Reviewerly/exHarmony huggingface dataset:
Perspectives on the Sustainability and Future Trajectory of Agile

Agile has gained speed and penetrated into many and varying organizations today. In tune, the term Agile has become representing a widely appreciated mind-set, principles, practices and methods, for varying domains. Despite these benefits of agility, the Agile consultants may evangelize it with commercial concerns resulting in “selling agility” to organizations as an object in the form of packaged practices, which may lead to end of Agile. Also, there are views about/on the Agile Manifesto as it is a product of the past and mainly developer centric. Those concerns have led us to investigate whether Agile is sustainable enough to live for a long time, what the future of Agile look like in the coming years or whether there is any point in the Agile Manifesto to be altered. To find out answers to these questions, fourteen interviews from nine companies in three major industries in Turkey including TechFin (Technology-Finance), Aviation, and Telecommunication were included to collect data. It seems that Agile will be lasting for a long time as it provides many benefits to the organizations. On the way, it may evolve into many different versions shaped around customized needs and contexts of the organizations. Organizations will probably focus more on people related factors, integrating and balancing Agile with other best practices, being-changed communication needs after the pandemic, seeking solutions for the outside of the “initial Agile” zone and Agile mindset. Commonly, the participants expect to see a change in the software focus on the Agile Manifesto, as well as an addition of value perspective on it.
Research on Vision-based pedestrian detection and tracking algorithm

Target detection and target tracking play an important role in many scenes. For example, intelligent monitoring, multi-target tracking in complex environments. This paper designs a pedestrian detection and tracking system. Based on Yolov5, the proposed detection algorithm introduces the deformable convolution to construct the Res-dcn component to replace the residual component of the Yolov5 backbone network, which can accurately locate the target feature points. Pedestrian tracking is based on DeepSort multi-target tracking algorithm, for the mismatch problem of pedestrian IDS in front and back frames in complex environment, the fusion of FHOG feature and CNN feature is introduced to the DeepSort multi-target tracking algorithm, the candidate feature matching mechanism is used to filter the trajectory set to improve the tracking performance. Experiment results with comparisons show the validity and the superiority of our developed tracking algorithm.
Identification and reading method of pointer meter based on CBAM D2-Net

In order to read the ambiguous and reflective pointer instruments collected on site, a D2-net model based on CBAM is proposed, and an attention mechanism is introduced into the feature extraction network to focus the feature point detection on the salient part to capture more It has a good visual structure, and takes the angle method as the core, and uses the RANSAC algorithm to fit the angle of the instrument for interpretation. Experiments show that the accuracy of the instrument identification and interpretation method in this paper is within the allowable error range in all cases, and the registration and reading of all samples are successful, and there is no obvious fluctuation in reflective interference and image blurring. The comparison shows that for the highest stability.
Study regarding the volatility of main cryptocurrencies

Abstract Developments in digital technologies are considered to be the most important innovations since the advent of the internet. In several countries, this has led to a significant change in the way payments are made, leading to new forms of payment, such as crypto-currencies. With regard to cryptocurrencies, it remains a complex issue involving especially volatility, but also money laundering and consumer protection issues. While most countries consider cryptocurrencies too volatile to be used as a payment alternative, crypto-currencies gain interest of investors in the last 10 years due to the possibility of obtaining large profits. The aim of the paper is to study the volatility of the first 5 cryptocurrencies (Bitcoin, Ethereum, Binance Coin, Cardano and Ripple) through GARCH models. The process of evaluating highly volatile cryptocurrencies is complex and depends on many parameters. Therefore, our results would be particularly useful in terms of portfolio and risk management and could help them to be more agile in evaluating their investments, in making optimal decisions and making future forecasts. We find that the GARCH (1.1) models provide the best fit, in terms of modelling of the volatility in the most popular and largest cryptocurrencies. The results show that for BTC, ETH and XRP the appropriate model is GARCH (1.1) and in the case of BNC and CARDANO GARCH-M explain better the volatility of the crypto-currencies. Therefore, more in depth analysis of the datasets may be required to confirm or deny possible structural change. The study can be complemented by carrying out an event study on the 5 cryptocurrencies analyzed or extending the analysis by applying other GARCH models, to research the optimal model for several cryptocurrencies.
Roaming Jingliao – The AR Based Learning Assistance for Design Cultural Creation Education

