Reviewerly/exHarmony huggingface dataset:
The Role of Instrumental Variables in Causal Inference Based on Independence of Cause and Mechanism

Causal inference methods based on conditional independence construct Markov equivalent graphs and cannot be applied to bivariate cases. The approaches based on independence of cause and mechanism state, on the contrary, that causal discovery can be inferred for two observations. In our contribution, we pose a challenge to reconcile these two research directions. We study the role of latent variables such as latent instrumental variables and hidden common causes in the causal graphical structures. We show that methods based on the independence of cause and mechanism indirectly contain traces of the existence of the hidden instrumental variables. We derive a novel algorithm to infer causal relationships between two variables, and we validate the proposed method on simulated data and on a benchmark of cause-effect pairs. We illustrate by our experiments that the proposed approach is simple and extremely competitive in terms of empirical accuracy compared to the state-of-the-art methods.
Design and analysis of a multi-agent e-learning system using prometheus design tool

Agent unified modeling languages (AUML) are agent-oriented approaches that supports the specification, design, visualization and documentation of an agent-based system. This paper presents the use of Prometheus AUML approach for the modeling of a Pre-assessment System of five interactive agents. The Pre-assessment System, as previously reported, is a multi-agent based e-learning system that is developed to support the assessment of prior learning skills in students so as to classify their skills and make recommendation for their learning. This paper discusses the detailed design approach of the system in a step-by-step manner; and domain knowledge abstraction and organization in the system. In addition, the analysis of the data collated and models of prediction for future pre-assessment results are also presented.
Peculiarities of Establishing Mediation in Uzbekistan as an Outof-Court Dispute Resolution

INTRODUCTION. The main purpose of current article is to analyze the measure taken in order to successfully implement mediation in the Republic of Uzbekistan and identify areas in need of further reform. This article reflects the existing situation on the implementation of the mediation procedure in the Republic of Uzbekistan, reveals the existing legislative framework and draws attention to the gaps. It contains changes in legislation made in connection with the entry into force of the Law “On Mediation”, namely, changes made in the Civil Procedure, Civil, Tax, Economic Procedure Codes, as well as to the Law “On Domestic Arbitration Courts”. MATERIALS AND METHODS. In preparation of the article the author studied the experience of Singapore, China, Germany, Italy and other states on implementation of mediation, there is a comparison of court annexed and private mediation, the advantages and disadvantages of mediation have also been noted, as well as features of the introduction of mediation have also been identified. There are several methods that have been applied during the research such as comparative, empiric, historical and legal ones. RESEARCH RESULTS. The article reveals some problems of introducing mediation at the present stage of development of society, briefly reveals the content of the law “On Mediation”, which regulates relations connected with the use of mediation to disputes arising from civil law relations, including in connection with business activities, as well as individual labor disputes and disputes arising from family relations. This article also provides some recommendations for the successful implementation of the institute of mediation in Uzbekistan, as well as the popularization of this institution among the population. DISCUSSION AND CONCLUSION. In the article there is the recommendation to develop a strategy for the development and promotion of mediation, to train all students of the jurisprudence in the “Mediation” discipline, to identify commercial mediation as one of the training directions for mediators, to introduce judicial mediation and to make the mediation procedure mandatory for a certain types of cases. First session of mediation is advised to be obligatory and the continuation of the procedure is left to the will of the parties. Besides that, there is a proposal to amend the legislation noting that organization with state ownership should add mediation clause to their contracts.
Dictance learning of mathematical disciplines for students of a pedagogical university taking into account health-saving factors

This article discusses the problems of teaching mathematical disciplines in the process of switching from traditional learning to distance learning. Modern possibilities of remote communication between a teacher and students and their application in higher education institutions, in particular, in teaching mathematical disciplines, are analyzed. The conditions for effective training in this form are outlined, including taking into account the preservation of health. The readiness of students and teachers to switch to distance learning is considered. A number of problems that arise during the transition from the traditional form of education to distance learning are identified, and the features of teaching mathematical disciplines are taken into account. Adverse health effects have been identified. A possible solution to these problems is proposed, taking into account health-saving factors, for which the results of the analysis of the initial approbation of the transition to the distance form of full-time students were used.
Implementation of Extended Play-Fair Algorithm for Client-Side Encryption of Cloud Data

