical accuracy, and the grammatical errors are mainly in the third person singular of verbs, singular and plural of nouns, and passive voice. The use of CALL in teaching oral expressions does not improve the variety of sentences. However, the application of CALL in oral teaching stimulates students’ enthusiasm for learning, improves their interest in learning spoken English, and increases their confidence in oral expression.
Key Technologies of Media Data In-Depth Analysis System Based on Artificial Intelligence-Based Big Data

At present, big data related technologies are developing rapidly, and major companies provide big data analysis services. However, the big data analysis system formed by the combination method cannot sense each other and lacks cooperation, resulting in a certain amount of waste of resources in the big data analysis system. In order to find the key technology of the data analysis system and conduct in-depth analysis of the media data, this paper proposes a scheduling algorithm based on artificial intelligence (AI) to implement task scheduling and logical data block migration. By analyzing the experimental results, we know that the performance of LAS (Logistic-Block Affinity Scheduler) is improved by 23.97%, 16.11%, and 10.56%, respectively, compared with the other three algorithms. Based on real new media data, this article analyzes the content of media data and user behavior in depth through big data analysis methods. Compared with other methods, the algorithm model in this paper optimizes the accuracy of hot topic extraction, which has important implications for media data mining. In addition, the analysis results of the emotional characteristics, audience characteristics, and hot topic communication characteristics obtained by the research also have practical value. This method improves the recall rate and F value by 5% and 4.7%, respectively, and the overall F value of emotional judgment is about 88.9%.
Text-based Image Retrieval Using Captioning

The retrieval of an image from a database containing a large number of images is often a cumbersome task and takes a lot of time. Most conventional approaches for image retrieval make use of metadata such as keywords and annotations. However, the manual generation of captions for images is strenuous and time consuming. Hence, to solve this problem, a captioning model equipped with an attention mechanism generates an annotation for each image in the database. A ranking system then compares the input query to the list of captions that were generated to find the most similar caption and the corresponding image is retrieved, thereby saving valuable time. The architecture proposed retrieved the exact image 61 percent of the time, and retrieved very close matches otherwise.
University Mobile Employment Network Information System in the Internet Age

Abstract The rapid development of mobile Internet has changed our consumption patterns and living habits, and mobile Internet has become a mainstream way of life for us. This article mainly studies the construction of college mobile employment network information system in the Internet era. In terms of software environment, because this system is developed based on architecture, it can adapt to most operating systems. After confirming the choice of B/S architecture, according to the system’s individualization and specific needs, this system will be developed using Portal technology. Specific functional modules are implemented through Portlet components, and the server uses JSP scripts to meet general work requirements. In the school computer room, 120 computers are used for access testing at the same time, and different roles are used for testing at the same time. At the same time, a large number of user login, system operations and query form operations are submitted. The system responds to each user’s request within 5 seconds. Experimental data shows that when n is 40, the recommended accuracy reaches the maximum. The results show that the system designed in this paper has fast response speed and high accuracy.
Generation of the chaotic keys on the fly for AES encryption system

This paper proposes a safe and effective method to generate the subkeys that are used in the Advanced Encryption Standard (AES) algorithm for data encryption applications. The suggested method relies upon the Pseudo-Random Number Generator (PRNG) that is created from the improved Lorenz chaotic system. The output of PRNG is exploited as a key schedule for generating AES subkeys where the output is characterized by having an infinite periodic length. This method can be used in high-speed applications since no hardware multipliers are utilized so it gives the best hardware performance. Simulation and Synthesis of the suggested method are done by using the ISE 14.7 and analyzed by MATLAB and the NIST mathematical suite that was included, this method only made use of 4% from the Virtex 5 FPGA kit's slices and operating frequency reached a maximum of 242MHz.
Rate-Distortion Optimized Encoding for Deep Image Compression

Deep-learned variational auto-encoders (VAE) have shown remarkable capabilities for lossy image compression. These neural networks typically employ non-linear convolutional layers for finding a compressible representation of the input image. Advanced techniques such as vector quantization, context-adaptive arithmetic coding and variable-rate compression have been implemented in these auto-encoders. Notably, these networks rely on an end-to-end approach, which fundamentally differs from hybrid, block-based video coding systems. Therefore, signal-dependent encoder optimizations have not been thoroughly investigated for VAEs yet. However, rate-distortion optimized encoding heavily determines the compression performance of state-of-the-art video codecs. Designing such optimizations for non-linear, multi-layered networks requires to understand the relationship between the quantization, the bit allocation of the features and the distortion. Therefore, this paper examines the rate-distortion performance of a variable-rate VAE. In particular, one demonstrates that the trained encoder network typically finds features with a near-optimal bit allocation across the channels. Furthermore, one approximates the relationship between distortion and quantization by a higher-order polynomial, whose coefficients can be robustly estimated. Based on these considerations, the authors investigate an encoding algorithm for the Lagrange optimization, which significantly improves the coding efficiency.
Privacy preserving divisible double auction with a hybridized TEE-blockchain system

