Reviewerly/exHarmony huggingface dataset:
The Temporal Distance Issues and their Mitigation Strategies in (GSD): A Systematic Literature Review

GSD offers several benefits that facilitate software organizations to adopt this trend, such as highly skilled employees, reduced cost, cheap labour, improved marketplace, etc. To achieve these benefits, communication among the distributed teams should be adequate. Communication is a significant challenge in development, particularly in GSD. Communication risk is further divided into temporal, Geographical, and sociocultural distance issues. Among these, the temporal distance issue is affecting more GSD activities. It produces a significant problem in distributed development. While using mitigation strategies, organizations can minimize these issues and achieve the desired quality within budget and time constraints. Therefore, the current study aims to recognize temporal distance issues and their mitigation strategies in GSD. A Systematic Literature Review (SLR) has been performed to find all the temporal distance issues and their mitigating strategies in GSD. A conceptual framework has been proposed.
Blockchain Technology Adoption in the South African Financial Service Sector: Perceived Advantages, Challenges and Potential Use Cases

Blockchain Technology (BCT) is a decentralized, trustless system that is set to transform, and possibly revolutionize, the financial services industry worldwide. In South Africa, perceptions concerning BCT are mixed, with some organizations unsure if it is just a hype or if it is indeed as disruptive as predicted. While there have been numerous studies on the use of cryptocurrencies, there is limited research on the alternative uses and intended adoption of the technology within the South African investment industry. This paper explores the intention of South African investment organizations to adopt blockchain and probes into future intended use cases. Semi-structured interviews were conducted with participants from eight financial services organizations. Factors from TOE, TTF, DOI, PCI as well as institutional theory were integrated. The study revealed many different factors influencing BCT adoption intentions within the South African investment industry. Several intended use cases specific to the investment industry were identified with smart contracts, equity post trade and securities registry being the most cited. Finally, the study identified several key factors that either had a positive or negative affect on blockchain adoption. The revised TOETI model can serve as a foundation for future studies on blockchain which in time could live up to its hype to be as fundamental and disruptive as predicted.
Parallel Decoders Guided Lexically Constrained Response Generation

Response generation is a fundamental function in conversational systems, where controllability of the response is a key problem. In this paper, we consider how to control the response by lexical constraints, namely lexically constrained response generation. The stochastic search-based methods have achieved promising performance in satisfying lexical constraints. The idea of these methods is modifying a sentence through the actions of insertion, deletion and replacement guided by an optimization algorithm. The core of our method is modifying the response by incorporating the lexical constraints and preserving the message-related parts. For this purpose, we propose the novel Parallel Decoders to guide response modification. The first decoder generates responses according to the given message and constraints. The second decoder calculates the relevance between the input and the response. Based on Parallel Decoders, during the modification, we could sample the positions in the response for editing according to the relevance score. Experiments show the proposed framework achieves better performance than the state-of-the-art generation models in terms of constraint relevance, sentence fluency, response diversity and human evaluation.
BPN: Bidirectional Path Network for Instance Segmentation

The feature pyramid network (FPN) has achieved impressive results in the field of object detection and instance segmentation by aggregating features of different scales, especially the detection of small objects. However, for some special large objects (such as tables, chairs, etc.), It is difficult to achieve good results for FPN. In this paper, we propose a new simple but effective network, the Bidirectional Path Network (BPN), for the problems that FPN cannot solve. In simple terms, it consists of a top-to-down FPN and bottom-to-up FPN. This bidirectional network structure can greatly enrich high-level semantic information and improve the detection effect of these large objects. And we also introduce dense connections to enrich the output features further. We tested our method on the COCO dataset. Firstly, on the object detection task, our method obtains comparable results with the state-of-the-art benchmark. Then, on the instance segmentation task, our method also achieved good results.
On the use of code-based cryptography in automotive applications

Cybersecurity is a critical aspect for automotive applications. In this paper we approach this issue from a cryptographic point of view. In particular, we propose the use of a post-quantum code based cryptosystem based on a special family of low-density parity-check (LDPC) codes, called fully connected monomial LDPC codes. We also investigate some properties of these codes.
Rotation Transformation: A Method to Improve the Transferability of Adversarial Examples

