Reviewerly/exHarmony huggingface dataset:
Visual Odometry integrated with Self-Supervised Monocular Depth Estimation

To solve the problem of poor robustness of multiview geometric visual odometry in a dynamic environment, we propose a visual odometry method base on deep learning that combines depth estimation and geometric pose determination. First, we remove the time-consuming dense optical flow prediction network in GeoNet. We propose a method for image reconstruction at one frame interval to solve large displacement between moving objects. Second, we propose a way to find the best pixel to solve the problem of pixel occlusion in the image. Third, to further solve the uneven illumination of the image and the degradation of image quality, we propose an adaptive histogram equalization based on limited contrast to enhance the image. A large number of experimental demonstrations have been carried out on the KITTI public dataset. The experimental results show that our network reduces the time cost and complexity on the general evaluation index. It has a significant improvement compared with GeoNet and has achieved more accurate in-depth and position prediction results.
Self-training with Masked Supervised Contrastive Loss for Unknown Intents Detection

The performance of many intent detection approaches will degrade when they meet open-set data because there is out-of-domain (OOD) noise. Some works utilize clean but expensive labeled data to supervise models more robust to the varied environment. However, a large number of labeled samples are scarce and their models no longer change after finishing training. To address this problem, we propose an iterative learning framework that can dynamically improve the model's ability of OOD intent detection and meanwhile continually obtain valuable new data to learn deep discriminative features. Concretely, the model can generate pseudo labels for unlabeled examples by self-training and use the local outlier factor (LOF) algorithm to detect unknown intents. Furthermore, we add mask operation on supervised contrastive loss (SCL) and use the masked-SCL to absorb new data effectively. Experiments on CLINC and SNIPS demonstrate that our proposed method can robustly realize intent detection in the presence of a high proportion of open-set.
A Fast Domain Adaptation Network for Image Super-Resolution

Most previous super-resolution (SR) methods are based on high-resolution (HR) images and corresponding low-resolution (LR) images obtained artificially through bicubic downsampling. However, in real scenes, LR images are usually obtained with complex degradation functions, which may result in the domain gap between LR images. And we observe that this can sharply weaken the performance of the SR model. In this work, we propose a Fast Domain Adaptation Network for SR to solve this issue. First, we train a domain adaptation module to transform source LR images to the bicubic downsampled LR images. Then, we apply this module on the top of any SR model pretrained on bicubically downsampled images. Abundant experiments demonstrate the effectiveness of our proposed method and show that our network outperforms previous state-of-the-art works in terms of both qualitative and quantitative aspects on real-world dataset and synthetic dataset.
A Classification Method of Social Network Members Based on Content Security

With extensive and deep applications of Social Networking Services (SNS), more and more security issues are unfortunately related to it. Research shows unsuitable classification of social network members may induce misinformation and privacy leak. Thus, we propose a novel classification method of social network members based on content security. The method adopts LDA (Latent Dirichlet Allocation) to identify the topics of social networking content, and then takes topic vector as label to annotate the talking member. Finally, all the members are periodically classified according to topic labels. Moreover, an algorithm is also introduced to update the labels, so that the labels may be consistent in the trust decay. Preliminary experiments show that the method achieves 70%-85% customers' satisfaction.
Efficient Data Flow Optimization for Internet Middleware on Application to Ideological Online Interactive System

Efficient data flow optimization for the Internet middleware on application to the ideological online interactive system is studied in this manuscript. Our application research is mainly to discover the calling relationship and calling methods between the application and the database, to then clarify the compatibility characteristics of the application modules and the database calling middleware, and to clarify the transformation points of the application in the conversion of each module. The superword-level parallel vectorization is used at a finer granularity, and then it will be applied for the data flow analysis. The designed model is then applied to the online interactive system. The proposed model can effectively improve the online class efficiency and the feedback information is positive.
Story-Plot Recommender Using Bidirectional LSTM Network

Most jobs today are being done manually and are backed up by machines. These technologies are easily available to us at our disposal, thus making the jobs and our lives comfortable. One example of this is a ‘Story-Plot Recommender.’ People often face difficulties in writing stories for many reasons like ‘writer’s bloc’ or lack of inspiration and ideas. Plot generators can be used to gain inspiration and originality. Moreover, they can be used as storytelling agents, in fantasy computer games, or even as an agent for education. The principal reason for this research is to propose a system that creates a consistent plot based on the concept of deep learning. The plot generated can be used as guidance to continue the process of story writing. To achieve this, natural language processing (NLP) is used for pre-processing the data. Furthermore, the model is built using a bidirectional long short-term memory network, also known as ‘bidirectional LSTMs.’ The generated output of the model is then followed by segmentation and punctuation for elucidation. This system produces a unique and random plot that is fictionally based on the input seed given which can further be pursued and changed according to the user.
Towards Binarized MobileNet via Structured Sparsity

