Reviewerly/exHarmony huggingface dataset:
A Comprehensive Evaluation Method for Container Auto-Scaling Algorithms on Cloud

Container technology becomes increasingly popular in public cloud platforms. Although many Container Auto-Scaling Algorithms (CASAs) have been proposed recently, there is still a lack of standardized frameworks to evaluate them comprehensively. This paper proposes a comprehensive evaluation method for CASAs. We firstly proposed a set of CASA evaluation metrics considering the requirements of cloud provider and user, and then designed a test data set based on real-world system load traces and 6 workload patterns. Experiments on some representative CASAs are conducted to demonstrate the effectiveness of the proposed evaluation method. Our research can provide cloud providers, operators and users with more comprehensive and systematic information about CASAs.
Towards Open Ended and Free Form Visual Question Answering: Modeling VQA as a Factoid Question Answering Problem

Visual Question Answering (VQA) is a multi disciplinary challenging problem involving various fields such as Natural Language Processing, Computer Vision, Deep Learning and Knowledge Representation, which has garnered much interest among researchers, especially with the recent advancements in machine perception. The problem at hand not only involves reasoning over visual elements present in the image or natural language understanding of the input query, but also may involve outside world knowledge in order to infer the answer. In this paper, we convert the VQA problem to a factoid question answering task over a set of natural language facts extracted from images in the Visual Genome Dataset []. Recent literatures in textual question answering have established the effectiveness of End to End Memory Networks (MemN2N) over the standard LSTMs. Inspired by the approaches incorporated by researchers in this direction, as a first step to create an explainable VQA model, this paper proposes the incorporation of MemN2N with soft attention for inferring the answer from a set of regional facts extracted from the image. We also experiment with the addition of a Bayesian Neural layer for posterior reasoning of the answer from a fixed vocabulary, as proposed in [] which shows a significant improvement in the accuracy score compared to the other models tested.
Investigation and Analysis on the Satisfaction Degree of Teaching Servicein Universities

Improving the quality of teaching service is conducive to improving the quality of university teaching, improving the quality of talent training, improving the students’ satisfaction of teaching service, and improving students' sense of identity and belonging to the school. Based on a questionnaire survey of 2468 students in a comprehensive university in Beijing, this paper discusses how to improve the satisfaction of teaching service for students. The survey data shows that the overall satisfaction of students to the school's teaching service is high, and the students' satisfaction is closely related to the teaching service content, teaching service environment, teaching service mode and teaching service team. On this basis, this paper puts forward important suggestions on how to improve the quality of teaching service and improve students' satisfaction.
An extension of Bravyi-Smolin’s construction for UMEBs

We extend Bravyi and Smolin’s construction for obtaining unextendible maximally entangled bases (UMEBs) from equiangular lines. We show that equiangular real projections of rank more than 1 also exhibit examples of UMEBs. These projections arise in the context of optimal subspace packing in Grassmannian spaces. This generalization yields new examples of UMEBs in infinitely many dimensions of the underlying system. Consequently, we find a set of orthogonal unitary bases for symmetric subspaces of matrices in odd dimensions. This finding validates a recent conjecture about the mixed-unitary rank of the symmetric Werner–Holevo channel in infinitely many dimensions.
A Node Classification Approach for Dynamically Extracting the Structures of Online Discussions

Online discussion platforms require extracting the discussion structure in order to support understanding the flow of these discussions. Towards this end, this paper proposes an approach that performs node classification which is the first step towards extracting the structure of online discussions. In this regard, the proposed approach employs a graph attention network (GAT) in order to directly learn the discussion structure. In specific, the GAT, which is a type of graph neural networks (GNNs), encodes the graph structures directly. In addition, the GAT, which is based on attention architecture, is able to deal with different graph structures. In order to evaluate the proposed approach, we have conducted a set of experiments on the persuasive essay dataset that is styled using the issue-based information system (IBIS). The experimental results show that the proposed approach is able to classify the nodes in online discussion structures accurately.
Gender-inspired Facial Age Recognition based on Reflexivity, Antisymmetry and Transitivity

We propose an age recognition method that compares a pair of image instances from the same gender for the three conditions of reflexivity, antisymmetry, and transitivity corresponding to older, similar, and younger than respectively. The approach uses a small number of reference set to address performance issues due to the lack of datasets for age recognition. Experimental results demonstrate that the use of a few optimally selected class references for the comparison resulted in a dramatic improvement on the baseline. Thus, with a Mean Absolute Error (MAE) of 4.07 and Cumulative score (CS) of 93.87% on the adience dataset, our proposal shows superiority among other gender-inspired age recognition proposals.
A Feature Fusion Based Object Tracking Algorithm

