Reviewerly/exHarmony huggingface dataset:
Reliable Cloud Software Development Architectures and Business Models Case Study

Development of cloud applications directly on the cloud infrastructure has become a common approach. Reliability concerns have also become more of a challenge during the last years. Our goal is to assert the potentials deriving from an integrated development environment, adopting software reliability concepts and fault tolerant techniques, as part of the cloud core services. As a methodology, we propose the implementation of a new cloud service Reliable Integrated Development Environment as a Service to become part of the existing core services. Our objectives are to 1) fulfill the need of having a development environment independent from personal desktop environments; 2) code development environment should be offered directly from the cloud service providers. With fault tolerant technique integration, we also suggest that the reliability of the cloud system should be handled from the cloud developers at software level when cloud quality assurance services fail to do so at their different levels. We also propose potential business models to become part of this core service for the major cloud providers. We introduce a new High Availability Coding model providing a roadmap for the future.
Topic Analysis of Microblog About “Didi Taxi” Based on K-means Algorithm

In the age of information and digitization, most users publish and obtain real-time information by microblog in social networks. Through effective means, we can accurately discover, organize, and utilize the valuable information hidden behind the massive short texts of social networks. Then we can explore hot topics in microblog, which is conducive to public opinion monitoring and marketing development. In today's society, Didi Taxi has become a necessary choice for many users to travel. This paper applied K-means clustering algorithm to topic analysis of Sina microblog short text on Didi Taxi. We crawled 17226 search results of microblog relevant to the topic of Didi Taxi from April 2019 to June 2019. After a series of data cleaning and data preprocessing steps, we used TF-IDF method to represent 15054 pieces of text data after processing. Through the evaluation of silhouette coefficient, we set the dimension of text 300 and the number of clusters 34 with K-means. Next, we extracted 8 topic clusters from 34 clusters, which include the advantages and disadvantages of Didi Taxi and its development status. Finally, we discussed the results by human check in semantic perspective. Through the topic analysis of microblog, we can understand the public’s attitude to Didi Taxi and provide the basis for the management of the government or company in the future.
Research review on Transformer Fire Protection Technology

Transformer is the core equipment in power plant and substation. Its safety problem directly affects the normal operation of the whole power plant or substation. In the event of fire, it not only affects the normal operation and system load of the system, but also affects the normal operation and load of the system. In this paper, the main causes of transformer fire, fire pre-warning technology and fire extinguishing technology are introduced comprehensively. A on-line detection fire warning technology without blackout is proposed by analyzing transformer operating noise. Finally, the substation fire protection technology from the aspect of civil engineering design is introduced briefly.
A Survey on Machine Learning Algorithms for the Diagnosis of Breast Masses with Mammograms

Breast cancer is leading cancer among women for the past 60 years. There are no effective mechanisms for completely preventing breast cancer. Rather it can be detected at its earlier stages so that unnecessary biopsy can be reduced. Although there are several imaging modalities available for capturing the abnormalities in breasts, mammography is the most commonly used technique, because of its low cost. Computer-Aided Detection (CAD) system plays a key role in analyzing the mammogram images to diagnose the abnormalities. CAD assists the radiologists for diagnosis. This paper intends to provide an outline of the state-of-the-art machine learning algorithms used in the detection of breast cancer developed in recent years. We begin the review with a concise introduction about the fundamental concepts related to mammograms and CAD systems. We then focus on the techniques used in the diagnosis of breast cancer with mammograms.
Philosophy of Fuzzy Logic as Fundamental of Decision Making Based On Rule

Abstract The logic of talking about rules of thought so that these rules can draw correct conclusions, but in reality in everyday life many things are found that cannot be stated that something is absolutely true or absolutely false. This study aims to explain that the philosophy of fuzzy logic is the basis for decision making based on IF-THEN rules. Related to the role of philosophy of science as the basis for the development of fuzzy logic, it cannot be separated from ontology, epistemology and axiology. The findings of this study are Ontology covering decision making and rules for decision making. Decision making is an election based on certain criteria for two or more possible alternatives while decision making rules are fuzzy rules in the form of IF-THEN. While epistemology of fuzzy logic is related to how the process to get output, how are the stages to take decisions. The process of getting output consists of fuzzification and inference. The output obtained by the Z ∗ value of 38541.67 and the rule-based fuzzy logic axiology in giving restaurant waiter tips is useful to be able to determine the total cash of tips that will be given to the restaurant waiter. The total cash of appropriate tips given based on service and food to the restaurant waiter is Rp.38.541.
Continuous assessment in software engineering project course using publicly available data from GitHub

