Reviewerly/exHarmony huggingface dataset:
Metasoftware: Building Blocks for Legal Technology

This Article develops a novel concept in information technology called metasoftware. It then applies the concept of metasoftware to developing legal technology.Metasoftware enables users to create the software of their choosing and stands in sharp contrast to traditional, functional software that is intended for a particular purpose or a defined range of tasks. Functional software is the default type of software that is currently produced and includes word processing, email, social networking, enterprise resource management, online marketplaces, and video game software. Metasoftware, by contrast, is not functional. Metasoftware presents the user with a blank slate upon which to build functional software. I argue that software is metasoftware to that extent that (1) it enables users to build user interface elements, workflow logic, and perform database operations, (2) provides connectivity with external data and software systems, and (3) is able to be stored and run independently from the platform that is used to build the software. In its purest form, metasoftware enables its users to build any functional software (given the existing state of technology), integrate with all open software platforms, and be hosted and run in the environment of the user’s choosing without being bound to a particular vendor or other proprietary software platform.My identification of metasoftware contributes to the academic literature on information systems (and technology). Metasoftware is a hitherto unrecognized category of software for analysis in terms of several foundational lines of information technology research including user acceptance and usage, diffusion within an organization, and impact on organizational innovation and success (e.g., business performance). I analyze three types of metasoftware platforms to determine how metasoftware characteristics are implemented in each and important inherent tradeoffs. These platforms are those closely tied to major producers of cloud-based software platforms, standalone proprietary “no code” software builder platforms, and open source visual development platforms. This Article also describes how metasoftware platforms can be used to build functional legal technology and analyzes how the tradeoffs between different types of metasoftware platforms turn impact how each type of platform should be approached as building blocks for legaltech software. I focus on four major categories of legaltech as illustrative of the potential for metasoftware to build functional software: legal research, legal matter management, contract automation, and a variety of applications of artificial intelligence.
State estimation for delayed positive neural networks under false data injection attack

This paper focuses on the state estimator for a class of delayed discrete-time positive neural networks. The utilization of network in control system for information mutual interaction brings great conveniences. But it will be surfer from the issue of information security, cyber-attacks. False data injection attacks occurring in information transmission are taken into consideration in this paper. The model of the delayed positive neural network with this kind of attacks is constructed. Through linear Lyapunov function, sufficient conditions for globally asymptotic stability of positive neural networks are proposed. The desired positive estimator is designed by putting forward to a linear programming approach. Finally, an example of application to water distribution network is given to demonstrate the effectiveness and applicability of the derived results.
Oxygen Cylinders Monitoring System in Hospital Warehouse Using CNN

In recent years, a shortage of oxygen cylinders was observed in hospitals causing numerous deaths. Thus, a need for efficient monitoring of the quantity of the oxygen cylinders in a hospital storage facility becomes the need of the hour. To address this issue, we have come up with a solution of a surveillance system to keep track of the quantity of the oxygen cylinders. A surveillance system typically uses a camera to spy and observe a specific target. The process of monitoring can be done in both real time (live video-based) and in offline, where the surveillance can happen at a later time on a recorded video. This project employs the real time tracking of the oxygen cylinders in hospital store house using convolution neural network.
The SURF System for Continuous Data and Applications Placement Across Clouds

In a hybrid cloud environment, as well as in a multi-cloud environment, an enterprise employs a number of local sites (or data centers) and cloud data center(s) that may be geographically distributed. The problem of where to place and replicate data and applications is complicated by multiple dynamically changing conditions. We describe two types of algorithms: data movement (conservative and optimistic of various types) and recovery from various system faults. They may be integrated into various system types. These systems may have their own correctness requirements. The system we provide is charged with creating an illusion that data is stationary. These algorithms are implemented on top of a ZooKeeper compact distributed database. The algorithms were extensively tested over three public clouds.
The Reimagined Schoolyard: Cryptocurrency's Adoption in Tomorrow's International Monetary Order