At present, most of the networks used to extract features in tracking algorithms are pre-trained on the ImageNet classification data set, such as VGG-16, AlexNet, etc. Most of the features used are feature maps output by the highest convolutional layer, so as to make full use of the powerful expressive power of convolutional features. But it will bring certain problems. The spatial resolution ability of convolutional neural network is not strong. If the target moves fast, exceeds the sampling area of the picture, or the target is occluded by background objects, the target tracking algorithm will fail. In order to further improve the spatial resolution ability of the convolutional neural network and solve the problem of tracking failure that may be caused by local cutting, a single target tracking algorithm based on feature fusion is proposed here. The feature map of the last layer is up-sampled and merged with the feature map of the previous layer to improve the spatial resolution ability. At the same time, the information of different feature models is used to improve the accuracy of the tracking algorithm. In addition to the local detection model, this article also proposes a classification detection model. When the local detection model cannot perform effective detection, multiple detection models are used instead to detect within the entire image to find the location where the target may appear. If the location where the target appears is found, the local detection model continues to be used at that location.
UNIFY - The University Recommender

As humanity move towards the digital age university education started becoming more and more important for any field in the market, but to understand the process of how the university admissions work a student has to invest a lot of time and money to get the required information with a possibility of getting scammed, to over this issue a sophisticated university recommender system is discussed in this paper and basic details on how to implement the system as well as difficulties faced while developing the said system is discussed in brief.
Бази даних цитувань та пошукові інструменти для науковців майбутнього

This popular science article discusses the history, present and future of citations databases and specialized search tools. The article talks about the most popular scientometric tools (Scopus, Web of Science and Google Scholar). Also, this article describes new alternative citation indexes, including those related to the development of the Initiative for Open Citations.
Impact of Leadership and Governance on Poverty Alleviation in Africa

Leadership is one of the most observed and least understood phenomena on earth (Afegbua & Adejuwon, 2013). A call for a focus on leadership and governance is timely, important and no doubt topical, reflecting the worldwide thrust toward political and economic liberalization (Afegbua & Adejuwon, 2013). The ubiquity of the governance phenomena in contemporary times has made most African and indeed, global multilateral organizations, like the World Bank, United Nations Development Programme (UNDP), and the United Nations Economic Commission for Africa (UNECA) to become "governance infatuated" by giving prescription and insisting that governance is the key to economic growth and development in Africa (Essien, 2012). Nonetheless, after two decades of unprecedented economic growth, how much have the lives of African families improved? A plethora of its citizens wallow in poverty hinged on the declining quality of governance, and indeed the spatial culture of non-existent leadership in Africa. The latest estimates from the World Bank suggests that the share of the African population in extreme poverty did decline- from 56% in 1990 to 43% in 2012. These are staggering numbers. In fact, the search for aptness in government and governance that is oriented toward development, the common good and well-being of the people of Africa has become something of a scandal and simply ironic (Anyanwu, 1998: 148).This paper engages in a conceptual examination of the nexus between leadership and governance focusing on the impact of their interaction on poverty alleviation. Using selected key governance indicators such as government effectiveness and control of corruption, voice and accountability, budgetary transparency, resource allocation and targeting of benefits, and the rule of law and human rights, this paper investigates the governance contents of member states, its Impact and the extent which it can transform leadership and promote good governance as a basis for achieving poverty alleviation in Africa.
Sememe Tree Prediction for English-Chinese Word Pairs

Sememe is the minimum unambiguous semantic unit in human language. The semantics of word senses are encoded and expressed by sememe trees in sememe knowledge base. Sememe knowledge benefits many NLP tasks. But it is time-consuming to construct the sememe knowledge base manually. There is one existing work that slightly involves sememe tree prediction, but there are two limitations. The first is they use the word as the unit instead of the word sense. The second is that their method only deals with words with dictionary definitions, not all words. In this article, we use English and Chinese bilingual information to help disambiguate word sense. We propose the Chinese and English bilingual sememe tree prediction task which can automatically extend the famous knowledge base HowNet. And we propose two methods. For a given word pair with categorial sememe, starting from the root node, the first method uses neural networks to gradually generate edges and nodes in a depth-first order. The second is a recommended method. For a given word pair with categorial sememe, we find some word pairs with the same categorial sememe and semantically similar to it, and construct a propagation function to transfer sememe tree information of these word pairs to the word pair to be predicted. Experiments show that our method has a significant effect of F1 84.0%. Further, we use the Oxford English-Chinese Bilingual Dictionary as data and add about 90,000 word pairs to HowNet, nearly expanding HowNet by half.
RGraph: Asynchronous graph processing based on asymmetry of remote direct memory access

