Reviewerly/exHarmony huggingface dataset:
Framework integrating RUP, Scrum and ISO 9126 to improve Quality during Software Construction in SMEs

This research proposes a framework integrating Rup and Scrum, two methodologies that successfully complement each other, combining their strengths and removing their limitations to improve quality during software construction in SMEs. To achieve this, the necessary guidelines are provided that help to contribute to the increase in quality; and to ensure said purpose, it is evaluated through indicators for each sub-characteristic, according to the quality standards suggested according to the ISO 9126 standard. The focus of this research is quantitative and with a quasi- experimental design, with only a posttest and a control group, for this, the instruments with observation guides are elaborated for each metric according to its dimension. To obtain the results it is necessary to specify the relative weights, levels, or scales; this percentage is based on the importance defined in each of the quality dimensions or factors. With this methodology applied to the Model Group (GM), an overall rating of 81.11% was obtained, which is within the target range at the scoring level, which shows that it has a degree of satisfaction. Our findings show that designing the framework helps developers reach set goals and improve software quality.
Comparative study on SMS spam message detection with different machine learning methods for safety communication

Over recent years, the utilisation of short message service (SMS) has been growing significantly. Along with it, there is a notable increase in spam messages from spammers. SMS spam is any kind of unsolicited text in the form of promotional content, Web links or any other irrelevant text note that is sent to your mobile phone for advertisement purposes. The low cost of SMS offered by telecom companies is one of the factors for high usage of SMS. The surge in unsolicited messages across all platforms including emails and SMS has created a need for the advancement and refinement to counteract spam messages, especially SMS spam messages. It really disturbs the users. Hence a variety of methods have been used to detect spams. We are using a dataset of real SMS spams from UCI Machine Learning Repository. The dataset contains a total of 5,574 SMS messages, in which 774 SMS messages were spam and 4827 were ham. In preprocessing of the data, we removed the stop words that do not give much significance to the data. For feature extraction, we use Wordnet Lemmatiser for tokenisation and Count Vectoriser for converting the words into vectors. Various machine learning algorithms are deployed to this dataset for training and testing. The machine learning algorithms are as follows: naive Bayes classifier, logistic regression, random forest classifier and decision tree classifier. We evaluate these models with the evaluation metrics such as precision score, recall score and accuracy score. We are taking accuracy as the primary evaluation metric to be considered for the most effective algorithm for detecting SMS spam messages. Among the abovementioned existing machine learning algorithms, random forest classifier is the most suitable algorithm as it possesses a higher accuracy of 98.13 percent for SMS spam detection.
Joint photon-number splitting attack on semi-quantum key distribution

Semi-quantum key distribution is based on the basic principle of quantum mechanics, which allows a classical user and quantum user to use information theory to have a secure shared key. In 2021, our research group proved the first proof-of-principle experimental demonstration of semi-quantum key distribution and verified its feasibility. Due to the limitations of existing science and technology, the experimental system still features a combination of multiphoton signal source and loss in the transmission line. This makes semi-quantum key distribution as susceptible to a photon-number splitting attack as quantum key distribution, leading to limitations of secure transmission distance. It seems that practical single-state semi-quantum key distribution can overcome photon-number splitting attack due to the SIRT bits (also known as the “sifted key”). However, its dual-channel feature still opens up an observation window to Eve. We present two joint photon-number splitting attacks suitable for a single-state semi-quantum key distribution system and show that through the joint photon-number splitting attack, Eve can obtain key information without being detected by Alice or Bob.
Poster

Blockchain sharding protocols randomly distribute nodes to different shards. They limit the quantity of shards to ensure that the adversary remains a minority inside each shard with a high probability. There can exist only a small number of shards. In this article, we propose a new sharding protocol that links the number of shards with the adversary population in real-time instead of a fixed upper-bounded population. The protocol is a two-phase design. First, several committee shards are constructed where the majority of nodes inside each are honest with high probability; then, each committee shard randomly splits into several worker shards with a high likelihood that at least one honest node is inside each. Each worker shard handles different transactions. Worker shard blocks that did not pass the unanimous voting are collected and voted by the committee shard using the majority voting. We show that (1) in the worst case (extremely unlikely) when all the transactions need to be handled by the committee shards, the transaction throughput and the data requirement only deteriorate to the same level as classical sharded blockchain; (2) when the worker shards handle most transactions, the overall transaction throughput is zoomed by two magnitudes securely while the data requirement for nodes remains at the same level.
Scientific Integrity: A Duty for Researchers [From the Editor]