Abstract Double auction mechanisms have been designed to trade a variety of divisible resources (e.g., electricity, mobile data, and cloud resources) among distributed agents. In such divisible double auction, all the agents (both buyers and sellers) are expected to submit their bid profiles, and dynamically achieve the best responses. In practice, these agents may not trust each other without a market mediator. Fortunately, smart contract is extensively used to ensure digital agreement among mutually distrustful agents. The consensus protocol helps the smart contract execution on the blockchain to ensure strong integrity and availability. However, severe privacy risks would emerge in the divisible double auction since all the agents should disclose their sensitive data such as the bid profiles (i.e., bid amount and prices in different iterations) to other agents for resource allocation and such data are replicated on all the nodes in the network. Furthermore, the consensus requirements will bring a huge burden for the blockchain, which impacts the overall performance. To address these concerns, we propose a hybridized TEE-Blockchain system (system and auction mechanism co-design) to privately execute the divisible double auction. The designed hybridized system ensures privacy, honesty and high efficiency among distributed agents. The bid profiles are sealed for optimally allocating divisible resources while ensuring truthfulness with a Nash Equilibrium. Finally, we conduct experiments and empirical studies to validate the system and auction performance using two real-world applications.
Optimal Image Steganography Content Destruction Techniques

The paper presents two approaches for destroying steganogrphy content in an image. The first is the overwriting approach where a random data can be written again over steganographic images whereas the second approach is the denoising approach. With the second approach two kinds of destruction techniques have been adopted these are filtering and discrete wavelet techniques. These two approaches have been simulated and evaluated over two types of hiding techniques, Least Significant Bit LSB technique and Discrete Cosine Transform DCT technique. The results of the simulation show the capability of both approaches to destroy the hidden information without any alteration to the cover image except the denoising approach enhance the PSNR in any received image even without hidden information by an average of 4dB.
Sequence Mining TV Viewing Data Using Embedded Markov Modelling

The advancement of Internet Protocol television (IPTV) has brought significant advantages to TV users in terms of the TV content viewing and control over the Electronic Program Guide (EPG) system, which has provided richer TV viewing behaviour data to gain insights from. The Set-Top-Box (STB) in an IPTV system typically collects user behaviour data as well as monitors system performance in the form of event logs. Sequence mining technologies have been widely used in the study of event logs, such as mining the most frequent items. This paper presents the analysis of event logs on an IPTV box (BT YouView Box), focusing on customers’ navigation paths on the EPG system. We propose a new methodology by utilising embedded discrete-time Markov Chains (DTMC) and dynamic programming, to generate maximum likelihood trajectories for customer’s navigation sequences. This paper has demonstrated that the DTMC fits well into event logs and has revealed insights into customers’ navigation paths. The analysis has also identified several potential areas for further work.
Design of Online Music Teaching System Based on B/S Architecture

Under the global epidemic situation, offline face-to-face teaching has not adapted to the current teaching environment. Using computer technology to carry out online assisted teaching has become an inevitable choice for music teaching in colleges and universities. Offline teaching also has many disadvantages, such as single and backward teaching methods, poor sharing of audio-visual resources, and inconsistent teaching contents. The survey found that music teachers urgently need an online teaching platform to enable teachers to obtain effective, standardized, and comprehensive teaching resources anytime and anywhere so that students can enjoy high-quality music teaching resources as much as possible. Based on these requirements, the development of online music teaching system under B/S architecture has an important practical significance.
Better Security-Efficiency Trade-Offs in Permutation-Based Two-Party Computation

We improve upon the security of (tweakable) correlation-robust hash functions, which are essential components of garbling schemes and oblivious-transfer extension schemes. We in particular focus on constructions from permutations, and improve upon the work by Guo et al. (IEEE S&P '20) in terms of security and efficiency.
An Experimental Performance Evaluation of OpenNebula and Eucalyptus Cloud Platform Solutions