Convolutional neural network models are fragile to adversarial examples. Adding disturbances that humans cannot observe in clean images can make the model classification error. Among the adversarial attack methods, white-box attacks have achieved a high attack success rate, but the "overfitting" between the adversarial examples and the model has led to a low success rate of black-box attacks. To this end, this paper introduces the data augmentation method into the adversarial examples generation process, establishes a probability model to perform random rotation transformation on clean images, improves the mobility of adversarial examples, and improves the success rate of adversarial examples under black-box setting. The experimental results on ImageNet show that the RO-MI-FGSM method we proposed has a stronger attack effect, achieving a black-box attack success rate up to 80.3%.
Dynamic Motion Planning for Driverless Vehicles via Decentralized Model Predictive Control

How to ensure safety of driverless vehicles has always been a challenging problem. In this letter, we introduce a novel algorithm based on decentralized model predictive control for online trajectory planning of driverless vehicles. By introducing obstacle information into the cost function, dynamic obstacle avoidance is successfully achieved. With the provided path, the proposed algorithm can predict the future states, calculate the optimal control sequence and track the reference path with less deviation. Besides, when the conflict between path tracking and avoiding collision occurs, agent running with this algorithm will give the priority to obstacle avoidance. Simulation results show that this approach can reach less deviation with the reference path than move_base, which is a famous and effective algorithm for robot navigation. Besides, the computation time is reduced by more than 85%.
Focal Point of Learning

This article presents an automatic way to discover the divisions of an image. These will surely have a positive influence on the learning outcome. Our main contribution is to provide a practical method to improve classification performance by selecting the best divisions of interest. After splitting the image into several rectangles, our method exhaustively searches for the rectangles that have the best precision among all possible combinations using MLP (Multi Layer Perceptron) [10]. Then we merge these rectangles into a single rectangle which is called the LFP learning focal point. Experimental studies on images of sardine cans have shown that the proposed methodology improves classification results.
Multi-scale Hierarchical Transformer structure for 3D medical image segmentation

Transformers have demonstrated great potential in computer vision tasks which is benefited from its ability of long-range dependency modeling. However, its core material multi-head self-attention (MHSA) has high computational and spacial complexity. Besides, tokens embedded with 1D position sequence are unable to represent inductive bias of locality and neighbor contextual information, which are essential for high-resolution downstream vision tasks. Thus, transformer is hard to use in image segmentation tasks, especially in 3D medical images. In this paper, we propose a novel multi-scale hierarchical framework (MSHT) that efficiently incorporates the local modeling ability of CNN and long-range modeling ability of transformer for accurate 3D medical image segmentation. Unlike many prior Transformer-based solutions, the proposed MSHT first adopts parallel CNN and transformer as encoder block to extract the global and local feature representations. As the core component for our MSHT, a position embedding method for 3D medical image (3D IPE) is proposed to generate relational and absolute position encoding for tokens fed in transformer block. We conduct an extensive evaluation on both Lits2017 dataset and Kits2019 dataset. The results indicate that our MSHT leads to a substantial performance over other CNN-based methods on 3D image segmentation.
An Efficient Infrared Pedestrian Segmentation Algorithm Based on Weighted Maximum Entropy Thresholding

Infrared pedestrian segmentation is a challenging problem in infrared image processing. Thresholding algorithm is one of the commonly used image segmentation techniques. Classical entropy-based thresholding methods cannot detect the pedestrian in the infrared image with complex background. In this paper, a weighted maximum entropy thresholding algorithm is proposed for the infrared pedestrian segmentation. The novel algorithm weighted the entropy of the object and the background by the Gaussian filtering histogram, which makes the optimal threshold not only has large entropy value, but also close to the flat valley of the histogram. The time complexity of the proposed algorithm is linear in the gray level of image. The weighted entropy thresholding algorithm can detect the pedestrian in the infrared images quickly and effectively. In the experiment, a number of infrared images in different scenes from the OTCBVS data set have been tested. The experimental results verify the performance of the weighted entropy thresholding method, which shows that the proposed algorithm is a simple and effective infrared pedestrian segmentation method.
A Novel Approach of Mutual Authentication in Fog Computing

Today, security is a major issue in the cyberworld. The data generated by the devices are more susceptible to threats and attacks than being used. The emergence of cloud computing and IoT changed the whole concept of handling and interacting with data. The framework that we proposed for the security of data, which is being generated by IoT devices is divided into multiple phases. The first starting phase is mutual authentication. This phase guarantees the communicating parties are genuine. The paper presents the novel scheme for mutual authentication between two parties that are IoT device and Fog system. In the paper, we discuss and figure out how the IoT device and the Fog system will interact with each other and authenticate each other.
KHDR: Kannada Handwritten Digit Recognition Using PCA and SVM Classifier

