Reviewerly/exHarmony huggingface dataset:
Adaptive hybrid technique for face recognition

One of the most important biometric features for personal identification is the face. In current paper, a new method of face verification upon on singular value decomposition (SVD) and standard deviation (SD) would be described. Due to many variations in real-life such as pose, illumination, or facial expression, there would be difficulty of face recognition. It should be mentioned that there are many approaches for face recognition, however, there is no one could be considered as the most suitable for many situations. One of the methods used is Singular value vector for an image detecting, but the drawback of this approach is the low rate of recognition, where one scale singular value vector is used for face acknowledgment. There an algorithm has been developed to expand the rate of the recognition. In this paper, an approach has been proposed to associate two feature sets obtained from SVD and SD method. It has noticed a good recognition rate could be obtained from the experimental results, where approximately more that 97.5% recognition rate has obtained on the ORL data base. The results from current proposed method have matched with some techniques and it has shown that this method is better than the existing approaches. An extensive experiment has demonstrated not only better performance, but it offers a great likely to achieve equivalent performance to other categories of state-of-the-art methods.
Review: Cloud Task Scheduling and Load Balancing

Load Balancing is fundamental for effective activities in conveyed conditions. As Cloud Computing is developing quickly with customers be requesting extra services and superior outcomes, load balancing in favor of the Cloud have turned into an extremely intriguing and imperative research region. Numerous schemes were proposed to give proficient components and schemes to allotting the customer's solicitations to accessible Cloud hubs. These methodologies plan to get better concert of the Cloud as well as offer the client all the extra gratifying and effectual tunes. In this article, we explore the diverse schemes planned to determine the concern of load balancing and task scheduling within Cloud Computing. We talk about with contrast these schemes with give a review of the most recent methodologies in the meadow.
Ab Antiquo: Proto-language Reconstruction with RNNs

Historical linguists have identified regularities in the process of historic sound change. The comparative method utilizes those regularities to reconstruct proto-words based on observed forms in daughter languages. Can this process be efficiently automated? We address the task of proto-word reconstruction, in which the model is exposed to cognates in contemporary daughter languages, and has to predict the proto word in the ancestor language. We provide a novel dataset for this task, encompassing over 8,000 comparative entries, and show that neural sequence models outperform conventional methods applied to this task so far. Error analysis reveals a variability in the ability of neural model to capture different phonological changes, correlating with the complexity of the changes. Analysis of learned embeddings reveals the models learn phonologically meaningful generalizations, corresponding to well-attested phonological shifts documented by historical linguistics.
Prediction of stroke probability occurrence based on fuzzy cognitive maps

Among neurological patients, stroke is the most common cause of mortality. It is a health problem that is very costly all over the world. Therefore, the mortality due to the disease can be reduced by identifying and modifying the risk factors. Controllable factors which are contributing to stroke including hypertension, diabetes, heart disease, hyperlipidemia, smoking, and obesity. Therefore, by identifying and controlling the risk factors, stroke can be prevented and the effects of this disease could be reduced to a minimum. Therefore, for the quick and timely diagnosis of the disease, we need an intelligent system to predict the stroke risk. In this paper, a method has been proposed for predicting the risk rate of stroke which is based on fuzzy cognitive maps and nonlinear Hebbian learning algorithm. The accuracy of the proposed NHL-FCM model is tested using 15-fold cross-validation, for 90 actual cases, and compared with those of support vector machine and k-nearest neighbours. The proposed method shows superior performance with a total accuracy of (95.4 ± 7.5)%.
Toward Continuous Assessment of the Programming Process

Assessment of software tends to focus on postmortem evaluation of metrics like correctness, mergeability, and code coverage. This is evidenced in the current practices of continuous integration and deployment that focus on software's ability to pass unit tests before it can be merged into a deployment pipeline. However, little attention or tooling is given to the assessment of the software development process itself. Good process becomes both more challenging and more critical as software complexity increases. Real-time evaluation and feedback about a student's software development skills, such as incremental development, testing, and time management, could greatly increase productivity and improve the ability to write tested and correct code. In my research, I develop models to quantify a student's programming process in terms of these metrics. By measuring the programming process, I can empirically evaluate its adherence to known best practices in software engineering. With the ability to characterize this, I can build tools to provide them with intelligent and timely feedback when they are in danger of straying from those practices. In the long term, I hope to contribute to the standardization and adoption of continuous software assessment techniques that include not only the final product, but also the process undertaken to produce it.
Item Characteristics on Pro-TEFL Listening Section

