Reviewerly/exHarmony huggingface dataset:
The Effect of Digital Intelligence on the Change in the Corporate Culture

The article discusses the influence of digital intelligence on the formation and change of the current corporate culture of the organization. The model “Digital Manager” is shown, which relies on the “Digital—IQ” approach. It has been proven that digital transformation interacts with the corporate culture of the organization and becomes a mantra where the company's individuality (branding) is reflected, the “spiritual guideline and program” of the business. It was shown, digital intelligence relies on the most necessary digitalization elements in the era, among which virtual cooperation, media light, transdisciplinary, digital thinking. Key tools of the future are given, which modern management is already applied in practice. The concept of “Digital First” is shown, within which each business is developing as much as possible in the electronic environment. An example of a technological company “Sber” is given, in which a single ecosystem is drawn up with the possibility of building the workflow in digital form, combining the team on a digital innovation platform.
Adaptation of young teachers to professional activities in an innovative educational environment

Introduction. Qualitative improvement of professional training of specialists, reduction of staff turnover in educational structures is a serious and well-known problem. The field of education is regularly replenished with a significant number of new graduates of pedagogical universities. Numerous studies have demonstrated the existence of a clear pattern: the initial period of "embedding" in the profession is characterized by the greatest intensity. This largely determines both the personal and professional development of a specialist. Having considered the features of the introduction of new educational standards in a number of educational institutions, we can conclude that among the basic factors of the formation of a teacher is the ability to professionally adapt to the new, to be ready to introduce various innovations. Materials and Methods. The article discusses the views of foreign and Russians scientists which studied the process of adaptation of future teachers to professional activities in an innovative educational environment. Methodological basis of the research: cultural approach, competence approach, environmental approach, individual elements of the strategic approach, as well as humanistic and system-structural principles. The main research methods: the method of expert questions, questionnaires, experimental methods, comparative and systematic analysis, the study of thematic publications, the author's own observations. Results. The result of the analysis and interpretation of the studied sources is on the problem of pedagogical support of future teachers to professional activities, it was revealed that the main purpose of pedagogical support is to help a novice teacher to understand himself, the causes of emerging professional problems, in order to mobilize his internal resources, knowledge and skills, for the development of new professional competencies and further self-development. Discussion and Conclusions. We have identified and considered the difficulties and complexities of the process of adaptation of future teachers to professional activities, justified the relevance of the study. We associate the further direction of the research with the development of a program for pedagogical support aimed at the successful adaptation of future teachers to professional activities in an innovative educational environment.
Attendance Marking System using Periocular Recognition with Temperature Monitoring (ASPR)

Of late the COVID pandemic has necessitated authorities to make sure that every candidates are being worn mask. So, this forces to quit from conventional attendance marking systems which only relies on face recognition for biometric identification that involves close proximity or body contact. Wearing of masks can definitely occlude a major area of face. So the proposed project aims at using periocular recognition for biometric identification. The system proposed uses a pre-trained Convolution Neural Network (CNN) model that is VGG16 trained on ImageNet dataset to achieve the target of periocular recognition. Here it involves only a smaller region of interest and so external factors cause only less constraints to periocular recognition. Mask detection, which is an image classification phase, is done with MobileNet V2. This includes training which serialises face mask detectors to disk and deployment which outputs images as ‘with mask’ or ‘without mask’ [1] [2]. Non-contact IR sensor, MLX90614 IR sensor will automatically detect body temperature to determine whether the candidate's temperature is exceeding a threshold value.
DCrowd: A Decentralized Service Model for Credible Crowdsourcing Based on Game Theory and Smart Contracts

Crowdsourcing provides a new way of group intelligence interaction in recent years. Traditional crowdsourcing service models rely on centralized third-party platforms, which are bottlenecks in credibility. Blockchain is a potential solution. We therefore propose DCrowd, a Decentralized and Credible crowdsourcing model based on game theory and smart contracts. The workers in DCrowd are organized in a decentralized manner. However, information on the blockchain is open, which may cause data leakage and privacy issues. To tackle the data transparency issue, a commitment scheme is leveraged for data submission among workers. Then, an unbiased random selection algorithm is further designed to select independent workers from the dispersed worker pool to avoid possible collusion. Through the Nash equilibrium principle, it is proved that workers in DCrowd have to perform honestly to maximize their rewards. Finally, the feasibility of our model design is demonstrated through experiments on Ethereum.
End-to-End Speech Recognition Model Based on Deep Learning for Albanian