Cloud computing is a kind of distributed computing that provides its customers on-demand, utility-based computing devices. It gives the ability to its customers to store and access their data on the cloud instead of keeping them on the local computer’s hard drive. Storing a large amount of critical information in centralized locations arises many concerns about data protection. A straightforward way to protect sensitive data is to encrypt those data using standard cryptographic tools. The selection of a cryptosystem suitable for encrypting the cloud data can be done in two steps: (i) choosing a suitable type of encryption and then (ii) picking the best algorithm within that class. In the first step, let us choose the symmetric key encryption, which is appropriate for encrypting most sensitive data requiring highest security. And in the second step, instead of using an existing symmetric key algorithm, the authors have tried modifying the simple play-fair cryptographic algorithm to some extent so that it can be used efficiently for securing the cloud data at rest.
Coding line structured light based on a line-scan camera and its calibration

In a conventional three-dimensional (3D) measurement technique of a line-scan camera, the projection system based on surface structured light is a compromise of traditional projection technology, which suffers from complex calibration, complex structure and low accuracy. To this end, the coding line structured light based on the coded line laser projection system is proposed to address the 3D measurement of a line-scan camera. The single-line projection and codeable characteristics of coded line laser projection system (constructed by a point laser and a micro-electro-mechanical system (MEMS) scanning galvanometer and modeled as the line projection model) are fully matched with the imaging mode of the line-scan camera. The 3D measurement model based on the height information, lateral information and absolute phase of the coding line structured light is derived. The multi-position flat display calibration method is proposed to calibrate the system parameters. In addition, in order to obtain the accurate absolute phase from the phase shift combined binary code, the periodic error correction method based on expansion-corrosion is proposed to correct the phase error. Contrary to conventional structured light methods based on a line-scan camera, the proposed method has the advantages of high measurement accuracy, high efficiency, more compactness and low cost. The experiments affirm that the coding line structured light is valid and the proposed calibration method is feasible. Experimental results also indicate that the proposed method performs well for both diffuse reflective surfaces and reflective surfaces that are difficult to measure with conventional structured light methods based on a line-scan camera.
Adaptive deep feature aggregation using Fourier transform and low-pass filtering for robust object retrieval

With the rapid development of deep learning techniques, convolutional neural networks (CNN) have been widely investigated for the feature representations in the image retrieval task. However, the key step in CNN-based retrieval, i.e., feature aggregation has not been solved in a robust and general manner when tackling different kinds of images. In this paper, we present a deep feature aggregation method for image retrieval using the Fourier transform and low-pass filtering, which can adaptively compute the weights for each feature map with discrimination. Specifically, the low-pass filtering can preserve the semantic information in each feature map by transforming images to the frequency domain. In addition, we develop three adaptive methods to further improve the robustness of feature aggregation, i.e., Region of Interests (ROI) selection, spatial weighting and channel weighting. Experimental results demonstrate the superiority of the proposed method in comparison with other state-of-the-art, in achieving robust and accurate object retrieval under five benchmark datasets.
Achieving the Heisenberg limit under general Markovian noise using quantum error correction without ancilla

Practical quantum metrology is generally hindered by environment noise which can demolish the advantage of breaching the shot-noise limit provided by quantum mechanics. Recently, quantum error-correction (QEC) codes have been developed for restoring the quantum advantage against Markovian noise for quantum metrology. This has been done by assuming either the existence of noiseless ancilla or specific noise types such as the spatially correlated noise and the so-called commuting noise. We consider the situation where all probing systems for metrology are infested by general uncorrelated Markovian noise; namely, there is neither noiseless ancilla nor spatial correlation of the noise. Under such circumstances, we have discussed the error-correction conditions in parallel metrology scheme where more than three probes are employed. We have shown the superiority of parallel schemes over sequential schemes in fighting Markovian noise by ancilla-free QEC with an explicit example. If we employ qubits for parameter sensing, it can be shown that one can restore Heisenberg limit (HL) via full and fast control without any ancilla, as long as the signal Hamiltonian of the probe system has nonvanishing component perpendicular to the noise. This is exactly the same requirement for restoring HL with the assistance of noiseless ancilla.
A Robust Multiple Moving Vehicle Tracking for Intelligent Transportation System