Ethics in science is essential for various reasons and is a duty for scientists. The full sense of the word <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ethics</i> may differ according to languages and countries. For instance, in France, we typically make a distinction between ethics and scientific integrity, while scientific integrity is a part of ethics in the United States. For instance, the article “The Submerged Part of the AI-Ceberg,” <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> published in the September issue of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IEEE Signal Processing Magazine</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SPM</i> ), discusses the ethical issues concerning the research in our domain, i.e., a set of philosophical reflections on the interest and usefulness of our work for humanity and for the Earth. These questions are complex and must prompt debates between scientists and society. Conversely, scientific integrity is a set of good practices in the sciences, which has not been discussed but is strictly applied. All of the learned scientific societies promote integrity. Of course, IEEE, as such and also as a publisher of many scientific journals, strongly supports scientific integrity. Scientific integrity and misconduct are at the core of a few talks <xref ref-type="bibr" rid="ref2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[2]</xref> at the annual IEEE Panels of Editors meeting, which bring together volunteers and staff members to discuss assorted publication-related topics. Serious failures related to scientific integrity include plagiarism and data falsification or fabrication, but misconduct related to authorship and during the review process are also unacceptable and must be met with punishment.
The Psychology of Architecture Decision Records

Architecture Decision Records (ADRs) have been characterized as a lean documentation technique, but this overlooks their cultural impact: writing ADRs can also help developers evolve into architectural thinkers. ADRs accomplish this by subtly influencing developers to change four key behaviors: improved appreciation of design’s value, increased design participation, better follow through on decisions, and stronger design engagement. These four complementary behaviors nudge developers to see themselves as software architects, which in turn transforms the team’s culture.
Blockchain Based Solution to Enhance Drug Supply Chain Management for Smart Pharmaceutical Industry

Counterfeit drugs are an immense threat for the pharmaceutical industry worldwide due to limitations of supply chain. Our proposed solution can overcome many challenges as it will trace and track the drugs while in transit, give transparency along with robust security and will ensure legitimacy across the supply chain. It provides a reliable certification process as well. Fabric architecture is permissioned and private. Hyperledger is a preferred framework over Ethereum because it makes use of features like modular design, high efficiency, quality code and open-source which makes it more suitable for B2B applications with no requirement of cryptocurrency in Hyperledger Fabric. QR generation and scanning are provided as a functionality in the application instead of bar code for its easy accessibility to make it more secure and reliable. The objective of our solution is to provide substantial solutions to the supply chain stakeholders in record maintenance, drug transit monitoring and vendor side verification.
Unsupervised Enhancement and Web Tool for Perceptually Invisible Type Degraded Image

Severe contrast degradation in an image result in a substantial loss of crucial visible information. A novel unsupervised image enhancement algorithm is proposed in this paper to unravel hidden visual details in perceptually invisible images. In this paper, new algorithms are designed to solve the range localization problem. The proposed algorithm intends to expand the image range to maximize its intensity level utilization. Outliers present in an image intensity range are truncated based on their power distribution. Based on the type of input image, maxima-minima, or frequency-based expansion of truncated levels, is exercised to avoid over-enhancement. Two novel parameters, namely the transmission map and zero-frequency spread, are coined. The visually enhanced result successfully exhibits the camouflaged information and avoids over-enhancement. Consumer gadgets, such as cameras, have difficulty taking pictures in very dark and cloudy settings. The suggested algorithm can improve low-contrast photos that are both very dark and severely attenuated. The unsupervised nature of the proposed algorithm makes it suitable for application in the field of consumer electronics like mobile phones and digital cameras. A consumer-focused, platform-independent app is created and made available to the general public for additional study.
Federated Learning Optimization Algorithm for Automatic Weight Optimal

