Reviewerly/exHarmony huggingface dataset:
End-to-end data-dependent routing in multi-path neural networks

Neural networks are known to give better performance with increased depth due to their ability to learn more abstract features. Although the deepening of networks has been well established, there is still room for efficient feature extraction within a layer, which would reduce the need for mere parameter increment. The conventional widening of networks by having more filters in each layer introduces a quadratic increment of parameters. Having multiple parallel convolutional/dense operations in each layer solves this problem, but without any context-dependent allocation of input among these operations: The parallel computations tend to learn similar features making the widening process less effective. Therefore, we propose the use of multi-path neural networks with data-dependent resource allocation from parallel computations within layers, which also lets an input be routed end-to-end through these parallel paths. To do this, we first introduce a cross-prediction-based algorithm between parallel tensors of subsequent layers. Second, we further reduce the routing overhead by introducing feature-dependent cross-connections between parallel tensors of successive layers. Using image recognition tasks, we show that our multi-path networks show superior performance to existing widening and adaptive feature extraction, even ensembles and deeper networks at similar complexity.
Face mask detection system using ML

According to the World Health Organization, the corona virus COVID-19 pandemic is producing a worldwide health catastrophe, and the most effective protective technique is wearing a face mask in public places (WHO). The COVID-19 epidemic compelled governments all over the world to implement lockdowns in order to limit viral spread. According to reports, wearing a face mask while at work significantly decreases the chance of transmission.An efficient and economic approach of using AI to create a safe environment in a manufacturing setup. A model using deep learning for face mask detection will be presented. A face mask detection dataset consists of with mask and without mask images , we are going to use OpenCV to do real-time face detection from a live stream via our webcam. We will use the dataset to build a COVID-19 face mask detector with deep learning using Python, OpenCV, and Keras. Our goal is to identify whether the person on image/video stream is wearing a face mask or not with the help of deep learning.
Research on Semantic Analysis Strategy in Intelligent Human-Computer Interaction System

To provide users with more intelligent information access interface in online answering system, this paper designs an automatic question and answer system based on LSA. The strategy uses space vector model as the representation of problems and sentences to improve the weight calculation of elements in word document matrix. Then, according to the requirements of generating semantic space process, a new answer extraction technology based on semantic role annotation is proposed. Finally, the paper provides the complete architecture and application development mode of the system through web service technology, and tests and analyzes the scheme proposed in this paper. The results show that the system can meet the requirements of users' questioning better, and it has the characteristics of saving resources, intelligence and high efficiency of execution.
Adversarial Robustness of Probabilistic Network Embedding for Link Prediction

In today’s networked society, many real-world problems can be formalized as predicting links in networks, such as Facebook friendship suggestions, e-commerce recommendations, and the prediction of scientific collaborations in citation networks. Increasingly often, link prediction problem is tackled by means of network embedding methods, owing to their state-of-the-art performance. However, these methods lack transparency when compared to simpler baselines, and as a result their robustness against adversarial attacks is a possible point of concern: could one or a few small adversarial modifications to the network have a large impact on the link prediction performance when using a network embedding model? Prior research has already investigated adversarial robustness for network embedding models, focused on classification at the node and graph level. Robustness with respect to the link prediction downstream task, on the other hand, has been explored much less.This paper contributes to filling this gap, by studying adversarial robustness of Conditional Network Embedding (CNE), a state-of-the-art probabilistic network embedding model, for link prediction. More specifically, given CNE and a network, we measure the sensitivity of the link predictions of the model to small adversarial perturbations of the network, namely changes of the link status of a node pair. Thus, our approach allows one to identify the links and non-links in the network that are most vulnerable to such perturbations, for further investigation by an analyst. We analyze the characteristics of the most and least sensitive perturbations, and empirically confirm that our approach not only succeeds in identifying the most vulnerable links and non-links, but also that it does so in a time-efficient manner thanks to an effective approximation.
BRDF Importance Sampling for Linear Lights

Abstract We introduce an efficient method to sample linear lights, i.e. infinitesimally thin cylinders, proportional to projected solid angle. Our method uses inverse function sampling with a specialized iterative procedure that converges to high accuracy in only two iterations. It also allows us to sample proportional to a linearly transformed cosine. By combining both sampling techniques through suitable multiple importance sampling heuristics and by using good stratification, we achieve unbiased diffuse and specular real‐time shading with low variance outside penumbrae at two samples per pixel. Additionally, we provide a fast method for solid angle sampling.
Effects of Governance on Growth in the Central Africa Economic and Monetary Community (CAEMC)

