Reviewerly/exHarmony huggingface dataset:
Diverse, Neural Trojan Resilient Ecosystem of Neural Network IP

Adversarial machine learning is a prominent research area aimed towards exposing and mitigating security vulnerabilities in AI/ML algorithms and their implementations. Data poisoning and neural Trojans enable an attacker to drastically change the behavior and performance of a Convolutional Neural Network (CNN) merely by altering some of the input data during training. Such attacks can be catastrophic in the field, e.g. for self-driving vehicles. In this paper, we propose deploying a CNN as an ecosystem of variants , rather than a singular model. The ecosystem is derived from the original trained model, and though every derived model is structurally different, they are all functionally equivalent to the original and each other. We propose two complementary techniques: stochastic parameter mutation , where the weights θ of the original are shifted by a small, random amount, and a delta-update procedure which functions by XOR’ing all of the parameters with an update file containing the Δ θ values. This technique is effective against transferability of a neural Trojan to the greater ecosystem by amplifying the Trojan’s malicious impact to easily detectable levels; thus, deploying a model as an ecosystem can render the ecosystem more resilient against a neural Trojan attack.
Generating Photos from Line Drawings of Chinese Classical Upper-class Ladies Based on Conditional GAN

The upper-class ladies are important objects of Chinese classical paintings, and line drawing is one of the important forms of expression. This paper presents to generate photorealistic digital images based on line drawings of Chinese classical upper-class ladies using Conditional GAN. There is no image dataset of the classical ladies, and there are no pre-trained models that can be directly used for training. Contemporary photos of ancient costumes are collected and processed into a paired image dataset for training. ReLU activation function and more constraints on the objective function are used to obtain good results. The project pix2pixHD is modified to implement the presented approach. Line drawings of Chinese classical upper-class ladies are used to test the generation of photorealistic digital images, and desired results are obtained.
Urdu Miner: An Application for Urdu Text Mining

A large amount of data is generated every day; it represents both a challenge and opportunity to understand statistics from existing and new data (datasets/documents). Urdu is Pakistan's national language and is also the official language of six Indian states. A vast number of books, documents, datasets are present in the Urdu language. To examine their content, we have no proper application/system that helps to understand and analyze their data with complete statistics, graphical representation options, dataset topics, and coverage of the dataset. We proposed a solution for Urdu-language datasets text mining, which includes comprehensive file statistics, better understandings using graphs/charts, topics extractions, comparison with other datasets, and findings related words with any specific word. We have manually compiled and cleansed 9 million Urdu words dataset. A new list of stop words for Urdu, consisting of 271 words that mainly occur in Urdu, is constructed. A unique Urdu words list composed of 0.2 million is made. System algorithms are optimized for text analysis, and their results are auspicious. This is the pioneering text mining system developed entirely for Urdu language text that covers various aspects of data analysis for the Urdu language.
Academy and Company Needs

This chapter presents a view of how the use of NLP knowledge might change the relation between universities and companies. Products from NLP analysis are expected in both ends of this at times not so reciprocal exchange. But history has shown the products developed by universities and companies are complementary for the development of NLP. The great volume of data the world is producing is requiring newer perspectives to provide understanding. These newer aspects found on big data may provide the comprehension of human language categorization and therefore possibly human language acquisition. But to process data more data need to be produced and not all companies have the time to dedicate for this task. This chapter aims to present through sharing literature review and experience in the field that partnerships are the most reliable resource for the cycle of knowledge production in NLP. Companies need to be receptive of the theoretical knowledge the university may provide, and universities must turn their theoretical knowledge for a more applied envionment.
Mistrust: Why Losing Faith In Institutions Provides The Tools To Transform Them Ethan Zuckerman. New York, NY: W. W. Norton &amp; Company, 2021. 275pp. $26.95 (hardcover)

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Research on Travel Personalized Recommendation Information Retrieval Based on Big Data Improvement Algorithm

Tourism, as a basic component of social and economic development and urban construction, is also the core content to meet the spiritual needs of residents. Therefore, on the basis of understanding the current integrated development form of the Internet and tourism, this paper analyzes how to build a recommendation information retrieval model with personalized tourism based on the Apriori algorithm, aiming at the improved algorithm proposed based on big data.
Improving Neural Architecture Search with Unlabeled External Data

