Reviewerly/exHarmony huggingface dataset:
Challenges in Agile Software Maintenance for Local and Global Development: An Empirical Assessment

Agile methods have gained wide popularity recently due to their characteristics in software development. Despite the success of agile methods in the software maintenance process, several challenges have been reported. In this study, we investigate the challenges that measure the impact of agile methods in software maintenance in terms of quality factors. A survey was conducted to collect data from agile practitioners to establish their opinions about existing challenges. As a result of the statistical analysis of the data from the survey, it has been observed that there are moderately effective challenges in manageability, scalability, communication, collaboration, and transparency. Further research is required to validate software maintenance challenges in agile methods.
REVIEW OF METHODS FOR DETERMINING THE TONATION OF TEXTS IN NATURAL LANGUAGES

The analysis of sentiment in user comments finds application in many areas, such as evaluating the quality of goods and services, analyzing emotions in messages, and detecting phishing advertisements. There are numerous methods for analyzing the sentiment of textual data in the Russian language, but automatic sentiment analysis of Russian-language texts is much less developed than for other major world languages. This article is part of a broader study on the creation of an information system for detecting dangerous content in the cyberspace of Kazakhstan. The purpose of this article is to provide an analytical review of the different approaches to sentiment analysis of Russian-language texts and to compare modern methods for solving the problem of text classification. Additionally, the article seeks to identify development trends in this area and select the best algorithms for use in further research. The review covers different methods for text data preprocessing, vectorization, and machine classification for sentiment analysis of texts, and it concludes with an analysis of existing databases on this topic. The article identifies some of the main unresolved problems in sentiment analysis of Russianlanguage texts and discusses planned further research.
Blockchain-Based Loyalty Management System

Loyalty platforms are designed to increase customer loyalty and thus increase consumers’ attraction to purchase. Although successful in increasing brand reach and sales, these platforms fail to meet their primary objective due to a lack of incentives and encouragement for customers to return. Along with the problem in originating sales, they bring excessive costs to brands due to the maintenance and infrastructure required to make the systems feasible. In that sense, recent blockchain technology can help to overcome some of these problems, providing capabilities such as smart contracts, which have the potential to reinvent the way loyalty systems work and solve current problems. Although blockchain is a relatively new technology, some brands are already investigating its usefulness and rebuilding their loyalty systems. However, these platforms are independent and linked directly to a brand. Thus, there is a need for a generic platform capable of creating and managing different loyalty programs, regardless of the size of the business. This paper explores the shortcomings of current loyalty programs identified through the literature review, and proposes a loyalty management system with blockchain integration that allows any retailer to create and manage their loyalty programs and have customers interact directly with multiple programs in a single application.
Software and Hardware Encoding of Omnidirectional 8K Video: A Performance Study

Omnidirectional (360-degree) video streaming has been on the rise dramatically in recent years. As a result, demand on the monitoring and evaluation of the visual quality of such videos is rapidly increasing. This paper deals with the performance study of hardware and software encoders for 360° video in resolution 8K, especially in terms of encoding speed on a consumer computer. New encoding standards significantly improve encoding efficiency, but also increase encoding time due to increased computational complexity. In this work, different implementations of the High Efficiency Video Codec (HEVC) for 360° videos at SK resolution are compared. Next, quality presets that balance visual quality and encoding speed at the same bitrate are also considered. Netflix’s Video Multimethod Assessment Fusion (VMAF) objective metric is used to measure video quality. Furthermore, attention is devoted to the study of power consumption and encoding speed, considering CPU and GPU performance limitations. The results show that hardware encoders can encode 8K360° videos in realtime with quality comparable to software encoding using x265 compression. However, the encoding speed is approximately 20 times faster.
Synwmd: Syntax-aware word Mover’s distance for sentence similarity evaluation

Word Mover’s Distance (WMD) computes the distance between words and models text similarity with the moving cost between words in two text sequences. Yet, it does not offer good performance in sentence similarity evaluation since it does not incorporate word importance and fails to take inherent contextual and structural information in a sentence into account. An improved WMD method using the syntactic parse tree, called Syntax-aware Word Mover’s Distance (SynWMD), is proposed to address these two shortcomings in this work. First, a weighted graph is built upon the word co-occurrence statistics extracted from the syntactic parse trees of sentences. The importance of each word is inferred from graph connectivities. Second, the local syntactic parsing structure of words is considered in computing the distance between words. To demonstrate the effectiveness of the proposed SynWMD, we conduct experiments on 6 textual semantic similarity (STS) datasets and 4 sentence classification datasets. Experimental results show that SynWMD achieves state-of-the-art performance on STS tasks. It also outperforms other WMD-based methods on sentence classification tasks.
Recognition of expiry data on food packages based on improved DBNet