The purpose of this study is to highlight the effects of governance indicators on the growth of CAEMC countries' economies.The analysis conducted using panel data econometrics, in accordance with the generalized method of moments, over a period from 2002 to 2015, showed that corruption, bureaucratic quality and political instability negatively influence the growth of CAEMC countries' economies.In contrast, regulatory quality, rule of law, and citizen voice and accountability had a positive impact on the growth of CAEMC economies.These results have given rise to economic policy implications.
Human abnormal behavior recognition based on two-stream convolutional neural network

With the gradual improvement of people’s security awareness, the need for intelligent video surveillance is becoming more and more urgent. As a hot spot in the field of computer vision, the recognition of abnormal human behavior has received widespread attention from the whole society. This article proposes a human action recognition algorithm by learning joints information using two-stream convolutional networks. The spatial convolutional neural network is used to describe the spatial position and appearance information of the human body in the video frame. The temporal convolutional neural network constructs a motion feature vector based on the changes in the position of the human skeleton joint points between frames to describe the human body's movement. Finally, the spatio-temporal two-stream is fused to recognize the abnormal behavior.
Stit semantics for epistemic notions based on information disclosure in interactive settings

We characterize four types of agentive knowledge using a stit semantics over branching discrete-time structures. These are ex ante knowledge, ex interim knowledge, ex post knowledge, and know-how. The first three are notions that arose from game-theoretical analyses on the stages of information disclosure across the decision making process, and the fourth has gained prominence both in logics of action and in deontic logic as a means to formalize ability. In recent years, logicians in AI have argued that any comprehensive study of responsibility attribution and blameworthiness should include proper treatment of these kinds of knowledge. This paper intends to clarify previous attempts to formalize them in stit logic and to propose alternative interpretations that in our opinion are more akin to the study of responsibility in the stit tradition. The logic we present uses an extension with knowledge operators of the Xstit language, and formulas are evaluated with respect to branching discrete-time models. We also present an axiomatic system for this logic, and address its soundness and completeness.
An Empirical Study: Automated Subdomain Takeover Threat Detection

Due to the development of E-commerce, phishing attacks are one of the major current cyber threats. Phishing attacks have become increasingly sophisticated and have exploited both individuals and organizations. For the enterprise, a successful duplicate phishing website may affect an organizations reputation or be the basis of a subdomain takeover attack. This latter attack can completely escape the detection of an SSL certificate and have a direct impact on the enterprise. A successful subdomain takeover attack has a higher threat level as a controllable subdomain owns the same SSL certificate with its parent website, and yet it does not require an advanced technical skill to exploit. In this paper, two techniques have been presented as potential solutions. One is a query approach based on machine learning for querying existing subdomains and the second is an auto-detection system to identify the potentially risky subdomains.
Self dual, reversible and complementary duals constacyclic codes over finite local Frobenius non-chain rings of length 5 and nilpotency index 4.

Abstract Over finite local Frobenius non-chain rings of length 5 and nilpotency index 4 and when the length of the code is relatively prime to the characteristic of the residue field of the ring, the structure of the dual of γ -constacyclic codes is established and the algebraic characterization of self-dual, reversible γ -constacyclic codes and γ -constacyclic codes with complementary dual are given.
Survey on Authentication Techniques for web applications

Every application which has client and server, which has sensitive data security and privacy Is the major concern. This means any other user than authorized user can access the data no matter what happens and why. There are great deal of Authentication strategies which are created and utilized over years. Most frameworks use Cookies and sessions which make servers statefull which gives obstructions for scaling and different issues. In this paper, survey on various previous authentication techniques with their advantages and disadvantages are done. Also a new authentication scheme Json Web Token(JWT) introduced by OAuth2.0 framework is also discussed and why to adopt this method other than the ones above mentioned are the key elements of the paper.
Neural Translation Models

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A New Method of Sign Detection in RNS Based on Modified Chinese Remainder Theorem