Federated learning (FL), a distributed machine-learning framework, is poised to effectively protect data privacy and security, and it also has been widely applied in variety of fields in recent years. However, the system heterogeneity and statistical heterogeneity of FL pose serious obstacles to the global model's quality. This study investigates server and client resource allocation in the context of FL system resource efficiency and offers the FedAwo optimization algorithm. This approach combines adaptive learning with federated learning, and makes full use of the computing resources of the server to calculate the optimal weight value corresponding to each client. This approach aggregated the global model according to the optimal weight value, which significantly minimizes the detrimental effects of statistical and system heterogeneity. In the process of traditional FL, we found that a large number of client trainings converge earlier than the specified epoch. However, according to the provisions of traditional FL, the client still needs to be trained for the specified epoch, which leads to the meaningless of a large number of calculations in the client. To further lower the training cost, the augmentation FedAwo ∗ algorithm is proposed. The FedAwo ∗ algorithm takes into account the heterogeneity of clients and sets the criteria for local convergence. When the local model of the client reaches the criteria, it will be returned to the server immediately. In this way, the epoch of the client can dynamically be modified adaptively. A large number of experiments based on MNIST and Fashion-MNIST public datasets reveal that the global model converges faster and has higher accuracy in FedAwo and FedAwo ∗ algorithms than FedAvg, FedProx, and FedAdp baseline algorithms.
An Improved Software Bug Triaging Approach Based on Topic Modeling and Fuzzy Logic

Software development is a modular approach which involves multiple developers and multi-tasking teams who are working together from various locations across the world. It is possible that a software bug may originate due to multiple reasons in multiple modules and can be fixed by multiple developers. Furthermore, there are a large number of software bugs that are unlabeled, vague, and noisy. As a result, it becomes a challenging task for the triager to find expert developers for fixing a newly reported bug from the available developers. To address the above problems, a combined approach of topic modeling and fuzzy logic-based bug triaging (TM-FBT) is proposed in this paper for efficient bug triaging. Topic modeling is used to create various topics of software bugs. Fuzzy logic is used to map developers with various topics to understand the multiple relationships between developers and software bugs. For a newly reported bug, the fuzzy similarity values are calculated and expert developers are identified by applying the fuzzy $$\alpha $$ -cut on the similarity values. The outcomes of the TM-FBT approach are compared with various machine learning algorithms and the fuzzy logic-based Bugzie model on benchmark data sets. On the Eclipse, Mozilla, and NetBeans data sets, the TM-FBT approach yields an accuracy of 0.903, 0.887, and 0.851 respectively. Similarly, the TM-FBT model outperforms all other state-of-the-art models in all other performance measures.
The Impact of ICT on Financial Development: Evidence from Southeast Asian Countries

This article aims to analyze the impact of Information &amp; Communication Technologies (ICT) on financial development in several Southeast Asian countries (Vietnam, Singapore, Thailand, Indonesia, Malaysia, Brunei, and the Philippines) from 1996 to 2019. Data in this study are collected from the World Bank (World Bank), the International Monetary Fund (IMF), and the Pooled-OLS, random-effects, fixed-effects, and Driscoll-Kraay estimation methods are used. The research results show that ICT has a positive impact on the financial development of some Southeast Asian countries. Based on the research results, some Southeast Asian countries need to have appropriate policies for increasing financial development through ICT.
Design and research of intelligent wheelchair robot based on lidar and ROS

In recent years, the research about robot operating systems has become more in-depth. A wide variety of robots have emerged, such as sweepers and nursing robots. With the increase in people's income and economic development, indoor robots such as nursing robots and sweeper robots gradually enter people's daily life. To meet the needs of disabled patients in their daily life, an intelligent wheelchair robot based on ROS is designed to provide indoor activities for the disabled with disabilities. The robot collects environmental depth information with lidar as its core, and has the functions of automatic navigation and multipoint navigation. In this paper, the performance of robot auto-navigation is evaluated experimentally. Through experiments, the proposed nursing robot based on lidar and ROS is verified to achieve synchronous positioning and map construction, and achieve multipoint cruise during autonomous navigation. Finally, the experimental results are demonstrated and the defects are put forward.
Brain2Pix: Fully convolutional naturalistic video frame reconstruction from brain activity

Reconstructing complex and dynamic visual perception from brain activity remains a major challenge in machine learning applications to neuroscience. Here, we present a new method for reconstructing naturalistic images and videos from very large single-participant functional magnetic resonance imaging data that leverages the recent success of image-to-image transformation networks. This is achieved by exploiting spatial information obtained from retinotopic mappings across the visual system. More specifically, we first determine what position each voxel in a particular region of interest would represent in the visual field based on its corresponding receptive field location. Then, the 2D image representation of the brain activity on the visual field is passed to a fully convolutional image-to-image network trained to recover the original stimuli using VGG feature loss with an adversarial regularizer. In our experiments, we show that our method offers a significant improvement over existing video reconstruction techniques.
Inference, Prediction, &amp; Entropy-Rate Estimation of Continuous-Time, Discrete-Event Processes