This Article looks to specific periods in the socio-legal history of money to make a series of predictive statements about cryptocurrency’s contemporary impact. New forms of currency have been more consequential than simply solving narrowly defined problems. They have shaped relationships between technology and government’s expansionary aims to produce important structural arrangements – sometimes at the cost of disrupting incumbent ones. In the prehistoric era, commodity based forms of money gave way to metal coins and systems of exchange that facilitated trade expansion and would eventually express political power in their physical design. The largescale circulation of paper notes in seventeenth-century England also responded to a mix of technological and governmental needs that created their own disruptive effects. This paradigm would repeat itself in America’s early history and ultimately lay the seedbed for today’s global political economy. In each of these historical periods, social participation remained a key element in the evolution of state power through its currency. This history gives rise to two observations about digital currency and its prospects. The first suggests cryptocurrency’s disruption will echo prior experiences with new currency adoption while attempting to address some perceived problem. The second observation argues that cryptocurrencies signal a new phase in the constantly evolving project of defining law's role in the relationship between currency, governance and regulation; and where the modern state remains interested in controlling money's production and movement.
Method for Selecting a Set of Image Files Similar to the Object of Interest

In this research, the scientific novelty is the development and testing of the method. This method will allow combining the high speed of the cascade classifier and the quality of the artificial neural network. As an approbation of the work in this article, we study the set of image files. The set of image files are available in the directory on the disk. Next, we select a set of files. The set of files is similar to the object of interest image files. It means, that we can't identical any objects. Then we rewrite files to the target directory. This set should later be used for training a neural network. Images of the object of interest can be in any orientation and on any background. This non-identity must be recognized by a trained neural network. For the purposes of forming such a set, we will need an identifier that has sufficient accuracy for these purposes. Here we do not need very high accuracy. The very identification error of this preliminary analyzer helps us of introducing some dissimilarity between the objects of the images. The task is complicated by the fact that objects can have different sizes (scale) and orientation in space. Our approach based on cascade classifiers and neural networks combines the high speed of the cascade classifier and the quality of the artificial neural network. And the stage of preparing training data for the network is of great importance.
Development of Neural Machine Translator for English-Assamese Language Pair

Machine translation aims to minimize the language barrier between people of different linguistic backgrounds. Machine translation is an automatic translation technique between pairs of different languages. Machine translation using neural network or neural machine translation came into the picture due to several limitation associated with its predecessors which are rule based and statistical based models. For large data sets with a rich range of vocabulary, the neural network machine translation system provides fair translation accuracy. We have observed that there remain very few machine translators dedicated to Indian languages, especially those spoken in the North-East area. So our paper mainly focuses on implementing a neural machine translation system for the English-Assamese language. In this paper we used five different neural machine translation models for English-Assamese language pair using LSTMs and GRUs, along with an attention layer. We have made a comparison analysis based on the performance results of these models. We used BLEU Score to calculate the accuracy of these five models, thus achieving a BLEU score of 34.168%, which was the highest among the five models.
ACE: A Context-Enhanced Model for Interactive Argument Pair Identification

In recent years, Interactive Argument Pair Identification has attracted widespread attention as an important task in argument mining. Existing methods for the task usually explicitly model the matching score between the argument pairs. However, identifying whether two arguments have an interactive relationship not only depends on the argument itself, but also usually requires its context to better grasp the views expressed by the two arguments. In the NLPCC-2021 shared task Argumentative Text Understanding for AI Debater, we participated in track 2 Interactive Argument Pair Identification in Online Forum and proposed ACE, A Context-Enhanced model which makes good use of the context information of the argument. In the end, we ranked 1st in this task, indicating the effectiveness of our model.
Program Synthesis for Musicians: A Usability Testbed for Temporal Logic Specifications

In recent years, program synthesis research has made significant progress in creating user-friendly tools for Programming by example (PBE) and Programming by demonstration (PBD) environments. However, program synthesis from logical specifications, such as reactive synthesis, still faces large challenges in widespread adoption. In order to bring reactive synthesis to a wider audience, more research is necessary to explore different interface options. We present The SynthSynthesizer, a music-based tool for designing and testing specification interfaces. The tool enables researchers to prototype different interfaces for reactive synthesis and run user studies on them. The tool is accessible to both researchers and users by running on a browser on top of a docker-containerized synthesis toolchain. We show sample implementations with the tool by creating dropdown interfaces, and by running a user study with 21 users.
On the Electronic Database of the Armenian Word-Formation