The proposed framework accurately counts and classifies the vehicle color of detected vehicles for a real-time video input. The proposed methodology maps the vehicle features for detecting the moving vehicles using background subtraction using KNN, a binary mask, and morphological operations such as erosion and dilation which efficiently eliminate dynamic shadow and extract the vehicle region. Further, unique Id is been assigned for detected vehicles to avoid repetitive counting of detected vehicles when it passes through the counting zone. Furthermore, the colors of the vehicles are been identified by estimating similarity measure between trained and tested using k-Nearest Neighbors machine learning classifier. The RGB intensity with a maximum frequency of occurrence is reflected as the color of vehicle. Experimental results show that the proposed system outperforms compared to other state-of-the-art methods such as mixture of Gaussian2 (MOG2) and Godbehere–Matsukawa–Goldberg (GMG) resulted in false detection due to its inefficiency in shadow removal resulting in an overall efficiency of 94% consuming less computational time for yielding the output.
Usage of Multilingual Indexing for Retrieving the Information in Multiple Language

There is a solid requirement for the real-time ordering of enormous measures of information streaming at the rate of 10 GB/s or more. This information should be scanned for designs and the query items are time-basic in fields as different as security reconnaissance, monetary services including stock exchanging, checking the basic well-being states of patients, atmosphere notice frameworks, etc. Here, the file will be required to age-off in a little time and thus will be of limited size. Notwithstanding, such situations cannot endure any infringement of indexing latency and severe pursuit reaction times. Likewise, future greatly parallel (multicore) structures with capacity class recollections will empower rapid in-memory constant indexing, where the index can be totally put away in a high limit storage class memory. As the Web is growing, it means a number of documents on the Web are also growing so index size will also grow. The approach would be to partition a single large search index into smaller partitions and assigned to each node in the cluster. Later, when a search request comes each node in the cluster will perform a search on the local index for each query term and send the result back to the central machine, which will later combine search results from all nodes in the cluster and then send it to the user.
The human unlikeness of neural language models in next-word prediction

The training objective of unidirectional language models (LMs) is similar to a psycholinguistic benchmark known as the cloze task, which measures next-word predictability. However, LMs lack the rich set of experiences that people do, and humans can be highly creative. To assess human parity in these models’ training objective, we compare the predictions of three neural language models to those of human participants in a freely available behavioral dataset (Luke & Christianson, 2016). Our results show that while neural models show a close correspondence to human productions, they nevertheless assign insufficient probability to how often speakers guess upcoming words, especially for open-class content words.
Hierarchical Coherent Anomaly Fall Detection Low Bandwidth System With Combination of Wearable Sensors for Identifying Behavioral Abnormalities

To build cleverly assisted life systems for elderly people, the identification of behavioral abnormalities in human life is essential. Fall detection is a significant public health concern, particularly for the elderly fitness is unavoidable, and consistent monitoring of negative effects is required on time. This paper introduces Hierarchical Coherent Anomaly Fall Detection Low Bandwidth System (HCAFDLBS) to effectively identify behavioral abnormalities in human life based on data obtained from wearable sensors and related location contexts. The machine tracks human body activity, identifies an efficient quaternion algorithm as a decrease in normal daily activities, and sends automatic requests for assistance to caregivers in the region of the patient. The probabilistic theoretical paradigm is used to identify three types of irregularities, including spatial deviations, timing anomalies, and anomalies of sequence. This technique relies on a hierarchical analytical network for the detection of complex behavior; the maximum approximate algorithm and the smoothing Laplace method are used to learn anomaly detection model parameters. The classification is spread on various sensor nodes, and a computer for a specific station, the distribution of multiple action classes is demonstrated to follow a mixture of subspace model for each action class and one subspace. The findings have confirmed the feasibility of the proposed background so that the behavior study can be incorporated in future intelligent elderly homes, this behavioral abnormality detection system is predicted, and their results have been verified.
HausaMT v1.0: Towards English–Hausa Neural Machine Translation

Neural Machine Translation (NMT) for low-resource languages suffers from low performance because of the lack of large amounts of parallel data and language diversity. To contribute to ameliorating this problem, we built a baseline model for English–Hausa machine translation, which is considered a task for low–resource language. The Hausa language is the second largest Afro–Asiatic language in the world after Arabic and it is the third largest language for trading across a larger swath of West Africa countries, after English and French. In this paper, we curated different datasets containing Hausa–English parallel corpus for our translation. We trained baseline models and evaluated the performance of our models using the Recurrent and Transformer encoder–decoder architecture with two tokenization approaches: standard word–level tokenization and Byte Pair Encoding (BPE) subword tokenization.
Evaluation of the Dynamic Pricing Model in Cloud

