Reviewerly/exHarmony huggingface dataset:
Comment on Security and Improvement of Partial Blind Signature Scheme and Revocable Certificateless Signature Scheme

Abstract Partial blind signature scheme (BLD-SS) can protect the specific content of the signed message by adding factor to blind the signed message and can also prevent the misuse of the signature by adding common information negotiated in advance. Revocable certificateless signature scheme (RCSS) can prevent the user’s privacy from leaking by adding time key in the signature process and signature verification process. BLD-SS and RCSS both have wide applications in electronic-voting, electronic-cash system and other fields. In this paper, it presents a security analysis on a partial blind signature scheme and a revocable signature scheme proposed recently. It points out that the BLD-SS does not satisfy the unforgeability. A malicious user can forge a partial blind signature on any message and any common information without known from PKG (Private Key Generation). It also shows that the RCSS does not satisfy the unforgeability. A malicious enemy can forge a signature on any message and on any user. The PKG also cannot update the time key immediately when a user’s key is leaked or his identity is expired. In order to overcome these problems, it proposes some simple improvement methods which are almost the same efficiency with the original signature scheme.
Machine learning for anonymous traffic detection and classification

Anonymity is one of the biggest concerns in web security and traffic management. Though web users are concerned about privacy and security various methods are being adopted in making the web more vulnerable. Browsing the web anonymously not only threatens the integrity but also questions the motive of such activity. It is important to classify the network traffic and prevent source and destination from hiding with each other unless it is for benign activity. The paper proposes various methods to classify the dark web at different levels or hierarchies. Various preprocessing techniques are proposed for feature selection and dimensionality reduction. Anon17 dataset is used for training and testing the model. Three levels of classification are proposed in the paper based on the network, traffic type, and application.
Preparation for Remote Activities in the University using Cisco Webex Education Offer

Face-to-face activities at universities have become difficult with the worldwide outbreak of COVID-19. In our university, we are making various efforts to prevent infection. In the first semester, all face-to-face classes were cancelled, and a remote lecture using LMS and a video conference system was conducted.
Development of an Integrated Security Monitoring System Based on the Analysis of the Goals of Actions of Subjects of Computer Systems

Security monitoring systems for computer systems and networks (CSN) are classified by areas of information infrastructure where security policy violations are detected. At the same time, there are security monitoring systems (SMS) focused on: workstation; applications; database management system (DMS); computer network. SMS of a workstation are executed on the protected node and monitor various security events, collect and analyze information reflecting the activity taking place in the operating system of a separate computer. Application-level SMSs and DMSs collect and analyze information from specific applications such as Web servers, firewalls, or database management systems. Network layer SMSs collect information from network traffic. They can be performed on ordinary or specialized computers, and also can be integrated into routers or switches. The article discusses the features of the practical implementation of SMS in CSN. Based on the analysis of the problems arising during the functioning of security monitoring tools in modern CSN, the main research area is highlighted - the development of methods and means of security monitoring that ensure an increase in the effectiveness of SMS against the actions of malicious intent by: predicting possible actions of intruders; dynamic analysis of risks of implementation of threats to the security of information resources; recommendations to the adaptive security management system for reconfiguring/modifying protection when the threat level changes. The developed complex SMS based on the analysis of the goals of the actions of the subjects of computer systems is described.
IHAC: Incorporating Heuristics for Efficient Rule Generation &amp; Rule Selection in Associative Classification

Associations rule mining along with classification rule mining are both significant techniques of mining of knowledge in the area of knowledge discovery in massive databases stored in different geographic locations of the world. Based on such combination of these two, class association rules for mining or associative classification methods have been generated, which, in far too many cases, showed higher prediction accuracy than platitudinous conventional classifiers. Motivated by the study, in this paper, we proposed a new approach, namely IHAC (Incorporating Heuristics for efficient rule generation &amp; rule selection in Associative Classification). First, it utilises the database to decrease the search space and then explicitly explores the potent class association rules from the optimised database. This also blends rule generation and classifier building to speed up the overall classifier construction cycle. Experimental findings showed that IHAC performs better than any further associative classification methods.
An attack-immune trusted architecture for supervisory aircraft hardware