The formal description of the languages has become more and more practical; however, it should be noted that the full formal description of the Armenian language has not yet been done. However, the fact that certain attempts have been made is undeniable. The Electronic database of the Armenian word-formation (https://formlang.am/) is the first stage of the complex project in the full formal description of the Armenian language. In the current article, we want to present the advantages of the electronic database of the Armenian word-formation. The electronic database containing the word-formation analysis of thousands of words can search for words and morphemes within them. It means that searching for any root or affix appears all the simple, compound, derived and derived-compound words made up of them. It enables us to reveal the regular structures and the variative forms, deflections, and irregularities with frequency data with their automatic analysis and the possibility of derivation. The database gives a great opportunity to study the Armenian word-formation on synchronic and diachronic points, to discover the basic patterns of the formation of new words, by the thousands of examples to find out the principles and ways of word-formation in the Armenian language, to have the full list of the distinguished simple, compound, derived and derived-compound Armenian words. The current database has not only practical great value to involve the Armenian language in the domain of the modern informational technologies as the communicative mean, but also significant theoretical value to present the accurate description of the vocabulary structure. Thus, it will give an excellent perspective for solving the problems of theoretical linguistics and the practical -applied tasks. It can be significant for the further development of Armenology.
Uterine Movement Analysis Based on Velocity Information by Convolutional Neural Networks

Female infertility patients have characteristic uterine movements. Since the movements are related to fertility, it is important to analyze the movements. We developed an automated analysis system for the uterine movements. The system was composed of convolutional neural networks. The system predicted the occurred movements from images with velocity information. The images were generated from Cine MRI images by calculating optical flow for each frame image. We confirmed that the accuracy of the model by the velocity images is higher than the model by the original images. We found that the uterine movement velocity is very important information to analyze the uterine movement patterns.
Toxic Messages Classification in Social Media

The aim of our work is to develop a software that could detect toxicity in the Russian segment of social media. In this paper, we investigated the problem of toxic detection in messages in Russian language. We implemented a set of features using selected vector models, trained some classifiers on the dataset about fourteen thousand annotated messages and compare results. Experiments were conducted with a calculation of accuracy, precision, and recall values. F1 measure reached the value 0.91, accuracy value is 0.87.
An Intelligent Method for Plastic Money Fraud Detection Using Isolation Forest Classifier

With the advent of the modernization, as most of the payment gateway are based on the digital and Credit card based payment transaction being one of them. Credit card transaction are used both for online as well as offline payment mode. This leads to increased fraud activities of forgery and theft. Usually in the existing scenario, detecting fraud once the theft had been performed. Such frauds are harmful for both merchants and to an individual customer too. There are different patterns for credit card online fraud and at times, it becomes bit tough to detail each fraud separately in isolation. Using Machine-learning algorithms through their predictive techniques can make it easier to detect such online transaction frauds, in which data mining techniques play a significant role. Since data being both continuous and nominal, with the use of statistical analysis methods fraud events can be prevented. Techniques like Logistic Regression, Genetic Programming, Fuzzy Logic, Bayesian and Probabilistic methods, these methods are already applicable for the purpose of credit-card fraud-detection. This paper gives a data mining technique of Isolation Forest Classifier approach in combination which could contribute in detecting such frauds and help in decrease of fraud occurrence rate. The Local Outlier Factor is Obtained 99% times we had considered using the Random Forest Classifier and the results we got using the Isolation Forest classifier are 99.65%. Therefore, we conclude that Random-Forest-Classifier is the best algorithm for Credit Card Fraud Detection.
Evaluation of MC-CNN Based Stereo Matching Pipeline for the CO3D Earth Observation Program

When it comes to 3D reconstruction, stereo matching step stands in the way of the light. But just as essential as it is, it remains very critical. Even more so for the CO3D world wide mission. 3D will be rendered for every continents, increasing the importance of generalizing well over different landscapes. This paper then focuses on the benefits of using MC-CNN neural network as stereo matching costs, instead of the Census adopted so far on CO3D studies. We compare them both over different areas. We train MC-CNN on Middlebury dataset and a stereo satellite dataset. We show that MC-CNN matching costs are much more trustworthy than Census ones and introduce a measure of ambiguity to illustrate this behavior. To help reproduce our results, we publicly release our work as a contribution to Pandora.
Passivity and Feedback Passification for Discrete-Time Switched Interval Type-2 Fuzzy Systems