To prevent products with missing character information from reaching the market, manufacturers need an automatic character recognition method. One of the key problems of this recognition method is to recognise text under complex package patterns. In addition, some products use dot matrix characters to reduce printing costs, which makes text extraction more difficult. We propose a character detection algorithm using DBNet as the base network, combined with the Convolutional Block Attention Module (CBAM) to improve its feature extraction of characters in complex contexts. After the character area has been located by the detection algorithm, it is intercepted and fed into a fully convolutional character recognition network to achieve print character recognition. We use ResNet as the backbone network and CTC loss for training. In addition, the CBAM module was added to the backbone network to enhance its recognition of dot matrix characters. The algorithm was finally deployed on the jetson nano. The experimental results show that the character detection accuracy reaches 97.9%, an improvement of 1.9% compared to the original network. As for the character recognition algorithm, the inference speed is doubled when deployed to the nano platform compared to the CRNN network, with an accuracy of 97.8%.
Multiple kernel learning-based rule reduction method for fuzzy modeling

As a model for complex processes, the Takagi-Sugeno fuzzy model (T-S fuzzy model) usually requires numerous rules, often leading to dimension disaster. To mitigate this problem while preserving modeling accuracy, a multiple kernel learning-based rule reduction method (MKLRR) is proposed here. First, several fuzzy sets in the original T-S fuzzy model are merged into a larger single set. Dynamic behavior in these big fuzzy sets is strongly nonlinear, and that between sets is often completely different, rendering ineffective the local linear model structure of traditional T-S fuzzy modeling. To solve this difficulty, we develop a multiple kernel learning strategy to project each big fuzzy set from a low-dimensional space into a high-dimensional space using the corresponding projection function. In each high-dimensional space, data has a linear relation which is easily represented by a linear model. The final model is constructed through fuzzy inference by combining all linear models from these high-dimensional spaces, greatly reducing the rule number and preserving modeling accuracy due to the good approximating ability of multiple kernel learning. Two solving strategies are developed to subsequently train the model. Additional analysis and proofs state the effectiveness of the proposed method. Case study indicates that the constructed model performs better with a fewer number of rules than the other methods with a greater number of rules, which effectively solves the problem of dimension disaster.
A co-evolutionary migrating birds optimization algorithm based on online learning policy gradient

A co-evolutionary migrating birds optimization algorithm based on online learning policy gradient (CMBO-PG) is proposed to address complex continuous real-parameter optimization problems. In CMBO-PG, a Gaussian estimation of distribution algorithm (GEDA), which enhances the exploitation tendency, is utilized to generate the solutions of the leading flock. The neighborhood solutions of the following flock are produced by a multi-strategy learning mechanism to promote exploration capability. The co-evolution of the leading flock and following flock is realized by the information-sharing mechanism and the operation of destruction and construction to keep the balance of exploration and exploitation. The nonlinear selection of mutation strategies is laborious due to the differences in the ability to address optimization problems. In the mechanism of multi-strategy learning, a long short-term memory (LSTM) is adopted as a selector of mutation strategies to predict the selection probability of three mutation strategies. The evolutionary procedure of the following flock is modeled as a Markov decision process (MDP). The policy gradient (PG) is employed as a model optimizer to control the parameters of LSTM based on the historical feedback information. The performance of CMBO-PG is testified on the CEC 2017 benchmark test suite. The experimental results show that CMBO-PG is superior to the 12 comparison algorithms, including state-of-art algorithms.
QR codes during the pandemic: Seamful quotidian placemaking

During the COVID-19 pandemic, one technology for contact tracing has come to dominate – QR codes. As a technology pioneered in Japan two decades ago and mainstreamed in China, QR codes have quickly become part of quotidian placemaking. While locations such as China have fully incorporated QR code technology into everyday contexts including public transport and mobile wallet applications, QR codes in the West were relatively overlooked. That was, until the pandemic. In this article, we examine some of the ways QR codes are being imagined and reimagined as part of public placemaking practices. In order to do so, we begin with a short history of QR codes – emerging in Japan, becoming mainstream in China and their consequent uptake globally. We then discuss the methods of our Australian study conducted during the pandemic and the seamful/seamless findings from our study.
Quality comparison of 360° 8K images compressed by conventional and deep learning algorithms