This paper describes an approach for assessment in a large software engineering project course. We propose an approach for continuously collecting information from a source code repository and collaboration tool, and using this information for assessing student contributions and also for assessing the course as a whole from the teacher's standpoint. We present how we display metrics for how the students perform in relation to some of the requirements of the course. We argue that continuous summative assessment feedback to the students on how they are performing in the project is a suitable strategy for ensuring active participation from the students for the duration of the project course.
On-chip deterministic optical SWAP gate

SWAP gate is an essential element of nearest neighbor qubit networks which carry out quantum information processing tasks. The customary way of applying the SWAP gate consists of consecutive probabilistic operations which requires considerable resources. We propose the theoretical design of a structure constructed from linear optical elements on-chip, which applies the SWAP gate deterministically. Using reconfigurable elements, the structure can apply either the SWAP or instead the identity gate. Utilizing components frequently employed in the design and physical implementation of photonic networks on-chip, the structure can be implemented on various platforms. With its compact structure requiring no detectors or ancilla qubits, our design substantially reduces the required resources for the implementation of the SWAP gate.
Multi-resolution Hashing for Fast Pairwise Summations

A basic computational primitive in the analysis of massive datasets is summing simple functions over a large number of objects. Modern applications pose an additional challenge in that such functions often depend on a parameter vector y (query) that is unknown a priori. Given a set of points X and a pairwise function w(x,y), we study the problem of designing a data-structure that enables sub-linear time approximation of the summation of w(x,y) for all x in X for any query point y. By combining ideas from Harmonic Analysis (partitions of unity and approximation theory) with Hashing-Based-Estimators [Charikar, Siminelakis FOCS'17], we provide a general framework for designing such data structures through hashing that reaches far beyond what previous techniques allowed. A key design principle is constructing a collection of hash families, each inducing a different collision probability between points in the dataset, such that the pointwise supremum of the collision probabilities scales as the square root of the function w(x,y). This leads to a data-structure that approximates pairwise summations using a sub-linear number of samples from each hash family. Using this new framework along with Distance Sensitive Hashing [Aumuller, Christiani, Pagh, Silvestri PODS'18], we show that such a collection can be constructed and evaluated efficiently for log-convex functions of the inner product between two vectors. Our method leads to data structures with sub-linear query time that significantly improve upon random sampling and can be used for Kernel Density, Partition Function Estimation and sampling.
Moving Vehicle Detection with Shadow Elimination Based on Improved ViBe Algorithm

Abstract Moving vehicle detection based on video processing has been widely used in intelligent transportation system recently. However there are also many problems, such as dynamic background, ghost region, and shadow of moving objects. This paper proposes an improved ViBe object detection algorithm. First, an accurate background image is obtained by using the multi-frame averaging method, and then the background model is initialized by this accurate background image, thus effectively reducing the generation of ghost region. Whenever there is no moving object for a fixed number of consecutive frames in the video, this frame is updated to the background image. Conservative update strategy and foreground point counting method are adopted to update the background and reduce the impact of dynamic background on the foreground detection. Next, the foreground image detected by improved ViBe algorithm is input into the shadow elimination method proposed in this paper. Shadows in foreground pixels are detected in RGB color space, and then the pixels determined as shadows are eliminated. Finally, accurate moving vehicles are obtained. Our algorithm can effectively eliminate the shadows of moving vehicles, quickly adapt to background changes and illumination mutation, and get accurate moving objects, which is helpful for vehicle contour extraction and subsequent image processing.
Verifying Source Citations in the Hadith Literature

Historians rely on hadiths (narratives about Muhammad) as a source for writing the history of early Islam. Each hadith is preceded by an isnād, which is a list of names purporting to give the sequence of individuals who transmitted it. Scholars apply a variety of methods to the isnāds of hadiths in order to determine their dates and geographic origins. These methods presuppose the absence of inadvertent mistakes in the names cited. Because the hadith literature is so voluminous and unwieldy, the systematic discovery and correction of these mistakes using manual methods is unfeasible. We introduce new computational and statistical methods for automatically detecting a subset of these errors and correcting them. We do so by investigating the citations of sources in the isnāds documented in the digital hadith repository, Gawāmiʿ al-Kalim.
Sensitive and Private Data Analysis