Deep learning technology nowadays is considered one of the most advances in machine learning that have led to significant and widespread improvements in how people interact with the world. Deep Learning is a technique that constructs artificial neural networks to mimic the structure and function of the human brain. Recently Deep Learning is well known for its applicability in speech recognition. This stems mainly by the flexibility and predicting power of deep neural networks. This research paper introduces an end-to-end speech recognition model applicable for Albanian language. The model is based on Recurrent Neural Network (RNN) architecture, which will be created and implemented in Pytorch tool. It is composed by two main neural network modules - N layers of Residual Convolutional Neural Networks (ResCNN) to learn the relevant audio features, and a set of Bidirectional Recurrent Neural Networks (BiRNN) to leverage the learned ResCNN audio features. The model will be trained and evaluated using a corpus in Albanian, created for this purpose and a corpus in English derived by LibriSpeech. The experimental results show a very satisfactory WER, CER and Loss.
Doubly excited states in a chiral waveguide-QED system: description and properties

Abstract In modern quantum optics chiral waveguide quantum-electrodynamical (wQED) systems are attracting a lot of attention from the perspective of fundamental science, and possible interesting applications. In our work we theoretically analyze the eigenstates in a two-excitation domain of an ensemble of two-level atoms that are periodically spaced, and asymmetrically coupled to a guided mode. We found that in a regime when all atoms emit photons in-phase, most eigenstates in such a system can be well-approximated and described through the eigenstates from a single excitation domain, while the rest present a superposition of bound states with two strongly attracting excitations, and states, for which the excitations strongly repel from each other occupying the opposite edges of the system.
Automatic Speech Recognition by Machines

Building machines to converse with human beings through automatic speech recognition (ASR) and understanding (ASU) has long been a topic of great interest for scientists and engineers, and we have recently witnessed rapid technological advances in this area. Here, we first cast the ASR problem as a pattern-matching and channel-decoding paradigm. We then follow this with a discussion of the Hidden Markov Model (HMM), which is the most successful technique for modelling fundamental speech units, such as phones and words, in order to solve ASR as a search through a top-down decoding network. Recent advances using deep neural networks as parts of an ASR system are also highlighted. We then compare the conventional top-down decoding approach with the recently proposed automatic speech attribute transcription (ASAT) paradigm, which can better leverage knowledge sources in speech production, auditory perception and language theory through bottom-up integration. Finally we discuss how the processing-based speech engineering and knowledge-based speech science communities can work collaboratively to improve our understanding of speech and enhance ASR capabilities.
Characteristics of Remote Education in a Time of Pandemic: An Analysis of Transformative Teaching Practices in a Higher Education Institution

The SARS-COV 2 disrupted all sectors of the economy and education was no different. We saw a shift in educational paradigm: new technologies were developed, others revised, and new learning formats created. The goal of the present article is to analyze, based on the results obtained from an internal survey, the educational practices used in an IHE of the state of Rio de Janeiro, Brazil, during the pandemic. The methodological structure contemplates a brief literature review, a contextualization of the complex educational environment of Brazilian society and a quantitative analysis of secondary data. As a result, the data we presented allows us to gauge the wide variety of tools utilized and the several distinct forms of the diffusion of teaching used by professors. To maintain the effectiveness of this learning process and to absorb/transmit the necessary knowledge, extra effort from students and professors is needed as well as a redesign of personal and institutional processes. We concluded that the new process that emerged from this period, if well-adjusted and understood by all parts, can reinvent the global education ecosystem. To that end, the introduction of new tools and a cultural shift of institutions, professors and students will allow the constructing of new educational spaces.
Fuzzy Intelligent System for Improving High Availability of a Docker-Based Hybrid Cloud Infrastructure

