Reviewerly/exHarmony huggingface dataset:
An Approach for Detecting Spear Phishing Using Deep Packet Inspection and Deep Flow Inspection

In the era of Internet Banking, most of the people may hear the term called “Phishing”, it is related to fraudulent over the Internet as well as Social Engineering. It is commonly done by sending an e-mail to target user with an embedded hyperlink to steal their user-name, password, transaction password, etc. Moreover, Phishers used to gain the access in personal credentials of targeted user for stealing cash from user’s bank account. Phishing can be done by using several other ways like sending text messages (called “Smishing” for SMS-Phishing), or via Phone Calls (often called “Vishing” for Voice-Phishing) or sometimes via social-media itself including sending an e-mail to user or organization. Phishing has several types in terms of the used medium as well as in terms of victim. Spear Phishing is a prevalent method of phishing and comes under the Advanced Persistent Threat (APT) category. The term “Spear Phishing” mainly defines that stealing the credential of a specific user or a specific company at a time by sending e-mail. This paper deals with Spear Phishing Emails that mainly organizations, companies and most of the time common people used to get by Phishers. This paper proposes an approach to detect the Spear Phishing e-mails with the help of Deep Packet Inspection and Deep Flow Inspection using Packet Filtering and Network Traffic Identification technique.
Study of malaria cases in central laboratory at tertiary care hospital, Vadodara, India

Introduction: Malaria is an endemic disease with high degree of severity in certain geographic areas of India.Gujarat is amongst one of them.The symptoms of Malaria ranges from mild to severe depending on the type of the plasmodium species, age of the patient, parasite load in the body of the host, season in which the infection occurs as well as endemicity of that region.These symptoms include fever, headache, nausea, vomiting.In case of P. Falciparum malaria, patient may develop convulsions, coma and death if cerebral malaria develops.In P.Vivax, there are high chances of relapse.So in such cases patient requires treatment after completion of routine treatment.That is why it is important to know the type of malaria and treat them accordingly.Aims and Objectives: To evaluate prevalence of various plasmodium species in clinically suspected cases of malaria and their demographic study.Material and Methods: Total 113 positive malaria cases were included in this study between June 2019 to May 2020 at the Central Laboratory, Department of Pathology, Medical College, Baroda.The samples included all age groups and both the gender of the patients.Routine hematological investigations including peripheral smear examination, thick and thin smear, rapid test whenever needed and complete hemogram were performed.Results: Two types of malarial parasites were found, Plasmodium vivax (68.1%) and Plasmodium falciparum (31.9%).The prevalence of malarial infection exhibited seasonal pattern with most of the cases in the month of September, October and November with peak in October.Infection prevalence in male patients was higher (71.68%) than in female (28.32%) patients.Age group ranged from 7 years to 75 years with 21-30 years had greatest risk.These findings compared with other findings of malaria endemic populations in India that a hospital-based diagnosis and surveillance for malaria reflects seasonal malaria transmission underlying demographic and geographic distribution. Conclusion:This study gives useful information regarding prevalence of malaria in Gujarat which can be useful at national level for National Malaria Prevention and Control Program, to minimize the cases, complications and fatality related to malaria.
QR Code Positioning Algorithm

With the advent of Internet of Things, barcode technology, which is important for information identification and collection, is developing rapidly. Quick response (QR) codes have wide applications in e-commerce, human recognition and advertisement marketing. At present, these codes can also be used in industrial and agricultural production lines. The performance of QR recognition technology depends on the pre-processed QR images. A complex background hinders the location and recognition of the QR code. Therefore, this study investigates the performance of QR code recognition technology under complex background. A QR image pre-processing method that combines image grey processing, filtering, projection-based initial location, binarisation, feature-based precise location, image rotation and geometric correction is proposed. In addition, new ideas for initial and precise locations are proposed. Results show that the proposed pre-processing method can achieve high effectiveness and efficiency in QR code positioning under complex background.
Clout Chasing for the Sake of Content Monetization: Gaming Algorithmic Architectures with Self-moderation Strategies

This short discussion paper addresses how controversy is monetized online by reflecting on a new iteration of the shock value in media production, identified on social media as the ‘clout chasing’ phenomenon. We first exemplify controversial behavior, and subsequently proceed to defining clout chasing, which we discuss this concept in relation to existing frameworks for the understanding of controversy on social media. We then outline what clout chasing entails as a content monetization strategy, and address the risks associated with this approach. In doing so, we introduce the concept of ‘content self-moderation’, which encompasses how creators use content moderation as a way to hedge monetization risks arising out of their reliance on controversy for economic growth. This concept is discussed in the context of the automated content governance entailed by algorithmic platform architectures, to contribute to existing scholarship on platform governance.
Apropos of Signal Processing

