Reviewerly/exHarmony huggingface dataset:
Dimensionality Reduction Techniques

Dimensionality reduction technique involves finding out the transformation matrix that maps from the random vector in the higher dimensional space to the lower dimensional space. This is obtained by identifying the orthonormal basis using PCA, LDA, KLDA, and ICA. In PCA, the basis vectors are identified in the direction of the maximum variance of the data. PCA doesn’t care about the class index associated with the training vectors. This is circumvented using LDA. In this case, the distance between the vectors within the class is minimized and the distance between the centroids of various classes is made apart. After subjected to PCA, the data are made uncorrelated. These are further made independent by projecting the data using ICA basis. In almost all the techniques in pattern recognition techniques, the individual classes are assumed as Gaussian distributed. But in real-time applications, data aren’t Gaussian distributed. Thus the Gaussianity of the data can be minimizing the absolute of the kurtosis measured using the given data or by maximizing the neg entropy of the given data.
Digital Video Steganography Technology For Security Application

The paper proposes a technique on the digital video steganography. Various techniques are employed in order to achieve a higher secure data transmission these includes the cryptography, watermarking and steganography the steganography finds its application in the audio-video synchronization, copyright control and T.V.broadcasting specially in military areas the paper analyses the various improvements made in the security f the data transmission as well as the various methodologies adapted in achieving the same.the major difference between the steganography and the cryptgraphy is the latter has the cipher text which is coded and then transmitted while the former is embedded within a image or in a video frame so as to achieve the intended use.
An HCI-Based Cognitive Architecture for Learning Process Observation

In this article, a cognitive framework for observing learning activities based on human-computer coupling is proposed. The observation is based on the vectorization of a learning situation along with human-computer interaction factors. An evolutionary high-dimensional topology of learning cognitive flow is introduced for human-computer interaction. In addition, the authors have selected a tree topology as the topological structure of a low-dimensional learning space to process the observations for online learning. Furthermore, the mechanism for the BSM (brain cognitive body-situation of coupling-manifold of information) the coupling morphism is presented. The principle for the coupled observation of objects in a cognitive or learning manifold is proposed. Finally, a special system for teaching and learning is programmed to observe and evaluate learning and mental arithmetic training processes. This system not only provides students with a new ergonomic learning model but also records the students' learning processes. Thus, the teachers can summarize the knowledge points automatically rather than manually.
Automated Reasoning with Restricted Intensional Sets.

Intensional sets, i.e., sets given by a property rather than by enumerating elements, are widely recognized as a key feature to describe complex problems (see, e.g., specification languages such as B and Z). Notwithstanding, very few tools exist supporting high-level automated reasoning on general formulas involving intensional sets. In this paper we present a decision procedure for a first-order logic language offering both extensional and (a restricted form of) intensional sets (RIS). RIS are introduced as first-class citizens of the language and set-theoretical operators on RIS are dealt with as constraints. Syntactic restrictions on RIS guarantee that the denoted sets are finite, though unbounded. The language of RIS, called L_RIS , is parametric with respect to any first-order theory X providing at least equality and a decision procedure for X-formulas. In particular, we consider the instance of L_RIS when X is the theory of hereditarily finite sets and binary relations. We also present a working implementation of this instance as part of the {log} tool and we show through a number of examples and two case studies that, although RIS are a subclass of general intensional sets, they are still sufficiently expressive as to encode and solve many interesting problems. Finally, an extensive empirical evaluation provides evidence that the tool can be used in practice.
Research on the Prediction of Popular Opinion Trend of Web News based on BP neural Network and LSTM

Abstract Current prediction methods on public opinion trend of network news are often carried out according to human experience or traditional time series prediction, but they sometimes lack scientific principle and immediacy. This paper researches on the prediction of public opinion trend of network news based on deep learning. Firstly, we use the BP neural network and LSTM to predict public sentiment trend, then combine word2vec with LSTM to classify the text sentiment and finally use doc2vec algorithm and k-means to cluster the text. Experiment result shows that the mean square error with good performance, which reveals the beneficial effect of our prediction method.
Mobile Physiological Sensor Cloud System for Long-Term Care