With the wide application of electronic hardware in aircraft such as air-to-ground communication, satellite communication, positioning system and so on, aircraft hardware is facing great secure pressure. Focusing on the secure problem of aircraft hardware, this paper proposes a supervisory control architecture based on secure System-on-a-Chip (SoC) system. The proposed architecture is attack-immune and trustworthy, which can support trusted escrow application and Dynamic Integrity Measurement (DIM) without interference. This architecture is characterized by a Trusted Monitoring System (TMS) hardware isolated from the Main Processor System (MPS), a secure access channel from TMS to the running memory of the MPS, and the channel is unidirectional. Based on this architecture, the DIM program running on TMS is used to measure and call the Lightweight Measurement Agent (LMA) program running on MPS. By this method, the Operating System (OS) kernel, key software and data of the MPS can be dynamically measured without disturbance, which makes it difficult for adversaries to attack through software. Besides, this architecture has been fully verified on FPGA prototype system. Compared with the existing systems, our architecture achieves higher security and is more efficient on DIM, which can fully supervise the running of application and aircraft hardware OS.
Are Teacher Students’ Deep Learning and Critical Thinking at Risk of Being Limited in Digital Learning Environments?

The 21st century is quite different from the 20th in regard to the skills people need for work, citizenship, and self-actualisation. Proficiency in the 21st century differs primarily due to the emergence of sophisticated information and communication technologies. In this chapter, we will discuss whether teaching students are sufficiently prepared regarding the need for 21st century skills and how learning in a digital age affects the need for high-level critical thinking. Based on 20 in-depth interviews of Norwegian and New Zealand teaching educators, teaching students’ low critical thinking skills seem to be understood as a global challenge and as connected to the digital revolution. Despite being from different sides of the globe, teaching educators from both countries expressed concern regarding students’ in-depth learning and critical thinking skills in an educational where learning is influenced by digital technology. This article discusses the dilemmas regarding having easier access to greater amounts of information, which requires a different form of critical thinking. We question whether we are and have been preparing students sufficiently for this educational change.
India and the Pain of the Last Mile

The third chapter focuses on various efforts to provide access to knowledge to villagers in rural South India by giving access to “the last mile” of telecommunications. In this study, Saji Matthew, Arun Kumar, and Ramesh Subramanian take a critical look at the multifarious efforts to bring Internet access to villagers, and the results that this brings in increased access to health information, to market resources, women’s empowerment and entrepreneurship, as well as democratization. While there have been many studies in this area, the authors of this chapter focus not just on the success stories, but also on the many failures and missteps that have occurred in efforts to provide access to communications infrastructure (and thus the Internet) to rural villages. The authors show, through several case studies, that while enhanced access to communications via mobile telephony has indeed increased access to knowledge, there are also several caveats that hinder progress, or even impede progress on achieving desirable results. Their case studies provide an excellent template that could be used by access providers and policy makers not only in India but also in several other developing economies.
Vision-Based Human Activity Recognition

Human activity analysis is an important problem studied widely in the vision community. Recognizing activities is crucial for a number of practical applications in different domains such as video surveillance, video indexing, autonomous navigation, human-computer interaction, active sensing, assisted living, etc. Most works on vision-based human activity recognition can be categorized as methods based on either handcrafted features or deep features. Handcrafted feature-based methods utilize various spatio-temporal feature descriptors such as STIP, dense trajectories, improved dense trajectories, etc. Deep feature-based methods utilize hierarchical feature representation produced by deep learning models. Several deep architectures have been explored in this direction, including 2D CNNs and 3D CNNs with both single-stream and two-stream models. In spite of achieving high recognition accuracy, two-stream models have their inherent limitations because of the additional computation cost imposed by the motion network fed with optical flow. Single stream methods, which take only raw video frames as input, are simple and efficient for processing, but lag behind in terms of accuracy. 3D CNNs trained on optical flow input still give remarkable accuracy boost when used in ensembles. There have been another line of work combining CNNs with LSTMs. In this chapter, we present a comprehensive overview of the recent methodologies, benchmark datasets, evaluation metrics and overall challenges of vision-based human activity recognition. We discuss about the recent advances and the state-of-the-art techniques in an attempt to identify the possible directions for future research.
Finance, Institutions and Private Investment in Africa

The study extends the debate on finance versus institutions and measurement of property rights institutions. We assess the relationships between various components of property rights institutions and private investment, notably: political, economic and institutional governances. Comparative concurrent relationships of financial dynamics of depth, efficiency, activity and size are also investigated. The findings provide support for the quality of institutions as a better positive correlate of private investment than financial intermediary development. The interaction of finance and governance is not significant in potentially promoting private investment, perhaps due to substantially documented surplus liquidity issues in African financial institutions. The empirical evidence is based on 53 African countries for the period 1996-2010. Policy measures are discussed for reducing financial deposits, increasing financial activity and hence, improving financial efficiency.
A Recommender Systems’ algorithm evaluation using the Lenskit library and MovieLens databases