Due to the accessibility of virtual reality in recent years, there has been a great interest in producing and streaming omnidirectional $(360 ^{\circ}$ field of view) high resolution images and videos. Since both high resolution and high quality are demanding for the storage and distribution of such content, the use of advanced compression methods is a key factor in achieving this goal. This paper provides an objective comparison of conventional image compression codecs (JPEG, JPEG XL, HEIC, AVIF, VVC Intra) and deep learning image compression algorithms with a JPEG AI framework recommendation. The visual quality evaluation is based on ten images from publicly available databases compressed to predetermined bit rates. Six full reference objective metrics (WS-PSNR, MS-SSIM, VIFp, FSIMc, GMSD, VMAF) are used to evaluate the visual quality of the compressed images. Modern image compression codecs outperform the oldest and most widely used codec JPEG in terms of bandwidth reduction but require more processing power and system resources.
Omnidirectional Image Quality Assessment Database (OMNIQAD): Description and Examples

This paper introduces a public (OMNIQAD) database of omnidirectional (360°) images distorted by various ways and techniques, which can serve as a basic source for various research in the field of objective and subjective quality assessment of an immersive multimedia content. Compressionbased distortions were applied using traditional JPEG and several emerging image compression standards, for instance, HEIC, AVIF and JPEG XL. Other types of distortion included Gaussian and impulse noise. The database offers eleven reference omnidirectional images of different character (374 distorted images in total). For the evaluation of distorted and compressionbased corrupted images, several objective quality metrics were adopted. Link to a repository containing the resulting objective scores and the database of images are included in this paper.
Research on dynamic path planning method of electric inspection robot based on fuzzy neural network

In order to better address the issue of the coverage rate about power inspection robot to the working area, solving the difficulties in the navigation modeling and path planning of the intelligent control program is the focus of this paper. This paper first analyzes a multi-information fusion SLAM robot positioning and navigation algorithm, then uses the Kalman filter technology to locate through Voronoi mapping. Second, this paper proposes a five-layer neural network approach to how robots can plan and control routes in high-density scenarios. Third, the robot takes the surrounding obstacle distance, the target angle and the target distance as input parameters then the output parameters of the fuzzy neural network are the wheel speed and the steering angle. The inspection robot uses the sensors on the body to detect the surrounding environment’s parameter information. The dynamic path planning algorithm changes the path accordingly, prevents collision, and finally reaches the preset target. Finally, the paper collects the features obtained by Voronoi mapping in a large-scale simulation environment. The fuzzy neural network algorithm for detecting robots is validated in a complex environment, proving the effectiveness and accuracy of the algorithm.
Do we really need a new theory to understand over-parameterization?

This century saw an unprecedented increase of public and private investments in Artificial Intelligence (AI) and especially in (Deep) Machine Learning (ML). This led to breakthroughs in their practical ability to solve complex real-world problems impacting research and society at large. Instead, our ability to understand the fundamental mechanism behind these breakthroughs has slowed down because of their increased complexity, while in the past breakthroughs often emerged from foundational research. This questioned researchers about the necessity for a new theoretical framework able to help researchers catch up on this lag. One of the still not well understood mechanisms is the so-called over-parametrization, namely the ability of certain models to increase their generalization performance (reduce test error) when the number of parameters is above the interpolating threshold (zero training error). In this paper we will show that this phenomenon can be better understood using both known theories (surveying them in the process) and empirical evidences for both shallow and deep learning algorithms.
Calibration method of spatial transformations between the non-orthogonal two-axis turntable and its mounted camera

In order to expand the field of view and measuremenst range, the camera is often mounted on a two-axis turntable to perform various visual tasks. And the calibration of the position and attitude relationship between the mounted camera and the two-axis turntable is a prerequisite for visual measurement. The turntable is considered an ideal orthogonal two-axis turntable in conventional methods. However, the rotation axes of the actual two-axis turntable may be neither vertical nor intersecting, and the optical center of the mounted camera is not always located in the rotation center of the turntable even for orthogonal two-axis turntables. The quite difference between the actual physical model of the two-axis turntable and the ideal model can cause large errors. Therefore, what we believe to be a novel position and attitude calibration method between a non-orthogonal two-axis turntable and the mounted camera is proposed. This method describes the spatial hetero-planar lines relationship between the azimuth axis and pitch axis of the turntable accurately. By the geometric invariant characteristics of the mounted camera in motion, the axes of turntable are recovered and the base coordinate system is established, and the position and attitude of the camera are calibrated. Simulation and experiments verify the correctness and effectiveness of our proposed method.
Compact Temporal Trajectory Representation for Talking Face Video Compression