With the rapid growth of IAAS (Infrastructure as a Service) cloud solutions, its flexibility, energy savings, cost, and optimal resource utilization, most organizations have turned to the right cloud to deploy the application that users need. There are a variety of open source cloud platforms such as OpenStack, Eucalyptus, Opennebula, etc. However, it is difficult for organizations to select an appropriate platform to host Web applications or deploy virtual machines for high availability. From this perspective, researchers have tended to compare the performance of cloud platforms in order to obtain the most suitable platform for cloud deployment. In this article, the performance of OpenNebula and Eucalyptus open-source clouds was evaluated through a series of hardware and software experiments ending in a user-friendly selection.
A survey on e-voting based on blockchain

The Blockchain is one of the recent technologies that have emerged in the last decade. Blockchain has become a topic of many researches in several fields and it was implemented in some industries like finance, energy, health care, and electronic voting. Blockchain presents some great potential solutions that help to tackle difficulties. In this work we will focus on e-voting using blockchain technology. As we all know the voting is a way that leads the governments to approach democracy in their countries by an electoral process. The blockchain voting has replaced the traditional vote paper, also it replaced the voting systems that store data in central database. That gave much efficiency to the blockchain technology through a decentralized system that requires anonymity, confidentiality and transparency.
Arithmetic autocorrelation of binary <i>m</i>-sequences

An m-sequence is the one of the largest period among those produced by a linear feedback shift register. It possesses several desirable features of pseudorandomness such as balance, uniform pattern distribution and ideal autocorrelation for applications to communications. However, it also possesses undesirable features such as low linear complexity. Here we prove a nontrivial upper bound on its arithmetic autocorrelation, another figure of merit introduced by Mandelbaum for error-correcting codes and later investigated by Goresky and Klapper for FCSRs. The upper bound is close to half of the period and hence rather large, which gives an undesirable feature.
Share of Toxic Comments among Different Topics: The Case of Russian Social Networks

With the widespread use of online social networks, it is becoming more and more difficult to monitor and analyse all the user-generated content. Toxic speech in online conversations should be treated as a matter with serious social gravity, since it may result in both negative impacts on mental health and violent actions in the physical world. Within this study, we identified the share of toxic comments among different topics in Russian-language comments from social network Pikabu. Firstly, for toxic comments classification, we manually labelled the training dataset and fine-tuned several language models. To provide further toxic comments studies with strong classification baselines, we made our pre-trained publicly available to the research community. Secondly, we proposed an approach for topics labelling based on six major objective and observable dimensions for objective wellbeing measurement used by intergovernmental and government organisations. Lastly, we conducted an analysis of Pikabu data. We found that the largest share of toxic comments was under posts about politics, while security and socioeconomic topics ranked second and third, and the rest of the topics showed roughly the same values.
Comparison of Min-Max, Z-Score and Decimal Scaling Normalization for Zoning Feature Extraction on Javanese Character Recognition

Javanese script is a Javanese letter that was once used to replace Javanese, Madurese, Sundanese, Balinese and Sasak texts. Javanese script is used when the kingdom of the archipelago is still victorious. Javanese script began to appear in the 17th century. In the 19th century, Dutch introduced Latin letters in Indonesia. Over time, Javanese script was replaced by Latin letters. There are several types of methods that can be used to extract features of a character image, one of which is the zoning method. Zoning itself has many types such as simple zoning, Image Centroid Zone (ICZ), Zone Centroid Zone (ZCZ), and a new approach introduced by Elima Hussain. In this paper, we discus Min-Max, Z-Score and Decimal Scaling Normalization for Zoning Feature Extraction on Javanese Character Recognition.
Algorithm-Hardware Co-Optimization for Energy-Efficient Drone Detection on Resource-Constrained FPGA

Convolutional neural network (CNN) based object detection has achieved very high accuracy, e.g. single-shot multi-box detectors (SSD) can efficiently detect and localize various objects in an input image. However, they require a high amount of computation and memory storage, which makes it difficult to perform efficient inference on resource-constrained hardware devices such as drones or unmanned aerial vehicles (UAVs). Drone/UAV detection is an important task for applications including surveillance, defense, and multi-drone self-localization and formation control. In this paper, we designed and co-optimized algorithm and hardware for energy-efficient drone detection on resource-constrained FPGA devices. We trained SSD object detection algorithm with a custom drone dataset. For inference, we employed low-precision quantization and adapted the width of the SSD CNN model. To improve throughput, we use dual-data rate operations for DSPs to effectively double the throughput with limited DSP counts. For different SSD algorithm models, we analyze accuracy or mean average precision (mAP) and evaluate the corresponding FPGA hardware utilization, DRAM communication, throughput optimization. Our proposed design achieves a high mAP of 88.42% on the multi-drone dataset, with a high energy-efficiency of 79 GOPS/W and throughput of 158 GOPS using Xilinx Zynq ZU3EG FPGA device on the Open Vision Computer version 3 (OVC3) platform. Our design achieves 2.7X higher energy efficiency than prior works using the same FPGA device, at a low-power consumption of 1.98 W.
A Survey of Challenges Posed by the Dark Web