A good test should be proven theoretically and empirically.A test is considered as a good one if its items have appropriate content, language and construct based on theoretical analysis.Besides, the empirical analysis on the test items need to do in order to describe the quality of the test.Empirical item analysis can be done based on the framework of Classical Test Theory (CTT) and Item Response Theory (IRT).This study analyzed empirically the test item characteristics based on the 1 parameter logistic (1-PL) IRT framework.The items were analyzed based on the difficulty indices.Before analyzing the test items, assessment on IRT assumptions was done; unidimensionality and local independence.The data were gathered through testing.The responses from 334 test takers on 50 items of listening section on Pro-TEFL test administered by the Center for Language Development were used as empirical data.The assessment on IRT assumptions (unidimensionality and local independence) was done by using Factor Analysis which was assisted by SPSS for windows program.The item parameter (the difficulty index) was estimated by using Program R. Results showed that the assessment on IRT assumptions can be fulfilled.Then, the analysis of item characteristics was done to estimate the item parameter based on the 1-PL IRT model.Based on the analysis on the difficulty index, there were 2 items that were considered as poor items because the difficulty indices are more than +2 and they are considered as too difficult items.
Large-Scale Training Framework for Video Annotation

Video is one of the richest sources of information available online but extracting deep insights from video content at internet scale is still an open problem, both in terms of depth and breadth of understanding, as well as scale. Over the last few years, the field of video understanding has made great strides due to the availability of large-scale video datasets and core advances in image, audio, and video modeling architectures. However, the state-of-the-art architectures on small scale datasets are frequently impractical to deploy at internet scale, both in terms of the ability to train such deep networks on hundreds of millions of videos, and to deploy them for inference on billions of videos. In this paper, we present a MapReduce-based training framework, which exploits both data parallelism and model parallelism to scale training of complex video models. The proposed framework uses alternating optimization and full-batch fine-tuning, and supports large Mixture-of-Experts classifiers with hundreds of thousands of mixtures, which enables a trade-off between model depth and breadth, and the ability to shift model capacity between shared (generalization) layers and per-class (specialization) layers. We demonstrate that the proposed framework is able to reach state-of-the-art performance on the largest public video datasets, YouTube-8M and Sports-1M, and can scale to 100 times larger datasets.
English Teaching Platform Based on the Campus Network Multimedia

The traditional teaching and the multimedia network teaching have their own advantages and limitations. The harmonious integration of these two teaching modes can make up for each other’s weaknesses and give full play to the best overall effects of our teaching. Guided by the constructivism theory, the humanism theory and the harmonious education theory, this paper explores the contradiction between the traditional foreign language teaching and the multimedia network teaching, expounds the characteristics of the two kinds of the teaching harmony, and puts forward solutions to how to achieve the harmony of the foreign language teaching.
Interleaving Search and Heuristic Improvement

Abstraction heuristics are a leading approach for deriving admissible estimates in cost-optimal planning. However, a drawback with respect to other families of heuristics is that they require a preprocessing phase for choosing the abstraction, computing the abstract distances, and/or suitable cost-partitionings. Typically, this is performed in advance by a fixed amount of time, even though some instances could be solved much faster with little or no preprocessing. We interleave the computation of abstraction heuristics with search, avoiding a long precomputation phase and allowing information from the search to be used for guiding the abstraction selection. To evaluate our ideas, we implement them on a planner that uses a single symbolic PDB. Our results show that delaying the preprocessing is not harmful in general even when an important amount of preprocessing is required to obtain good performance.
Model AI Assignments 2019

The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of ten AI assignments from the 2019 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at http: //modelai.gettysburg.edu.
Rule-Based Method to Develop Question-Answer Dataset from Chest X-Ray Reports

Available and objective clinical documents are important for research of assistant diagnosis, development of algorithms, and education. To facilitate the readability and variability of clinical documents, this paper presents a rule-based approach to develop a question-answer dataset for chest X-rays from a public collection of radiology examinations, including both images and radiologist narrative reports. Our method simplified the complicated reports via hand-selected keywords, generated more than 63 thousand question-answer pairs via hand-written patterns, and augmented the question-answer dataset to more than 130 thousand pairs via rule-based question answering. To the best of our knowledge, this is the first generated question-answer dataset for chest X-rays by rule-based method. The dataset is promising for future researches and applications such as visual question answering, computer-aided diagnosis and so on.
Expert Guided Rule Based Prioritization of Scientifically Relevant Images for Downlinking over Limited Bandwidth from Planetary Orbiters

