Reviewerly/exHarmony huggingface dataset:
Enabling MapReduce based Parallel Computation in Smart Contracts

Smart Contracts based cryptocurrencies such as Ethereum are becoming increasingly popular in various domains: but with this increase in popularity comes a significant decrease in throughput and efficiency. Smart Contracts are executed by every miner in the system serially without any parallelism, both inter and intra-Smart Contracts. Such a serial execution inhibits the scalability required to obtain extremely high throughput pertaining to computationally intensive tasks deployed with such Smart Contracts. While significant advancements have been made in the field of concurrency, from GPU architectures that enable massively parallel computation to tools such as MapRe-duce that distributed computing to several nodes connected in the system to achieve higher performance in distributed systems, none are incorporated in blockchain-based distributed computing. The team proposes a novel blockchain that allows public nodes in a permission-independent blockchain to deploy and run Smart Contracts that provide concurrency-related functionalities within the Smart Contract framework. In this paper, the researchers present “ConCurrency,” a blockchain network capable of handling big data-based computations. The technique is based on currently used distributed system paradigms, such as MapReduce, while also allowing for fundamental parallelly computable problems. Concurrency is achieved using a sharding protocol incorporated with consensus mechanisms to ensure high scalability, high reliability, and better efficiency. A detailed methodology and a comprehensive analysis of the proposed blockchain further indicate a significant increase in throughput for parallelly computable tasks, as detailed in this paper.
Reasoning with Ambiguity

Abstract We treat the problem of reasoning with ambiguous propositions. Even though ambiguity is obviously problematic for reasoning, it is no less obvious that ambiguous propositions entail other propositions (both ambiguous and unambiguous), and are entailed by other propositions. This article gives a formal analysis of the underlying mechanisms, both from an algebraic and a logical point of view. The main result can be summarized as follows: sound (and complete) reasoning with ambiguity requires a distinction between equivalence on the one and congruence on the other side: the fact that $$\alpha $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>α</mml:mi> </mml:math> entails $$\beta $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>β</mml:mi> </mml:math> does not imply $$\beta $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>β</mml:mi> </mml:math> can be substituted for $$\alpha $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>α</mml:mi> </mml:math> in all contexts preserving truth. Without this distinction, we will always run into paradoxical results. We present the (cut-free) sequent calculus $$\mathsf {AL}^{\textit{cf}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow> <mml:mi>AL</mml:mi> </mml:mrow> <mml:mi>cf</mml:mi> </mml:msup> </mml:math> , which we conjecture implements sound and complete propositional reasoning with ambiguity, and provide it with a language-theoretic semantics, where letters represent unambiguous meanings and concatenation represents ambiguity.
Forensic Block Chain and it's linkage with Artificial Intelligence: A new Approach

Forensic block chain is an emerging technique in the field of forensic science. It is a system integrated tool that helps in maintaining and representing the chain of custody, which ought to be maintained throughout the investigation. Forensic block chain is an approach wherein the investigators or law enforcement officials store the history of criminal case in a digitalized form on block chains which in turn stores the information on remote servers. The data regarding the criminal records are highly encrypted providing less probability for intruders to gain access into the system. Implementing artificial intelligence will help to manage and sort the data related to case records/criminal profile. AI helps in creating and analyzing a case report, and further processing of evidence. This will help the investigator to create more efficient hypothesis. Present study aims to link Artificial Intelligence to forensic block chain and develop a digitalize chain of custody of evidences that will help the forensic investigators to link the cases worldwide and help in solving crimes efficiently.
Syntactically and Semantically Annotated Hindi Corpus for an Opinion Mining System

Opinion mining, a field of Information Retrieval and Text Processing, has variety of applications in domains like business analytics, social reforms, politics, agriculture, youth psychology analysis, and mapping of future research directions. Lack of annotated corpus and lexical resources in Hindi is one of the reasons why opinion mining applications for Hindi web contents do not scale up to their English counterparts. In an ongoing research, the authors of this paper have developed a semantically and syntactically annotated corpus in the domain of home remedies from Hindi Weblogs. The objective of the development of corpus, methodologies used and outcomes in its current state are discussed in this paper. The corpus is being developed with an objective to develop an opinion mining system in the domain of food and health issues for Hindi text from weblogs. The corpus is expected to be useful to other researchers developing applications for Hindi web contents in the area of opinion mining and related fields.
A Posteriori Outlier Rejection Approach Owing to the Well-ordering Property of a Sample Consensus Method for the Stitching of Drone-based Thermal Aerial Images

