Reviewerly/exHarmony huggingface dataset:
BFF: foundational and automated verification of bitfield-manipulating programs

Low-level systems code often needs to interact with data, such as page table entries or network packet headers, in which multiple pieces of information are packaged together as bitfield components of a single machine integer and accessed via bitfield manipulations (e.g., shifts and masking). Most existing approaches to verifying such code employ SMT solvers, instantiated with theories for bit vector reasoning: these provide a powerful hammer, but also significantly increase the trusted computing base of the verification toolchain. In this work, we propose an alternative approach to the verification of bitfield-manipulating systems code, which we call BFF. Building on the RefinedC framework, BFF is not only highly automated (as SMT-based approaches are) but also foundational---i.e., it produces a machine-checked proof of program correctness against a formal semantics for C programs, fully mechanized in Coq. Unlike SMT-based approaches, we do not try to solve the general problem of arbitrary bit vector reasoning, but rather observe that real systems code typically accesses bitfields using simple, well-understood programming patterns: the layout of a bit vector is known up front, and its bitfields are accessed in predictable ways through a handful of bitwise operations involving bit masks. Correspondingly, we center our approach around the concept of a structured bit vector---i.e., a bit vector with a known bitfield layout---which we use to drive simple and predictable automation. We validate the BFF approach by verifying a range of bitfield-manipulating C functions drawn from real systems code, including page table manipulation code from the Linux kernel and the pKVM hypervisor.
Discriminant non-stationary signal features’ clustering using hard and fuzzy cluster labeling

&lt;p&gt;Current approaches to improve the pattern recognition performance mainly focus on either extracting non-stationary&lt;/p&gt; &lt;p&gt;and discriminant features of each class, or employing complex and nonlinear feature classifiers. However, little&lt;/p&gt; &lt;p&gt;attention has been paid to the integration of these two approaches. Combining non-stationary feature analysis with&lt;/p&gt; &lt;p&gt;complex feature classifiers, this article presents a novel direction to enhance the discriminatory power of pattern&lt;/p&gt; &lt;p&gt;recognition methods. This approach, which is based on a fusion of non-stationary feature analysis with clustering&lt;/p&gt; &lt;p&gt;techniques, proposes an algorithm to adaptively identify the feature vectors according to their importance in&lt;/p&gt; &lt;p&gt;representing the patterns of discrimination. Non-stationary feature vectors are extracted using a non-stationary&lt;/p&gt; &lt;p&gt;method based on time–frequency distribution and non-negative matrix factorization. The clustering algorithms&lt;/p&gt; &lt;p&gt;including the K-means and self-organizing tree maps are utilized as unsupervised clustering methods followed by a&lt;/p&gt; &lt;p&gt;supervised labeling. Two labeling methods are introduced: hard and fuzzy labeling. The article covers in detail the&lt;/p&gt; &lt;p&gt;formulation of the proposed discriminant feature clustering method. Experiments performed with pathological&lt;/p&gt; &lt;p&gt;speech classification, T-wave alternans evaluation from the surface electrocardiogram, audio scene analysis, and&lt;/p&gt; &lt;p&gt;telemonitoring of Parkinson’s disease problems produced desirable results. The outcome demonstrates the benefits&lt;/p&gt; &lt;p&gt;of non-stationary feature fusion with clustering methods for complex data analysis where existing approaches do not&lt;/p&gt; &lt;p&gt;exhibit a high performance.&lt;/p&gt;
Bridging the Computer Science -- Law Divide

Many pressing societal questions can be answered only by bringing experts from different disciplines together. Questions around misinformation and disinformation, platform power, surveillance capitalism, information privacy, and algorithmic bias, among many others, reside at the intersection of computer science and law. We need to develop institutions that bring together computer scientists and legal scholars to work together on issues like these, and to train new innovators, thought leaders, counselors, and policymakers with hybrid training in both disciplines. In Universities, the disciplines of Computer Science (CS) and Law are separated by many wide chasms. Differences in standards, language, methods, and culture impede professors and other academic researchers who want to collaborate with colleagues on the other side of this divide. Universities place CS and Law in different schools, on different campuses, on different calendars, etc. Researchers in the two disciplines face differing incentives and reward structures for publishing, teaching, funding, and service.
Comprehensive Database and Machine Learning Put Focus on Gut Microbiome

A technology company in Gaithersburg, MD, USA, has developed a three-pronged approach to help researchers analyze microbiome samples. It includes a thorough microbiome database, an algorithm to search it, and a cloud platform to return useful information.
Utilizing Prediction Intervals for Unsupervised Detection of Fraudulent Transactions: A Case Study

