Reviewerly/exHarmony huggingface dataset:
Effects of Side Channel Attacks on Data Security and Their New Age Countermeasures

Data are the new oil in today’s age. Data and information are extremely important in our day-to-day lives in one way or another. Ranging from finding the nearest hospital to finding one’s favorite fast-food joint, data are everywhere. The exact location of one’s workplace, the distance from home, and the time it will take to reach there: everything depends on the availability of data. With the emergence of large amounts of data, concerns about its security have arisen. There are various algorithms and mechanisms for providing security to the data flowing across public networks, the most common of which is the internet, but we can surely say that security on the internet is not at all robust. Can the existing algorithms be the method of choice? That is a question we need to answer. The existing algorithms are hailed as being sufficient to sustain a barrage of attacks, but whether they stand a chance against AI-based cryptanalysis would be a tough question to answer. Continuous ongoing research on quantum computers and their increasing performance is constantly threatening the existing symmetric cryptosystems. There is also a constant threat from an indirect attack on the existing cryptographic systems, called the side channel attack, or from power analysis attacks, which attack the implementation of the algorithm rather than going for the algorithm itself. It was proven to crack the RSA 4096 algorithm completely in 2013. In this chapter, we will discuss some of the existing techniques of information security and also discuss the various ways in which these ciphers have been cracked or can be cracked in the foreseeable future. Toward this end, the various intelligent systems based new age cryptographic alternatives will also be discussed, as will a possible replacement for the existing algorithms. We are also going to briefly touch on how intelligent systems based on deep learning could complement the existing cryptographic and steganographic techniques by providing better security and robustness against attacks.
Memory Acquisition and Analysis for Forensic Investigation

Conventionally, digital forensics encompasses the exploration and examination of artifacts confined in digital devices such as computers, mobile devices, hard drives, etc. Now with the advancement in computer attacks, the attackers are clever enough to avoid footprints on digital devices. Therefore, an investigation of memory also needs to be performed for deeper forensic analysis. Many researchers have made various incredible memory forensics tools that magnify the span of advanced crime scene investigation to incorporate the assessment of volatile memory too. Volatile memory might contain a lot of information like users logged on, system time, files that are currently open, network-based indicators: URLs, IP addresses, etc., information about processes: currently running, their creation and exit time, etc., memory acquired by processes, network status, clipboard contents, and much more. This chapter discusses the importance of live memory acquisition and analysis by taking a scenario and investigating it further by acquiring a memory dump using FTK Imager and then later performing memory analysis on it using two tools: the Volatility Framework and Volatility Workbench.
Cloud Mining Actionable Pattern Discovery In Big Data : A Survey

Everyday several tons of data are generated by Social Media and Education sectors. Mining this data can provide a lot of meaningful insights on how to improve user experience in social media and how to improve student learning experience. Action Rule Mining is a method that can extract such actionable patterns from large datasets. Action Rules provide actionable suggestions on how to change the state of an object from an existing state to a desired state for the benefit of the user. There are two major frameworks in the literature of Action Rule mining namely Rule-Based method where the extraction of Action Rules is dependent on the pre-processing step of classification rule discovery and Object-Based method where it extracts the Action Rules directly from the database without the use of classification rules. Hybrid Action rule mining approach combines both these frameworks and generates complete set of Action Rules. The hybrid approach shows significant improvement in terms computational performance over the Rule-Based and ObjectBased approach. This paper gives a brief survey of previous works on action rule mining algorithms, and discusses Action Rule Mining on BigData and in a distributed environment. The applications of actionable action rules in BigData include business opinion mining, educational opinion mining, medical data decision support systems, text emotion detection, and social media data recommendations.
Masquerading Email Detection