Each day an extensive amount of data is produced from various organisations, such as e-commerce, IT, hospitals, retail and supply chain, etc. Due to the expansion of computer devices and advances in technology this immense amount of data has been collected and analysed to support decision making. The examination of such data is advancing businesses and contributing advantageously to society in numerous diverse areas. However, serious privacy concerns are raised due to the storage and flow of potentially sensitive data [31]. Strategies that permit the knowledge extraction from the data, while protecting privacy, are known as privacy-preserving data mining (PPDM) techniques. This paper surveys the analysis of private and sensitive data using various PPDM algorithms and techniques. We also highlighted their advantages and limitations within various contexts.
An expert system for diagnosing plant diseases using certainty factor and backward chaining based on android

Abstract Rice is a producer of basic human needs that have a very important role. Every use of new seeds can cause disease in new plants and often the results obtained are not optimal because they do not know more precisely the type of disease that attacks the rice plants. Most rice farmers still use manual methods to find out the existence of the disease in the rice plants, namely by seeing it directly in the rice fields so that often rice farmers are too late to know these symptoms due to the lack of more knowledge and understanding of rice plants. To overcome the problems that occur it takes a system that has the knowledge like an expert. In this study, an expert system with an Android-based design was designed to make it easier for farmers to access information and can be used as a tool to diagnosediseases in rice plants so that they can be accessed wherever and whenever to overcome the problems faced by farmers. In this system applying the backward chaining method as the basis of the rules and the certainty factor method as weighting, so that by using the method that has been applied in testing various types of diseases and symptoms, the diagnosis of the type of blast disease is 68,83%.
Sentiment Analysis on Arabic Content in Social Media

In recent years, social networks become an information goldmine provides analyzes and inferences rich environment which can be exploited for the development of knowledge in various fields.
W-LDMM: A Wasserstein driven low-dimensional manifold model for noisy image restoration

The Wasserstein distance originated from the optimal transport theory is a general and flexible statistical metric in a variety of image processing problems. In this paper, we propose a novel Wasserstein driven low-dimensional manifold model (W-LDMM), which tactfully embraces the Wasserstein distance between two noise histograms and low-dimensional manifold regularization for noisy image restoration. Specifically, with the Wasserstein distance, the discrepancy between the estimated noise histogram and the reference noise histogram is minimized to improve the noise estimation accuracy. Moreover, based on the fact that the patch manifold structures of many natural images are low dimensional, the dimension of the image patch maniflod is utilized as regularization constraint. The key idea of W-LDMM is that the Wasserstein distance for noise constraint and the low-dimensional manifold for image regularization are complementary to each other, rather than isolated and uncorrelated. Together, they promote the W-LDMM to exhibit excellent restored results in Gaussian denoising and noisy image inpainting. Finally, extensive experiments confirm that our W-LDMM can greatly improve visual and quantitative performance compared to several popular image restoration models.
Polynomial-Time Updates of Epistemic States in a Fragment of Probabilistic Epistemic Argumentation

Probabilistic epistemic argumentation allows for reasoning about argumentation problems in a way that is well founded by probability theory. Epistemic states are represented by probability functions over possible worlds and can be adjusted to new beliefs using update operators. While the use of probability functions puts this approach on a solid foundational basis, it also causes computational challenges as the amount of data to process depends exponentially on the number of arguments. This leads to bottlenecks in applications such as modelling opponent's beliefs for persuasion dialogues. We show how update operators over probability functions can be related to update operators over much more compact representations that allow polynomial-time updates. We discuss the cognitive and probabilistic-logical plausibility of this approach and demonstrate its applicability in computational persuasion.
MemGANs: Memory Management for Energy-Efficient Acceleration of Complex Computations in Hardware Architectures for Generative Adversarial Networks

Generative Adversarial Networks (GANs) have gained importance because of their tremendous unsupervised learning capability and enormous applications in data generation, for example, text to image synthesis, synthetic medical data generation, video generation, and artwork generation. Hardware acceleration for GANs become challenging due to the intrinsic complex computational phases, which require efficient data management during the training and inference. In this work, we propose a distributed on-chip memory architecture, which aims at efficiently handling the data for complex computations involved in GANs, such as strided convolution or transposed convolution. We also propose a controller that improves the computational efficiency by pre-arranging the data from either the off-chip memory or the computational units before storing it in the on-chip memory. Our architectural enhancement supports to achieve 3.65x performance improvement in state-of-the-art, and reduces the number of read accesses and write accesses by 85% and 75%, respectively.
Constructing Higher Nonlinear Odd-Variable RSBFs With Optimal AI and Almost Optimal FAI