Handwritten digit recognition is one of the classical problems in the field of image classification, a subfield of computer vision. In this work, we propose an approach for recognition of handwritten digits for Kannada language. We have used Kannada-MNIST dataset for digit recognition to evaluate the performance of Support Vector Machines (SVM) and Principal Component Analysis (PCA). Efforts were made previously to recognize handwritten digits of different languages with this approach. However, due to lack of standard MNIST dataset for Kannada numerals, Kannada handwritten digit recognition was left behind and very less work has taken place in this aspect. With the introduction of MNIST dataset for Kannada digits, we budge towards solving the problem statement and show how applying PCA for dimensionality reduction before using the SVM classifier. The system is trained using 60,000 and 10,000 images were used for testing. We were able to achieve accuracy of 95.44%. We have also evaluated our technique based on precision.
Research on Face Recognition Algorithm Based on Deep Learning

In this paper, a feasible face recognition algorithm based on CNN method and TensorFlow deep learning framework is proposed for face multi-pose and occlusion recognition problems. Firstly, a face detection model was established. Under the TensorFlow learning framework, the MTCNN face detection model was established and trained with Wider Face data set. Secondly, the face recognition model was established, the Facenet algorithm model was established, the CASIA- Webface dataset was used for training, and the LFW was used for model testing. The test results on LFW data set show that the face recognition accuracy of the proposed algorithm can reach 98.78<sup>&#x0025;</sup>.
A Constrained Generative Approach to Generalized Zero-shot Object Recognition

Generative adversarial network (GAN)-based methods for zero-shot learning (ZSL) use the base category data as output and the semantic descriptors as conditional input for training so that the network can synthesize data for the novel classes. Using these generated data, the ZSL problem is converted into a supervised learning problem. Since the GAN is trained on only the base categories, the model prediction is biased towards the base categories. Also, the generated data for the novel categories might not accurately represent the ground truth. To address these problems, we propose a three-way solution. Firstly, we constrain the generation process such that the generated data from the novel classes can be highly discriminated from that of the base classes. This constraint tries to get rid of the biasness problem. Secondly, we enforce semantic consistency by reconstructing the semantic attributes from the generated data. Finally, we selectively adapt and transform the generated data from the novel classes to be close to the ground-truth unlabeled test data. We evaluated our framework on five standard datasets for ZSL and found our method to be highly competitive when compared with previous work. We also carried out additional studies to better understand our framework.
As a mechanism that achieves the goal of decision management

Working with different data depending on the appearance and content of the object. Formation of types of objects and content of processes within the framework of human activity. Based on this, the control mechanism is also different. Management effects in the learning system are understood as the transfer of knowledge in the subject area to the student using the methods and tools formed by the teacher in the educational environment, taking into account all the factors generated in this process. The control system in this view is a simple system with a nonlinear feedback section. It combines complexes consisting of technical, software, telecommunications and methodological tools used in the educational process. They can be used to form alternative solutions not only by delivering parts of the training materials, but also by collecting and processing the information needed to manage the learning process. The training method is chosen according to the requirements of the learning environment and depends on the topic. If the chosen method is accepted, the training part is provided, which is fixed (question-answer or in another form), which makes it possible to work independently.
Hadoop with Wavelet Support for Medical Big Data

Medical data is becoming more dense and complicated day by data. After COVID-19, the medical information is entirely expended from terabytes and petabytes. An accurate diagnosis needs a sophisticated mechanism and the support of information technology. Hadoop ecosystem is facilitating big data management for various health care applications. As dense patient history leads to better diagnosis; Hadoop architecture supports patient data accommodation, retrieval, update, and many similar functions like information assortment, information intricacy, information stockpiling, information investigation, information security, and protection.
Creative Activity Outcomes and Optimal Task Scheduling

Activities that require creativity will continue to be performed by humans in the future because it is difficult to replace creative individuals with artificial intelligence and robots. In contrast to design and production, creative activities such as idea generation do not always produce results in proportion to the man-hours and effort invested in them. It is, therefore, necessary to understand the characteristic of creative activities for their implementation, but their outcomes process has not yet been clarified. The purpose of this research is to improve the quality of the results and the productivity of creative activities through an appropriate schedule when available resources are limited. This study attempts to define the concept of outcomes of creative activities. These outcomes are not always significant because we need to create a new conceptual space or adapt to new rules in the initial stage and only manifest abruptly during the final stage of the process. We will test this concept through a creativity game and demonstrate the results by analyzing the collected data in the future.
Improving Automatic Quality Inspection in the Automotive Industry by Combining Simulated and Real Data

