Reviewerly/exHarmony huggingface dataset:
A Systematic Survey of the Chatbot Evolution

With the advancement of technology and the emergence of intelligent machines, the concept of chatbot has come into the scene, which is a computer program that stimulates and processes human conversation, either written or spoken, allowing humans to interact with digital devices as if they were communicating with a real human. The aim of the present work is to make an explicit survey on the state-of-the-art of the chatbot and the methodologies used in the same. Some recent publications have been reviewed from the last 5 years and found that the methodologies used to a great extent were mainly hybrid algorithms, natural language processing, machine learning, and deep learning. As per the survey, the various uses of chatbots have been observed, from educational purpose to business and administration. The main focus of this survey is the use of chatbot in personalized learning systems and the various techniques and methods applied to achieve the same.
The Study of the Issues Related to Orphan Blocks

Cryptocurrency is replacing the centralized system with a decentralized network of Internet-based miners who generate and handle foreign currency and transactions in a more secure manner. They use blockchain as decentralized ledger and tightly close them with proof of work. Bitcoin’s rule for attaining consensus is deciding on the longest chains and discarding the different chains as orphan and stale. It is observed that this rule has a weak point toward selfish mining in which the egocentric miner exploits the variance in the blockchain technology through party retaining blocks. The paper explains the different methods that can be used to minimize the orphan risk in blockchain like Bitcoin and other different methods that can be used to resolve the issue if they are still being formed.
Classification of Fashion Items Based on Shape Features Using Machine Learning

A cloth or outfit classification in images has many practical applications in e-commerce and online advertising. This paper proposes a computer vision-based recognition model for clothes classification. The input image includes the fashion catalog of both men and women with the clothes exposed completely and worn by the people. The model of classification includes three main steps: (i) segmentation, identifying, and locating the cloth region in an image using pixel annotation method; (ii) the feature extraction, Zernike moments, Fourier shape descriptors and chain codes are applied to each segmented image to generate a feature vector; (iii) the classification, support vector machine classifier is utilized for classifying different classes of outfits. Our database includes 485 images, including both men and women, with 7 different clothing images, including a jacket, shirt, suit, sweater, t-shirt, polo shirt. The experiment shows that this model can accurately classify categories of clothes 82.36%.
A Comprehensive Study on Blockchain-Based Data Security in IoT to Build Reputation for Intelligent Transport Systems

Transportation is the most convenient way of movement for any object or person from one location to another. Modern day transportation has become a big concern due to the increasing accident rates, traffic congestion, carbon emissions, air pollutions, etc. Such complexities needed to be resolved using the virtual technology for transportations, popularly known as Intelligent Transport System (ITS). Whilst digitalizing the transport system, one of the major concerns was collecting the user data, authenticating them, and storing them in a tamper-proof manner. This paper helps us to explore how Blockchain can be used to build a tamper-proof database for ITS. ITS is the application which can sense, analyse, control and communicate information with the physical transportation. ITS has already been applied in several applications, few of them could be seen in car navigations, traffic signal control systems, container management systems, automatic number plate recognition systems, security CCTV systems, feedback systems, parking guidance and information systems, weather information systems, and many more.
Controlled Chaos Position Transformation and Noise Generation-Based Keyless Speech Encryption Technique

The exponential developments in the field of communication, data transfer, and computational technologies have created tremendous growth in the development of encryption techniques. In this contest, there will always be a healthy demand for efficient and strong encryption techniques to store data in encrypted domain to avoid the possibility of data breach in the shared networks. In this paper, a novel approach for multi-level keyless speech encryption technique based on controlled chaos position transformation and controlled noise generation is developed. In the proposed algorithm, speech sample positions are randomized with the help of controlled chaos position transformation system and then noise is generated in the randomized speech samples with the help of controlled noise generation system. Secret noise generation level of the noise generation system increases the security of the developed algorithm. It is then attached in the end of encrypted speech with the help of combinational masking scheme, which makes the developed encryption technique keyless. The developed algorithm is tested over a huge number of speech samples and the obtained result shows improvement in UACI and SNR by 17.4% and 49.29%, respectively, than the related work found in the literature. The proposed algorithm is compared with different relevant works and found superior to them.
A Survey on Medical Image Diagnosis Systems: Problems and Prospects