Money laundering operations have a high negative impact on the growth of a country’s national economy. As all financial sectors are increasingly being integrated, it is vital to implement effective technological measures to address these fraudulent operations. Machine learning methods are widely used to classify an incoming transaction as fraudulent or non-fraudulent by analyzing the behaviour of past transactions. Unsupervised machine learning methods do not require label information on past transactions, and a classification is made solely based on the distribution of the transaction. This research presents three unsupervised classification methods: ordinary least squares regression-based (OLS) fraud detection, random forest-based (RF) fraud detection and dropout neural network-based (DNN) fraud detection. For each method, the goal is to classify an incoming transaction amount as fraudulent or non-fraudulent. The novelty in the proposed approach is the application of prediction interval calculation for automatically validating incoming transactions. The three methods are applied to a real-world dataset of credit card transactions. The fraud labels available for the dataset are removed during the model training phase but are later used to evaluate the performance of the final predictions. The performance of the proposed methods is further compared with two other unsupervised state-of-the-art methods. Based on the experimental results, the OLS and RF methods show the best performance in predicting the correct label of a transaction, while the DNN method is the most robust method for detecting fraudulent transactions. This novel concept of calculating prediction intervals for validating an incoming transaction introduces a new direction for unsupervised fraud detection. Since fraud labels on past transactions are not required for training, the proposed methods can be applied in an online setting to different areas, such as detecting money laundering activities, telecommunication fraud and intrusion detection.
Speckle Simulation for Metrology

In most imaging applications, speckle is a nuisance, and various methods are used to attempt to suppress it. However, in the field of metrology, speckle can be a friend rather than a foe, and many methods for using speckle in measurement have been devised. Here we will simulate only a few of such methods.
Simulation of Speckle in Free-Space Propagation

Calculating the diffraction patterns of clear apertures of various shapes is a common task. However, similar calculations for apertures with a complex internal structure are less common. In the case studied here, the internal structure of a rectangular aperture is a random pattern of phase representing a diffuser. To perform the simulation of propagation, choices must be made for the number of samples within the aperture (the parameter in what follows) and the total number of samples in the simulation (the parameter in what follows), as well as the method of calculation. Guidelines for choices of these parameters and methods for clear apertures do not apply in this more complex case.
Appendix B - Some Subtleties in Dealing with Mathematica Images

In the chapters of this book, we have often had occasion to use the Mathematica commands <strong>Image[]</strong> and <strong>ImageData[]</strong> when moving between numerical data and images. Given an array of real numbers, an image can be obtained by using the command <strong>Image[</strong><i>array</i><strong>]</strong>, while to go in the opposite direction, from an image to real numbers, the command <strong>ImageData[</strong><i>image</i><strong>]</strong> is used. There are some peculiarities of these commands that we wish to point out here.
Semidecoupled decomposition-based fractional-order variational model for low-light enhancement

Low-light enhancement is an important technique for improving image quality. This is because low-light enhancement is expected to improve image visibility while maintaining visual naturalness of the image. In recent years, many methods have been researched to enhance low-light images, including histogram-based, fusion-based, and learning-based methods. The most representative and widely used low-light image enhancement method is the so-called Retinex-based method. However, they tend to have many limitations. The limitations of the Retinex-based method are as follows. (1) Due to strong imaging noise or less-effective image decomposition, this results in a large number of artifacts in the enhanced results. (2) Although the first problem can be partially solved by exploring prior information, it often complicates the optimization process. (3) Small-magnitude details are often lost in enhanced results. To overcome these drawbacks, we propose a model called the fractional-order Retinex model. At the same time, Retinex images are decomposed in an effective semidecoupled way. More concretely, the illumination layer T is gradually estimated only with the observed image S based on the proposed variation model, whereas the reflectance layer R is jointly estimated by the intermediate T and S. Experimental results demonstrate the effectiveness of our method.
Neural Network Based Task Scheduling in Cloud Using Harmony Search Algorithm

Cloud computing is a field that is growing in popularity every passing day for computation and storage purposes. Having already been adopted by companies like Google, Microsoft, IBM, etc., it is slowly but steadily making its way into the mainstream market, which is evident from the exponential increase in the size of the cloud servers over the past few years. Being a computational process where extensible resources are conveyed as facilities to customers using online methods, Cloud computing needs to have a method for choosing the correct resources for executing processes within a given framework. This is called task-scheduling. Various task-scheduling models are in use currently, but the one that we will be focusing on is the NN (Neural Network)-based model. This model was set up to estimate the task execution status for resource allotment among the candidates. An NN-based model makes use of various scheduling algorithms to formulate optimum results in terms of quality of service (QoS), total cost, service satisfaction, etc. Through our work, we are simulating various task-scheduling algorithms in a virtual environment and comparing their efficiency based on the results we obtain from these simulations. While our focus will be on an emerging metaheuristic optimization algorithm called the Harmony Search Algorithm, we are also running simulations for the Moth algorithm, and the Genetic Algorithm.
First-Order Statistics of Speckle Amplitude