In this study, we propose a Mobile Physiological Sensor Cloud System for Long-term Care (MPCLC), the main functions of which are collecting carereceiver’s physiological data by using sensors and analyzing and reporting the carereceiver’s healthy condition. With the features of small in size, high convenience for use, and good immediacy of request response, the MPCLC can partially solve the problems of long-term care which are insufficiency of required labors and hard for tracking the results of traditional medical treatment. After the data measured by using sensors is collected, the data is sent to the cloud for storage. The health reports will be generated by the cloud, and delivered to caregivers and medical staffs for reference under their requests. This can save the time that people go and come between hospitals and the places where carereceivers stay for measuring patients’ psysilogical data. One of the other key functions is to prevent the carereceivers from disease in advance.
Quantum locally linear embedding

Reducing the dimension of nonlinear data is crucial in data processing and visualization. The locally linear embedding algorithm (LLE) is specifically a representative nonlinear dimensionality reduction method with well maintaining the original manifold structure. In this paper, we present two implementations of the quantum locally linear embedding algorithm (qLLE) to perform the nonlinear dimensionality reduction on quantum devices. One implementation, the linear-algebra-based qLLE algorithm utilizes quantum linear algebra subroutines to reduce the dimension of the given data. The other implementation, the variational qLLE algorithm utilizes a variational quantum-classical hybrid procedure to acquire the low-dimensional data. The classical LLE algorithm requires polynomial time complexity of $N$, where $N$ is the global number of the original high-dimensional data. In data preprocessing, we invoke the quantum $k$-nearest neighbors algorithm ($k$-NN) to find out the $k$ nearest neighbors of the given data with quadratic speedup. For the main part of the qLLE algorithm, compared with the corresponding classical algorithm, the linear-algebra-based qLLE algorithm proposed in this paper achieves an exponential speedup in $O(\mathrm{poly}(\log N))$. The variational qLLE algorithm can be implemented on the near term quantum devices. In addition, the corresponding quantum circuits of the qLLE algorithm are also presented.
Digital Forensics vs. Due Process

Cybersecurity and digital forensics are closely related to current and even more to future criminal proceedings due to the fact that digital evidence is more and more dominating the body of evidence in criminal trials. It is also fair to say that digital forensics to a large extent performs in cybersecurity as a production of digital evidence.
Silhouette: Efficient Intra-Address Space Isolation for Protected Shadow Stacks on Embedded Systems.

Embedded systems are increasingly deployed in devices that can have physical consequences if compromised by an attacker - including automobile control systems, smart locks, drones, and implantable medical devices. Due to resource and execution-time constraints, C is the primary programming language for writing both operating systems and bare-metal applications on these devices. Unfortunately, C is neither memory safe nor type safe. In this paper, we present an efficient intra-address space isolation technique for embedded ARM processors that leverages unprivileged store instructions. Using a compiler transformation, dubbed store promotion, which transforms regular stores into unprivileged equivalents, we can restrict a subset of the program's memory accesses to unprivileged memory while simultaneously allowing security instrumentation to access security-critical data structures (e.g., a shadow stack) - all without the need for expensive context switching. Using store promotion, we built Silhouette: a software defense that mitigates control-flow hijacking attacks using a combination of hardware configuration, runtime instrumentation, and code transformation. Silhouette enforces Control-Flow Integrity and provides an incorruptible shadow stack for return addresses. We implemented Silhouette on an ARMv7-M board and our evaluation shows that Silhouette incurs an arithmetic mean of 9.23% performance overhead and 16.93% code size overhead. Furthermore, we built Silhouette-Invert, an alternative implementation of Silhouette, which incurs just 2.54% performance overhead and 5.40% code size overhead, at the cost of a minor hardware change.
Software Development Process Evolution in Malaysian Companies

GSD is a phenomenon mainly associated with the outsourcing of software development projects to some offshore company. Reduction in software development cost increased productivity and advantage of multisite development with respect to time are the main benefits that software development companies (SDCs) get from GSD. Besides benefits, a number of challenges associated with GSD are also observed. Consequently, the traditional processes to develop software are evolving and being replaced with a new set of processes which are lightweight and outcome-based. The process evolution has been deeply investigated in the context of companies mostly in Europe, Australia, USA and mainly other countries in those regions. In this regard, limited research has been carried out on Malaysian companies. The present research investigates the process evolution phenomenon in Malaysian companies. The current software development processes and the reasons for the evolution of software processes in Malaysian software companies have been identified. A qualitative approach using structured interviews has been followed for the collection of data collection and its analysis. The findings explain that software processes in most of the Malaysia companies are increasingly evolving or have been evolved. The companies are overwhelmingly adopting agile methods because of their support to GSD. Some of the companies are using ad hoc approaches for software development. The size of the company and project has been found as one of the main factors behind using ad hoc approaches. Mainly the small and medium-size companies and projects are involved in this practice.
Bootstrap Validation of the Estimated Parameters in Mixture Models Used for Clustering