With recent advances in artificial intelligence (AI) and machine learning (ML)-based techniques, clinical problems are being solved by diagnosing medical images for different purposes. Such methods are used to extract medical information that helps in offering other solutions easily. In this paper, we have contemplated different medical image diagnosis systems in detail. These systems primarily analyze a particular type of medical image (X-ray or MRI) and identify abnormalities out of it. In this survey, we have explored different types of medical images, their significance, and possible exceptions for each of these images. For a particular kind of image, existing medical diagnosis systems have also been reviewed. We have compared these approaches and analyzed their performance to furnish ideas for future researchers. We have also offered ways to boost these performances and combine these systems into one intelligent system. This system is expected to recognize any medical images, identify their possible abnormalities, and offer possible solutions/cures.
Transfer Learning and Transformer Architecture for Financial Sentiment Analysis

Rehman, Tohida Bose, Raghubir Chattopadhyay, Samiran Sanyal, Debarshi KumarFinancial sentiment analysis allows financial institutions like banks and insurance companies to better manage the credit scoring of their customers in a better way. Financial domain uses specialized mechanisms which makes sentiment analysis difficult. In this paper, we propose a pre-trained language model which can help to solve this problem with fewer labelled data. We extend on the principles of transfer learning and transformation architecture principles and also take into consideration recent outbreak of pandemics like COVID. We apply the sentiment analysis to two different sets of data. We also take smaller training set and fine-tune the same as part of the model.
A Study of Different Encryption Techniques to Generate Keys in Cloud Computing Security

Akilashri, P. S. S. Ahadha Parveen, A.Today, the size of the data grows from GB to TB every day, mainly due to a large evolution of real-time facts. The most important data is delivered via storage in the cloud. As cloud system offers online services, several attackers and envious users are involved. Attackers continuously try to access the confidential huge data of users without taking the right of access. They sometimes substitute bogus data for the actual data. Security has therefore recently become a major concern. Encryption also ensures data privacy and integrity over communication channels during transmission. In this review paper, different encryption algorithms are reviewed to assess the significance of the keys used for encryption as a vital component for the asset of the method. In most businesses, the relevance of encryption techniques is acknowledged; consequently, data encryption has become vital, since data security is a fundamental component for data and information safety in various institutions.
The Study of Major Web Application Vulnerabilities and Their Implementation for Learning Purpose

Web application vulnerabilities are on the rise with an increase in the digitization of everything taking place. These vulnerabilities are exploited regularly and there is a need for security researchers to have a better understanding. This work is an approach to provide a better platform to the learners to prepare themselves with the hands-on practice of the vulnerabilities and proper documentation for understanding the underlying concepts. Node.js back end helps prepare with the new technologies as we have seen a lot of such applications made in PHP, and further such applications can be made in all the different back end technologies so that the user gets a varied experience.
Analysis of Digital Transformation of Enterprise Accounting Talents from the Perspective of Blockchain

In order to improve the digital transformation effect of enterprise accounting talents, this paper combines intelligent methods to carry out the digital transformation of enterprise accounting talents from the perspective of blockchain. Moreover, this paper studies the sliding window CS-SCHT algorithm in depth. Based on the theoretical derivation of the sliding window CS-SCHT based on the gray code kernel, the algorithm is implemented on the computer platform, and the test experiment of the operation time is carried out. In addition, this paper explores the application of the sliding window CS-SCHT algorithm in adaptive filtering. The experimental results show that the adaptive filter based on the CS-SCHT algorithm can obtain a higher signal-to-noise ratio than the sliding window DFT algorithm. Finally, this paper constructs an intelligent accounting digital information processing system. The research shows that the system proposed in this paper can play an important role in the digital transformation of enterprise accounting talents from the perspective of blockchain.
Automatic online quantification and prioritization of data protection risks

Data processing systems operate in increasingly dynamic environments, such as in cloud or edge computing. In such environments, changes at run time can result in the dynamic appearance of data protection vulnerabilities, i.e., configurations in which an attacker could gain unauthorized access to confidential data. An autonomous system can mitigate such vulnerabilities by means of automated self-adaptations. If there are several data protection vulnerabilities at the same time, the system has to decide which ones to address first. In other areas of cybersecurity, risk-based approaches have proven useful for prioritizing where to focus efforts for increasing security. Traditionally, risk assessment is a manual and time-consuming process. On the other hand, addressing run-time risks requires timely decision-making, which in turn necessitates automated risk assessment.
Forensic analysis of Tor in Windows environment: A case study