A wide variety of algorithms can be found in the recommender system's literature for computing predictions and obtaining recommendation lists. These algorithms' performance can be evaluated by numerous metrics depending on which business goal is intended to be optimized. However, performance can vary drastically depending on the selected algorithm as well as the selected metric. In addition, algorithmic performance can be heavily influenced by the experimental setup such as: dataset, cross-validation strategy, recommender framework and the model hyper-parameters chosen. In this work we will compare nine different algorithms used for generating recommendations across seven different metrics. One vital aspect of our analysis is to provide a detailed and clear explanation of the methodology carried out as well as the experimental setup in order to ensure future reproducibility. Moreover, performance and computation times will be introduced for further comparison. Finally, optimal model hyper-parameter combinations will be presented for benchmarking purposes.
Text Document Summarization Using POS tagging for Kannada Text Documents

Humongous amount of data available on the world wide web has been a constant issue pertaining to better Information Retrieval (IR) techniques. Text document summarization is there around for the past several decades, but providing a succinct summary has been challenging as ever. This work focuses on extractive summarization techniques using POS tagging, where the goal is to tag individual words with its parts of speech in a document and do the extractive summarization with more grammatical meaning. The Hidden Markov Model (HMM) is used for tagging the dataset. The idea is to use sentence ranking to produce the summary of a given document. This method of summarization uses the key phrase extraction, where the goal is to select individual words or sentences to tag a document to create text document summary.
A Comprehensive and Adversarial Approach to Self-Supervised Representation Learning

Self-supervised representation learning aims to generate effective representations for data instances without the need for manual labels, also known as unsupervised embedding learning, which has been a critical challenge in many existing semi-supervised and supervised learning tasks. This paper proposes a new self-supervised learning approach, called Super-AND, which extends the memory-based pretraining method AND model [13]. Super-AND has its unique set of losses that combines data augmentation in neighborhood discovery for more accurate anchor selection in embedding learning and further presents an adversarial training manner to learn more confident embeddings under the unsupervised setting. Experimental results exhibit that Super-AND outperforms all existing state-of-the-art self-supervised representation learning approaches and achieves an accuracy of 89.2% on the image classification task for CIFAR-10.
Automatic Speaker Recognition (ASR)

This article focuses on the study and optimization of the monitoring system for People Living with HIV (PLWHIV) in the cross-border area between the Gambia, Senegal and Guinea Bissau. Difficulties in adherence to treatment are accentuated, among other things, by the stigmatization of HIV patients and the high mobility of the population. These two situations and the absence of a reliable identification system for PLWHIV lead to restarting treatment for certain patients, which can cause resistance.
Cohousing IoT: Technology Design for Life in Community

This paper presents a research-through-design project to develop and interpret speculative smart home technologies for cohousing communities—Cohousing IoT. Fieldwork at multiple sites coupled to a constructive design research process led to three prototypes designed for cohousing communities: Cohousing Radio, Physical RSVP, and Participation Scales. These were brought back to the communities that inspired them as a form of evaluation, but also to generate new understandings of designing for cohousing. In discussing how they understand these prototypes, this paper offers an account of how research though design generates knowledge that is specific to the conditions and issues that matter to communities. This contributes to design research more broadly in two ways. First, it demonstrates how contemporary ideas of smart home technology are or could be made relevant to broader ways of living in the future. Second, it provides an example of how a design research process can serve to uncover community values, issues, and goals.
WITHDRAWN: Modified color pixel based image segmentation using fbmc algorithms used with real time image database

Image mining is an astonishing concept for data mining. For image mining, prior information is more essential to understand the data mining concept. Image mining is associated with extracting implicit information, associations with image data or other patterns not embedded directly in the images. It is the method of analyzing large collections of domain-specific data and consequently extracting information and insights for the decision-making process in the form of new relationships, patterns or clusters. Tiger become a reserved animal. Conservation of tiger has been a challenging task. This work would add a small account to the herculean task of conserving the species. Several scientific researchers have carried out their research on the tiger reserve conservation. This research work proposes a method to find the age of the tiger, using color as a parameter. Color pixel based image classification and clustering techniques has been used to identify the age of the tiger. This research work mainly focuses on RGB color spaces, which is implemented on the real time tiger images. The objective of the research work is to be done on assessing the age of the tiger using the color pixel based image classification and clustering is the main task of the research work and to optimize the image filtering and enhancement methods that are used to remove the noise and to improve the quality of pixels or images and assessing the processing Time, Retrieval Time, Accuracy and Error Rate by generating the better results is real time tiger image database.
Cultural Symbol Recognition Algorithm Based on CTPN + CRNN