The dark nets’ anonymity has provided the organised crime groups, terror groups, and paedophiles alike a place to communicate, recruit, purchase and disseminate illegal materials across the world instantly without much fear of retribution. As the exit nodes of the TOR browser can be set up by anyone across the globe, policing requires a significant degree of both skill and resources. Undercover operations within law enforcement agencies have been largely successful with paedophile rings being disrupted, as well as illicit cryptomarket places being monitored and shut down. However, this activity is incredibly time-consuming and costly. This chapters provides a discussion of some of the existing challenges that the darknet poses to law enforcement and other security agencies.
Telegramming Hate: Far-Right Themes on Dark Social Media

Background: This study empirically examines the multimodal discourses of far-right groups on mobile apps. Many fringe groups find Telegram a convenient platform to spread hate speech without the need to censor their content or fear being blocked from the platform. Analysis: This study collected all the posts from 15 far-right Telegram channels. The data was analyzed using a mixed-method approach, including an examination of profile images, hashtags, mentions, and emojis that have been weaponized to assist in hate dissemination. Conclusion and implications: The findings show that one major theme on Telegram revolves around white peoples’ perceived grievances and discussions on conservatism followed by the minorities as the problem.
Interactive Visual Facets to Support Fluid Exploratory Search

Exploratory search starts with ill-defined goals and involves browsing, learning, and formulating new targets for search. To fluidly support such dynamic search behaviours, we focus on devising interactive visual facets (IVF), visualising information facets to support user comprehension and control of the information space. To do this, we reviewed existing faceted search interfaces and derived two design requirements (DR) that have not been fully addressed to support fluid interactions in exploratory search. We then exemplified the requirements through devising an IVF tool, which coordinates a linear and a categorical facet representing the distribution and summarisation of items, respectively, and providing context for faceted exploration (DR1). To support rapid transitions between search criteria (DR2), the tool introduces a novel design concept of using facets to select items without filtering the item space. Particularly, we propose a filter-swipe technique that enables users to drag a categorical facet value sequentially over linear facet bars to view the items in the intersection of the two facets along with the categorical facet dynamically summarizing the items in the interaction. A user study of 11 participants with realistic email search tasks shows that dynamic suggestions through the timeline navigation can help discover useful suggestions for search; the novel design concept was favoured over using facet values as filters. Based on these practices, we derive IVF design implications for fluid, exploratory searches.
Two-dimensional quaternion linear canonical series for color images

The quaternion-based moments and transforms are among the essential tools for color image processing, and received increasing attention and interest from researchers. In this paper, a novel two-dimensional quaternion linear canonical series (2-D QLCS) for describing color images is introduced. The capabilities of the proposed 2-D QLCS on image representation and reconstruction are analyzed. The proposed 2-D QLCS can be acquired from the common two-dimensional linear canonical transform series (2-D LCTS). The relationships between 2-D QLCS and 2-D LCTS are investigated in detail. Experimental results verify the correctness of the proposed method and show that 2-D QLCS has ideal performance in terms of scale invariance, numerical stabilities and image representation.
Machine-learning the Sato–Tate conjecture

We apply some of the latest techniques from machine-learning to the arithmetic of hyperelliptic curves. More precisely we show that, with impressive accuracy and confidence (between 99 and 100 percent precision), and in very short time (matter of seconds on an ordinary laptop), a Bayesian classifier can distinguish between Sato-Tate groups given a small number of Euler factors for the L-function. Our observations are in keeping with the Sato-Tate conjecture for curves of low genus. For elliptic curves, this amounts to distinguishing generic curves (with Sato-Tate group SU(2)) from those with complex multiplication. In genus 2, a principal component analysis is observed to separate the generic Sato-Tate group USp(4) from the non-generic groups. Furthermore in this case, for which there are many more non-generic possibilities than in the case of elliptic curves, we demonstrate an accurate characterisation of several Sato-Tate groups with the same identity component. Throughout, our observations are verified using known results from the literature and the data available in the LMFDB. The results in this paper suggest that a machine can be trained to learn the Sato-Tate distributions and may be able to classify curves much more efficiently than the methods available in the literature.