The email which contains suspicious links or malicious files is one of the considerable threats in the digital world nowadays and because of these types of emails, enormous social and financial losses occur. The different approaches of detection of masquerading emails, which are already proposed, are unceasingly being renovated, the outcomes of that are not more impressive. Moreover, the masquerading email crisis is intensifying at a higher rate in the past few years. Because of the rapid increase in masquerading email threats, there is a need to develop new techniques to eliminate these types of masquerading email which is the present need of the hour. In our proposed work, the structure of the email was thoroughly analysed and then fed on recurrent convolutional neural networks. After that the Recurrent convolutional neural network model processes the email header and body of the email for detecting the suspicious or malicious files, elements and codes at character and word level of particular email; if the suspicious elements, file extension is detected then it will automatically transfer to the spam folder. For increasing the veracity of the design, the training of the model is performed with a balanced and unbalanced data set due to which improves the efficiency to distinguish between the legitimate and non-legitimate emails. Thus, the result of the experiment implies that the filtering or recognition probability of non-legitimate email is much closer to the value one, which also symbolizes that certainty of the proposed model enhances. As a result, classification of the spam and non-spam email is done by using the neural network classifier. The masquerading emails contain the suspicious links when the user clicks on the link the redirection takes place and the user reaches the suspicious website where the confidential information is asked from the user. The details shared by users results in financial and social losses.
[RETRACTED] Vardan Male EnhancementFDA Warns Consumers to Avoid Certain Male Enhancement and Weight Loss Products Sold Through Amazon, eBay and Other Retailers Due to Hidden, Potentially Dangerous Drug Ingredients v1

[RETRACTED] . However, most of these medications have adverse side effects. There are male boosters that promise to enhance male sexual health naturally.
A New Dynamic Stable Treemapping Method

Dynamic stabilility is a desired property for treemapping methods. Since in real world, the dataset is usually in a continuous way to be proceed, and the treemap we visualized will be in a dynamic flow. In this paper, We present a dynamic stable treepping method. This method has two steps, the first step is to lay out each rectangle into the treemap in a near-uniform way, and the second step is to resize each rectanle into its real size. This method is a novel method which focuses on the dynamic stable issue of treemapping methods. We use three different real world datasets to test our method, and compare our method with 14 existing methods.
Roadmap to Create an Augmented Reality Application

Augmented Reality (AR) combines real and virtual objects and makes them coexist in the same space, which highlights reality. This characteristic makes the creation of Augmented Reality applications attractive in several industries, such as medicine, education, entertainment, and so on. With this heyday of Augmented Reality, it is imperative to know what is required to create Augmented Reality applications. This paper explains what Augmented Reality is, defines the tools necessary to create an Augmented Reality application, and presents a software selection methodology. This paper stated that four tools are required to create an Augmented Reality application. First, the Integration tool manages the integration of all the tools. Second, the Virtual Object tool handles the creation of virtual objects. Third, the Coding tool allows to write code, locate its errors, and compile the code. Finally, the fourth tool is the Development tool, which provides functions that other tools do not offer, such as marker recognition. Although four tools are required to create an Augmented Reality application, this does not mean that four software should be used, it may be more or less. The amount of software to use in creating an application is a unique number for each project.
Spoiler Detection Using Machine Learning

Over the course of the lockdown, television shows, web series, and movies have been viewed more than ever before. In order to pick a show or movie to start, we always need to browse through several reviews as it feels like a significant investment of our time, and we want to be sure that they will be genuinely entertaining and gauging. However, these reviews often contain information that reveal things about the plot that a viewer should not know prior to watch the respective show or movie. These bits of information are popularly known as spoilers and they possess the potential of greatly impacting a viewer’s experience. These viewers may lose interest and in turn the production companies will to suffer from a loss of revenue. The issue at hand is that we want to read reviews before starting a movie or show, but we do not want to read a spoiler. How can we be sure that a review does not contain a spoiler? We need some warning, and that is why we are attempting to build a machine learning model that uses Natural Language Processing (NLP) to predict whether or not a particular review contains a spoiler, hence, serves as a warning. We will be using an IMDB review dataset to train and test our model.
Development of Signal Recognition Technology for Automation Rule Based Diagnosis

Rule based diagnosis is a machine condition diagnostic technology, and the result obtain through its responses to attributes consists of expert knowledge and experience. Accordingly, unlike machine learning, data and general-purpose aspects have advantages as they do not require big data and learning. However, rule-based diagnosis requires the user to respond to attributes, resulting in individual errors or time costs. Hence, it needs to be performed automatically. This paper develops a signal recognition technique by analyzing the diagnostic parameters of the rule-based diagnostic attributes. The diagnostic parameter consists of two characteristics and is recognized using different techniques. It is based on signal recognition, confirming its diagnostic potential, Further, this study is expected to enhance the automation of this diagnosis.
Taking a Preliminary Computational Look at Classical Japanese Poetry

