Reviewerly/exHarmony huggingface dataset:
DATA EXTRACTION FROM PASSWORD PROTECTED MOBILE PHONE BY USING CHIP-OFF METHOD –A FORENSIC CASE STUDY

The current digital era is full of digital devices and to ensure the safety of their data, users utilize the protective armor of passwords using the fingerprints lock, face lock, pin codes and password locks. The trial-and-error method possesses an infinite possibility of passwords in patterns/ pin locks for unlocking the devices. The levels of security ensure that only a limited number of trials are possible before any device blocks itself or makes unlocking harder. In these circumstances, especially when a victim is a deceased person (e. g. an abetment in suicide case), the password is practically impossible to attain through the authorities. However, the data can be retrieved with the unique and distinctive method of Chip-off analysis. In this study, a password protected mobile phone was retrieved from a deceased individual and only the analysis of his/her mobile could lead to investigative insights for catching the perpetrator. The Chip-off method has several difficulties and a password/ pin protected device increases the difficulties for analysis several folds. This study highlights the significance of chip-off analysis in achieving accurate importing and extraction of maximum data along with the use of the hardware/software, MSAB, XRY and other software.
MapReduce fuzzy C-means ensemble clustering with gentle AdaBoost for big data analytics

Big data clustering is one of the significant processes employed in numerous application domains. Existing clustering algorithms do not cope with large-scale data, resulting in higher false positive rate. In order to cluster such large datasets with higher accuracy, MapReduce gradient descent gentle AdaBoost clustering (MGDGAC) technique is proposed. The MGDGAC technique designs MapReduce fuzzy C-means (MFCM) clustering where the large dataset is initially subdivided into a number of chunks which are executed in parallel on different nodes to effectively perform clustering processes with minimal time. The data with larger membership value are grouped in the cluster with help of mappers. Then, reducer in MFCM clustering re-estimates the centroid value and iteratively fed to the mapper again until it attains a particular iteration and groups the similar data together. Finally, MGDGAC technique applies gentle AdaBoost with intention of reducing the training error of large data clustering.
2. Frameworks of lexical decomposition of verbs

Starting from early approaches within Generative Grammar in the late 1960s, the article describes and discusses the development of different theoretical frameworks of lexical decomposition of verbs. It presents the major subsequent conceptions of lexical decompositions, namely, Dowty’s approach to lexical decomposition within Montague Semantics, Jackendoff’s Conceptual Semantics, the LCS decompositions emerging from the MIT Lexicon Project, Pustejovsky’s Event Structure Theory, Wierzbicka’s Natural Semantic Metalanguage, Wunderlich’s Lexical Decompositional Grammar, Hale and Kayser’s Lexical Relational Structures, and Distributed Morphology. For each of these approaches, (i) it sketches their origins and motivation, (ii) it describes the general structure of decompositions and their location within the theory, (iii) it explores their explanative value for major phenomena of verb semantics and syntax, (iv) and it briefly evaluates the impact of the theory. Referring to discussions in article 7 [Semantics: Foundations, History and Methods] (Engelberg) Lexical decomposition, a number of theoretical topics are taken up throughout the paper concerning the interpretation of decompositions, the basic inventory of decompositional predicates, the location of decompositions on the different levels of linguistic representation (syntactic, semantic, conceptual), and the role they play for the interfaces between these levels.
Hybrid enhanced shuffled bat algorithm for data clustering

Enhanced shuffled bat algorithm (EShBAT) is a recently proposed variant of bat algorithm (BA) which has been successfully applied for numerical optimisation. To leverage the optimisation capabilities of EShBAT for clustering, HESB, a hybrid between EShBAT, K-medoids and K-means is proposed in this paper. EShBAT works by dividing the population of bats into groups called memeplexes, each of which evolve independently according to BA. HESB improves on that by employing K-medoids and K-means to generate a rich starting population for EShBAT. It also refines the memeplex best solutions at the end of every generation by employing K-means algorithm. Both these modifications combined together produce an efficient clustering algorithm. HESB is compared to BA, EShBAT, K-means and K-medoids, over ten real-life datasets. The results demonstrate the superiority of HESB.
React-Bootstrap

CSS frameworks like Bootstrap ( ) and Foundation ( ) have changed the way people build their websites. These tools make it a lot easier to make a web application look professionally styled and be responsive (that is, make it adapt to mobile screens nicely). Of course, the downside is that these existing frameworks may not give you fine-grained customizability, and your application will look like many others. But, even if you do have the luxury or the capability to create your own end-to-end custom styles, I suggest that you start off with these frameworks. That’s because there’s a lot to learn from the patterns these frameworks use.
Analysis of parameter selection for solar radiation prediction and global solar radiation prediction model using polynomial regression