The rising demand for deploying convolutional neural networks (CNNs) to mobile applications has promoted the booming of compact networks. Two parallel mainstream techniques include network compression and lightweight architecture design. Despite these two techniques can theoretically work together, the naive combination results in dramatic accuracy degradation. In this paper, we present Binarized MobileNet-Sp for mobile applications, by compression-architecture co-design. We first reveal the connection between MobileNets and low-rank decomposition, showing that decomposition-based architecture is not quantization friendly. Then, by adopting the view of sparsity, we propose the Binarized MobileNet-Sp, which significantly enhances the robustness to binarization. Experiments on ImageNet show that the proposed Binarized MobileNet-Sp achieves 61.2% top-1 accuracy, outperforming the naive binarization method by about 10% higher top-1 accuracy. Compared to the Bi-Real net which achieves 56.4% top-1 accuracy on the more heavy-weight and redundant ResNet-18 (which has comparable baseline accuracy with MobileNet in full-precision representation), the Binarized MobileNet-Sp achieves much higher accuracy with a significant reduction in computing complexity.
A Method to Compute the Schrieffer–Wolff Generator for Analysis of Quantum Memory

Quantum illumination uses entangled light that consists of signal and idler modes to achieve higher detection rate of a low-reflective object in noisy environments. The best performance of quantum illumination can be achieved by measuring the returned signal mode together with the idler mode. Thus, it is necessary to prepare a quantum memory that can keep the idler mode ideal. To send a signal towards a long-distance target, entangled light in the microwave regime is used. There was a recent demonstration of a microwave quantum memory using microwave cavities coupled with a transmon qubit. We propose an ordering of bosonic operators to efficiently compute the Schrieffer-Wolff transformation generator to analyze the quantum memory. Our proposed method is applicable to a wide class of systems described by bosonic operators whose interaction part represents a definite number of transfer in quanta.
AI Threat Detection and Response on Smart Networks

The main goal of smart cities is to dynamically optimize the quality of life, through the application of information and communication technologies (ICT). The involved networks, require a continuous increase in data exchange, in order to intelligently control services and in particular, mechanisms that activate a higher degree of automation in the city. As many critical services are interconnected, the need for cyber security is increasing, in order to ensure data exchange protection, privacy, and better health and safety services for all citizens. The security and evolution of smart cities is based on the security of their smart networks which are activated by specific automation mechanisms, such as the SCADA networks and the pre-eminent automation systems. This paper presents the AnomaTS, an advanced Machine Learning system, for anomaly detection in sensors of SCADA networks, taking into account the temporal state of their mechanisms.
An Adaptive Federated Control Strategy for Participant Selection in Multi-Client Collaboration

The federated ecology provides a new paradigm for breaking the isolated data island problem and fully activating the potential of big data and artificial intelligence, especially in multi-client collaboration tasks. Participant selection strategy in multi-client collaboration is the main limitation for increasing the convergence speed and lowing the communication costs. However, in the face of the unbalanced and non-IID data distributions, the performance of federated optimization algorithms will also decrease. To solve the above problems, we propose an adaptive federated control strategy for participant selection in multi-client collaboration based on the Mann-Kendall test, named FedMK. By adaptively selecting weak participants for training rather than random selection, FedMK can speed up model convergence and reduce communication costs. Experiment results show that our method outperforms the baseline method in non-IID scenarios, and the number of communication rounds on CIFAR-10 and synthetic datasets reduced by more than 15% and 10 %,9 respectively.
Conclave: A Collective Stake Pool Protocol