Inferring models, predicting the future, and estimating the entropy rate of discrete-time, discrete-event processes is well-worn ground. However, a much broader class of discrete-event processes operates in continuous-time. Here, we provide new methods for inferring, predicting, and estimating them. The methods rely on an extension of Bayesian structural inference that takes advantage of neural network’s universal approximation power. Based on experiments with complex synthetic data, the methods are competitive with the state-of-the-art for prediction and entropy-rate estimation.
Looking into the Crystal Ball

If we had a crystal ball, what would it show for the future of identity management? As the swirling mists parted in our all-seeing globe, we believe it would undoubtedly show identity management becoming increasingly necessary in the future for several reasons. First, it is unlikely that hackers will stop hacking, which means we’ll continue to need security measures to protect against increasingly diverse and potentially more automated threats. At the same time, there will be many innovative new services and devices that will be beneficial in our lives, but they’ll need adequate identity management to reduce the risk of them being used against us. We’ll also see a rise in autonomous entities requiring identification, authentication, and authorization just like humans. The notion of identity will need to spread from humans to all manner of devices, agents, and robots acting on our behalf, and such entities will need identity management as part of their defenses against malicious attacks. In addition, the Covid-19 pandemic has greatly increased the number of employees working from home or other locations outside the corporate network. This has accelerated a shift from older enterprise security models that depended on a network perimeter to a different approach, based on Zero Trust principles, that relies more heavily on identity and access management to control access to digital resources that reside both within and outside a corporate network. For all these reasons, the need for identity management in the future will be more important than ever before, and we’ll need to find ways to make it easier to implement and manage effectively.
Text Spotting towards Perceptually Aliased Urban Place Recognition

Recognizing places of interest (POIs) can be challenging for humans, especially in foreign environments. In this study, we leverage smartphone sensors (i.e., camera, GPS) and deep learning algorithms to propose an intelligent solution to recognize POIs in an urban environment. Recent studies have approached landmark recognition as an image retrieval problem. However, visual similarity alone is not robust against challenging conditions such as extreme appearance variance and perceptual aliasing in urban environments. To this end, we propose to fuse visual, textual, and positioning information. Our contributions are as follows. Firstly, we propose VPR through text reading pipeline (VPRText) that uses off-the-shelf text spotting algorithms for word spotting followed by layout analysis and text similarity search modules. Secondly, we propose a hierarchical architecture that combines VPRText and image retrieval. Thirdly, we perform a comprehensive empirical study on the applicability of state-of-the-art text spotting methods for the VPR task. Additionally, we introduce a challenging purpose-built urban dataset for VPR evaluation. The proposed VPR architecture achieves a superior performance overall, especially in challenging conditions (i.e., perceptually aliased and illuminated environments).
Physical education in E-learning

Forming the need for a healthy lifestyle, the transfer of knowledge, skills and abilities in the field of physical culture and sports are an urgent pedagogical problem in the transition from the “classical” forms of organization of the educational process to E-learning. The solution of this problem by means of well-known programming languages is impossible due to the insufficient descriptive potential of the latter, in particular, the lack of the possibility of representing human knowledge, probable or possible future events, actions, results. The purpose of our research is to develop theoretical provisions and to verify in practice the possibilities of formalizing the presentation of the individual educational goal of the student and the conscious need for a healthy lifestyle as its basic component in declarative languages, as well as its implementation by means of the educational platform of the university. The researcher considers the possibility of describing the individual educational goal of a student, including a conscious need for a healthy lifestyle as its basic component, gaining knowledge, forming and developing skills in the field of physical culture and sports, as well as the possibility of building an individual educational trajectory of its achievements in the organization of E-learning at the university in the language of first-order predicate logic. A number of features of the predicate logic language that are essential for the problem under consideration are singled out. A pedagogical experiment was conducted on the basis of the educational platform of Voronezh State Pedagogical University. The study involved 158 students and undergraduates. The descriptions of individual educational goals of the student, formalized by the language of logic of predicates, suitable for implementation by means of computer technology are obtained, on the basis of which the optimal trajectory for achieving this goal is calculated for each participant. The practical implementation of the educational process was carried out, data on a conscious need for a healthy lifestyle, gaining knowledge, forming and developing skills in the field of physical culture and sports were obtained. The obtained results were compared with similar studies and a conclusion was made about the ways to improve the suggested approach to the formation of a conscious need for students to lead a healthy lifestyle, gain knowledge, form and develop their skills in the field of physical culture and sports in the context of the transition from “classical” forms of organization of the educational process to E-learning.