Reviewerly/exHarmony huggingface dataset:
#Fortheculture: Generation Z and the Future of Legal Education

Generation Z, with a birth year between 1995 and 2010, is the most diverse generational cohort in U.S. history and is the largest segment of our population. Gen Zers hold progressive views on social issues and expect diversity and minority representation where they live, work, and learn. American law schools, however, are not known for their diversity, or for being inclusive environments representative of the world around us. This culture of exclusion has led to an unequal legal profession and academy, where less than 10 percent of the population is non-white. As Gen Zers bring their demands for inclusion, and for a legal education that will prepare them to tackle social justice issues head on, they will encounter an entirely different culture—one that is completely at odds with their expectations. This paper adds depth and perspective to the existing literature on Generation Z in legal education by focusing on their social needs and expectations, recognizing them as critical drivers of legal education and reform. To provide Gen Z students with a legal education that will enable them to make a difference for others—a need deeply connected to their motivators and beliefs—law school culture must shift. Reimagining, reconstituting, and reconfiguring legal education to create a culture of inclusion and activism will be essential and necessary. Engaging in this work “for the culture” means getting serious about diversifying our profession by abandoning exclusionary hiring metrics, embedding social justice throughout the law school curriculum, and adopting institutional accountability measures to ensure that these goals are met. Gen Zers are accustomed to opposing institutions that are rooted in inequality; law schools can neither afford, nor ignore the opposition any longer. We must begin reimagining legal education now—and do it, for the culture.
Evaluation of Speckle Noise Reduction and Feature Enhancement in Prolapsed Mitral Valve Leaflet Echocardiography

Abstract Rheumatic heart disease has a substantial impact on morbidity and mortality for both men and women in developing countries. It is a complication of autoimmune phenomenon known as acute rheumatic fever in response to group A streptococcus bacteria. It often causes damage to valves and lacks its functionality. Initial manifestations of prolapsed valves are evident in echocardiography in the form of valve bulging, commissural fusion and restricted leaflet motion. However, these echocardiogram images are inevitably degraded by multiplicative noise known as speckle noise at the time of image acquisition and transmission. Hence despeckling is of vital importance for enhancing ultrasound image quality. In this paper, a comparative analysis of well established despeckling filters compiled for speckle suppression and feature enhancement has been performed. In addition to this, the performance of these filters is validated using different quantitative metrics. The experimental result shows that feature enhanced Fast Non-Local Means (FNLM) filter outperforms other state-of-art filtering techniques in the aspect of higher PSNR value and preserves structural similarity in terms of SSIM value near to unity. In conclusion, the preprocessing intends to suppress speckle noise and enhancing perceived visual quality that aids for further development of virtual cardiac model.
Increasing the General and Local Endurance of Students with Rock Climbing as a Form of Youth Recovery

Purpose: The article describes the problems of physical fitness of introductory course students, and defines measures aimed at increasing the general, local and power endurance of students through classes in sports climbing sections; some results of the research on the effect of classes in sports climbing groups on the indicators of development of local endurance of students are presented. Design/Methodology/Approach: For the rapid adaptation of novice climbers to the specific loads of the sport, the authors, in September 2016, introduced a technique aimed at improving the strength, general and local endurance of students engaged in sports climbing in sections. In the preparation of sections of the program, to improve the strength of trainees, such standard technologies were used as: the method of unintended burdens; the method of limit and near-limit aggravations; method of unintended aggravations with limit number of repetitions Findings: The authors, for several years, tracked and identified specific features of endurance training in climbers of various sports levels. Based on observations, muscle groups were identified that require increased training loads, due to the specifics of climbing, as a sport. Generalized experience shows that among all types of endurance, the development of local endurance of the forearm muscles, requires more attention. Originality/Value: The methodology developed by the authors of the program can be used as part of training sessions with students of all study groups. The studied direction of formation of “local” endurance is relevant in training process of various sports, requires further study and corresponding medical-biological and coaching support.
Dynamic Chunkwise CNN for Distantly Supervised Relation Extraction