Proof-of-Stake (PoS) distributed ledgers are the most common alternative to Bitcoin’s Proof-of-Work (PoW) paradigm, replacing the hardware dependency with stake, i.e., assets that a party controls. Similar to PoW’s mining pools, PoS’s stake pools, i.e., collaborative entities comprising of multiple stakeholders, allow a party to earn rewards more regularly, compared to participating on an individual basis. However, stake pools tend to increase centralization, since they are typically managed by a single party that acts on behalf of the pool’s members. In this work we propose Conclave, a formal design of a Collective Stake Pool, i.e., a decentralized pool with no single point of authority. We formalize Conclave as an ideal functionality and implement it as a distributed protocol, based on standard cryptographic primitives. Among Conclave’s building blocks is a weighted threshold signature scheme (WTSS); to that end, we define a WTSS ideal functionality and propose two constructions based on threshold ECDSA, which enable (1) fast trustless setup and (2) identifiable aborts.
Research and practice of three-phase signal source design based on DDS technology

This paper analyzes the working principle and basic structure of DDS, combined with the design points of three-phase signal source based on DDS technology, including software development platform, hardware description language, sinusoidal signal, AM module, FM module, ASK modulation and demodulation, system assistant, system top, etc. The purpose is to improve the application value of DDS technology and the application effect of three-phase signal source.
Review of Zottola (2021): Transgender Identities in the Press: A Corpus-Based Discourse Analysis

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Criminological Aspects of the Unauthorized Activities within the Cyber Space

In the modern conditions of human life and the functioning of economic entities, government agencies is rapidly expanding the use of electronic computing machines (computers), automated systems, computer networks or networks or telecommunications networks. Their role increased significantly during the COVID-19 pandemic, as the implementation and application of electronic services in a variety of areas intensified to minimize personal contact. Meanwhile, it generates risks of unauthorized interference in their work by criminals who pursue their own selfish goals. According to the results of the research, the elements of the criminal offense from unauthorized interference in the work of electronic computing machines (computers), automated systems, computer networks or telecommunication networks registered by the internal affairs bodies of Ukraine were singled out. The article highlights the results of the judicial practice analysis and makes a generalization in accordance with the court verdicts during the period under study, as well as identifies the characteristics of the offender. Quantitative and qualitative descriptors of criminal offenses registered by the internal affairs bodies of Ukraine are analyzed. In the course of the research a hypothesis was put forward about the influence of probable factors on the result indicator - the number of recorded offenses with unauthorized activities within the cyber space. Such factors are proposed to include the number of the existing population in the country; number of Internet subscribers; number of unemployed population; the volume of the country’s GDP; the volume of GDP per 1 person of the current population; the level of the country’s shadow economy. To prove the hypothesis, the expediency of using the mathematical apparatus of artificial neural networks with radial-basis functions. The experiment proved the relationship between hypothetical factors influencing the level of criminal offenses with unauthorized activities within the cyber space, as well as the feasibility of using mathematical apparatus of artificial neural networks with radial-based functions for forecasting the number of investigated criminal offenses and the development of state policy to combat computer crime.
Deep convolutional neural network based facial expression identification using face-parsing method

Face parsing methodology may be a one amongst the advancements in pc vision that analyses the surface synthesis of the external body part, to amass bits of information on options needs correct pixel segmentation of various components of face like (mouth, nose, eyes etc.). Same means the analysis on feeling recognition plays a eventful role in communication and interactions of humanity and additionally relevant to psychological activities. Considering the disadvantage that totally different completely different components of face contain different quantity of knowledge for face expression and also the weighted perform are not an equivalent for various faces. In keeping with analysis, the image classification task ordinarily drives North American country to the notable Convolutional Neural Network (CNN) during which we tend to ar victimization VGG19 model. beyond exploring around however CNN, sometimes performs for greyscale photos, we tend to selected to start from 3 consecutive convolutional layers followed by a most pooling layer, basic exploit work for convolutional layer and “relu” is used, even as an analogous artefact pattern. The highlights to be known victimization the convolutional layer distended to 128 layers from thirty-two, it is suggestable that multi-layered structure (with increasing layers) that performs and results the most effective outcomes for the DNN model. At last, the CNN layer is 1st smoothened and afterwards expertise 2 many dense layers to reach the yield layer during which SoftMax activation perform is used for multiclass classification. We tend to victimization Cohn-Kanadre face expression dataset of seven expressions like contempt, anger, disgust, happiness, fear, disappointment and surprise.
Opportunities for professional psychological support in a situation of optimization of technological process control in the transport industry