Solar irradiance is available in abundance and can be harvested to satisfy ever-growing energy demand. Installation of photovoltaic (PV) solar panels at any desired location is not often economically feasible and hence prediction of solar radiation is crucial. Relative humidity and temperature at a specific location Chumukedima, Nagaland (latitude 25.86 N, longitude 93.75 E) in India have been used for the present study along with the solar irradiance outcome. Significant parameter contributing towards the solar radiation outcome is derived with the help of design of experiments (DOE) and analysis of variance (ANOVA). Polynomial regression model is developed to predict the solar irradiation. From the results obtained, it is evident that the humidity contributes more towards the solar irradiation prediction.
Design and Implementation of Secure Communication using Elliptic Curve Cryptograph between Wireless Sensor Nodes

Wireless Sensor Networks is an emerging trend and has become gradually popular across wide range. A key distributional protocol is intended to safely provide authentic motes with secret keys system using Elliptic Curve Cryptography functions. The convention is a variation of the Diffie_Hellman convention utilizing Elliptic Curve Cryptography. It’s agreement of a key convention that permits pair of gatherings, both having a elliptic open and closed key, to build up a mutual secret key over an uncertain channel. This shared secret key may be either utilized as key or used to determine another key which would then be able to be utilized to subsequent correspondences utilizing a cipher of symmetric key. The use of advanced encryption standard for the encrypt and decrypt of the data also ensures that security is never flawed. Therefore Elliptic curve cryptograph is a best candidate for providing secure communication between Wireless Sensor Networks.
The Point of Diminishing Return on Cyber Risk Investment

This chapter begins with a literature review in the field of economics of information security, challenges for cyber risk management cost optimization, challenges in quantifying security costs, challenges in determining the optimal level of investment in security and risk, and game theory in security investment. It then presents current models for cyber risk cost optimization, cost models for projection, and cost models for investment, i.e., determining how much should be spent on cyber risk, including Return on Security Investment (ROSI), Net Present Value (NPV), and Internal Rate of Return (IRR). The chapter closes with decision models for optimal risk management strategies, i.e., when the point of diminishing return on cyber risk investment is reached.
TaskTracker aware scheduler with resource availability control for Hadoop MapReduce

Schedulers are playing a vital role in task assignment for Hadoop MapReduce. In some scenario, the default schedulers of Hadoop spawn tasks in TaskTracker without checking the external dependency and may fail. As a result, Hadoop should rerun the tasks in another TaskTracker. To address this issue, TaskTracker aware scheduler has been introduced. This paper focuses the resource availability control of TaskTracker aware scheduler. The proposed scheduler will not allow a task to run and fail if the load of the TaskTracker reaches its threshold for the Job. The performance of this scheduler may increase if the scheduler is aware of the status of the resources present in the TaskTracker nodes. The main features of this scheduler are user controllability of jobs and configuration based resource utilisation control for task allocation.
Design and Fabrication of Automated Urinal Flushing System Using Mechanical Elements With Disinfectant

In India, One of the most important problem is Infected Public Toilets especially Urinals that no one worried about the Flushing of Urinals. Hence, the infected urinals cause diseases to the users by spreading infectious germs, therefore complete cleaning of urinals is essential to maintain the hygienic conditions. In this paper, to tackle this problem by the development of Urinal flushing system with a less usage of water. This flushing system is made of mechanical elements. Automatic flushing system is one of the essential technologies in India as it is used for maintaining hygienic conditions especially in public urinals. This flushing system does not require an electric power as making it highly useful and reliable in public places like garden and public urinals. Also, this system conserves the water and the electricity. In this setup, Rocker Disc is connected with the mechanical linkages for the flexibility of valve operations.
Obfuscation

As data ecosystems grow in size due to the IoT, researchers are developing obfuscation techniques that issue fake search engine queries, undermine location tracking algorithms, or evade government surveillance. These techniques raise two conflicts: one between each user and the machine learning algorithms which track the users, and one between the users themselves.
In re Qapsule Techs., Inc. United States Court of Appeals of the Federal Circuit, 2019 No. 2018-1772, 2019 WL 1110899