By first-order statistics of speckle we mean the statistics observed at a point in space or a point in time, with the statistics being over an ensemble of rough surfaces or rough diffusers. First we consider the statistics of speckle complex amplitude, relevant when using ultrasound or microwave illumination of surfaces that are rough on the scale of their individual wavelengths. For such imaging modalities, it is possible to measure both the magnitude and the phase of the wavefields. In Chapter 3 we turn to the statistics of speckle intensity, which is the most relevant quantity for the optical region of the spectrum, where a detector can measure only intensity unless some form of interferometry is used.
Appendix A - Some Subtleties in Speckle Simulation With the 4f Imaging System

As pointed out in Section 1.3, there are some subtleties associated with the generation of simulated speckle using the 4f imaging system that arise from the finite size of the arrays used in the simulation. The phenomenon involves a complicated relationship between the size of the arrays used and the diameter of the focal-plane stop. It is possible to choose these parameters in such a way that the contrast of the speckle (i.e., the standard deviation of intensity normalized by the mean intensity) never achieves its ideal value of unity.
Organizational readiness and innovation in the Jordanian telecommunication companies

Purpose The purpose of this study is to examine the effect of organizational readiness (OR) dimensions (organizational culture, climate and capability) on three types of innovations (INs) (service, process IN and entering new markets) in telecommunication companies. The study also tests the mediating role of employee engagement (EE) in the causal relationship between OR and IN. Design/methodology/approach In the theoretical framework, a deep and broad review of the literature was presented to determine the study variables and hypotheses that were tested in the field study. The study sample consisted of 306 respondents distributed to the headquarters of the three companies (Zain, Orange and Umniah) working in the Jordanian telecommunications sector. The number of questionnaires retrieved and valid for analysis was 255 (83%). Findings Results indicate a positive effect of organizational climate and organizational capacity on process IN and entering new markets. While organizational culture had no significant effect on the three types of IN EE did not have a mediating role in the relationship between OR and IN. Research limitations/implications The results of this study are related to the telecommunications sector as a highly competitive service sector and more able to work remotely with regard to customers, so its results cannot be generalized to other sectors such as the industry sector, which has suffered in recent years from the epidemic more than other sectors. Practical implications The study of OR as a concept, dimensions and effects provides great experience for leaders and managers facing the challenges of competition and threats posed by the Covid-19 pandemic. This study also helps researchers to study OR in new areas and in relation to other concepts. Social implications The OR covers a wide field that includes the individual, the group and the company. Therefore, readiness includes a social experience that can extend from the company to the community. Originality/value The study gains an important value by revealing that organizational culture as a dimension of readiness does not have a significant impact on IN. With the readiness to respond quickly to challenges, culture can be more inclined to the status quo and the prevailing routine than to IN and change.
SECURING BANGLA TEXT COMMUNICATION USING IMAGE STEGANOGRAPHY WITH DYNAMIC SUBSTITUTION IN IOT ENVIRONMENT

Privacy and security of information are the prime concerns of today’s internet users. They want to get better and reliable services without sacrificing privacy from the current paradigm like the Internet of Things (IoT). In order to ensure IoT as a truthful service platform for the vast users, we need to protect the information that evolves throughout the internet and in the storage. In this paper, we propose a steganographic method for Bangla text communication over the unsecured internet based on RGB color image with the help of secret key for better protection of information. The proposed system consists of four main components namely preprocessing, embedding, extraction, and post-processing. We use dynamic positioning in case of substitution of (Bangla) text into the image. The secret key is transmitting throughput the image so that there is no extra hassle for communicating the secret key. We justify our proposed approach by using simulation with respect to imperceptibility, capacity, and robustness. We compare the result of the proposed method with other existing methods and get a better result over several existing efficient methods.
A Hybrid Architecture Based on Blockchain to Ensure Security, Privacy, and Trust in IoT