Many of the companies started moving towards cloud computing because of its characteristics like pay as you go, easy to use, scalable, multi-tenant, secure, etc. There are many cloud providers providing cloud services over the internet. There are number of pricing policies for the user. These pricing policies can be categorised as static and dynamic pricing. This paper represents the enhancement and evaluation of the proposed dynamic resource pricing model for cloud resources based on the consumer behaviour. For model evaluation, one tail two sample t-test has been used. The results show the model will be helpful to the cloud provider for profit maximisation.
CleanPage: Fast and Clean Document and Whiteboard Capture

The move from paper to online is not only necessary for remote working, it is also significantly more sustainable. This trend has seen a rising need for the high-quality digitization of content from pages and whiteboards to sharable online material. However, capturing this information is not always easy nor are the results always satisfactory. Available scanning apps vary in their usability and do not always produce clean results, retaining surface imperfections from the page or whiteboard in their output images. CleanPage, a novel smartphone-based document and whiteboard scanning system, is presented. CleanPage requires one button-tap to capture, identify, crop, and clean an image of a page or whiteboard. Unlike equivalent systems, no user intervention is required during processing, and the result is a high-contrast, low-noise image with a clean homogenous background. Results are presented for a selection of scenarios showing the versatility of the design. CleanPage is compared with two market leader scanning apps using two testing approaches: real paper scans and ground-truth comparisons. These comparisons are achieved by a new testing methodology that allows scans to be compared to unscanned counterparts by using synthesized images. Real paper scans are tested using image quality measures. An evaluation of standard image quality assessments is included in this work, and a novel quality measure for scanned images is proposed and validated. The user experience for each scanning app is assessed, showing CleanPage to be fast and easier to use.
The JHU Submission to the 2020 Duolingo Shared Task on Simultaneous Translation and Paraphrase for Language Education

This paper presents the Johns Hopkins University submission to the 2020 Duolingo Shared Task on Simultaneous Translation and Paraphrase for Language Education (STAPLE).We participated in all five language tasks, placing first in each.Our approach involved a language-agnostic pipeline of three components: (1) building strong machine translation systems on general-domain data, (2) finetuning on Duolingo-provided data, and (3) generating n-best lists which are then filtered with various score-based techniques.In addition to the language-agnostic pipeline, we attempted a number of linguistically-motivated approaches, with, unfortunately, little success.We also find that improving BLEU performance of the beam-search generated translation does not necessarily improve on the task metric-weighted macro F1 of an n-best list.
Using Mobile Instant Messaging in Teaching Vocabulary to Pre-intermediate EFL Learners: The Case of WhatsApp

Smartphones are becoming an increasingly common part of our daily lives. Applications used in these systems are extremely varied due to their extraordinary efficiency. With respect to language teaching, the use of these technologies has opened up new doors, changing innovatively the way teachers teach and students learn. Accordingly, this study aimed to find out the impacts of using WhatsApp as a mobile instant messaging application on teaching vocabulary among pre-intermediate EFL learners. To this end, 60 Iranian pre-intermediate EFL learners were selected from a private English Language Institute. Then, they were randomly assigned to two equal groups- an experimental group and a control group. They were pre-tested by a researcher-made vocabulary test. Then, the treatment was applied to both groups. As the treatment, the control group was taught with traditional activities whereas the experimental group was taught with the use of mobile application (WhatsApp). At the end of the instruction, a researcher-made vocabulary post-test was administered. The results of independent samples t-test and one-way ANCOVA indicated that the experimental group had better performance on the post-test compared to control group revealing the usefulness of the mobile application utilized in this study on learning vocabulary.
A daily activity feature extraction approach based on time series of sensor events

Activity recognition benefits the lives of residents in a smart home on a daily basis. One of the aims of this technology is to achieve good performance in activity recognition. The extraction and selection of the daily activity feature have a significant effect on this performance. However, commonly used extraction of daily activity features have limited the performance of daily activity recognition. Based on the nature of the time series of sensor events caused by daily activities, this paper presents a novel extraction approach for daily activity feature. First, time tuples are extracted from sensor events to form a time series. Subsequently, several common statistic formulas are proposed to form the space of daily activity features. Finally, a feature selection algorithm is employed to generate final daily activity features. To evaluate the proposed approach, two distinct datasets are adopted for activity recognition based on four different classifiers. The results of the experiment reveal that the proposed approach is an improvement over the commonly used approach.