This paper proposes a cultural symbol recognition algorithm based on CTPN + CRNN. The algorithm uses the improved VGG16 + BLSTM network to extract the depth features and sequence features of the text image, and uses the Anchor to locate the text position. Finally, the task of cultural symbol recognition is carried out through the CNN + BLSTM + CTC deep network, The algorithm is an ideal cultural symbol recognition scheme in terms of the recognition efficiency and accuracy.
P2A: Privacy Preserving Anonymous Authentication Based on Blockchain and SGX

Modern Identify-as-a-Service solutions solve the problems of burdensome user credential management and non-uniform security strength, by introducing an Identity Provider (IdP) that holds the users’ identities and grants a user one-time access tokens when he/she tries to login to different online applications (known as the Relying Parties, RPs). However, the non-negligible problem of privacy leakage during authentication largely remains unattended. In this paper, we propose a Privacy Preserving Anonymous Authentication Scheme (P2A) with Blockchain and Intel Software Guard Extensions (SGX). The IdP in P2A manages the users’ identities by issuing different kinds of transactions in the Blockchain, covering the registration, update, freeze/thaw, and deletion of identities. When the user wants to login to an RP, instead of asking for an one-time token from the IdP, he can generate an identity proof locally with SGX and login to the RP with an RP-specific pseudonym (PN). By resorting to the Blockchain, the RP will be convinced that the PN is associated with some registered identity on IdP and specific attributes of the user are satisfactory, without obtaining the real identity and raw attributes of the user. In this way, privacy leakages to the IdP and RPs are eliminated. P2A has a few exciting new features and security analysis shows it can resist various attacks even under strict assumptions.
Multi-level Feature Fusion Network for Single Image Super-Resolution

Recently, deep convolution neural networks have achieved remarkable performance in the task of single image super-resolution (SISR). However, effectiveness of existing networks highly relies on their receptive field, which always increases with the depth of the network. In this work, we propose a novel module, named as residual group, to effectively learn feature maps by using dynamic receptive field. This residual group firstly uses a selective kernel convolution layer to dynamically learn multi-scale information from its input features. Then, several residual blocks are employed to further refine the learned feature. In addition, we also propose a selective feature fusion module to fuse appearance information in multi-level features. Within this module, the low-level features and high-level features are selectively fused to complement the high-level ones. Finally, by combining these two methods, we introduce a multi-level feature fusion network (MLFFN) for single image super-resolution (SISR). Through comprehensive experiments, we demonstrate that the proposed MLFFN achieves state-of-the-art performance both quantitatively and qualitatively.
Measurement of Information Security Awareness Level: A Case Study of Digital Wallet Users

Abstract Awareness of a person is an important factor in maintaining information security and reducing the fraud gap. The existence of various types of digital wallet application fraud requires someone to have an awareness of information security. This study was conducted to measure the information security awareness of digital wallet users. The research was conducted by distributing questionnaires to 156 respondents of digital wallet users. These respondents represented users of digital wallet applications in Indonesia. Information security was measured based on the user’s knowledge-attitude-behavior. The assessment of information security awareness level was calculated using the Analytic Hierarchy Process (AHP) method. The results showed that the score was at the awareness level of good (80.78%). However, several focus areas still showed score at the average level which was one of the reasons why there are still many information security breaches against users of the digital wallet.
Opinion Spam Detection and Analysis by Identifying Domain Features in Product Reviews

With the rapid advent of technology there is an exponential number of users who purchase online products and express their opinions in form of reviews. It is observed that recommending the deserving products to people often depend on the sentiment expressed in the reviews. Classification of the reviews as genuine and fake is one of the hardest problems in the current world. Feature Engineering is an active area of research in text analytics and opinion mining and plays a significant role in extracting features from reviews. Genuine reviews often contain higher percentage of concrete information or domain specific information as these reviews are written based on experience, but spam reviews often lack this information This paper focuses on, using Latent Dirichlet Allocation topic model to extract domain features in product reviews and use this as one of the main features to identify fake reviews.