The study has taken the issue of “AR in education” into consideration in design cultural creation domain. The specialized areas such as AR development and user satisfaction have been taken into account. The purpose is to develop an AR application for helping students to learn local culture while having Field Learning Activities. Roaming Jingliao is a LBS AR to provide local cultural information and attract students to learn in the field course. The study carried out experiments to assess the impact of exploiting AR in learning local culture. The author brought students to discover Jingliao Old Street in Houbi District by using Roaming Jingliao. The participants were asked to take the satisfactory questionnaire and a short interview. Afterwards, they worked in groups to design and produce e-books based on the cultural knowledge they’ve obtained, and further register for an e-book contest. Meanwhile, these works were also assessed by three professional judges for cultural and creative scoring. There were 9 e-books produced in the study and 3 of them earned excellent works award in the contest. It is shown that the participants have a positive attitude and high interests of using Roaming Jingliao to learn local cultures.
Using Immersive Virtual Reality to Explore the Learning Performance and Cognitive Load of Students in STEAM Electronic Circuits Learning

In the recent era applying advanced emerging technologies to educational issues has become a subject for wide discussion, also students have a high scope to obtain much more learning experience and resources through these exclusive technologies. Many such studies have used this immersive virtual reality to explore the feasibility of STEAM education for their students. However, learning activities through immersive virtual reality may have some negative effects as well, indeed which may indirectly reduce student learning performance. Therefore, this study has built an extremely realistic virtual reality system to present the materials of electronic circuits. The students can make use of immersive virtual reality devices and handheld sensing devices to operate and practice the electronic circuit learning tasks in a virtual learning environment. In addition, this study has recruited 20 students to have an investigation on the effect of immersive virtual reality on learning performance and cognitive load in electronic circuit learning. According to this result, the experimental group using the virtual reality system for STEAM learning activities can significantly have an emerging improvement in the learning performance. Additionally, the cognitive load of the experimental group was also lower than that of the control group in the process of STEAM learning activities.
Acquisition and cognition information of human body swing

Man plays an important role as the moving sensor platform in information systems, including 6G communication and intelligent applications. In particular, personal self-position and attitude are critical for firefighters performing rescues, athletes competing in sports, and the shooter with a shoulder launcher aiming at a target. Due to the swinging of the human body and respiratory fluctuations, the real-time miniature inertial measurement unit (MIMU) data combines body motion noise and degrades navigation precision. Two scenarios are discussed in this paper, including aiming at a stationary and moving target. The angular rate and acceleration of MIMU data for a stationary target are obtained from the turntable and shooter's shoulder, respectively. Data are collected via a stimulated launcher mounted on the shooter's shoulder and the theoretical solution for a moving target. A technology known as error cumulative amplitude spectrum (CAS) is adapted to extract the specific frequency caused by the shooter's body swing, which exhibits low-frequency characteristics. Thus, a mixed filter frame composed of an adaptive notch filter (ANF) and a cubature Kalman filter (CKF) is described in detail to reduce both high- and low-frequency measurement errors. The results of series tests indicate that the acceleration and angular rate errors caused by shooter swing are reduced by more than 98% and more than 81%, respectively, in the stationary and moving cases. As a result, the presented method effectively suppresses body swing and enhances navigation performance.
Clause-level Relationship-aware Math Word Problems Solver

Automatically solving math word problems, which involves comprehension, cognition, and reasoning, is a crucial issue in artificial intelligence research. Existing math word problem solvers mainly work on word-level relationship extraction and the generation of expression solutions while lacking consideration of the clause-level relationship. To this end, inspired by the theory of two levels of process in comprehension, we propose a novel clause-level relationship-aware math solver (CLRSolver) to mimic the process of human comprehension from lower level to higher level. Specifically, in the lower-level processes, we split problems into clauses according to their natural division and learn their semantics. In the higher-level processes, following human′s multi-view understanding of clause-level relationships, we first apply a CNN-based module to learn the dependency relationships between clauses from word relevance in a local view. Then, we propose two novel relationship-aware mechanisms to learn dependency relationships from the clause semantics in a global view. Next, we enhance the representation of clauses based on the learned clause-level dependency relationships. In expression generation, we develop a tree-based decoder to generate the mathematical expression. We conduct extensive experiments on two datasets, where the results demonstrate the superiority of our framework.
Stroke-Based Autoencoders: Self-Supervised Learners for Efficient Zero-Shot Chinese Character Recognition