The Tor browser is a popular tool that is used by many users around the world. The browser is common among cyber criminals who use the tool to hide their activities. Until now, little research has been conducted by forensics researchers on the Tor browser, its application, and the data that can be obtained from the artefacts generated from its execution. In this work, we present a forensics analysis of the footprint left by the Tor application in the Windows environment. Our analysis focuses on three critical areas that are examined: network, memory, and hard disk. We provide a methodology that allows a structured forensic investigation. In this work, we examine multiple tools’ abilities in obtaining artefacts. The artefacts were identified not only when the Tor browser was running, but also when it was closed and uninstalled. We provide a methodology to analyse Tor applications with a focused case study of the Tor browser, allowing investigators to analyse Tor browsers and reproduce our results.
RIPEMB: A framework for assessing hardware-assisted software security schemes in embedded systems

Memory corruption bugs remain one of the biggest threats to software security. The increasing complexity of SoCs and prevalence of connected embedded devices require larger software support packages that inevitably contain more bugs. Unfortunately, as of now, hardware-assisted security measures are not widely available in smaller embedded devices based on MCUs. Even if they are, vendors might configure them inadequately and validating the correct behavior of such important features is advisable.
Network Steganography Through Redundancy in Higher-Radix Floating-Point Representations

Higher-radix floating-point representations have the potential for higher performance, lower energy footprint, and reduced gate count in embedded systems when compared to traditional binary floating-point numbers. Thus, they might also appear in transmission of sensor data values. However, these number formats introduce redundancies, which can be exploited for steganographic message transfer. We present a covert channel that exploits this redundancy and can trade steganographic bandwidth against introduced error and thus detectability. In the basic variant, the covert channel is fully reversible, i.e., not detectable from the data. Experiments with an implementation illustrate that detectability via compressibility metric, Shannon entropy and bi-grams is possible depending on how aggressive bandwidth is pushed.
Applying a cryptographic metric to post-quantum lattice-based signature algorithms

Measuring the security of cryptographic systems is not a simple task. Nevertheless, there is an increasing need for a cryptographic metric which could assist in decision making when choosing between various candidates. The National Institute of Standards and Technology (NIST) has launched a process to standardize quantum-resistance public key encryption, key encapsulation and digital signature algorithms. This is NIST’s response to the threat posed by quantum computers against classical public key cryptography. In this paper, we apply a metric taxonomy, produced by earlier studies, to two NIST third round finalist digital signature algorithms Dilithium and Falcon in order to asses the effectiveness and extensiveness of the metric. Although, our results show that clear differences can be found with used metrics, we propose some improvements to them to allow more comprehensive analysis.
Cyber-Security Culture Assessment in Academia: A COVID-19 Study

Times of crisis have long been combined with an increase in cybercrime, exploiting the general instability; therefore, in such times, systems and infrastructures face greater exposure to vulnerabilities. On top of that, the COVID-19 crisis has increased our reliance on the internet, while working-from-home has been the daily reality for a large proportion of the population worldwide. Increased cyber-security awareness becomes a necessity for everyone, starting from a more knowledgeable audience; IT professionals, and software engineers. In this context, this paper aims to assess the cyber-security culture readiness of representatives studying or working within a European Polytechnique Academic Institution, during the COVID-19 crisis. Towards that end, a targeted evaluation campaign was launched for two weeks, from 28th February 2022 to 13th March 2022. The campaign consisted of four questionnaires of increased difficulty and a phishing quiz, all assessing the security culture of the participants against three dimensions; their security attitude, their competency, and their actual behavior. The campaign results have been thoroughly analyzed, and the findings were unforeseen in many cases, supporting the identification of security awareness weaknesses and assisting in drafting targeted, customized training programs.
Privacy-Preserving Polyglot Sharing and Analysis of Confidential Cyber Threat Intelligence

Sharing cyber threat intelligence helps organizations analyze and protect against a growing number and sophistication of security threats. However, organizations are reluctant to share their locally collected cyber threat intelligence with third parties because of the the risk of incidentally disclosing sensitive business data or personally identifiable information, and the subsequent reputational harm or even financial repercussions imposed by the GDPR. To address the different confidentiality needs of threat intelligence producers and consumers, we present and evaluate a practical polyglot solution for privacy-preserving sharing and analysis of confidential or private information, and this on top of a contemporary cyber threat intelligence platform. Additionally, we investigate the security impact and computational overhead of these techniques to analyze correlations between threat events in a privacy-preserving manner and across sharing organizations.
MalRec: A Blockchain-based Malware Recovery Framework for Internet of Things