A noun/verb ratio has been used to explain various aspects of language and language production, including the elucidation of a noun orientation in SVO languages such as English and a verb preference in SOV languages such as Japanese. This study presents a preliminary investigation into a noun/verb ratio in classical Japanese poetry to illuminate how poets leveraged grammatical resources for aesthetic ends. To that end, this study focuses on 短歌 (tanka) (literally, ‘short poem’), as represented by the 13th-century anthology of tanka, 小倉百人一首 (Ogura hyakunin isshu), to answer a set of three related research questions: (1) What is a noun/verb ratio at the moraic level of tanka? (2) How is that ratio expressed in the patterned use of nouns and verbs at the phrase level of tanka? and (3) To what extent can such ratios function as indicators of syntactic and rhetorical complexity in tanka? This study reports a marked noun orientation in classical Japanese from multiple perspectives while shedding further light on the noun/verb ratio as it relates to the characteristics of the Japanese language in general and the attributes of tanka as an enduring poetic form of Japanese utterances.
Building the quantum communication networks of the future

Turning young, but promising, technologies into a full-fledged quantum internet requires careful planning and foresight.
Winners of the Governor’s Prize 2021. Album-catalogue about Baikal

The article presents an illustrated research album-catalogue “Baikal on postcards. 1898-1917. Historical and bibliographical album-catalogue” which received the Governor’s Prize 2021. The sites included in the catalog are briefly characterized. The innovative character and importance of the project is emphasized. The book review was published in Project Baikal journal (Medvedev, 2020).
Simultaneous Recognition Algorithm of Human Activity and Phone Position Based on Multi-sensor Data Fusion

For human activity recognition based on phone sensors, the position of the phone is an important factor of the recognition accuracy. To improve the recognition accuracy of behavioral activities and the position of the phone placed, this paper proposes a classification recognition algorithm based on accelerometer and gyroscope sensors. First, sensor data collected from seven different body positions are used as inputs to a deep stacked bidirectional long and short-term memory neural network; then the activity type and the phone position are used as labels to train the neural network for simultaneous recognition of human activity and phone position; finally, the performance of the proposed method is evaluated by cross-validation. The experimental results show that placing the phone on the waist and thigh achieves the highest recognition accuracy rate. The accuracy of the simultaneous recognition of activity and position is over 90%, which is 18% higher than existing algorithms.
Van Fossen et al., 2022 SSM Accepted Manuscript

Objective: Contact tracing (CT) can limit the spread of infectious diseases, however its effectiveness hinges on public participation. We evaluated perceptions of the financial and health risk posed by COVID-19 and trust in information about COVID-19 provided by the state health department that manages CT as predictors of comfort and willingness to comply with CT. We further examined the moderating effect of political ideology on these relationships. Method: We used structural equation modeling to test hypotheses in data from a cross-sectional survey completed by a representative sample of Michigan residents (N = 805) in 2020. Results: Perceptions of the risk of COVID-19 to one’s health (but not finances) was negatively related to comfort and willingness to participate in CT. Trust in information about COVID-19 and liberalism were positively related to comfort and willingness. There was also a moderating effect of political ideology, such that conservatives were less comfortable and willing at greater perceptions of health risk. Conclusions: Conservatives and those who perceive a greater health risk may require targeted messaging and more deliberate engagement strategies to increase CT participation.
The (Collaborative Pianist’s) Art of Pedaling, Part I

The (Collaborative Pianist’s) Art of Pedaling: general information about the piano’s three pedals and their possibilities, particularly for the performing collaborative pianist. Instructions and ideas for use of the sostenuto pedal describes technique also. Includes an exercise for getting to know a piano quickly and well via its damper pedal.
Image Retrieval Framework for Anomalies Detection in X-Ray Radiographical Images

Medical imaging is well-known since ages and has become very popular now-a-days. Medical imaging solely based on datasets. The medical dataset consists of highly sensitive and valuable information. Since last two decades, lots of images are added to the domain, hence an efficient image retrieval is on the radars of many researchers. The Content Based Medical Image Retrieval System (CBMIRS) retrieve an image of interest from large set of medical images. The heart of CBMIRS is image retrieval strategy. There are numerous strategies developed, but does not address all classes of images. Different modalities and orientation of images makes the retrieval system difficult for searching and retrieving. This paper uses the histogram of image-based strategy for image retrieval, which is insensitive for any orientation and modality of the image. The validation is carried out with large set of X-rays radiographical images. The results suggest efficiency of the used strategy.
Comparative Analysis of Fruit Fly-Inspired Multi-Robot Cooperative Algorithm for Target Search and Rescue