Chinese characters carry a wealth of morphological and semantic information; therefore, the semantic enhancement of the morphology of Chinese characters has drawn significant attention. The previous methods were intended to directly extract information from a whole Chinese character image, which usually cannot capture both global and local information simultaneously. In this paper, we develop a stroke-based autoencoder(SAE), to model the sophisticated morphology of Chinese characters with the self-supervised method.Following its canonical writing order, we first represent a Chinese character as a series of stroke images with a fixed writing order, and then our SAE model is trained to reconstruct this stroke image sequence. This pre-trained SAE model can predict the stroke image series for unseen characters, as long as their strokes or radicals appeared in the training set. We have designed two contrasting SAE architectures on different forms of stroke images. One is fine-tuned on existing stroke-based method for zero-shot recognition of handwritten Chinese characters, and the other is applied to enrich the Chinese word embeddings from their morphological features.The experimental results validate that after pre-training, our SAE architecture outperforms other existing methods in zero-shot recognition and enhances the representation of Chinese characters with their abundant morphological and semantic information.
An Efficient Algorithm for Human Abnormal Behaviour Detection Using Object Detection and Pose Estimation

This paper presents a novel approach to detect and recognize human behaviour. The primary purpose of this work is to demonstrate the detection of abnormal behaviour within the restricted domain. The proposed work automatically detects the human being from the feed of video surveillance or regular videos. Once the human being is noticed in a frame, the human's pose is estimated and the action is recognized. The action is then classified as normal or abnormal. The proposed work includes models such as the FAST R-CNN model for human detection within the video frame or image and the SSD MOBILE NET for human action recognition. The fast R-CNN model is one of the best algorithms for object detection and thus it is used to locate human beings within a specific frame. SSD-Mobilenet is used to estimate the human pose and recognize the action being performed. The combination of these two models makes the proposed model faster and more accurate. The proposed network is trained using the HMDB database. It is an extensive human motion database that consists of 51 kinds of actions. Results of the proposed work are better than state-of-the-art performance. The proposed work detects the human beings in a video-frame and then recognizes the action being performed by the human detected. Once the action is recognized, it is then classified as Normal or Abnormal actions. The proposed work can be used at various public places like metro stations, bus stands, banks, railway stations, theatres, etc. to prevent mishappening.
Improving effort estimation of software products by augmenting class point approach with regression analysis

Software products are essential parts of many organizations on-going business up to a large extent. The main factors contributing to the successful delivery of a software product are its timely completion within the allocated budget and its quality compliance. Customer goodwill and profitability are very important for a software organization’s continued business. A large proportion of software products are delivered late or go over-budget causing significant inconvenience to the customers. This work proposes an accurate development effort estimation approach for software products. The Class Point (CP) approach with regression analysis method has been used for estimation of the development effort. This work uses a two step estimation approach. In the first step, an enhanced CP approach is used to evaluate the development effort of the system. In the second step, regression analysis models are utilized to refine the estimated effort accuracy. The results derived by applying the proposed two step approach confirmed the validity and the accuracy of this approach. It was observed that the SVR with RBF kernel is providing the best accuracy compared to other approaches.
Solving Speed Reducer Design Problem by Memorized Differential Evolution

Real-life optimization problems (RLOPs) are common in numerous research disciplines. In literature, evolutionary algorithms (EAs) have been deemed an emerging field to solve RLOPs. Among popular EAs, Differential Evolution (DE) is clever algorithm to solve RLOPs. DE and its varied alternatives are highly influenced by improper operators like mutation and crossover. Mostly, DE doesn&#x2019;t compel to learn the optimum properties achieved in primary phase of the former aristocrats. In this article, a new DE (named as <sup>mb</sup>DE) created on memory mechanism is offered to solve a renowned RLOP, specifically speed reducer design. It enclosed different crossover &#x0026; mutation (swarm, crossover and mutation) made through particle swarm optimization (PSO) context. Experimental outcomes endorse the competency of <sup>mb</sup>DE over various procedures.
Efficient Approach for Attendance Management using Local Binary Pattern

In an academic institution, attendance management is a tedious task. Automatic real-time attendance management system is needed. We propose an efficient, novel, and intelligent attendance management system using Local Binary Patterns (LBP). It first detects the face of a student and enrolls the attendance automatically. Second, the subject teacher can export attendance in real-time. The proposed method overcomes existing face detection limitations, such as pose variation, illumination variation, and occlusion. Experimental results show significant performance improvement as compared to the existing attendance management system.
Caption Generation for Images with Deep Neural Networks

In an evolving environment of technologies, Deep Learning and the models have enabled the implementation of complex problem statements. Human beings learn to observe and detect, and produce or express descriptions of them and as they grow, can do so for the various aspects in their surrounding and how they interact with each other. Caption generation for images is one such task as it involves the extraction of features and then also further seeing the interactions amongst the recognised objects/aspects and producing descriptions of what and how it is. The various CNN models and how they contribute to the problem statement has been an interesting point of motivation and that along with other architectures are to be implemented for the purpose of caption generation.
A Symmetric Fusion Learning Model for Detecting Visual Relations and Scene Parsing