Hybrid cloud infrastructure offers several benefits including high availability. However, one of the biggest challenges for the hybrid cloud infrastructure is to maintain the same high availability of the local or traditional infrastructure as this is normally maintained by the organisation. Docker Swarm facilitates the development of a hybrid cloud infrastructure based on Swarm nodes consisting a high availability feature, which guarantees the successful running of a Docker Swarm cluster for a certain number of failures depending on the number of primary managers in the cluster. However, beyond this limit, there is no implicit guarantee of high availability. Moreover, the biggest risk is the failure of the Docker machine on which the Swarm manager node is running because that is beyond the control of Docker Swarm. This risk increases in severity if these Docker machines are maintained on local servers in a hybrid cloud infrastructure, and there is no risk management facility offered by cloud providers for on-premises failures. Therefore, this paper presents a fuzzy intelligent system to address this issue and improve the high availability of Docker machines, particularly, if they are running the Docker Swarm managers locally. This fuzzy intelligent system observes the health of Docker machines, indicates their current status and generates alerts for underuse or overuse states. Therefore, an appropriate action can be taken to avoid any failures. The paper proposes and illustrates the possible solutions which can be applied depending on the underuse or overuse state of the machine. This is a generic solution and in addition to the Docker-based hybrid cloud infrastructure, it can be used with any other hybrid cloud infrastructure for improving the high availability of virtual machines.
Edge Detection-Based Optical Flow Estimation Method

Due to the edge jagged and blurred problem in conventional deep learning-based optical flow estimation methods, an edge detection-based optical flow model (EDOF) is proposed in this paper to improve the accuracy of optical flow estimation. In this model, the feature extraction module EDNet is used to obtain the features with the edge information of the objects in images, while the other feature extraction module OFNet extracts the convolutional features with other common features such as the texture and color information of the object and others. The two kinds of features are fused to EstiNet, and then the estimation result is obtained by input the fused features into the optical flow network. Experiments on the public MPI Sintel and Flying Chairs datasets show that the EDOF method can reduce the average endpoint error of optical flow estimation.
Stabilized magnetic spin dimer entanglement using a genetic algorithm

Genetic algorithm optimized time varying magnetic field functions for stabilizing and maximizing entanglement in a decoherence-free Heisenberg spin dimer are presented. The oscillation in entanglement in a decoherence free magnetic spin dimer is presented. The effect on the coherence and population terms in the density matrix due to the time-varying magnetic field functions is discussed and viewed using two dimensional time varying plots of entanglement and three-dimensional Bloch sphere diagrams. This work shows that it is possible to remove the natural oscillatory nature of decoherence free entanglement and maximize entanglement using time varying transient magnetic field stabilizing functions.
MatTrip: Multi-functional Attention-based Neural Network for Semantic Travel Route Recommendation

Travel route recommendation aims to recommend a sequence of point of interests (POIs) for visitors based on their personal interests. Previous studies utilize user interest features and POI spatial information to provide travel route recommendation service. However, most of them fail to consider the implicit information in user traveling patterns, which is the key to improve recommendation quality. Additionally, few deep learning based travel route recommendation systems provide comprehensive trip planning functionalities, which is critical to improve the user experience. To alleviate these two problems, we propose a multi-functional attention-based neural network for route recommendation (named MatTrip). We first introduce an encoder-decoder structure with a novel dual bi-directional LSTM encoder as the sequence generation model to learn other users' traveling records and generates a semantic travel route based on user preference and geographical features of start/end POI. Next, multiple user-specific functionalities are supported in MatTrip by grid beam search. The functionalities include weather dependency, POI opening hours, restricted sequence length, mandatory POIs, and dynamic route revision. In addition, MatTrip adopts an online learning approach to learn from user deviation behaviors to improve recommendation performance. Experiments on two real-world datasets show that our model achieves a 20.98% improvement in performance, compared with state-of-arts.
Visualizing Metric Trends for Software Portfolio Quality Management

Software portfolios of today’s companies are comprised of a variety of heterogeneous, modular, and often polyglot software solutions. Ensuring high-quality standards across these entire portfolios raises additional challenges for quality engineers and requires new strategies and visualization approaches to support software quality management decisions. In this paper, we study the information needs of software quality engineers that drive these decisions on portfolio-level and propose Portfoliotrix -an expert visualization prototype for a portfolio-wide analysis and comparison of software quality metric trends. For this, we first introduce our four-phase fully automated data-mining process for gathering and aggregating quality metric data from the underlying software repositories. We then present our application frontend that implements different visualization and filtering concepts to satisfy eight specific information needs derived from our prior qualitative semi-structured expert interviews. A final scenario-based expert evaluation confirms the practical relevance of our prototype. Experts took less time to complete quality-related management tasks with our tool than they had estimated when relying only upon existing toolsets. Moreover, our visualization system reached the System Usability Scale (SUS) score of "good" with 76.7 points.
Review of Artificial Intelligence Applications in the ROS Ecosystem