Sentence representation learning is a key component in distantly supervised relation extraction. Text chunks (i.e., a group of n consecutive words) are meaningful units to understand relations. However, existing works suffer from extracting useful structural features from text chunks for relation extraction due to two challenges: (1) Prepositions often occur in text chucks but their semantics are hardly captured. (2) Chunk structures vary in different sentences with different sizes. In this paper, we propose a new model, dynamic chunkwise CNN (DCW-CNN), to tackle these challenges. We develop structural convolution to extract chunk features from sentences, and design a dynamic chunk module to dynamically determine the "most proper" chunk size for sentences of varying structures and contents. We have conducted experiments on two benchmark datasets. The experimental results show that our proposed model improves performance significantly compared with the state-of-the-art methods.
Teaching Physics in higher education: use of information and communication technologies and digital resources

This paper presents a methodology for the implementation of the Physics course in the distance modality through the use of information and communication technologies (ICT) and digital educational resources for industrial engineering careers within the Institute Technological of San Luis Potosí (ITSLP). The course was implemented using the Moodle platform, the teacher made the programming of the proposed exercises and activities, such as readings, simulators, calculators and evaluations, and the Webex platform was used as a support tool to develop synchronous sessions of class. As a sample, 4 groups from the Xilitla, Ahualulco, El Naranjo and Moctezuma campuses were considered. The intention was to verify if the applied method is effective and meets the objectives of learning the subject, in addition to providing students with the necessary tools to achieve basic skills for their professional development. It is concluded that, by applying the methodology appropriately, with carefully selected digital resources and the support of synchronous sessions between the teacher and the students, the learning objectives and the generation of significant knowledge were achieved.
Algorithm for constructing the optimal trajectory A* for an unmanned aerial vehicle in three-dimensional space

Abstract This article discusses the adaptation of the algorithm for constructing the optimal trajectory A * to three-dimensional space, as well as computer modeling of this algorithm in the MatLab environment and the analysis of the results.
A Proficient Model for Action Detection Using Deep Belief Networks

Picture processing and also personal computer vision have acquired a massive advance within the part of printer mastering strategies. Some of the primary exploration places in just printer learning are in fact pattern recognition as well as action detection. Motion recognition is really completely new improvements of pattern recognition methods by which the activities created by every measure or perhaps lifestyle getting is in fact tracked as well as administered. Motion recognition continues to encounter many difficulties which should be browsed on as well as conduct realize the activities is an incredibly very little time. Networks such as neural networks as well as SVM are in fact accustomed to instruct the networking inside such a manner that they are proficient to determine a pattern of excitement when a novice driver frame is really supplied. With this particular newspaper, we have suggested a design that detects patterns of measures by a picture or perhaps a video clip. Bounding cardboard boxes are in fact accustomed to determine the activities as well as localize it. Serious belief network is actually used to instruct the product by which numerous pictures owning steps are supplied as soon as an instruction set. The overall performance evaluation was completed on the item, and it is discovered that the activities are detected by it truly properly when a novice driver impression is actually supplied to the network.
Geological Field Guide: Malvan (Maharashtra, India)

We present a geological field guide for a lesser known location to (Indian) geologists from Maharashtra (India): Malvan. Geomorphological and structural geology exercises have been explained, and a few results have been shown from the 2016 field report of the third author.
Aspect Based Sentiment Analysis for E-Commerce Using Classification Techniques