When a mixture model is used to perform clustering, the uncertainty is related both to the choice of an optimal model (including the number of clusters) and to the estimation of the parameters. We discuss here the computation of confidence intervals using different bootstrap approaches, which either mix or separate the two kinds of uncertainty. In particular, we suggest two new approaches that rely to some degree on the model specification considered as optimal by the researcher, and that address specifically the uncertainty related to parameter estimation. These methods are especially useful for poorly separated data or complex models, where the selected solution is difficult to recreate in each bootstrap sample, and they present the advantage of reducing the well-known label-switching issue. Two simulation experiments based on the Hidden Mixture Transition Distribution model for the clustering of longitudinal data illustrate our proposed bootstrap approaches.
Towards a Call Behavior-Based Compositional Verification Framework for SysML Activity Diagrams

SysML activity diagram is a standard modeling language for complex systems. It supports systems’ composition by providing the operator ‘call behavior’. In general, the verification of systems modeled with those diagram inherit the limitations of the developed built-in tools, especially the case of model checking. To address this shortcoming, we propose a compositional verification framework based on the call behavior operator to alleviate the state space explosion problem of model-checking. The framework decomposes a property into local sub-properties and verify them separately on the composed behavioral diagrams. Further, we propose to ignore the diagrams’ artifacts that are useless with respect to the property under verification. We prove the soundness of the proposed approach by showing that the result deduced from the verification of the local properties is always preserved. The verification results are obtained by encoding SysML activity diagrams in the probabilistic model checker ‘PRISM’. Finally, we demonstrate the effectiveness of our framework by verifying a set of properties on two use cases that require a large amount of memory and a considerable time processing.
Policy and Regulatory Challenges of Distributed Ledger Technology and Digital Assets in Asia

Since the launch of Bitcoin in 2009, cryptocurrencies and their underlying blockchain technology have risen to global attention. It is now clear Bitcoin and a number of other cryptocurrencies were the focus of one of the largest speculative bubbles in history. This chapter explores blockchain, cryptocurrencies and Initial Coin Offerings (ICOs), as well as policy and regulatory responses in Asia. It demystifies key aspects of blockchain systems, while also disentangling concepts that are often (incorrectly) used interchangeably, such as distributed ledgers and blockchains. The chapter provides data on total capital raised through ICOs and analyzes the distribution of ICOs by country and region. Based on this framework, it conducts a comprehensive analysis of regulatory statements and disparate policy approaches in Asian countries toward digital assets, focusing on cryptocurrencies, blockchain, and ICOs.
DLT smart contract platforms for software lifecycle management

After its introduction the smart contract platforms got practical use when they become key part of the Ethereum public blockchain and defined the concept of distributed applications (dApps). Smart contract platforms, based on distributed ledger technologies (DLT), are used in various industries such as banking, government and law, healthcare, insurance, and transportation. One area of a DLT and smart contracts could also be used is the area of the software lifecycle management (SLM). Complex SLM procedures involve many parties such as customer, software provider, technical and business consultants, auditors, hardware providers, third party software vendors, and others. In this paper we investigate the applicability of DLT-based smart contract platforms to support multi-party SLM processes for complex customer systems, components of which are running on premise, in the cloud and on edge devices.After its introduction the smart contract platforms got practical use when they become key part of the Ethereum public blockchain and defined the concept of distributed applications (dApps). Smart contract platforms, based on distributed ledger technologies (DLT), are used in various industries such as banking, government and law, healthcare, insurance, and transportation. One area of a DLT and smart contracts could also be used is the area of the software lifecycle management (SLM). Complex SLM procedures involve many parties such as customer, software provider, technical and business consultants, auditors, hardware providers, third party software vendors, and others. In this paper we investigate the applicability of DLT-based smart contract platforms to support multi-party SLM processes for complex customer systems, components of which are running on premise, in the cloud and on edge devices.
Bounds on the frequencies of tuples on parts of the period of linear recurring sequences over Galois rings