Biotechnology Law ReportVol. 38, No. 3 As presented by Chris HolmanIn re Qapsule Techs., Inc. United States Court of Appeals of the Federal Circuit, 2019 No. 2018-1772, 2019 WL 1110899Edited for Biotechnology Law Report by CHRISTOPHER M. HOLMAN*Published Online:7 Jun 2019https://doi.org/10.1089/blr.2019.29123.cmhAboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 38Issue 3Jun 2019 InformationCopyright 2019, Mary Ann Liebert, Inc., publishersTo cite this article:In re Qapsule Techs., Inc. United States Court of Appeals of the Federal Circuit, 2019 No. 2018-1772, 2019 WL 1110899.Biotechnology Law Report.Jun 2019.201-204.http://doi.org/10.1089/blr.2019.29123.cmhPublished in Volume: 38 Issue 3: June 7, 2019PDF download
A latent semantic analysis-based image tag optimisation method

According to the nominal tags, the adverb and adjective high-level semantic tags which is described human emotion are easily to exceed the handling scope. Furthermore, the majority of optimisation methods exists many problems (e.g., although inputting a high application cost, it cannot get a satisfactory effect). This paper proposed an effective image tag optimisation algorithm which was composed of two important parts: firstly, a random walk model was used to process the initial image tag information, the image relationship diagram and the tag relationship diagram were structured based on image vision similarity and tag relevancy respectively, then, the image and tag information was spread through a dual-diagram (image relationship diagram and tag relationship diagram) walk mode randomly; secondly, a new image tag optimisation model was built according to three factors, i.e., semantic uniformity, noise sparsity and result matrix sparsity. In the experimental stage, the effectiveness of this model and the superiority of pre-processing were verified by experiment. Compared to other methods, the experimental result indicates that this algorithm is more reasonable and efficient.
Privileged Account Management Implementation

Organizations increasingly recognize that properly securing and controlling privileged credentials ranks as one of the best defenses against attacks from external hackers as well as from malicious insiders. For optimal results, a privileged access management solution should protect identities, accounts, passwords, and keys at all stages of the privileged attack vector kill chain (Chapter 1 ) by implementing comprehensive layers of control and audit. The overall objectives for your implementation should include the following:
Data Understanding

The main goal of data understanding is to gain general insights about the data that will potentially be helpful for the further steps in the data analysis process, but data understanding should not be driven exclusively by the goals and methods to be applied in later steps. Although these requirements should be kept in mind during data understanding, one should approach the data from a neutral point of view. Never trust any data as long as you have not carried out some simple plausibility checks. Methods for such plausibility checks will be discussed in this chapter. At the end of the data understanding phase, we know much better whether the assumptions we made during the project understanding phase concerning representativeness, informativeness, data quality, and the presence or absence of external factors are justified.
Design of Digital Filter by Genetic Algorithm

This paper relatively less difficult words it may be said that during Genetic Algorithm (GA) an evolutionary technique solves problems and the final end result is the pleasant fittest answer is evolve. A brief description of the herbal increase is mentioned to knowledge GA. In nature all living organisms essentially include cells. Each cell includes a hard and fast of chromosomes. Each chromosome, in flip, is a string of genetic fabric and serves as a model for the entire organism. A chromosome is essentially a collection of genes, in which each gene can be described as a block of genetic cloth and encodes a particular protein. In other phrases it may be said that each gene encodes a trait. These settings are known as alleles. Each gene has a selected role in a chromosome and that function is named as locus. As complete set of genes is referred to as a chromosome, like smart a genome includes a entire set of chromosomes. The great greatest fee of the enter entities performed the usage of genetic algorithm technique.
Technology, Innovation, and Experience of Managerial Culture Formation

In the article, the managerial culture is considered as the main component of the evaluation of the effectiveness of the contemporary teacher, who meets the requirements of training a competitive specialist. The emphasis is made on managerial training of a teacher, who is able and willing to organize a productive educational process. The content of organizational and managerial skills is influenced by psychological characteristics of the coordinator of an activity, such as enhanced responsibility for the effectiveness of professional work, subjects of professional interaction; creative approach to organization and management in solving professional problems; and specificities of communication in the course of solving organizational and managerial problems. It is an indisputable fact that organizational and managerial activities are a substructural component of the integral professional activity of the subject of labor. It involves the presence of formed motives, knowledge, skills, personal and behavioral qualities that enable the manager to effectively organize the process of a particular type of professional activity. This understanding of organizational and managerial activities allows concluding that the readiness of the subject of labor for this activity is closely related to the training system of professionals at high school. The above allows concluding that managerial culture for the education manager is a criterion of efficiency of his organizational and managerial activities, which is essentially identical to professional activity, while the development of this parameter is most effective during the study period at the university.
The future of the nanoparticle

Engineered nanoparticles that seem like today's water treatment panacea, could be tomorrow's ecological dynamite. This article plots a roadmap to a future in which we can take advantage of the benefits of nanotechnology in water and mitigate against the drawbacks.
From Auto-encoders to Capsule Networks: A Survey