Summary The scale of real‐world graphs is constantly growing. To deal with large‐scale graphs, distributed graph processing has attracted much research efforts. Existing distributed graph processing systems are commonly built on traditional TCP/IP communication stack, which leads to network bottleneck because of low bandwidth and heavy kernel stack operations. Meanwhile, in real power‐law graphs, the average number of mirror vertices after graph partitioning is very large, resulting in significant communication overhead among nodes. The emerging high‐performance Remote Direct Memory Access (RDMA) network has the features of low latency, high bandwidth, and low CPU overhead, which brings new opportunities for distributed graph processing systems. Existing RDMA‐assisted graph processing systems focus on synchronous execution, which imposes barriers between consecutive iterations. Synchronous execution transfers bulk data among nodes and thus only needs a small number of network transfers. However, synchronous execution is usually less efficient than asynchronous execution because of bulk synchronization. Asynchronous execution accelerates graph processing by eliminating barriers, which in turn requires to transfer a large amount of small size data. In this paper, we propose RGraph, an RDMA‐assisted asynchronous distributed graph processing system. RGraph distributes edges into two parts to isolate master and mirror vertices. RGraph exploits the asymmetry of RDMA to accelerate the one‐to‐many communication between master and mirror vertices. We implement RGraph on top of PowerGraph and conduct comprehensive experiments with large‐scale real graphs to evaluate its performance. Results show that compared to existing designs, RGraph reduces the execution time by up to 81%.
A Hybrid Inpainting Model Combining Diffusion and Enhanced Exemplar Methods

Image inpainting is a technique in the world of image editing where missing portions of the image are estimated and filled with the help of available or external information. In the proposed model, a novel hybrid inpainting algorithm is implemented, which adds the benefits of a diffusion-based inpainting method to an enhanced exemplar algorithm. The structure part of the image is dealt with a diffusion-based method, followed by applying an adaptive patch size–based exemplar inpainting. Due to its hybrid nature, the proposed model exceeds the quality of output obtained by applying conventional methods individually. A new term, coefficient of smoothness, is introduced in the model, which is used in the computation of adaptive patch size for the enhanced exemplar method. An automatic mask generation module relieves the user from the burden of creating additional mask input. Quantitative and qualitative evaluation is performed on images from various datasets. The results provide a testimonial to the fact that the proposed model is faster in the case of smooth images. Moreover, the proposed model provides good quality results while inpainting natural images with both texture and structure regions.
Vision System Actualization for Object Localization Robot

The accuracy of object localization by an autonomous robotic system depends on the efficiency of the vision system. The key issue for vision system is proper segmentation and detection of an object under noise and low light. A number of image enhancement algorithms have been proposed so far such as adaptive histogram equalization (AHE) and contrast limit adaptive histogram equalization (CLAHE) to combat the low light and high noise in the captured image. However, both of these algorithms have some advantages and disadvantages based on image quality. Therefore to incorporate the advantages of both algorithms, we design a vision system actualization (VSA) system based on automatic image tuning (AIT) system that selects the suitable algorithm for image enhancement on the basis of image noise variance and light intensity. The VSA system is implemented on a 3-DOF robotic system using embedded control system (ECS). The proposed method shows optimum results up to 92 percent and minimizes the processing times up to 0.005 seconds for the VSA system. In addition, we developed a human-robot interface (HRI) to control and optimize the vision performance.
Applying Augmented Reality to Learn Basic Concepts of Programming in U-Learning Environment

Learning the basic concept of programming, such as control structures, is considered difficult due to their complexity and require a high level of students’ abstraction. Faced with this problem and taking advantage of ubiquitous learning that allows learning without being limited to a specific space or time, developing a ubiquitous learning app that helps students learn these concepts more tangible for them has been considered challenging. For this reason, we developed a software application (App) that, through augmented reality techniques, shows an object to the student, who must move in search of it. As a result, the App generates a program with the actions carried out. This article presents the ubiquitous learning App and the principal modules’ development: the compilation module and the 3D manager module. The first one allows translating the student’s cell phone’s geographical coordinates into a programming language’s instructions. The second one allows the student to visualize the objects using augmented reality. The tests carried out on both modules are also shown, demonstrating the feasibility of translating the actions and displaying the objects.
Siamese pyramid residual module with local binary convolution network for single object tracking