Rotation symmetric Boolean functions (RSBFs) are nowadays studied a lot because of its easy operations and good performance in cryptosystem. This paper constructs a new class of odd-variable RSBFs with optimal algebraic immunity (AI). The nonlinearity of the new function, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2^{n-1}-\binom {n-1}{k}+2^{k-4}(k-3)(k-2)$ </tex-math></inline-formula> , is the highest among all existing RSBFs with optimal AI and known nonlinearity, and its algebraic degree is also almost highest. Besides, the class of functions have almost optimal fast algebraic immunity (FAI) at least for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n &lt; 17$ </tex-math></inline-formula> , which is actually the highest possible value for the designated number of variables.
Trajectory Privacy Protection Based on Location Semantic Perception

A personalized trajectory privacy protection method based on location semantic perception to achieve the personalized goal of privacy protection parameter setting and policy selection is proposed. The concept of user perception is introduced and a set of security samples that the user feels safe and has no risk of privacy leakage is set by the user’s personal perception. In addition, global privacy protection parameters are determined by calculating the mean values of multiple privacy protection parameters in the sample set. The concept of location semantics is also introduced. By anonymizing the real user with [Formula: see text] collaborative users that satisfy the different semantic conditions, [Formula: see text] query requests which do not have the exact same query content and contain precise location information of the user and the collaborative user are sent to ensure the accuracy of the query results and avoid privacy-leaks caused by the query content and type. Information leakage and privacy level values are tested for qualitative analysis and quantitative calculation of privacy protection efficacy to find that the proposed method indeed safeguards the privacy of mobile users. Finally, the feasibility and effectiveness of the algorithm are verified by simulation experiments.
An Algorithmic Approach Based on CMS Edge Detection Technique for the Processing of Digital Images

Research in the field of digital image processing (DIP) has increased in the current scenario. Edge detection of digital images is considered as an important area of research in DIP. Detecting edges in different digital images accurately is a challenging work in DIP. Different methods have been introduced by different researchers to detect the edges of images. However, no method works well under all conditions. In this chapter, an edge detection method is proposed to detect the edges of gray scale and color images. This method focuses on the combination of Canny, mathematical morphological, and Sobel (CMS) edge detection operators. The output of the proposed method is produced using matrix laboratory (MATLAB) R2015b and compared with Sobel, Prewitt, Roberts, Laplacian of Gaussian (LoG), Canny, and mathematical morphological edge detection operators. The experimental results show that the proposed method works better as compared to other existing methods in detecting the edges of images.
A Nelsonian Response to ‘the Most Embarrassing of All Twelfth-century Arguments’

Alberic of Paris put forward an argument, ‘the most embarrassing of all twelfth-century arguments’ according to Christopher Martin, which shows that the connexive principles contradict some other logical principles that have become deeply entrenched in our most widely accepted logical theories. Building upon some of Everett Nelson’s ideas, we will show that the steps in Alberic of Paris’ argument that should be rejected are precisely the ones that presuppose the validity of schemas that are nowadays taken as some of the most trivial logical truths: (A∧B) →AB A and (A∧B) →AB B, i.e. Simplification.
A Complete Planner for Temporal Answer Set Programming

In this paper we present tasplan, a complete planner for temporal logic programs. The planner receives a planning specification as input, having the form of a temporal ASP program, and obtains as output one or several alternative (shortest) plans, if the problem is solvable, or answers that no solution exists, otherwise. The tool allows different search strategies, including informed search algorithms if the user defines a domain-dependent heuristics with additional program rules.
Software-Based Buffering of Associative Operations on Random Memory Addresses

An important concept for indivisible updates in parallel computing are atomic operations. For most architectures, they also provide ordering guarantees, which in practice can hurt performance. For associative and commutative updates, in this paper we present software buffering techniques that overcome the problem of ordering by combining multiple updates in a temporary buffer and by prefetching addresses before updating them. As a result, our buffering techniques reduce contention and avoid unnecessary ordering constraints, in order to increase the amount of memory parallelism. We evaluate our techniques in different scenarios, including applications like histogram and graph computations, and reason about the applicability for standard systems and multi-socket systems.