Visual relationship detection (VRD) aims to locate objects and recognize their pairwise relationships for parsing scene graphs. To enable a higher understanding of the visual scene, we propose a symmetric fusion learning model for visual relationship detection and scene graph parsing. We integrate objects and relationship features at visual and semantic levels for better relations feature mapping. First, we apply a feature fusion for the construction of the visual module and introduce a semantic representation learning module combined with large-scale external knowledge. We minimize the loss by matching the visual and semantic embeddings using our designed symmetric learning module. The symmetric learning module based on reverse cross-entropy can boost cross-entropy symmetrically and perform reverse supervision for inaccurate annotations. Our model is compared with other state-of-the-art methods in two public data sets. Experiments show that our proposed model achieves encouraging performance in various metrics for the two data sets investigated. The further detailed analysis demonstrates that the proposed method performs better by partially alleviating the impact of inaccurate annotations.
Secure Two-Party Computation Based on Fast Cut-and-Choose Bilateral Oblivious Transfer

In secure two-party computation, each party has its input and wants to jointly compute a function from which it obtains the output corresponding to its respective inputs. For achieving security against a malicious adversary, an effective approach is using cut-and-choose, which requires the circuit constructor P1 to construct S copies of the circuit C (C is used to compute the function F). The circuit evaluator P2 selects S∕2 circuits to open for the check. If these S∕2 circuits are correctly constructed, P2 assumes that the remaining S∕2 circuits are also correctly constructed and uses the remaining circuits to compute. However, this method introduces significant computational complexity and interactive rounds, mainly due to more circuits that must be used for security purposes and the need for multiple interactions to transmit the keys. In this paper, regarding the issue above, we present a novel secure two-party computation protocol, and it can achieve security against the malicious adversary. Concretely, we still use the idea of cut-and-choose but improve the cut-and-choose oblivious transfer (CCOT) of the usual secure two-party computation protocol into cut-and-choose bilateral oblivious transfer (CCBOT) and propose a variant of it that we call batch single-choice CCBOT, which makes our protocol only needs two rounds of interaction to complete the transmission of all keys and 28Sl of exponentiations. In addition, we use a check mechanism to prevent the case that p1 cheats, but P2 is powerless. Our proposed protocol with an error probability of 2–s of P1 significantly optimizes the communication rounds and computation overheads, solves the selective failure attack, and ensures the consistency of the input.
Audio Steganography Technique using DCT-SWT with RC4 Encryption

The presence of digital devices and the internet has made information and communication technologies rapidly expanding. Security and confidentiality-related technologies are still much complained of by many to date, while technology has brought enormous benefits to the interests of individuals and groups. In this case, security and confidentiality are essential addressed. Therefore, steganography is one solution to the problem. In this study, a steganography audio system was designed with the disrete cosine transform-stationary wavelet transform (DCT-SWT) encrypted method of RC4. Messages before being inserted first performed the encryption process with the RC4 algorithm to increase message security. DCT changes the signal at the time domain into the frequency domain, and the SWT decomposes the signals into low and high-frequency sub-bands. The performance of the steganography audio system is analyzed and measured based on quality parameters. After the entire attack, the process optimization of the parameters is conducted by evaluating the high BER value. The proposed audio steganography technique is resistant to LPF, BPF, Resampling, TSM, and LSC attacks. Messages with RC4 and without RC4 have a similar quality value, which means that RC4 does not significantly inhibit a steganography audio system's quality value and performance. Many character messages significantly affect the performance of a steganography audio system. The more the character of the message, the less the quality and the performance of a steganography audio system.
Anti-jamming Method of Radio Fuze Based on KNN

In order to improve the anti-jamming ability of radio fuze and maximize the effectiveness of radio fuze in complex electronmagnetic environments, it is necessary to distinguish targets and jamming signals effectively. This research takes a sample of FM radio fuze as an example, mainly considers the sine amplitude modulation frequency-sweeping (Sine-AM) jamming signal and noise amplitude modulation frequency-sweeping (Noise-AM) jamming signal, which are pose the greatest threat to the FM radio fuze. The frequency exponential entropy and norm entropy of the fuze output signal are extracted. Then these features are input to K nearest neighbors classifier, in this way, the target signals and jamming signals can be effectively distinguished, Further improved the FM radio fuze ability to anti-jamming signal. The expermient result shows that using KNN classifier can get a classification accuracy more than 99%.