A sign detection is a non-modular operation in the residue number system (RNS), that requires calculating a position characteristic of the number represented in RNS. This paper proposes a new method of sign detection based on the Chinese remainder theorem (CRT) with fraction values. A hardware modeling on FPGA for the proposed method shows that it requires 1.4 - 30.9 times fewer hardware costs compare with CRT based method and 5.9 - 42.2 times fewer hardware costs compare with the method based on mixed radix conversion based. The proposed method of sign detection can be used in RNS based devices to implement magnitude comparison and division operations.
Interrelation of defense mechanisms and motivational orientations of corporate leaders at different stages of business development

This article is dedicated to examination of the degree of defense mechanisms and their interrelation with the motivational orientation of corporate leaders at different stages of business development. The subject of this research is the motivational orientations and defense mechanisms of corporate leaders at different stages of business development. The aim goal consists in studying the interdependence of defense mechanisms and motivational orientations of the leaders of companies founded less than 3 years ago (crisis state) and over 10 years (sustainable development). The article employs cross-sectional method based on the questionnaire &amp;ldquo;Life Style Index&amp;rdquo; (LSI) developed by R. Plutchik, H. Kellerman, and H. R. Conte; the method of diagnostics of socio-psychological attitudes of the individual in the motivational and consumer sphere developed by O. F. Potemkina. The analysis of the acquired results with determination of statistically significant interdependences of psychological indicators was carried out by means of the method of mathematical statistics. The novelty of this work consists in examination of the impact of motivational orientations of corporate leaders upon manifestation of their defense mechanisms during the crisis stages of business development in accordance with the theory of psychoanalysis. Such research is carried out for the first time. The practical importance of the results lies in the application of knowledge of psychoanalytic concepts for revealing the latent pathogenic factors in the company that affect personnel&amp;rsquo;s behavior. This would allow the corporate psychologists to choose effective recommendations for balancing such factors. The conclusion is made that the protective mechanisms of the business owner and corporate leadership are interrelated with their motivational orientations; the degree of defense mechanisms of leaders of newly established companies and long established companies differ. The author's special contribution to this research consists in interpretation of the results through the prism of psychoanalytic approach.
Quantum generative models for data generation

In recent years, many generation-based machine learning algorithms such as generative adversarial networks, Boltzmann machine, auto-encoder, etc. are widely used in data generation and probability distribution simulation. On the other hand, the combined algorithms of quantum computation and classical machine learning algorithms are proposed in various styles. Especially, there exist many relevant researches about quantum generative models, which are regarded as the branch of quantum machine learning. Quantum generative models are hybrid quantum-classical algorithms, in which parameterized quantum circuits are introduced to obtain the cost function of the task as well as its gradient, and then classical optimization algorithms are used to find the optima. Compared with its classical counterpart, quantum generative models map the data stream to high-dimensional Hilbert space with parameterized quantum circuits. In the mapping space, data features are easier to learn, which can surpass classical generative models in some tasks. Besides, quantum generative models are potential to realize the quantum advantage in noisy intermediate-scale quantum devices.
Adaptive Neuro-fuzzy System (ANFIS) of Information Interaction in Industrial Internet of Things Networks Taking into Account Load Balancing

The main aim of the Internet of things is to improve the safety of the device through inter-Device communication (IDC). Various applications are emerging in Internet of things. Various aspects of Internet of things differ from Internet of things, especially the nodes have more velocity which causes the topology to change rapidly. The requirement of researches in the concept of Internet of things increases rapidly because Internet of things face many challenges on the security, protocols and technology. Despite the fact that the problem of organizing the interaction of IIoT devices has already attracted a lot of attention from many researchers, current research on routing in IIoT cannot effectively solve the problem of data exchange in a self-adaptive and self-organized way, because the number of connected devices is quite large. In this article, an adaptive neuro-fuzzy clustering algorithm is presented for the uniform distribution of load between interacting nodes. We synthesized fuzzy logic and neural network to balance the choice of the optimal number of cluster heads and uniform load distribution between sensors. Comparison is made with other load balancing methods in such wireless sensor networks.
Neural Architecture Search Using Covariance Matrix Adaptation Evolution Strategy