In recent years, Neural Architecture Search (NAS) algorithms have gained significant attention in attempts to find state-of-the-art neural architectures for various computer vision tasks. While most existing NAS methods typically utilize only well-curated datasets with the full label information, in this paper we show that NAS can be further improved by additionally leveraging external unlabeled data in the wild. In particular, we consider a new NAS objective that synthesizes the supervised loss for the given labeled samples and the self-supervised loss for the unlabeled, external samples. This allows us to search for a better generalizable architecture than existing ones that only focus on the given dataset and task for the architecture search. Our experiments demonstrate that the proposed method achieves consistent performance improvement over various existing NAS algorithms.
Theoretical Aspects in Forming Complex Structure Signal

Receiving and transmitting paths of modern radio communication systems are built on the basis of an open structure that provides hierarchical differentiation of access to the provided telecommunication services. However, this approach does not exclude the possibility of access to the transmitted content by unauthorized users. Hiding information by methods of cryptographic protection in such a situation only activates additional interest in transmission, therefore the most pragmatic solution is to use signals of a complex structure, which significantly complicate or even exclude the extraction of information from them by third-party users. The problem of regulating access selection in the development and design radio system elements is rather multifaceted and has a high degree of complexity. One of the directions for solving problems in this subject area is based on the well-known approaches to expanding the signal base, however, algorithms for their practical implementation were obtained without taking into account the limitations on the allocated resource and the very fact of using these algorithms. Based on the theory of systems and the general theory of communication, an approach to the formation of signal structures of a complex structure has been developed, which ensures an increase in the properties of their structural secrecy in relation to unauthorized users. At the same time, the known solutions at the physical level of signal spaces were refined, which made it possible to formalize the procedures for the formation of radio signals with specified properties. The method of formalizing the function of displaying the signal space based on the allocation of stochastic properties of pseudo-random sequences has been substantiated, which made it possible to ensure the uncertainty of their structure in case of unauthorized processing. The approbation of the proposed approach is given on the example of the formation of quadrature modulation signals, taking into account the subsequent analysis of their properties from various positions of legitimate and illegitimate users. The results obtained confirm the uncertainty during illegitimate processing with a slight deterioration in the noise immunity properties of radio communication systems. In general, this allows to conclude the adequacy of theoretical solutions. As an example, constellation diagrams of signals at the output of a quadrature receiver are presented. The set of proposed technical solutions presented in the work determines the novelty of this approach. The scientific problem to be solved belongs to the class of problems of synthesis of signals of complex structures.
The Reliability Assessment for Advanced Persistent Threat Defense based on Correlation Evidence Reasoning Rule

Advanced Persistent Threat (APT) is a cyber-attack method targeting large corporations, national institutions, and research institutes. APT attacks are multi-stage, long duration, and difficult to detect. Hacking actions that carry out APT attacks to steal important data and disrupt systems have become more frequent and rampant in recent years. Therefore, assessing the reliability of APT defense for complex network systems helps to develop plans to upgrade defense methods. In this paper, a framework including four aspects is established, and a correlation evidence reasoning (CER) model is proposed to assess the reliability of APT defense. Finally, a case study is constructed to illustrate the actual effect of the proposed method.
Throughput Maximization of Virtual Machine Communications in Bandwidth-Constrained Data Centers

In this paper we study a new algorithmic problem that maximizes the throughput of virtual machine (VM) communication in bandwidth-constrained data centers. Given a set of VM pairs with different bandwidth demands that are already placed inside cloud data centers, we study how to allocate the network bandwidth to the VM pairs to accommodate maximum number of VM communication while considering that cloud data centers have limited bandwidths. We refer to this throughput maximization problem as VMB. Due to the massive growth of cloud communication traffic in recent years and that service providers attempt to accommodate as many VM applications as possible in order to maximize their profits, VMB is an important problem to study. First we prove that VMB is NP-hard. Then we propose a suite of algorithms to solve VMB. In particular, we propose an approximation algorithm that achieves approximation ratio of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1 /\left(2 \cdot\left\lceil\frac{B}{b}\right\rceil \cdot\vert E\vert^{1 /\left(\left\lceil\frac{B}{b}\right\rceil+1\right)}+1\right)$</tex> , where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\vert E\vert$</tex> is the number of edges in the data center network, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$B$</tex> is the average bandwidth capacity on edges, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$b$</tex> is the average bandwidth demand of each request. We show through simulations that our algorithms are effective in accommodating large number of VM communications under different network parameters. In particular, our approximation algorithm accommodates more than 60% of total VM communications, and up to 38% more VM pairs compared to existing research.
Offensive and Defensive Countermeasure Technology of Return-Oriented Programming