Abstract In this study, the authors generate panoramic images using feature-based registration for drone-based aerial thermal images. In the case of drone aerial images, the distortion of the photographing angle due to the unstableness in the shooting altitude deteriorates the performance of the stitching. Furthermore, for the thermal aerial images, the same objects photographed at the same time zone may have different colors due to the relative temperature, which may lead to a more severe condition to be stitched. Applying the scale-invariant feature transform descriptor, they propose a posteriori outlier rejection scheme to estimate the hypothesis of the mapping function for the stitching of consecutive thermal aerial images. By extension of the method of optimal choice of initial candidate inliers (OCICI) and a posteriori outlier rejection scheme using cross-correlation calculus, the authors obtained elaborate stitching of thermal aerial images. Their proposed method is numerically verified for its quality by comparing it with other possible approaches of post-outlier rejection treatments employed of OCICI. Also, after the Poisson blending using the finite difference method is conducted, the stitching performance is compared with some benchmark software such as Matlab-toolbox, OpenCV, Autopano Giga, Hugin, and PTGui.
Bow image retrieval method based on SSD target detection

The query image is usually a simple and single object in image retrieval, and the reference images in the database usually have many distractions. The precision of image retrieval can be greatly improved If the target regions in the database image are extracted during retrieval. So this paper proposes a Bow image retrieval method based on SSD target detection. First, the training gallery is manually annotated to record the location and size information. Second, the SSD target detection model is trained with the labeled training gallery to obtain the target object SSD model. Third, the SSD model is used to locate the similar target regions of the reference image and the query graph. Finally, the target region information is mapped into the convolutional features, and these feature vectors are used for image similarity matching. The performance of the proposed method is evaluated on Paris6k, Oxford5k, Paris106k and Oxford105k databases. The experimental results show that the accuracy of image retrieval will be greatly improved by adding optimization methods in the proposed image retrieval framework. The image retrieval accuracy of this method is higher than that of similar methods in recent years.
Remote State Design for Efficient Quantum Metrology with Separable and Non-Teleporting States

Measurements leading to the collapse of states and the non-local quantum correlations are the key to all applications of quantum mechanics as well as in the studies of quantum foundation. The former is crucial for quantum parameter estimation, which is greatly affected by the physical environment and the measurement scheme itself. Its quantification is necessary to find efficient measurement schemes and circumvent the non-desirable environmental effects. This has led to the intense investigation of quantum metrology, extending the Cramér–Rao bound to the quantum domain through quantum Fisher information. Among all quantum states, the separable ones have the least quantumness; being devoid of the fragile non-local correlations, the component states remain unaffected in local operations performed by any of the parties. Therefore, using these states for the remote design of quantum states with high quantum Fisher information can have diverse applications in quantum information processing; accurate parameter estimation being a prominent example, as the quantum information extraction solely depends on it. Here, we demonstrate that these separable states with the least quantumness can be made extremely useful in parameter estimation tasks, and further show even in the case of the shared channel inflicted with the amplitude damping noise and phase flip noise, there is a gain in Quantum Fisher information (QFI). We subsequently pointed out that the symmetric W states, incapable of perfectly teleporting an unknown quantum state, are highly effective for remotely designing quantum states with high quantum Fisher information.
Correlations among Software Testability Metrics

Testability is a relevant characteristic to evaluate software quality and it is addressed in most quality models. This characteristic is directly related to software testing activities and defines how easily a program can be tested. However, testability is not an intrinsic characteristic of the software and, therefore, it is necessary to identify attributes and measures for evaluating the software testability that, respectively, may influence and assess it. Most studies on testability focus on evaluating it on white box perspective. Thus, the goal of this study is to establish the correlation between the testability metrics into two perspectives (white and black box) and based on this correlation, we can evaluate the testability on other software artifacts such as Test Specification. We inittially identified a set of attributes (13) and measures (30) related to testability. Next, we proposed a set of 10 metrics to evaluate Testability from a Test Specification. Then, we conducted the empirical study in two mobile systems from industry to evaluate 3 attributes and 25 measures regarding the testability in those perspectives. As a result, 8 metrics that we proposed to measure testability of a test specification (black box level) had a significant Pearson’s correlation with 11 metrics commonly used to evaluate the testability in the white box level.
CloudMatrix Lite: A Real Trace Driven Lightweight Cloud Data Center Simulation Framework