FIELD GRAND CHALLENGE article Front. Signal Process., 18 May 2021 https://doi.org/10.3389/frsip.2021.686341
Dual Attention with the Self-Attention Alignment for Efficient Video Super-resolution

By selectively enhancing the features extracted from convolution networks, the attention mechanism has shown its effectiveness for low-level visual tasks, especially for image super-resolution (SR). However, due to the spatiotemporal continuity of video sequences, simply applying image attention to a video does not seem to obtain good SR results. At present, there is still a lack of suitable attention structure to achieve efficient video SR. In this work, building upon the dual attention, i.e., position attention and channel attention, we proposed deep dual attention, underpinned by self-attention alignment (DASAA), for video SR. Specifically, we start by constructing a dual attention module (DAM) to strengthen the acquired spatiotemporal features and adopt a self-attention structure with the morphological mask to achieve attention alignment. Then, on top of the attention features, we utilize the up-sampling operation to reconstruct the super-resolved video images and introduce the LSTM (long short-time memory) network to guarantee the coherent consistency of the generated video frames both temporally and spatially. Experimental results and comparisons on the actual Youku-VESR dataset and the typical benchmark dataset-Vimeo-90 k demonstrate that our proposed approach achieves the best video SR effect while taking the least amount of computation. Specifically, in the Youku-VESR dataset, our proposed approach achieves a test PSNR of 35.290db and a SSIM of 0.939, respectively. In the Vimeo-90 k dataset, the PSNR/SSIM indexes of our approach are 32.878db and 0.774. Moreover, the FLOPS (float-point operations per second) of our approach is as low as 6.39G. The proposed DASAA method surpasses all video SR algorithms in the comparison. It is also revealed that there is no linear relationship between positional attention and channel attention. It suggests that our DASAA with LSTM coherent consistency architecture may have great potential for many low-level vision video applications.
Legal and Regulatory Considerations for Digital Assets

The report examines legal and regulatory challenges arising from the emergence of digital assets. The aim is to frame future legal and regulatory discussions around digital assets, as well as to identify general trends and concepts across jurisdictions rather than to provide granular analysis of any given jurisdiction’s legal position.Key highlights:- Existing taxonomies of digital assets developed by national and international authorities have failed to fully capture the relevant features of digital assets and the true novelty introduced by crypto assets. Regulatory authorities should focus on the substance of the underlying asset and the rights associated with it, rather than its form, unless the form changes the substantive nature of the asset.- Digital assets, for the most part, pertain to existing and well-known legal concepts: they effectively represent a set of rights embodied in a new digital form. Consequently, the regulatory perimeter for regulating digital assets and associated activities should be determined by identifying the legal concept(s) behind a given digital asset.- However, digital assets may warrant adjustment or revisions to existing legislation, in particular whether digital assets are fitting objects of property rights. Generally, further clarification may be needed from national legislatures on the legal recognition of purely digital objects to understand whether (and how) dealing with a digital token has legal consequences for real world rights. In particular whether intangible, digital representation can be recognized as an object of property rights.
Sentiment Analysis: A Tool for Mining Opinions and Emotions

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PrivateDrop: Practical Privacy-Preserving Authentication for Apple AirDrop

Apple's offline file-sharing service AirDropis integrated into more than 1.5 billion end-user devices worldwide. Wediscovered two design flaws in the underlying protocol that allow attackers tolearn the phone numbers and email addresses of both sender and receiverdevices. As a remediation, we study the applicability of private setintersection (PSI) to mutual authentication, which is similar to contactdiscovery in mobile messengers. We propose a novel optimized PSI-based protocolcalled PrivateDrop that addresses the specific challenges of offlineresource-constrained operation and integrates seamlessly into the currentAirDrop protocol stack. Using our native PrivateDrop implementation for iOS andmacOS, we experimentally demonstrate that PrivateDrop preserves AirDrop'sexemplary user experience with an authentication delay well below one second.We responsibly disclosed our findings to Apple and open-sourced our PrivateDropimplementation.
Accuracy-enhanced coherent Ising machine using the quantum adiabatic theorem