In an unforeseen or human-made disaster, any search and rescue agency&#x2019;s primary concern are to locate the survivor immediately. For the victim&#x2019;s survival, even minutes may play a critical part. To strengthen the performance of the SAR community, the research community could introduce some creative technologies. With faster victim searches, initial assessment and visualization of the environment, and real-time assessment and management of search and rescue operations, multi-robot programs can significantly increase the productivity of search and rescue operations. This article proposes an intelligent algorithm based on the fruit fly optimization algorithm&#x2019;s food search pattern. To update the fruit flies&#x2019; position and speed, it uses particle swarm optimization. Repeated research simulations have confirmed the feasibility of the proposed process in a forest-like environment. The simulation results under different scenarios and performance comparison with existing techniques show the proposed algorithm&#x2019;s accuracy and robustness to be quite efficient in terms of detection rate and search and rescue time. The detection rate improves 10-25&#x0025; and search and rescue time by 5 to 15&#x0025;.
Web Extension for Lexical Simplification of Text

Lexical simplification means the process of providing alternatives to the complex words in the sentence with texts that are much more simpler to understand, while also preserving the context and grammar of the original text to make the whole sentence more easier to understand. All of the recent work involving lexical simplification relies on unsupervised tasks to learn simpler alternatives of complex words. But the drawback of most of these researches has been the fact that they provide simpler words without taking the context of the complex word in the sentence in account. In this paper, we are proposing a lexical simplifier which is based on contextual learnings from the sentence. We have applied the pre-trained representation model, BERT. It is a very powerful tool which can make use of the wider context of the sentence in both forward and backward direction. We have also taken the word frequency indicator from the Subtlex list, to produce results that will be more correct both semantically and grammatically. We have also added a web extension for the simplification of the text on the webpage, which takes the input from the user, processes the text on the server end, and gives the result in return after computation is over.
Privacy-Preserving Detection of Poisoning Attacks in Federated Learning

With federated learning, local learners train a shared global model using their own data, and report model updates to a server to aggregate and then update the global model. Such a learning paradigm may suffer from two attacks: privacy attacks by the untrusted server; adversarial attacks (e.g., poisoning attacks) by malicious learners. There is extensive research on addressing each of the attacks separately, but there is no scheme that can address both of them. In this paper, we pro-pose a scheme that enables both privacy-preserving aggregation and poisoning attack detection at the server, by utilizing additive homomorphic encryption and a trusted execution environment (TEE). Our evaluation based on an implemented prototype system demonstrates that our scheme can attain a similar level of detection accuracy as the state-of-the-art poisoning detection scheme, and that the increased computational workload can be parallelized and mostly executed outside of the TEE. A privacy analysis shows that the proposed scheme can protect individual learners’ model updates from being exposed.
The technology of informative features searching method applied for the problem of classifying areas of natural hyperspectral images

This article describes the process of applying the method of selecting informative features of the hyperspectral image area for classification. Methods and algorithms for searching for features of belonging to a certain area and the technology of their application are described. Further prospects for the development of the algorithm are indicated.
Subvocal Interface in Assistive Information Technology

The article describes a human-machine interface based on speech recognition without pronouncing it. The devices under consideration could be an alternative for people who have lost the ability to speak due to the removal of parts of the speech apparatus, as well as for the elderly or debilitated people who cannot clearly voice requests for their needs to their caregivers
HAD-IDC: A Hybrid Framework for Data Anomaly Detection based on Isolation, Density, and Clustering

Anomaly Detection, or Outlier detecting, refers to the problem of finding patterns in data that do not conform to expected behavior, which is of great significance in intrusion detection, fraud detection, and public health anomaly detection. Isolation Forest (iForest) is a widely-used algorithm with excel-lent performance and a low memory requirement, compared to traditional distance-based methods. However, iForest is sensitive to global outliers and is weak in distinguishing local outliers from low-density normal clusters. To address this problem, a hybrid anomaly detection framework based on Isola-ton, Density and Clustering (HAD-IDC) is proposed. It first uses iForest which has low complexity to quickly scan through the dataset to obtain two subsets of the original dataset, which are called the outlier candidate set and the inlier candidate set. And then, Local Outlier Factor (LOF) and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) are used on these two subsets, respectively. LOF is used in the inlier candidate set to detect local outliers that were not detected by iForest in the first step, and HDBSCAN is used to sift the low-density normal clusters from local outliers on the outlier candidate set. The experimental results obtained on official benchmark datasets show that HAD-IDC can significantly improve the detection accuracy compared to the existing methods, in term of AUC (area under curve), while reducing the computational time consumption.