Abstract We study the frequencies of tuples in linear recurring sequences (LRS) of vectors over Galois rings. By means of an estimate of an exponential sum some nontrivial bounds on the frequencies of elements in LRS are derived. It is shown that these bounds are in some cases sharper than known results.
Adjusted Transformation Methods for Reproduction Quality Control

n this paper we introduce adjusted transformation methods for off-line quality control. The proposed methods result in proper performance measures for the determination of the controllable factors that affect the mean and variability of the response variable of interest. The details of the proposed adjusted transformations are provided and compared with the well-known Box and Coxtransformation and the safeguard method of Logothetis. The performance abilities of the proposed methodology are demonstrated on quality control data, considering both a real dataset and a simulated one.
A Supervised Laplacian Eigenmap Algorithm for Visualization of Multi-label Data: SLE-ML

A novel supervised Laplacian eigenmap algorithm is proposed especially aiming at visualization of multi-label data. Supervised Laplacian eigenmap algorithms proposed so far suffer from hardness in the setting of parameters or the lack of the ability of incorporating the label space information into the feature space information. Most of all, they cannot deal with multi-label data. To cope with these difficulties, we consider the neighborhood relationship between two samples both in the feature space and in the label space. As a result, multiple labels are consistently dealt with as the case of single labels. However, the proposed algorithm may produce apparent/fake separability of classes. To mitigate such a bad effect, we recommend to use two values of the parameter at once. The experiments demonstrated the advantages of the proposed method over the compared four algorithms in the visualization quality and understandability, and in the easiness of parameter setting.
The Impact of Partial Weak Staining in Normal Breast Epithelium on the Reliability of Immunohistochemistry Results in HercepTest-positive Breast Cancer: Methodological Issues on Reliability and Correlation

I was interested to read the paper by Farra and colleagues published in the October 2019 issue of Clinical Breast Cancer. 1 Farra C. Fedda F. Tfayli A. et al. The impact of partial weak staining in normal breast epithelium on the reliability of immunohistochemistry results in HercepTest-positive breast cancer. Clin Breast Cancer. 2019; 19: 340-344 Abstract Full Text Full Text PDF PubMed Scopus (3) Google Scholar Normal epithelial cells do not show human epidermal growth factor receptor-2 (HER2) gene amplification and should lack membrane staining by HER2 immunohistochemistry (IHC); but HER2 staining in benign breast epithelium is occasionally encountered. The authors aimed to assess the correlation between HER2 IHC 3+ cases of breast cancer with normal epithelium staining (NES) and their corresponding fluorescence in situ hybridization (FISH) results, and to suggest recommendations for interpretation. They reviewed 154 cases of breast cancer with HER2 IHC 3+. NES was scored as present or absent. All study cases were sent for FISH testing. All cases, and particularly those that showed false positivity for IHC (positive IHC, negative FISH), were examined for NES. They reported that of the 154 cases, 146 cases were FISH-positive (94.8%) and 2 failed FISH testing (1.3%). Conversely, 22% (34/154) of the cases showed NES for HER2. Of these 34 cases, 23 (67%) were FISH-amplified, 9 (26%) were FISH not amplified, and 2 failed FISH testing. Notably, all of the false-positive (FISH-negative) cases of breast cancer showed some degree of positivity in normal breast epithelium.
Techno Entrepreneurship Adoption: An Intention Based Assessment Study of Start-Ups in the Kingdom of Saudi Arabia

Establishment of Start-up firms is providing great solutions to the important problems of unemployment and underutilization of resources across the economies. As such they have assumed a great significance and function in the economic growth policies and strategies of the nations throughout the world. Recognizing this reality, Saudi Arabia has given high priority in their vision 2030 for the development of Start-ups across the Kingdom. However, in today’s start up development paradigms, it is not only the establishment of start-ups but the pace of techno adoption of these start-ups that significantly determines their success and sustenance. It is in this context that this paper explores to assess the levels of intention related to technology adoption among the start-ups in the Kingdom of Saudi Arabia. The results of this research are expected to contribute towards the development of a country level start-up techno-entrepreneurship adoption policy framework. For this research, stratified random sampling survey method was used and the data collected was put to different statistical tests like Descriptive Analysis like Correlation and Predictive Analysis like Ordered Logistic Regression etc. The results obtained depict that start-ups in Saudi Arabia show a high-level of intention towards technology adoption in their enterprises, as they believe that it enhances the basic start up entrepreneurial process capabilities and efficiencies like business-ideation, concept & prototype development, feasibility analysis etc. However, as per the data results there are certain limitations that come in the way of techno-entrepreneurship integration for the start-ups in Saudi Arabia. Among these limitations Financial Support and skill/expertise serve as the key constraints. The resultant empirical information will be of high use to enhance a Start-up Techno entrepreneurship adoption modular framework that could facilitate the easy adoption and utilization of technology by the start-ups in the country.