Considerable amounts of data are required for a deep learning model to generalize to unseen cases successfully. Furthermore, such data is often manually labeled, making its annotation process costly and time-consuming. We propose using unlabeled real-world data in conjunction with automatically labeled synthetic data, obtained from simulators, to surpass the increasing need for annotated data. By obtaining real counterparts of simulated samples using CycleGAN and subsequently performing fine-tuning with such samples, we manage to improve a vehicle part’s detection system performance by 2.5%, compared to the baseline exclusively trained on simulated images. We explore adding a semantic consistency loss to CycleGAN by re-utilizing previous work’s trained networks to regularize the conversion process. Moreover, the addition of a post-processing step, which we denominate global NMS, highlights our approach’s effectiveness by better utilizing our detection model’s predictions and ultimately improving the system’s performance by 14.7%.
Securing MQTT Architecture Using a Blockchain

Since MQTT (Message Queuing Telemetry Transport) protocol is designed for resource-constrained environments, it includes only elementary security measures, which are mainly sufficient for transmitting non-critical data. However, for applications that transmit critical data, it is recommended to add some security measures. Many articles have used blockchain to improve security in the IoT environment; however, most of them neglect that many IoT systems are already functional. It is tough to rebuild the architecture from scratch. This article aims to propose a secured MQTT architecture using blockchain and smart contracts without affecting the overall operation of the MQTT protocol. Instead, it adds some parameters to the standard MQTT messages. The new architecture is based on blockchain and smart contract to control and store the transactions between clients and brokers. It is composed of two phases: The registration phase, where a client asks for a token to use for publishing or receiving data, and the Publishing phase, where the brokers network verifies the validity of the token and the integrity of the data before allowing the client publication. By comparing the current architecture and the improved one, we can conclude that the architecture proposed in this article will improve the security of MQTT protocol by using tokens for authentication, topic key for ensuring data confidentiality, brokers network to avoid a single point of failure and data hash to ensure the integrity of data. Also, the overall operation of the protocol was not affected.
Human-dimension systems of information society and risks of social relations regress

The development of human-dimension systems (e.g. «human–technique»systems) is supposed to play the most important role in the further evolution of society in the context of informatization. However, the risk-generating potential of these systems significantly exceeds the risks of socio-technical systems characteristic of industrial society due to the exponentially rapid development of so-called «dangerous knowledge». On the one hand, information technologies offer unprecedented opportunities for human creativity. On the other hand, a significant majority of people still use them as passive consumers. They are ready to automate not only some aspects of professional activity but also interpersonal interactions, i.e. a conscious component of social relations. This means that we are losing consciousness of individual life and social processes. We are less and less willing to make reflexive efforts. This leads to a «virtual objectification» of person, a loss of identity, reduces person to the state of an element of the network mechanism, unwilling and unable to deeply understand and comprehend social relations. «Artificial virtuality» displaces the «natural virtuality» of individual and social consciousness. Since in the current sociocultural context, information technology can destroy a person’s capacity for such understanding and comprehension, we consider knowledge associated with information technology to be potentially «dangerous». We see a response to this challenge in the following. Firstly, it is necessary to develop a scientific concept of consciousness as essentially autonomous and not reducible to its material foundations. Secondly, education should be developed in the direction of «Writing and Thinking» technology, which forms the competence of reflective, critical and systematic thinking.
Role of Forensic Audit as a Tool for Fraud Detection and Prevention in Micro Finance Industry

Microfinance plays an important role in ensuring financial inclusion among the poor. However, commercialization of microfinance in conjunction with weak regulations in the Micro Finance Industry has created a fertile ground for perpetrating fraud which is detrimental to the sustainability of Microfinance Institutions in our country. The present paper is an attempt to highlight the significance of Forensic Audit as a potent tool for fraud detection and prevention in the Microfinance Industry.
Deep Learning for Body Parts Detection using HRNet and EfficientNet

Human body parts detection is an important field of research in computer vision. It can serve as an essential tool in surveillance systems and used to automatically detect and moderate non-appropriate online content such as nudity, child pornography, violence, etc. In this work, we introduce a novel two-step framework to define ten body parts using joints localization. A new architecture with EfficientNet as a backbone is proposed and compared to HRNet for the first step of pose estimation. The resulting joints are then used as an input to the second step, where a set of rules is applied to connect the appropriate joints and to define each body part. The developed algorithms were tested using MPII human pose benchmark. The proposed approach achieved a very interesting performance with a 90.13% Probability of Correct Keypoint (PCK) for the pose estimation and an average of 89.80% of mean Average Precision (mAP) for the body parts detection.