The article considers the main directions of optimization of technological process management in the transport industry in the context of the implementation of professional psychological support procedures (on the example of JSC "Russian Railways"). Railway transport is one of the dynamically developing industries, with complex automated technological processes, which requires highly qualified personnel involved in all branches and structural divisions of JSC "Russian Railways". The interrelation between the implementation of the Transport Strategy of the Russian Federation and the need for qualified performance of official duties of employees who meet the requirements of current professional standards is noted: successful performance of professional activities by employees with a high level of professional competencies and versatility of labor functions is necessary for modern transport organizations. The main principles, types and stages of professional psychological support are highlighted. The analysis of the directions of personnel work of JSC "Russian Railways" and its interaction with educational organizations of higher education in terms of professional psychological support is carried out. The significant role of industrial practice, the implementation of mentoring in the course of educational and professional activities, training programs for students of higher educational institutions are noted. In the process of professional psychological support of the professional activities of transport personnel performing their official duties related to automation and control of technological processes in the transport industry, there is not just a study of their professional abilities, but an assessment of their current state, professional readiness and professional suitability for work. Professional psychological support is considered as a system of continuous interaction between the subject of labor and accompanying persons, within which an active role belongs to all participants in the labor process of support.
Straggler Node Detection and Prevention using supervised Job Scheduling Approach

Load balancing the distributed resource management and matchmaking techniques to meet the tangible job to an actual resource using scheduling techniques.  Distributed data processing systems expedite jobs by turning them back into distributed processing tasks. Nevertheless, the system performance can be enhanced by relatively slow or straggler functions than with the actual charge on a data cluster, resulting in delayed work completion and unsustainable resource consumption. Established straggler avoidance methods wait to identify stragglers and then restart them, slowing the identification of stragglers and wasting money. We are developing a framework that determines when there will be stragglers and must identify gaps to prevent those circumstances. System developed different frameworks for every network and workflow to capture node and configuration heterogeneity, involving the time-consuming compilation of important training data. We introduce multi-task learning mechanisms in this paper, which exchange knowledge between the different versions. The system first collects log data from available data nodes and applies an unsupervised machine learning technique to detect the straggler. Moreover, once a straggler has been detected by the learning algorithm, it tries mitigating such nodes instead to eliminate of nodes. The major benefit of this research, the system can handle massive data nodes with minimum data nodes or even system efficiently work with already heated nodes in a distributed environment. Our compositions absorb the shared context in our data, enhancing sweeping statement efficiency on minimal data, unlike naive inter learning frameworks.
Weight attention layer‐based document classification incorporating information gain

Abstract The performance of document classifiers largely depends on their internal representations of text data. Recent studies have been conducted to identify areas of focus and find latent data spaces to increase the representativeness and the performance of classifiers. In this study, we propose a weight attention layer (WAL) that uses an additional feature of words when computing their attention weights for deep learning models based on attention mechanisms. In the WAL, the attention distribution is calculated through the dot product of the attention weight matrix and a word weight matrix. We utilized information gain, which is one of the feature selection algorithms for the additional feature. To evaluate the proposed method, datasets of helpful reviews, sentiment reviews, and fake reviews were used. These datasets were applied to two deep learning models based on attention mechanisms, including an attention‐based bidirectional long short‐term memory (LSTM) and a hierarchical attention network. As a result of 10‐fold cross validation, the improved performance of the models in terms of accuracy and F1‐score when using WAL is demonstrated.
Secured And Transparent Voting System Using Biometric And Face Recognition

The country's "Democracy" permits citizens to elect their representatives, and we must ensure that the power granted to them is properly employed. The traditional way of voting was the ballot paper system, which was later replaced by the electronic Voting System (EVM). The EVM is simple to manipulate since it involves a lot of human participation, which could jeopardize election results by casting bogus votes. It has been observed that there are numerous issues linked with the conduct of elections in the country, including low voter turnout (usually less than 60%), election rigging, and so on.
Proposal of an Evaluation Method of Individual Contributions using the Function Point in the Implementation Phase in Project-Based Learning of Software Development

In recent years, software development is generally done in the form of a team project. Correspondingly, educational institutions have adopted project-based software development exercises (called project-based learning (PBL) of software development in this paper). The PBL of software development can perform various processes in software development, which will be a great experience for students to play an active role in their profession in the future. However, an individual evaluation of students in such a team project is difficult and involves several problems. Therefore, to evaluate students fairly, this research proposes an evaluation method to measure the contributions of individuals in the implementation phase in the PBL of software development. Particularly, it is an evaluation method that applies the function point method, which is known as the size estimation method.