Instruments onboard spacecraft acquire large amounts of data which is to be transmitted over a very low bandwidth. Consequently for some missions, the volume of data collected greatly exceeds the volume that can be downlinked before the next orbit. This necessitates the introduction of an intelligent autonomous decision making module that maximizes the return of the most scientifically relevant dataset over the low bandwidth for experts to analyze further. We propose an iterative rule based approach, guided by expert knowledge, to represent scientifically interesting geological landforms with respect to expert selected attributes. The rules are utilized to assign a priority based on how novel a test instance is with respect to its rule. High priority instances from the test set are used to iteratively update the learned rules. We then determine the effectiveness of the proposed approach on images acquired by a Mars orbiter and observe an expert-acceptable prioritization order generated by the rules that can potentially increase the return of scientifically relevant observations.
Literal or Pedagogic Human? Analyzing Human Model Misspecification in Objective Learning

It is incredibly easy for a system designer to misspecify the objective for an autonomous system (robot''), thus motivating the desire to have the robot learn the objective from human behavior instead. Recent work has suggested that people have an interest in the robot performing well, and will thus behave pedagogically, choosing actions that are informative to the robot. In turn, robots benefit from interpreting the behavior by accounting for this pedagogy. In this work, we focus on misspecification: we argue that robots might not know whether people are being pedagogic or literal and that it is important to ask which assumption is safer to make. We cast objective learning into the more general form of a common-payoff game between the robot and human, and prove that in any such game literal interpretation is more robust to misspecification. Experiments with human data support our theoretical results and point to the sensitivity of the pedagogic assumption.
Blockchain as an External Enabler of New Ventures: Emerging industry Value Architectures

We apply the external enabler concept to examine how blockchain technology facilitates new venture creation. We integrate the concept of industry architecture to explain why particular enabling mechanisms are activated by new ventures using a single foundational technology. Through our analysis of 36 white papers published by early stage ventures, we identify a radical redistribution of value creation and appropriation in the music sector. Artists and rights holders are benefiting from smart contracts and tokenization while many actors in the incumbent value chain are being disintermediated. Significantly, music fans are moving from a simple consumption role to co-creators of value. They can now participate in a reward economy where they benefit from supporting an artist. We conclude our theorizing by reflecting on implications for blockchain technology and entrepreneurship beyond the music industry.
Detection and Classification of Earthquake Images from Online Social Media

Natural disasters present a huge challenge for Government and non-Governmental organizations, as the individuals and communities are heavily affected. Disasters like earthquakes are seismic events that result in shaking of the earth’s surface causing large-scale death and destruction. Therefore, disaster management and control are of utmost importance. Nowadays individuals on site use socialLayek, Ashish Kumar media platforms (like Twitter, Facebook, etc.) for sharingChatterjee, Amreeta information and seeking remedy. If the extent of destruction can be estimated, one can assessChatterjee, Debanjan the amount of relief needed. Thus, the data gathered from social media platforms can be used to evaluate the extremity of architecturalBiswas, Samit damage induced by disasters like earthquakes. This paper proposes a CNN based segmentation of earthquake affected regions in images that are posted on online social media and classifying them into various classes according to the severity of damage. The approach is trained and tested using a dataset of images collected from social media posts during earthquakes and the results are encouraging.
The study of the formation of relations of educational cooperation of younger schoolchildren