This paper investigates the passivity analysis and feedback passification problems for a class of discrete-time switched interval type-2 fuzzy systems. The interval type-2 T-S fuzzy model is employed to represent switched nonlinear systems subject to parameter uncertainties. With the help of the IT2 fuzzy set theory, the parameter uncertainties can be captured by upper and lower membership functions. Based on the multiple storage functions and the state-dependent switching strategy, the solvability conditions for feedback passification are derived by solving a set of linear matrix inequalities. Moreover, the switched IT2 fuzzy passive controllers are designed simultaneously. Finally, a simulation example is provided to illustrate the effectiveness and applicability of the proposed method.
Research on Weakly Centralized Trading Mechanism of Distributed Energy Based on Smart Contract of Blockchain

Based on the distributed energy marketization trading mechanism, this paper applies Blockchain technology to build a weakly centralized distributed energy trading mechanism, and proposes a weakly centralized distributed energy trading architecture and trading process based on the smart contract of Blockchain, identify the main types, the trading execution process and the core stored data. Based on the whole process of distributed energy trading, which including trade request release, risk assessment and plan management, market quotation and bidding trading, price matching, security check, blocking management, settlement and clearing, etc., smart contracts are designed for risk assessment, price matching, security check, settlement and clearing. On the one hand, fundamentally ensure the reliability and traceability of trading, improve the efficiency of the system. On the other hand, improve the fairness and transparency of the system, ensure the participation of market players, and promote the consumption level of distributed energy.
An Improved Differential Evolution Scheme for Multilevel Image Thresholding Aided with Fuzzy Entropy

Image segmentation problem has been solved by entropy-based thresholding approaches since decades. Among different entropy-based techniques, fuzzy entropy (FE) got more attention for segmenting color images. Unlike grayscale images, color images contain 3-D histogram instead of 1-D histogram. As traditional fuzzy technique generates high time complexity to find multiple thresholds, so recursive approach is preferred. Further optimization algorithm can be embedded with it to reduce the complexity at a lower range. An updated robust nature-inspired evolutionary algorithm has been proposed here, named improved differential evolution (IDE) which is applied to generate the near-optimal thresholding parameters. Performance of IDE has been investigated through comparison with some popular global evolutionary algorithms like conventional DE, beta differential evolution (BDE), cuckoo search (CS), and particle swarm optimization (PSO). Proposed approach is applied on standard color image dataset known as Berkley Segmentation Dataset (BSDS300), and the outcomes suggest best near-optimal fuzzy thresholds with speedy convergence. The quantitative measurements of the technique have been evaluated by objective function’s values and standard deviation, whereas qualitative measures are carried out with popular three metrics, namely peak signal-to-noise ratio (PSNR), structural similarity index measurement (SSIM), and feature similarity index measurement (FSIM), to show efficacy of the algorithm over existing approaches.
Mobile Technology Pedagogy: Improved Student Engagement for Improved Self-Assessment

There is minimal research which makes the connection between mobile technology and improved student achievement. This study addressed the gap in the literature and considered the dynamic and fluid engagement mobile technology brings to the teaching and learning environment to improve student essay scores in the composition classroom. Freshman composition essay scores were collected over the course of five semesters: two semesters where the instructor did not teach with mobile application technology, and three semesters after adoption of mobile technology. There was a statistically significant difference in mean writing project scores, with the mobile technology curriculum producing higher mean scores for all three writing projects. The purpose of this study was to expand current research to consider the effects of student achievement when using mobile application technology, and to inform current pedagogical practices across all disciplines.
Pointing at Moving Robots: Detecting Events from Wrist IMU Data

We propose a practical approach for detecting the event that a human wearing an IMU-equipped bracelet points at a moving robot; the approach uses a learned classifier to verify if the robot motion (as measured by its odometry) matches the wrist motion, and does not require that the relative pose of the operator and robot is known in advance. To train the model and validate the system, we collect datasets containing hundreds of real-world pointing events. Extensive experiments quantify the performance of the classifiers and relevant metrics of the resulting detectors; the approach is implemented in a real-world demonstrator that allows users to land quadrotors by pointing at them.
Syntax and Coherence - The Effect on Automatic Argument Quality Assessment

In this paper, we focus on the task of automatic argument quality assessment. Prior empirical methods largely ignore the syntax structure in one argument or depend on handcrafted features that have shallow representation ability. In contrast, we proposed a method that directly models syntax and topic coherence. Our method can acquire both topic coherence and syntactic information from an argument that explicitly utilizes various types of relationships among words, thus can help with argument quality assessment. Experimental results suggest that our method significantly outperforms the previous state-of-the-art method and strongly indicates syntax and coherence correlate with argument quality.