Convolutional Neural Networks are a very powerful Deep Learning algorithm used in image processing, object classification and segmentation. They are very robust in extracting features from data and largely used in several domains. Nonetheless, they require a large number of training datasets and relations between features get lost in the Max-pooling step, which can lead to a wrong classification. Capsule Networks (CapsNets) were introduced to overcome these limitations by extracting features and their pose using capsules instead of neurons. This technique shows an impressive performance in one-dimensional, two-dimensional and three-dimensional datasets as well as in sparse datasets. In this paper, we present an initial understanding of CapsNets, their concept, structure and learning algorithm. We introduce the progress made by CapsNets from their introduction in 2011 until 2020. We compare different CapsNets series to demonstrate strengths and challenges. Finally, we quote different implementations of Capsule Networks and show their robustness in a variety of domains. This survey provides the state-of-the-art of Capsule Networks and allows other researchers to get a clear view of this new field. Besides, we discuss the open issues and the promising directions of future research, which may lead to a new generation of CapsNets.
Correlation Matrices

In this chapter we consider correlations over Boolean functions and iterated Boolean transformations. Correlations play an important role in cryptanalysis in general and linear cryptanalysis in particular.
Medical Question Entailment based on Textual Inference and Fine-tuned BioMed-RoBERTa

Information understanding and retrieval have al-ways been a challenge in natural language processing (NLP), especially in specific domains like the medical one. Natural language inference (NLI) is the task that focuses on under-standing the information using inference, while recognizing question entailment (RQE) is used for question answering systems, establishing if two questions can have the same answer. NLI and RQE have gained popularity in the medical field with the increased language model performance. However, the number of approaches is still limited. This study uses the state-of-the-art language model, BioMed-RoBERTa, to be fine-tuned for NLI and RQE tasks on medical data. We propose a method to automatically build a dataset for question entailment(QE) based on a generalized definition of QE according to which there is an entailment between two questions iff there is an entailment between their answers. The built dataset was used for fine-tuning a model which obtains good results on MEDIQA 2019 RQE task.
Data Storage and Retrieval with Deduplication in Secured Cloud Storage

The Cloud Storage can be depicted as a service model where raw or processed data is stored, handled, and backed-up remotely while accessible to multiple users simultaneously over a network. Few of the ideal features of cloud storage is reliability, easy deployment, disaster recovery, security for data, accessibility and on top of that lesser overall storage costs which removes the hindrance of purchasing and maintaining the technologies for cloud storage. In this modern technology world, massive amount of data are produced in day to day life. So, it has become necessary to handle those big data on demand which is a challenging task for current data storage systems. The process of eliminating redundant copies of data thereby reducing the storage overhead is termed as Data Deduplication (DD). One of the ultimate aim of this research is to achieve ideal deduplication on secured data of client side. On the other hand as the client’s data are encrypted with different keys, the cross user deduplication is merely impossible as having a single key encryption among multiple user’s leads to an in secure system resulting in fragile to client’s expectations. The proposed research adapts Message Locked Encryption (MLE) technique that looks for redundant files in cloud before uploading the client’s file which eventually reduces the storage. Since the redundant files are swept, the network bandwidth is considerably reduced with respect to the redundant contents uploaded several times.
Android adopts biometrics over passwords

Google has taken a significant step in backing biometrics over passcodes, by enabling Android operating system users to access online services using their fingerprints.
An IoT-Based Body Area Network in Medical Care System: Related Challenges and Issues

The Internet of Things (IoT) is the collection of open intelligent devices. The fast development of IoT and the huge expansion of mobile technology are unveiling fresh growth opportunities in several areas, including education, transport, and farming and, in particular, healthcare. Introduction of IoT via medical applications has several advantages including cost savings by dropping hospital costs, health care costs, and transportation costs, costs of human resources and costs of insurance. Recently, the academic world has produced major progress in research and security development for IoT-based applications, particularly on IoT-based healthcare devices. It contributes to an additional benefit of enhanced healthcare quality. However, the increased use of IoT facilities in e-health applications, particularly in the insurance field, has resulted in increased security and privacy issues. Medical applications are prone to data violations and widening security problems due to the increase in access points to sensitive data through electronic medical records and the increasing popularity of wearable technology. So there is a need of an efficient lightweight, secure verification system, providing important security levels against various attacks, such as attacks by impersonation, a man in the middle attack and unidentified key-sharing attacks for IoT foundation E-health. This article presents a survey of problems, difficulties, and challenges a short analysis of multiple problems and problems suggested by different authors using different techniques and methods.