Introduction: the need to study the current state of relations of educational cooperation of younger students is determined by the increasing role of communicative universal educational activities in the development of modern society. However, a number of studies it prove that often the life of a modern school does not provide any opportunities to use the potential resources of such an important component of the development of the student, as educational cooperation. Traditional school due to many restrictions does not always provide the possibility to build a student-centered relationship of educational cooperation in combination with a differentiated approach to younger students. Numerous studies often describe the interaction of students with adults and peers separately, that prevents making unified approaches to the child's socialization. The initial step towards solving this problem should be the complex study of the areas of interaction and diverse study of the formation of relations of educational cooperation of younger schoolchildren Materials and Methods: in article the diagnostic program-the complex made of the techniques giving the chance to get the idea of a modern state of development of the relations of educational cooperation in elementary school is declared. They were: a technique with the working title “ the Carpet”, allowing to establish levels of development of skills of group interaction of younger schoolchildren in the lessons; “Sociometric research”, intended for identification of the social status of the child in children's group; “Self-esteem ladder”, designed to determine the adequacy of children's self-esteem in the sphere of business contacts of the child with classmates. Results: the results of the study of the formation of relations of educational cooperation among younger schoolchildren with peers in a specially created innovative conditions in the context of a given procedure of learning and initiated by the child communication. The idea of teachers about the most important for younger schoolchildren’s psychological conditions of development of relations of educational cooperation is revealed. Discussion and Conclusions: the study suggests that for the effective development of relations of educational cooperation with children it is necessary to create a special innovative learning environment, which provides a high motivational basis for younger students to achieve their goals, initiatives, independence, strengthen their social status in the group of peers, increase self-esteem.
How Integrated are Cryptocurrencies

The present study focuses on five cryptocurrencies co-movements physiognomies both in time and frequency domain.The present study highlighted several interesting facts related to cryptocurrencies co-movements both in time and frequency domain that have high policy and investment implications.Overall wavelet coherence diagrams clearly indicate about the very short and long contagion effect among the cryptocurrency pairs for the whole study period.The contagion effect is different at different time scales.Finally wavelet clustering diagram indicates that by investing only in XBP and BitCoin cryptocurrencies investors are not going to get any benefit from diversification.This predictable co-movements pattern among the cryptocurrencies could be the basic investment strategies to gain maximum profit by diversifying the risk in cryptocurrency investments.
Editorial Platform in Blockchain for Application in Higher Education

In the current publishing industry, all stakeholders are faced with problems ranging from copyright management, to piracy, to the lack of transparency of the actual number of sales and the unfair division of profit between the publisher and the author. In this paper, we propose an editorial platform for application in higher education based on Blockchain technology in order to eliminate the problems that publishers, writers, translators and readers currently deal with. The proposed platform aims to eliminate the prices and rules that publishers impose on authors, redefining their responsibilities so that they are only in charge of the printing and distribution of content. This proposal also allows to the royalties received by the author to increase by 50-90% of the value of the sale, a substantial increase compared to the 10-25% they receive in the current panorama. In this way, new writers and other lesser-known writers who produce content related to the field of education will have a greater opportunity to publish their content and be fairly rewarded.
Differential Privacy meets Verifiable Computation: Achieving Strong Privacy and Integrity Guarantees

Often service providers need to outsource computations on sensitive datasets and subsequently publish statistical results over a population of users. In this setting, service providers want guarantees about the correctness of the computations, while individuals want guarantees that their sensitive information will remain private. Encryption mechanisms are not sufficient to avoid any leakage of information, since querying a database about individuals or requesting summary statistics can lead to leakage of information. Differential privacy addresses the paradox of learning nothing about an individual, while learning useful information about a population. Verifiable computation addresses the challenge of proving the correctness of computations. Although verifiable computation and differential privacy are important tools in this context, their interconnection has received limited attention. In this paper, we address the following question: How can we design a protocol that provides both differential privacy and verifiable computation guarantees for outsourced computations? We formally define the notion of verifiable differentially private computation (VDPC) and what are the minimal requirements needed to achieve VDPC. Furthermore, we propose a protocol that provides verifiable differentially private computation guarantees and discuss its security and privacy properties.
Determinants of Access and Use of ICT Based Sources of Market Information Amongst Finger Millet Farmers in Teso South, Kenya

In the traditional field of economics of information, marketing information is an important input to reduce the uncertainties faced by participants in the agricultural markets. In most developing countries, efforts have been directed towards improving farmers’ access to ICT based market information with an intention of providing requisite and real time market information to augment farmers’ access to input and output markets. This study analyses the quality of market information provided by radio and mobile phones and assesses the determinants of access and use of ICT based sources of market information amongst finger millet farmers in Teso South district. The results show that use of both mobile phone and radios as sources of market information is still low amongst the finger millet farmers (about one in four finger millet farmers uses ICT sources), thus transforming the market information system entirely into an ICT based system may preclude most farmers who are semi illiterate hence reliant on non ICT based sources of market information. Integration of both ICT based sources of market information and non-ICT sources is inevitable to ensure all the farmers are given the opportunity to access and use market information.