IoT devices have been considered an attractive target for malware (e.g., botnets) due to their low computational resources and lack of security measures. The literature focuses on detecting malware, but less attention is given to recovery solutions. In addition, with the development of data processing regulations in different countries, a need for transparent recovery systems that can help organizations present their due diligence arises. This work proposes a blockchain-based backup policy enforcement framework for IoT where an organization can formalize backup policies and enforce them. We have run our solution under extensive tests that show that it can be deployed in real-life IoT environments, despite the limited computational resources of IoT devices.
BISCUIT - Blockchain Security Incident Reporting based on Human Observations

Security incidents in blockchain-based systems are frequent nowadays, which calls for more structured efforts in incident reporting and response. To improve the current status quo of reporting incidents on blogs and social media, we propose a decentralized incident reporting and discussion system. Our approach guides users (security novices) towards a classification of their observations using a tiered taxonomy of blockchain incidents. Questions based on previous incidents interactively support the classification. Post submission a security incident response committee then discusses these observations on our decentralized platform to decide on an appropriate response. For evaluation, we implement our model as a decentralized application and demonstrate its practical suitability in a preliminary user study.
Using Telegram as a carrier for image steganography: Analysing Telegrams API limits

Telegram is a messaging platform with millions of users per month. For this reason, it is a possible vector for steganographic messages. We investigate the feasibility of using Telegram as a messenger service for images with steganographic content, specifically we use F5 as a proof of concept. We evaluate the optimal resolution and quality settings to achieve the highest possible payload size. In order to support longer message transfers over Telegram, we design a cover channel with a regular schedule of images to have a high bandwidth. We found that the optimal resolution for message transfers is 2560x2560 at JPEG quality settings of 82. And that this configuration allows us to send an average of 81 kilobytes of data per image.
Accuracy of Musical Pitch Control Through Finger Pushing and Pulling

An experiment with trained musicians was conducted to investigate how finger pushing and pulling gestures that control pitch bending of a synthesizer tone affect accuracy with respect to a given target pitch. Additionally, the effect of different interactive vibrotactile feedback was assessed. A self-developed haptic interface called TouchBox was used for this purpose. Finally, subjective evaluations were collected for perceived agency, confidence of correctness, and pleasantness of the experience.
Introducing quest

Machine learning models are everywhere now; but only few of them are transparent in how they work. To remedy this, local explanations aim to show users how and why learned models produce a certain output for a given input (data sample). However, most existing approaches for are oriented around images or text data and, thus, cannot leverage the structure and properties of tabular data. Therefore, we present Quest, a new framework for generating explanations that are a better fit for tabular data. The main idea is to create explanations in the form of relational predicates (called queries hereafter) that approximate the behavior of a classifier around the given sample. In an initial evaluation, we show anecdotally how Quest can be used on a tabular data set compared to existing approaches that can be applied on tabular data.
Exploratory training

Data systems often present examples and solicit labels from users to learn a target concept in supervised to semi-supervised learning. This selection of examples could be even done in an active fashion i.e., active learning. Current systems assume that users always provide correct labeling with potentially a fixed and small chance of mistake. In several settings, users may have to explore and learn about the underlying data to label examples correctly, particularly for complex target concepts and models. For example, to provide accurate labeling for a model of detecting noisy or abnormal values, users might need to investigate the underlying data to understand typical and clean values in the data. As users gradually learn about the target concept and data, they may revise their labeling strategies. Due to the significance and non-stationarity of errors in this setting, current systems may use incorrect labels and learn inaccurate models from the users. We report preliminary results for a user study over real-world datasets on modeling human learning during training the system and layout the next steps in this investigation.
Remote implementation of a discrete temperature control by hysteresis

The practices of face-to-face or conventional laboratories are essential for the training by competences of the student of electronic engineering. However, as a result of the COVID-19 pandemic at FIEE-UNMSM, it has been decided to carry out virtual laboratory practices using only simulators, harming the practical training of the student. This has motivated the implementation of remote laboratory practices, where the student can experiment with real physical devices at a distance. In this sense, as a prototype, the laboratory practice of discrete temperature control by hysteresis has been developed, using the Arduino Uno card programmed in Bascom-AVR, LabWindows/CVI for the graphical interface and the Team Viewer software for the remote connectivity of users. Obviously, the learning outcome compared to simulated practices is superior and is expected to be as close as possible to the laboratory presence.