The high complexities and coupling among modules of existing Cloud data center (CDC) simulators have greatly hindered their deployment, expansion and redevelopment. In order to resolve the issue, in this paper, we introduce a lightweight CDC simulation framework, CloudMatrix Lite. CloudMatrix Lite is driven by real virtual machine (VM) traces and mainly simulates the server consolidation of the CDC in four phases: Physical machine (PM) underloading detection, PM overloading detection, VM selection for migration and VM placement. With the low coupling in the design of CloudMatrix Lite, users are able to swiftly add and extend customized functions and modules. Also, CloudMatrix Lite supports most current mainstream open-source machine learning (ML) frameworks such as Pytorch. Hence users can conveniently develop ML-based methods to predict the fluctuated workloads in order to conclude resource management decisions more efficiently.
Anomaly Detection in Admission or Selection Examinations using Data Mining Techniques

The paper presents the utilization of data mining techniques to detect anomaly or fraud in admission or selection examinations. University authority and government often arrange admission examinations to narrow down the student admission process and recruit employees. However, in developing and underdeveloped countries, convicted people manage to supply question papers before examination or provide answers to candidates in different ways, including digital miniature contact devices. To address this issue, we propose an algorithm that utilizes Jenks natural breaks clustering method and FP-tree generation method to identify the anomaly or fraud in the exam by evaluating the subject wise marks. It is easier to classify using the proposed method due to the answer pattern, similar in nature, delivered to their candidates by criminals. Therefore, our proposed work helps investigate those anomalies with clustering of the evaluation marks and fetch the suspicious number pattern. Our experimental results show that the average F1 score for identifying suspicious patterns is 0.876, which is quite a satisfactory result.
Bayesian Filtering for Nonlinear Stochastic Systems Using Holonomic Gradient Method with Integral Transform

This paper proposes a symbolic-numeric Bayesian filtering method for a class of discrete-time nonlinear stochastic systems to achieve high accuracy with a relatively small online computational cost. The proposed method is based on the holonomic gradient method (HGM), which is a symbolicnumeric method to evaluate integrals efficiently depending on several parameters. By approximating the posterior probability density function (PDF) of the state as a Gaussian PDF, the update process of its mean and variance can be formulated as evaluations of several integrals that exactly take into account the nonlinearity of the system dynamics. An integral transform is used to evaluate these integrals more efficiently using the HGM compared to our previous method. Further, a numerical example is provided to demonstrate the efficiency of the proposed method over other existing methods.
Anomaly Detection Between Judicial Text-Based Documents

The problem of searching for anomalies or outliers are extremely important in various fields with problems like fraud detection, crime research, network reliability analysis, medical diagnostics etc.What is an anomaly in the judicial system? A court case is to be considered as an anomaly if the judge's decision differs significantly from existing decisions in similar cases.In most cases, the existing outlier's search methods use high-dimensional domains in which data can contain hundreds of dimensions. Such an approach requires lots of resources and clearly is not efficient.Objectives: In this article, the authors:present two methods (or two models) for searching for anomalies in judicial practice; give a comparative analysis of the results of the effectiveness of both methods. Methodology: The First method for searching for anomalies is a mix of two models: classification and similarity algorithms. Here algorithms like Logistic regression, Extreme Gradient Boosting (XGBoost), Tensorflow for classification and Latent Dirichlet Allocation (LDA), Latent semantic indexing (LSI) to find similar documents. The Second method shows the usage of the Bidirectional Encoder Representations from Transformers (BERT) embedding model and the Annoy indexing model.Findings: The second method shows better and fast results for searching outliers.Data source: Authors used the set of acts provided by the Supreme Court of the Republic of Kazakhstan. The dataset contains 1 million text documents and metadata.
The Morphological Annotation of Reduplication-Circumfix Intersection in Indonesian

In this paper, I report on the implementation of a morpheme-level annotation scheme for Indonesian [1], particularly the annotation of reduplications. Utilizing the NooJ program [2] and a set of novel linguistic resources, the majority of reduplications formed according to a number of distinct patterns were successfully annotated. However, one reduplication-circumfix intersection pattern could not be annotated. This is because the current NooJ morphological grammar is not designed to read the hyphen symbol, an orthographical cue that connects a root to its copy in Indonesian reduplication. Failure to read this symbol disconnects the opening and closing elements of the circumfix that surrounds its reduplication base. To overcome this problem, I introduced additional circumfix rules into the current morphological grammar without using any hyphen symbols in the rule definitions. The circumfix elements in the rules are linked as a single dependent unit using syntactic grammar whose rules NooJ allows to contain the hyphen symbol. However, this method promotes undesirable ambiguities. To overcome this side effect, I have modified the existing syntactic grammar to eliminate these ambiguities.
Analysis of electronic educational resources for distance learning