IoT is an enormous network of interconnected devices that gather data and share it over the internet. Today, IoT suffers from malevolent behaviours and actions that aim to damage and compromise privacy and security. Moreover, these issues and the new IoT requirements are not effectively covered by current architectures. Thus, this paper is an extended work of the authors' previous paper, which suggested a new architecture adhering to the IoT needs that have evolved. It suggests a hybrid architecture that integrates our previous one with a Blockchain-based architecture, to combine their significant features. The hybrid architecture fulfils the emerged requirements of IoT and exploits the Blockchain to increase trust, security and privacy. Further, the hybrid architecture delivers a novel structure to build the policy header, which is an access control list used to regulate the local network and was inherited from the Blockchain-based architecture. The Cooja simulator is used to compare the two structures, revealing that the new structure can use 87% less storage and 83% less computing.
Web-Based Mail Search Using the Levenshtein Distance Algorithm

The use of information technology in filing letters has been widely applied, especially in government institutions. In the Administrative Affairs of the Police Information Technology Bureau, the archiving of incoming and outgoing letters is still done manually, by utilizing records in a large agenda book and stored in a large folder. The absence of a system capable of assisting mail processing causes work to be inefficient, especially in terms of searching for letters. The purpose of this research is to produce a website that can help search letters by applying the Levenshtein Distance Algorithm. The design begins with needs analysis, design, implementation, verification (system testing), operation and maintenance. A website that can search letters by applying the Levenshtein Distance Algorithm was generated. This website is very helpful in the process of searching for letters to the Administrative Affairs of the Police Information Technology Bureau.
The Development of Student Arena Universitas Sumatera with Green Architecture

With a total number of 32,000 people, it is the reason to develop a student arena building that is more appropriate and sufficient to accommodate all students from 15 faculties. The location chosen was Medan Baru District is the Center for Education and Activities Trade / Business with a land area of 1.5 Ha. Design using a green architectural theme approach is such a way that the building can still maintain the image of the campus, which is a green campus while minimizing adverse impacts on the environment, which is still very natural. This building has supporting buildings that can support student activities from all faculties.
An Exploration of the Potential Role of Augmented Reality-Based Pedagogical Tools

Augmented Reality (AR) is an Information Communication Technology (ICT) that is rapidly growing in today’s modern world. Many industries are striving to improve and carry through the AR. When it comes to the education sector, specially the pedagogical tools for elementary education, the ICT tools like AR can prove to be of a great potential. The quest for such interactive education has become all the more relevant in the times of COVID-19, when the teachers are struggling on the question of how to engage the kids better. The current paper provides the readers with the knowledge of the use and impact of using AR in elementary education.
Tabular machine learning using conjunctive threshold neural networks

We propose a novel three-layer neural network architecture with threshold activations for tabular data classification problems. The hidden layer units correspond to trainable neurons with arbitrary weights and biases and a step activation. These neurons are logically equivalent to threshold logic functions. The output layer neuron is also a threshold function that implements a conjunction of the hidden layer threshold functions. This neural network architecture can leverage state-of-the-art network training methods to achieve high prediction accuracy, and the network is designed so that minimal human understandable explanations can be readily derived from the model. Further, we employ a sparsity-promoting regularization approach to sparsify the threshold functions to simplify them, and to sparsify the output neuron so that it only depends on a small subset of hidden layer threshold functions. Experimental results show that our approach outperforms other state-of-the-art interpretable decision models in prediction accuracy. • Proposed CT-Net achieves high accuracy for tabular data classification problems. • Minimal explanations can be readily derived from proposed neural network. • Sparsity-promotion training is leveraged to improve and simplify the network.
RANCANGAN PENATAAN ARSIP KARYA SENI PERTUNJUKAN UNTUK MENDUKUNG PENGEMBANGAN TRIDHARMA PERGURUAN TINGGI DI ISI SURAKARTA

This study aims to identify the types of art archives and arrange their arrangement designs in order to support the development of the tridharma of higher education at the Indonesian Institute of the Arts (ISI) Surakarta. The archives studied are the archives of performing arts that are the result of the Tridharma organization. The research was conducted using a qualitative descriptive method. That is describing a situation based on the facts that appear. The results of field observations show that the performing arts document consists of text, audio, and video. Its creation cannot be separated from previous works of art as a reference or source of works of art. Artwork documents are archives because they are the result of the tridharma process, but have only been managed as a library. The archives are dominated by final works of art from study programs at the Faculty of Performing Arts, results of artistic research from the Research Centre, and results of community service works of art from the Centre for Community Service. ISI Surakarta Archives Retention Schedule stipulates the archive as a static archive (permanent) after the retention period expires. Its arrangement requires a design according to archival standards. The design is made by taking into account the principle of provenance and original order. The design includes archive creators, archive classification (final project reports, research, and community service), archive indexes (reports, audio visual recordings, and intellectual property certificates), archive cross-linking to link different types of archives for the same archive, information archives, access conditions and archive service procedures. Keywords: archive, arrangement, design, performing arts, tridharma