Artificial intelligence has developed rapidly in recent years. It is permanently established in the field of robotics and science. It is seen as an intelligent decision maker which interacts with the environment or material and can work to achieve the best possible outcome. The biggest factors are the constant increase in computing power and the greater availability of data. With a large amount of data and computational power, solutions to complex problems can be created through machine learning. An application that can independently exploit the information provided to it and make decisions based on it can be considered an AI application. This can be utilized in robotics, for example, by teaching a chess robot to recognize different game pieces from each other. Machine learning can be used to teach a robot to adapt to different situations in a job, for example, to identify chess pieces from different perspectives or to find the most effective solution to a given problem. This article examines the use of artificial intelligence in ROS robotics applications.
Randomly connected neural networks for self-supervised monocular depth estimation

A plethora of deep learning-based methods have been proposed for self-supervised monocular depth estimation. The majority of these models utilise a U-Net-based architecture for disparity estimation. However, this architecture may not be optimal, and as shown in self-supervised approaches, replacing standard U-Net encoder with more complex architectures like the ResNet encoder achieves superior performance. In monocular depth estimation, the success of the method is attributed to the model architecture design capable of extracting high order features from a single image and the loss function for enforcing a strong feature distribution. In this work, a novel randomly connected encoder-decoder architecture has been designed for self-supervised monocular depth estimation. To enable efficient search in the connection space, the ‘cascaded random search’ approach is introduced for the generation of random network architectures. We introduce a new U-Net topology capable of utilising the semantic information of feature maps. For high quality image reconstructions, a loss function has been proposed, which efficiently extends perceptual and adversarial loss to multiple scales. We conduct performance evaluation on two surgical datasets, including comparisons to state-of-the-art self-supervised depth estimation methods. The performance evaluation analysis verifies the superiority of our randomly connected network architecture.
Are We Skillful or Just Lucky? Interpreting the Possible Histories of Vulnerability Disclosures

Coordinated Vulnerability Disclosure (CVD) stands as a consensus response to the persistent fact of vulnerable software, yet few performance indicators have been proposed to measure its efficacy at the broadest scales. In this article, we seek to fill that gap. We begin by deriving a model of all possible CVD histories from first principles, organizing those histories into a partial ordering based on a set of desired criteria. We then compute a baseline expectation for the frequency of each desired criteria and propose a new set of performance indicators to measure the efficacy of CVD practices based on the differentiation of skill and luck in observation data. As a proof of concept, we apply these indicators to a variety of longitudinal observations of CVD practice and find evidence of significant skill to be prevalent. We conclude with reflections on how this model and its accompanying performance indicators could be used by various stakeholders (vendors, system owners, coordinators, and governments) to interpret the quality of their CVD practices.
Performance Analysis of PointPillars on CPU and GPU Platforms

Recently, the safety of ADAS (Autonomous Driving Assistance System) has become a critical issue in industrial areas. Modern intelligent automobile control systems adopt various sensors for recognizing environmental obstacles, and LiDAR (Light Detection and Ranging) sensor has been selected due to its consistently accurate sensing abilities regardless of external conditions. Also, many neural network models have been proposed to process LiDAR data. Short inference latency and high accuracy matter for ADAS. From this perspective, the encoder of Pointpillars shortens inference latency by converting the 3D LiDAR point cloud into a 2D pseudo-image for object detection.In this paper, we study the architecture of PointPillars and analyze its performance by VTune [15] on CPU and NVIDIA profiler on GPU [16]. It has been observed that RPN (Region Proposal Network) shows the most dominant execution time to the overall model due to its internal convolution and transposed convolution operation. Therefore, the adoption of PointPillars to real ADAS requires RPN network optimization.
An effective image compression technique based on burrows wheeler transform with set partitioning in hierarchical trees

Abstract In this article, the efficient image compression which consists of Burrows–Wheeler transform (BWT) with set partitioning in hierarchical trees (SPIHT). The main phases of the proposed system are: partitioning, compression of non‐ROI areas, Fusion and compression of ROI areas. To enhance the propose of the proposed methodology, the morphological functions are updated by dividing two types of images with the consideration of dilation and erosion control. After that, the convolution and correlation in the deformation provide good accuracy of the segmentation at the fastest speed. In this proposed methodology, SPIHT encryption is a lossy compression technique aimed at understanding the non‐ROI area, while the BWT is a lossless compression technique performed to achieve a summary of the ROI area. Finally, separating these two parts of the image merges the image and reconstructs it to the desired quality. The test sends a variety of images and analyzes the performance of the proposed system using compression ratio and PSNR measurements.