In this paper, we propose to compactly represent the nonlinear dynamics along the temporal trajectories for talking face video compression. By projecting the frames into a high dimensional space, the temporal trajectories of talking face frames, which are complex, non-linear and difficult to extrapolate, are implicitly modelled in an end-to-end inference framework based upon very compact feature representation. As such, the proposed framework is suitable for ultra-low bandwidth video communication and can guarantee the quality of the reconstructed video in such applications. The proposed compression scheme is also robust against large head-pose motions, due to the delicately designed dynamic reference refresh and temporal stabilization mechanisms. Experimental results demonstrate that compared to the state-of-the-art video coding standard Versatile Video Coding (VVC) as well as the latest generative compression schemes, our proposed scheme is superior in terms of both objective and subjective quality at the same bitrate. The project page can be found at https://github.com/Berlin0610/CTTR.
SFHN: Spatial-Frequency Domain Hybrid Network for Image Super-Resolution

Deep convolutional neural networks (CNNs) have demonstrated tremendous success in image super-resolution (SR). According to the frequency principle, the vanilla CNNs fit the target function from low to high frequencies during the training process. It implies an implicit bias that CNNs tend to fit the training data by a low-frequency function. This is detrimental to SR task which essentially recovers the missing high-frequency cues from degraded images. To address this issue, a novel spatial-frequency domain hybrid network (SFHN) is proposed for image SR in this paper. More specifically, it contains multiple spatial-frequency domain hybrid convolution blocks (SFBlocks) for extracting both spatial and frequency components. In particular, frequency information is complementary to spatial one, mitigating the disadvantage of the spatial CNNs in capturing high-frequency content resulting from the inherent bias of the networks. In contrast to convolution blocks in the previous SR methods, the proposed SFBlock is configured with additional frequency-domain convolution branch driven by the Fourier transform, which allows to process frequency content. In addition, we introduce a spectral loss based on Fast Fourier Transform (FFT) to fully leverage the performance of our SFHN, as it prevents the loss of important frequency content during training. Experimental results on public benchmarks demonstrate that our SFHN achieves promising performance superior to the state-of-the-art methods.
Energy-Efficient Wireless Federated Learning: A Secrecy Oriented Design via Sequential Artificial Jamming

Wireless federated learning (FL) is envisioned as a promising paradigm of distributed learning in wireless networks without disclosing users' data privacy. However, radio channel leads to a potential risk of eavesdropping attack when sending the trained model data in wireless networks. To address this eavesdropping attack, in this work, we propose an energy-efficient wireless FL by using artificial jamming. Specifically, the group of wireless devices (WDs) adopt the time division multiple access (TDMA) approach to send their locally trained models to the FL server for model aggregation subject to the eavesdropping attack of a malicious node. When one of the WDs sends its local model, all the other WDs send the artificial jamming signals to interfere with the eavesdropper, which helps increase the secrecy throughput of the targeted WD for uploading its local model. Different from many existing studies using stochastic gradient descent (SGD), we adopt the stochastic average gradient (SAG) method in the local training to improve the convergence of FL and derive the corresponding lower bound of the FL convergence rate via SAG. Furthermore, we formulate an optimization problem that aims at minimizing the energy consumption of the WDs by jointly optimizing different WDs' local training time, their uploading transmission time in TDMA and the transmit-powers for providing artificial jamming, as well as the FL configurations of the local/global iterations. We also propose an efficient algorithm for solving this non-convex optimization problem. Numerical results are illustrated to validate the advantages of our design of wireless FL and the corresponding algorithms. In particular, the results demonstrate that our secrecy oriented energy-efficient FL can significantly outperform the other heuristic FL schemes.
Comments on "An Efficient Identity-Based Provable Data Possession Protocol with Compressed Cloud Storage"

This letter addresses some security issues of an identity-based provable data possession protocol with compressed cloud storage (published in IEEE TIFS, doi:10.1109/TIFS.2022. 3159152). Some serious flaws are identified and an attack to the protocol is designed. This attack is able to recover the ephemeral secret keys from two encrypted blocks with high probability to reveal the original plaintext file completely. Moreover, an adversary can impersonate a data owner to outsource any file to the cloud in a malicious way. The main ingredients of the attack is some classical number theoretic results.