Visual object tracking has made rapid progress in the past few years, which has received more and more attention. It is known that the texture information plays an essential role in numerous applications, for instance, in the person reidentification, image classification and so on. In this work, we propose a kind of new framework based on Siamese networks for single object tracking, called Siamese pyramid residual module with local binary convolution network for single object tracking (SPLBCT). A local binary convolution (LBC) module is proposed for the extraction of the texture feature of the tracked objective, which can improve in-variance to lighting. A pyramid residual Module (PRM) is used to study different levels of features and achieve the fusion of multi-scale features. The combination of the LBC and PRM enhances the feature extraction of the image data. According to two object tracking benchmarks (VOT2015 and VOT2016) and online tracking benchmark (OTB100), the final experimental results show that the proposed method performs better compared with some previous trackers.
Report of the ALCTS Cataloging and Metadata Management Section (CaMMs) Catalog Management Interest Group Meeting, ALCTS Virtual Interest Group Week, June 2020

The 2020 ALA Annual Conference & Exhibition scheduled for June 25–30, 2020, in Chicago, IL, was canceled. In lieu of face-to-face meetings at the Annual Conference, ALCTS held a Virtual Interest Gr...
Geometric Surface Image Prediction for Image Recognition Enhancement

This work presents a method to predict a geometric surface image from a photograph to assist in image recognition. To recognize objects, several images from different conditions are required for training a model or fine-tuning a pre-trained model. In this work, a geometric surface image is introduced as a better representation than its color image counterpart to overcome lighting conditions. The surface image is predicted from a color image. To do so, the geometric surface image together with its color photographs are firstly trained with Generative Adversarial Networks (GAN) model. The trained generator model is then used to predict the geometric surface image from the input color image. The evaluation on a case study of an amulet recognition shows that the predicted geometric surface images contain less ambiguity than their color images counterpart under different lighting conditions and can be used effectively for assisting in image recognition task.
Research and Improvement of Multi-granularity Method for Pedestrian Tracing System

Abstract In the field of video surveillance, with the continuous development of the Internet and artificial intelligence, the related technology for single device has been very mature, but how to accurately realize pedestrian tracing under cross-devices is still an active research field. In this paper, a multi-granularity pedestrian traceability method is proposed, which is based on the attribute extraction of pedestrian image and the person re-identification algorithm based on attention mechanism retrieves the same pedestrian image cross a lot of devices. The results show that the video surveillance system has good performance, which reduces the occurrence of tracking loss, and the track is visualized at the platform.
Educating minds to empower the future

Education is the basic need for economic and social growth. India being one of the fastest growing economies, has focused on an input-driven educational system. To provide free and compulsory education to all citizens of the country is a state priority. At Capgemini, we believe education is the key differentiator for development, both at the community and national level. We focus on access to, the quality of, and the inclusivity of education as part of our intervention agenda. We have adopted 126 schools across six states and nine cities, it was pertinent to leverage on the government network and ensure their support and participation. Further, the Model School approach has been adopted in twenty-seven schools across nine locations in which we have focused on bringing quality education through digital interventions. Our key priorities are Making education relevant and engaging for the children Enabling children to enjoy their childhood and learning like InnoSTEAM (Science, Technology, Engineering, Arts, Mathematics) Providing regular training to teachers and empowering them to be the change leaders Strengthening school management committees to be an influential element in continuing these processes These efforts as part of school adoption go a long way in making government school institutions equipped for children to learn and enjoy their childhood.
Research and Application of performance Evaluation Model of Rural Preschool Teachers based on big data Algorithm

In this paper, based on the research of the existing relevant results of teaching performance evaluation, referring to the information classroom performance evaluation system, the classroom teaching performance evaluation system in line with the characteristics of rural early childhood teaching classroom teaching. In this study, the Apriori analysis is adopted to analyze the data of classroom teaching performance evaluation of rural children teaching by using the big data algorithm method. Valuable knowledge of improving classroom teaching performance of rural children teaching is mined. First performance of education, teaching performance evaluation, rural children teaching classroom teaching and the analysis on the relevant situation do big data algorithm, mainly analyses the rural preschool education present situation of the application of classroom teaching, rural children education problems in classroom teaching, the demand of the rural preschool teaching classroom teaching evaluation, and on the basis of existing teaching performance evaluation research expounds the rural children teaching the basic content of the performance evaluation of classroom teaching, summed up the rural preschool teachers' performance evaluation model.