The problem of buffer overflow in the information system is not threatening, and the system&#x0027;s own defense mechanism can detect and terminate code injection attacks. However, as countermeasures compete with each other, advanced stack overflow attacks have emerged: Return Oriented-Programming (ROP) technology, which has become a hot spot in the field of system security research in recent years. First, this article explains the reason for the existence of this technology and the attack principle. Secondly, it systematically expounds the realization of the return-oriented programming technology at home and abroad in recent years from the common architecture platform, the research of attack load construction, and the research of variants based on ROP attacks. Finally, we summarize the paper.
Priorities of Vocational Training of Educators of Engineering Universities in the Formation of Their Psychological and Pedagogical Competency

The article is devoted to problems of professional psychological and pedagogical training of teachers of engineering universities; proposed a set of criteria for assessing the scientific and educational activities of teachers of technical disciplines. The authors describe the achievements of domestic and inter- national practice of training teachers of technical universities; identified ways to improve the efficiency of this process.
Lifelong Learning as a Strategy for Digital Literacy in University Teachers

The outbreak of the COVID-19 pandemic generated the transition from a face-to-face teaching modality to a virtual teaching modality. For continuity deliverance to the university educational system's activities, it was crucial to generate permanent training courses to manage virtual classrooms, virtual educational resources, and video conferencing platforms to establish contact with students. This research aims to corroborate whether permanent training constitutes a meaningful and valuable digital literacy strategy for university teachers. Following the paradigm of a qualitative research design, a survey application before and after starting the permanent training courses explicitly selected for the teachers from Escuela Superior Politécnica de Chimborazo of the Public Health faculty. As a result, there is a significant difference between the digital skills that teachers initially possess and those developed after participating in permanent training courses. The data was corroboree by obtaining a p-value of 0.007. Lifelong learning acquires a leading role when digital literacy and digital skills development are necessary for an emerging situation.
Object Size Measurement from CCTV footage using deep learning

Closed-circuit television (CCTV) cameras are being extensively installed in the cities, apartments, homes, restaurants, educational institutions, shops and so on producing lots of data every second. Storing, processing and analyzing these video footages effectively is a challenging task. In the course of analysis of these video sequences, measurement of size of the objects in the scene is an interesting and a very challenging task. Though there are systems that can determine the height of a person, they are not computationally cost effective and very few of these works for CCTV footages.There are so many applications to find out the size of an object or a person with higher cost of maintenance and there is no application which finds the height of a person in video captured by CCTV. This project mainly focuses on the detection and calculation of height of a person in the CCTV footage using deep learning algorithm.The main objective is to build the system which determines the measurements of an object or a person in a pre-recorded video or CCTV footage or in image. The methodology adapted in the project is to find the bounding boxes around all the objects found in an input and is to determine the object class it belongs to. The python programming and machine learning techniques like YOLO algorithm, R-CNN is used for the object recognition.The posture of a person changes accordingly as his/her actions.The posture of a person effects the calculation of height as likely in a bending position of knees, the height may look less and determined accordingly. The input dataset is having various numbers of predefined classes in it, so that the detection of the various objects in the frames will be easier. The model detects the objects in images and objects in CCTV video footage with 81% accuracy. The custom dataset has been used to train and test the model.
Do You Get Me: Controlled Adaptive Mental Models for Analysis and Support Processes

In this chapter, a self-modeling mental networkNetwork model is presented for cognitiveCognitive analysis and support processes for a human. These cognitiveCognitive analysis and support processes are modeled by internal mental modelsMental model. At the base level, the model is able to perform the analysis and support processes based on these internal mental modelsMental model. To obtain adaptationAdaptation of these internal mental modelsMental model, a first-order self-model is included in the networkNetwork model. In addition, to obtain controlControl of this adaptationAdaptation, a second-order self-model is included. This makes the networkNetwork model a second-order self-modeling networkNetwork model. The adaptive networkAdaptive network model is illustrated for a number of realistic scenarios for a supported car driver.
Methods of Information Security in Cloud Storages