This article analyzes modern trends of educational processes stipulated by features of modern society development. Modifications in surrounding reality dictate necessity of updating the learning process towards active transition to distance education. Increased demand of students for electronic educational resources stipulated necessity to analyze representation of electronic platforms and identification of their features. This work is aimed at analysis of the electronic educational resources being most popular among students, and identification of features of their use in distance education. The study was performed using analysis of scientific publications devoted to the research subject, survey and results of its analysis, analysis of electronic educational resources aiming at identification of their features for distance education. This work is comprised of two stages. At the first stage, the students of two learning levels were surveyed (universal bachelor degree course and high school), which allowed to reveal common requirements for the two levels of preparation for electronic educational resources and features of their selection by respondents. At the second stage, the most demanded for distance learning electronic educational platforms and their resources were analyzed to assist teacher to develop algorithm of combination of various electronic resources in the frames of content of certain course. A teacher should be aware of the content of the most popular educational platforms of opensource information educational environment, determine reasonability of access to this or that electronic resource and promote students to active participation in distance form.
Developmental Test and Requirements: Best Practices of Successful Information Systems Using Agile Methods

This article provides insights into the current state of developmental testing (DT) and requirements management in Department of Defense information systems employing Agile development. The authors describe the study methodology and provide an overview of Agile development and testing. Insights are described for requirements, detailed planning, test execution, and reporting. This work articulates best practices related to DT and requirements management strategies for programs employing modernized Software Development Life Cycle practices.
A Survey of Automatic Text Summarization Technology Based on Deep Learning

With the rapid development of the Internet, the amount of network text data is increasing day by day. It is increasingly becoming a challenge to quickly mine useful information from massive amounts of text data. The emergence of automatic summarization technology provides new ideas and methods for solving this problem. Compared with extractive summarization model, abstractive summarization model more closely resembles the process of human summarization, giving it important research significance. In recent years, with the development of deep learning methods, text summarization technology based on deep learning has made unprecedented breakthroughs. Based on the current mainstream sequence-to- sequence framework, we summarize the state-of-the-art abstractive summarization models, compare the advantages of different models and applicable scenarios, and provide a clear context for researchers in related fields. Furthermore, we also make statistics on the Chinese and English datasets. Finally, we put forward some thoughts on the common problems in the field of automatic text summarization.
Wavelet Classification for Non-Cooperative Non-Orthogonal Signal Communications

Non-cooperative communications using non-orthogonal multicarrier signals are challenging since self-created inter carrier interference (ICI) prevents successful signal classification. Deep learning (DL) can deal with the classification task without domain-knowledge at the cost of training complexity. Previous work showed that a tremendously trained convolutional neural network (CNN) classifier can efficiently identify feature-diversity dominant signals while it fails when feature-similarity dominates. Therefore, a pre-processing strategy, which can amplify signal feature diversity is of great importance. This work applies single-level wavelet transform to manually extract time-frequency features from non-orthogonal signals. Composite statistical features are investigated and the wavelet enabled two-dimensional time-frequency feature grid is further simplified into a one-dimensional feature vector via proper statistical transform. The dimensionality reduced features are fed to an error-correcting output codes (ECOC) model, consisting of multiple binary support vector machine (SVM) learners, for multiclass signal classification. Low-cost experiments reveal 100% classification accuracy for feature-diversity dominant signals and 90% for feature-similarity dominant signals, which is nearly 28% accuracy improvement when compared with the CNN classification results.
UAV Based Vehicle Detection with Synthetic Training: Identification of Performance Factors Using Image Descriptors and Machine Learning

Vehicle detection on aerial imagery plays an important role in autonomous systems especially on board of UAVs (Unmanned Aerial Vehicles). Deep learning based object detectors are often used to overcome the resulting detection challenges. To achieve the necessary variance, real training data are extended or completely replaced by synthetic data sets from virtual simulation environments. Differences between these two domains can lead to performance differences in the later real application, which are called reality gap. Our current research interests are focused on the identification of image properties that on the one hand allow a distinction between real and synthetic domains and on the other hand are related to detection performance. We present a method to rank the most relevant image descriptors using a machine learning based classification chain. In the first part of the investigations real and synthetic image pairs from own UAV flights and from the publicly available KITTI/Virtual KITTI data set will be analyzed. Finally, it will be shown which image descriptors are responsible for the false detections of a purely synthetic trained YOLOv3 vehicle detector and which conclusions can be drawn for future training data generation.