The coherent Ising machine (CIM) implemented by degenerate optical parametric oscillator (DOPO) networks is a novel optical platform to accelerate computation of hard combinatorial optimization problems. Nevertheless, with the increase of the problem size, the probability of the machine being trapped by local minima increases exponentially. According to the quantum adiabatic theorem, a physical system will remain in its instantaneous ground state if the time-dependent Hamiltonian varies slowly enough. Here, we propose a method to help the machine partially avoid getting stuck in local minima by introducing quantum adiabatic evolution to the ground-state-search process of the CIM, which we call A-CIM. Numerical simulation results demonstrate that A-CIM can obtain improved solution accuracy in solving MAXCUT problems of vertices ranging from 10 to 2000 than CIM. The proposed machine that is based on quantum adiabatic theorem is expected to solve optimization problems more correctly.
Man as a subject of creative activity in the conditions of the formation of a digital risomatic society

A new digital ideology is being actively introduced into the consciousness of modern society, which determines the direction of sociocultural development of modern society and man, an ideology in which a person, in order to survive must correspond to the techno-environment or as it is called today, is rapidly developing and it is impossible to stop its growth. The term "eco-environment" is actively used by Sber, MTS and other leaders of digitalization. Digitalization gradually leads to the fact that a person as a subject of creative activity "goes into the shadows", becomes "extra" and it is replaced by something else, devoid of creative potency, thing with artificial intelligence. As part of the work presented, we have attempted to address the main problems faced by a person as a subject of creative activity; prospects and contradictions of its development in the conditions of the formation of a digital society.
bcdata: An R package for searching retrieving data from the B.C. Data Catalogue

bcdata is an R package that connects publicly available metadata and data sets in the British Columbia (B.C.) Data Catalogue (DataBC Program (2020)) to the diverse array of mapping, modeling and data processing capabilities of the R ecosystem.bcdata enables the efficient retrieval of British Columbia's geospatial data, and supports repeatable and reproducible analysis of hundreds of open-licensed British Columbia public sector data sets.By enabling programmatic access to the B.C. Data Catalogue using familiar R dplyr syntax (Wickham et al. (2020)), bcdata helps both novice and experienced R users find and use British Columbia government public and open data holdings.
Measure of Nonlinearity With Application to Bearings-Only Target Tracking

Using the recently proposed measure of nonlinearity (MoN), the authors try to find the magnitude of nonlinearity for passive target tracking with bearings-only measurements in underwater environment. The method derived to measure the nonlinearity is completely based on the state covariance matrices of the filters. It is tried to find the allowable magnitude of nonlinearity in terms of MoN with which a filter can perform to estimate the target motion parameters with required accuracy. In this paper, MoN values for different filters are computed for different scenarios. Results obtained in the Monte Carlo simulation are presented.
A New Fragile Watermarking based on Distributed Hamming Code

In this paper, a novel fragile watermarking scheme is proposed for both tamper detection and tampered image recovery based on Hamming code. To serve this purpose, the authentication code (check bits) is computed using Hamming code from data bits. In this work, data bits were selected from the five Most Significant Bits (5_MSB) of the pixel values and authentication code is embedded into the three Least Significant Bits (3LSBs) to preserve image quality. Hamming (7,4) has been extended, in this paper, to (8,5) and is used for embedding, error detection and correction. Each instance of coding is applied on eight pixels (one bit per pixel) located in sufficient far parts of the image. Hence, for tampers smaller than a threshold, the recovery can be done perfectly. According to the experimental results, the proposed method achieves better performance in terms of recovering the tampered areas, compared to state-of-the-art.
Securing Query results for Cloud Databases

Cloud computing offers several computing service. Among these services is the Database Service, which stores outsourced databases and provides all the database services to users. However, despite, the attractiveness of the database services to users, security issues remain a main concern to database owners, as the data and the execution of database is out of their control. To ensure the outsourced database confidentiality and integrity, which are two of the most important security concerns in data outsourcing, we propose the design and implementation of an efficient Secure scheme to provide Confidentiality and Integrity of Query results for Cloud Databases based on Merkle B-Tree (SCIQ-CD). We develop a technique to convert the Merkle B-Tree into data authentication tables and store it within the relational database in DSP. We have implement query rewrite algorithms for trusted third party to generate the SQL statements to speed up the query processing, which will be sent within the query by the user to DSP. The performance analysis shows that our proposed scheme is secure and eﬃcient for practical deployment. The experimental results show that our scheme imposes a low overhead for queries executions with confidentiality and integrity assurance.
BLENDED LEARNINGAS AN EFFECTIVE TOOL FOR THE PROFESSIONAL DEVELOPMENTOF OUT-OF-SCHOOL EDUCATION TEACHERS