Outsourcing with the use of leased cloud storage is a promising way of storing and processing corporate information, including the JSCO "RZD". However, a serious obstacle to this is the problem of trust in providers. The paper describes various ways to protect user information. The method of bitwise dissection and separation is discussed. It is noted that with an increase in the length of the dissection and separation key, the security of information which is protected by this method may grows faster than according to the exponential law. The number of storages is not important at the same time.
Multi-Signature Scheme Resistant to Randomness Injection Attacks - A Bitcoin Case

We propose a modification of a multi-signature scheme, which was previously used as an enhancement of multi-signatures for the Bitcoin cryptocurrency. Our scheme is secure in a new stronger security model in which we allow the forger to inject or control the ephemeral (randomness) values in the end-user's signing device. Thus, our modified scheme is resistant to ephemeral key leakage attacks. We also provide a proof of concept implementation of our scheme, providing a time complexity and performance analysis.
Al Feasibility Study on Case Detection

In the current academic field, there are few researches on the connection between artificial intelligence (Al) and case detection, so it needs to find a new direction to make a breakthrough. Existing research mainly focuses on the integration of related technologies into public security applications, such as identity recognition technology in access control systems and image recognition technology for identity inspection in the railway station. However, in the deeper field of public security, such as criminal investigation, there is no trend to popularize the technology. This paper will focus on the principles of available Al technology, the problems in the detection of existing cases, how to integrate Al technology into the traditional model, and the possible hidden dangers in the future. The conclusion is that people can apply the existing artificial intelligence technologies such as voiceprint recognition and image recognition to criminal investigation
A Method of False-target Elimination Based on Asynchronous Fusion

For the problem of multi-false-target deception jamming, the netted radar system is one effective anti-jamming method. This paper describes a method of false-target elimination based on the asynchronous centralized fusion of the netted radar system and applies it to the range false-target deception jamming and the velocity false-target deception jamming. The method first fuses the measurements from different node radars to generate tracks using the asynchronous sequential fusion technique. Then, based on the spatial position distribution correlation characteristics of the measurements obtained by different node radars, three attribute features of an obtained track, i.e., the number of data source node radars, the average data rate and the ratio for each node radar’s measurements used for updating the track, are extracted to determine whether a track is real or false. For a velocity false target, its measurements are automatically absorbed at the data association stage of the fusion filtering processing by the nearest neighbor (NN) association criterion and no false track are generated. Simulations are performed to validate the effectiveness of the method.
Cryptocurrencies Transactions in the UK Real Estate Market: Threat or Opportunity for Anti-Money Laundering?

In recent years, the use of cryptocurrencies in various sectors of national economies has become extremely popular, attracting praise and criticism. These transactions have sparked debate about the potential money laundering risks associated with the use of cryptocurrencies. In particular, these concerns pertain to their high-level anonymity and the danger of criminal infiltration of important economic sectors such as the real estate market. However, the discussion on the topic is quite scattered and unsatisfactory, with many unanswered questions and legislative initiatives that have only partially tackled the issue. This chapter focuses on the use of cryptocurrencies in the UK real estate market, assessing some of the critical points that emerge from their employment in property transactions. The UK real estate market is a particularly relevant case study. Indeed, recently, it has been at the centre of debate for its vulnerability to money laundering practices and the challenges it presents for implementing AML regulation. The author addresses the question of whether the use of cryptocurrencies represents a threat or an opportunity for the AML regime concerning the property sector. This study aims to identify key aspects that future legislative and policy actions should consider to strike a balance between using cryptocurrencies and the need to protect the real estate market from criminal activities.
Enhanced Cloud Optimization Model for CSP, Tenant and User Through Container

The system leverages an optimization scheme for the tenant, client and CSP. This guided optimization model design acts as a intermediate SP (service provider) that guides the user for effective data streaming and resource allocation. A proper resource allocation strategy by checking the availability, size, security, and cost-effective service providers are deliberated. A deep neural learning is emphasized to produce a complete analysis on cloud. An optimization technique used to systemize the information in cloud. A new systematic Enhanced profit/loss (EPF) calculator implemented to calculate the profit or loss that are established during resource allocation. In case the loss rate is more then it gets controlled during the transaction itself. By analyzing the ratings, comments and the report a feedback record produced that helps in choosing a trustworthy container to the tenant. The tenant suggestthe particular trustworthy container to the user likewise the cyclic recommendation process is proceeded. From the proposed optimization model the experimental results are deliberated. The results demonstrates a profit for several users and CSP bu eduring a organized allocation scheme.