Evolution-based neural architecture search methods have shown promising results but they require high computational resources since these methods involve training each candidate architecture from scratch and then evaluating its fitness which results in long search time. Covariance Matrix Adaptation Evolution Strategy (CMA-ES) has shown promising results in tuning hyperparameters of neural networks but has not been used for neural architecture search. In this work, we propose a framework called CMANAS which applies the faster convergence property of CMA-ES to the deep neural architecture search problem. Instead of training each individual architecture seperately, we used the accuracy of a trained one shot model (OSM) on the validation data as a prediction of the fitness of the architecture resulting in reduced search time. We also used an architecture-fitness table (AF table) for keeping record of the already evaluated architecture, thus further reducing the search time. The architectures are modelled using a normal distribution, which is updated using CMA-ES based on the fitness of the sampled population. Experimentally, CMANAS achieves better results than previous evolution-based methods while reducing the search time significantly. The effectiveness of CMANAS is shown on 2 different search spaces for datasets: CIFAR-10, CIFAR-100, ImageNet and ImageNet16-120. All the results show that CMANAS is a viable alternative to previous evolution-based methods and extends the application of CMA-ES to the deep neural architecture search field.
“Mi chiamo Ana, cioè Anna”. Un’indagine sugli effetti del prestigio linguistico rispetto ai nomi dei migranti stranieri in Italia

The name represents a distinctive feature of personal identity and is an essential aspect within the personality building and the relationship with other individuals. This research presents a qualitative analysis of some linguistic changes related to the names of L2 speakers in Italy, in order to formulate some considerations on linguistic prestige and the consequent intercultural impact on the migrants' minorities, deepening the theme of the name variations.
THE EDUCATION VALUES OF FULL-TIME AND PART-TIME STUDENTS OF A MINISTERIAL UNIVERSITY

The article is based on the results of the sociological study on the value orientations of students in two traditional forms - full-time and part-time, the peculiarities of their educational trajectories, the motivation for choosing the form of education. The article tests the hypothesis about the presence (absence) of a pronounced connection between the organizational form of education at a university and the subsequent distribution of students, based on estimates of their level of education. The collection of empirical material on the research problem has been carried out in the course of planned research work devoted to the study of the image of the Academy of Management of the Ministry of Internal Affairs of Russia. A number of associations about the university among those who received education in different forms of education have been identified and described Two approaches of value expectations from education have been identified : the first is closely related to the strengthening of the resource of the prestige of academic education, the second - with the increase in the educational potential of the educational institution. It has been proved that in full-time students the desire to improve themselves as individuals and professionals who can compete on the basis of improved personal competencies, and not on the symbolic attributes of the social hierarchy prevail; the educational efforts of a significant part of students by correspondence are focused mainly on achieving a formal learning outcome - an increase in status, and do not consider a diploma as a document that reflects the qualifications and level of education of the owner. The article makes a substantiated conclusion about the need to differentiate the status of the received educational document depending on the form of study at a university.
Accurate Mapping of Thermal Camera Measurements on Planar Deformations

Synchronous measurement of temperatureTemperature distribution with infrared cameras has been increasingly used in the thermomechanical analysis of sheet material behavior. Measured temperatures are used, however, only qualitatively due to the absence of well-developed coordinate mapping methods applicable to thermographic measurements. The study introduces a primitive, easy to use, yet effective approach for accurate coordinate mapping from real to image coordinates, and inverse, applicable to even highly oblique thermographic and photographic measurements of planar deformations. The method is not limited to any camera model and precisely counteracts inhomogeneous image distortion. It can be used as a stand-alone opticalOptical measurement approach, but at the same time, it allows flawless correlation with available digital image correlation systems by enabling accurate mapping between thermographic and photographic measurements of planar deformation in real and image coordinates.
Exploring Experiences of Using SETA in Nordic Municipalities

User behavior is a key aspect of cybersecurity and it is well documented that insecure user behavior is the root cause of the majority of all cybersecurity incidents. Security Education, Training, and Awareness (SETA) is described by practitioners and researchers as the most important tool for improving cybersecurity behavior and has been for several decades. Further, there are several ways to work with SETA found in academic literature and a lot of research into various aspects of SETA effectiveness. However, the problem of insecure user behavior remains revealing a need for further research in the domain. While previous research have looked at the users' experience of SETA, this study looks at SETA adoption from the perspective of the adopting organization. For this purpose, a survey was sent out to all Nordic municipalities with the intent of measuring if and how SETA is conducted, and how the respondents would ideally like to conduct SETA. The results show that a majority of the participating organizations use SETA and that e-learning is the most common delivery method. However, the results also show that gamification and embedded training is seldom used in practice nor a part of the participants' picture of ideal SETA.