The paper is dedicated to the problem of out-of-school education teachers’ professional development in the conditions of growing requirements to such education quality. The authors of the article note that the professional development of teaching employees of out-of-school education institutions requires a review of existing tools and the search for new approaches. In the current conditions, advanced training courses for the teachers of out-of-school education institutions remain to be the prevailing form, but along with the development of e-education, increase of the freedom of a teacher to choose the forms, methods, terms of professional growth, they need a certain "reset". In a situation of quarantine restrictions, the qualification advancement courses are carried out in a remote format, but it has a number of pitfalls which impact the training effectiveness. Mastering the methods of reflection of one’s own activity by teachers allows to reduce this influence and to increase the quality of professional refresher training. The authors of the article focused their view on the introduction of blended learning during the qualification advancement courses, as such that should provide positive dynamics in the professional development of the teachers of out-of-school education institutions. Researchers see as its most effective models: the "flipped classroom", the self-blend model and the model of enriched virtual learning. They point to a number of advantages of these models compared to traditional learning, which include: flexibility, variability in the organization of the educational process and forms of presentation of educational material, as well as their impact on increasing the motivation of students to learn. The issues of increasing the requirements for the teacher's activity, which should offer optimal and rational methodological techniques and technological solutions for constructive interaction with students not only for the convenience of monitoring their learning, but also so that they can feel constant support, are considered separately. The authors of the article came to the conclusion that in each case the optimally selected model of the blended learning is the one that contains potential opportunities for professional development of the heads of out-of-school education clubs. The article provides statistics data for its confirmation.
Comparison of methods for automatic classification of Russian-language texts

Abstract In this paper, a comparative analysis of methods for automatic classification of text information using various machine learning algorithms based on artificial neural networks is carried out. The paper presents results of the text classification considering various ways of a vector space formation, such as bag-of-words, n-gram, word embedding, and various architectures of neural networks. The research is based on the text corpus in Russian.
Mobile Authentication using Hybrid Modalities (MAHM) in Pervasive Computing

Cloud computing facilities using mobile phones has become a necessity more than being an alternative. Cloud storage, social networks and email communications using mobile phones allows for mobility and on the go access to these cloud services. Despite providing such conveniences to the users, cloud access via mobile devices is vulnerable to security breaches especially when a third party gets hold of the  mobile phone. A third party may have access to the applications and features of the mobile phone provided that the unauthorized user managed to break the security login of the phone. This gives avenue for the unauthorized user to gain access to the cloud services without further verification. Because most of the cloud services provide a single sign-on features whereby the password is used by cloud services (gmail for example) for the first time only. After the first authentication, an authentication token which is downloaded to the mobile phone is used for subsequent authentications. Although this may sound secure without the use of password, the possibility for an unauthorized user access to the cloud services is still possible because the authentication token is stored in the phone itself and does not require further validations. Moreover, the authentication token is stored as plaintext inside the mobile phone and may be susceptible to security breach. Therefore, an identity management system based on user behavior patterns can be used to further validate the authorized user of the cloud services. The main aim of the research is to design MAHM, for cloud service identity management using human behavior patterns classification. There are various biometrics-based authentication using physiological and psychological have been in place but, environmental and human factors can influence these. In this research, unique behavior patterns will be used to identify distinct characteristics of certain individual. The framework will consist of various user behavior such as keypad tapping behavior, keystroke dynamics and phone swiping behavior as authentication scheme to the cloud applications
Evaluating a LSTM Neural Network and a Word2vec Model in the Classification of Self-admitted Technical Debts and Their Types in Code Comments

Context: Software development teams constantly opt for faster, lower quality solutions to solve current problems without planning for the future. This situation will have a negative long-term impact and is called technical debt. Similar to a financial debt, technical debts require interest payments and must be managed and detected so that the team can evaluate the best way to deal with them. One way to detect technical debts is through classification of source code comments. Developers often insert comments warning of the need to improve their own code in the future. This is known as Self-Admitted Technical Debt (SATD). Objective: Combine Word2vec for word embedding with a Long short-term memory (LSTM) neural network model to identify SATDs from comments in source code and compare with other studies and LSTM without word embedding. Method: We plan and execute an experimental process with model’s effectiveness data validation. Results: In general, the classification improves when all SATD types were grouped in a single label. In relation to other studies, the LSTM model with Word2vec achieved better recall and f-measure. The LSTM model without word embedding achieves greater recall, but perform worse in precision and f-measure. Conclusion: We found evidence that LSTM models combined with word embedding are promising for the development a more effective SATD classifier.