Tremendous accumulations of shopper audits for items are currently accessible on the Web. These audits contain rich stubborn data on different items. They have turned into an important asset to encourage shoppers in understanding the items preceding settling on buying choices, and bolster makers in fathoming purchaser suppositions to successfully enhance the item contributions. In any case, such audits are frequently sloppy, prompting trouble in data route and information securing. It is wasteful for clients to accumulate general suppositions on an item by perusing all the shopper audits and physically investigating assessments on each survey. In this undertaking, we can actualize item surveys rating from item audits, which intend to naturally distinguish critical item perspectives from online buyer surveys. The imperative viewpoints are recognized by two perceptions: the vital parts of an item are typically remarked by an expansive number of shoppers; and buyers' conclusions on the essential angles significantly impact their general sentiments on the item. Specifically, given customer audits of an item, we initially recognize the item angles by marking the surveys and decide buyers' feelings on these perspectives by means of a slant classifier. The proposed research can be execute SVM and Naive Bayes arrangement to recognize the supposition words by at the same time thinking about the surveys gathering and the impact of purchasers' assessments given to every perspective on their general sentiments. The exploratory outcomes on prevalent portable item surveys show the adequacy of our approach. We additionally apply the survey positioning outcomes to the utilization of assessment order, and enhance the execution essentially.
Segmentation of Brain Tissues from Infant MRI Records Using Machine Learning Techniques

The automatic segmentation of medical images is an intensely investigated problem, due to the quick rise of medical image data amount created day by day, which cannot be followed by the number of human experts. This paper searches for the most suitable classical machine learning method to be employed in the segmentation of various tissue types from volumetric multi-spectral MRI records of 6-month infant patients. Model training and model based prediction is performed using the 10 records of the train data set available at the iSeg-2017 challenge. All MRI records are treated with histogram normalization and feature generation, and then fed to six machine learning methods, which use them as train and test data according to the leave-one-out technique. The output of the classification algorithms is evaluated with statistical methods. The best segmentation accuracy is achieved by the random forest based approach, with a correct decision rate of 83.4%.
Linguistic tricks to grab your online audience’s attention

Virtual meetings can suck the life out of a presentation. Pausing, probing and varying your pitch will keep your muted listeners hooked. Virtual meetings can suck the life out of a presentation. Pausing, probing and varying your pitch will keep your muted listeners hooked.
FP-ExtVP: Accelerating Distributed SPARQL queries by Exploiting Load-adaptive Partitioning

As the scale of RDF data increases gradually, the requirements for efficient processing technologies on RDF data in the distributed environment has become a hot topic. Most existing methods have two main issues. First, massive storage redundancy often occurs to materialize semi-join results, but only a part is frequently accessed in most queries. Second, the queries are allocated to the whole cluster, causing a considerable duplication on intermediate results and hence increasing the communication cost. In this paper, we propose a new approach named FP-ExtVP, using a hybrid partitioning schema to reduce the storage requirement without damaging the query performance. In addition, we create an index named P-tree to allocate the queries only to the mentioned nodes, instead of the whole cluster, in order to reduce the shuffle cost. The experiments on several benchmarks demonstrate that, comparing to the native S2RDF, our method outperforms on Star, Snowflake and Complex queries, by reducing nearly a half of the storage requirements.
Competence-based model for modernization of higher library education: Ukraine’s experience

Transition of higher library education in Ukraine from the qualification to competence-based model is characterized. The three-level system, i.e. “Bachelor – Master – Doctor of Philosophy”, being implemented within the new integrated program of study “Information, Librarianship and Archivistics” is reviewed. The competence-based approach was applied in the processes of development and approval of higher education standards for the program “Information, Librari-anship and Archivistics” for the first (bachelor's), second (master's) and third (educational and scientific) levels. The authors emphasized that the systematic ap-proach to selecting essential competences for the program was applied. The experience of developing the program for Ph.D. candidates in this subject area with the focus on cooperation with the applicants and their future employers, individu-al capabilities, their scientific and educational potential, and self-motivation, is discussed. The need for the quality management system based on monitoring and continuing review of the content and structure of educational programs, is sub-stantiated. Among these criteria are: projecting and objectives of educational program, its structure and content; forms and methods of education, its availability; monitoring and assessing learning outcomes; quality of human resources; comfortable educational environment and its material and technical resources; domestic education quality assurance system; transparency and publicity of educational programs.
Recurrent Multi-column 3D Convolution Network for Video Super-Resolution

Super-resolution (SR) aims to recover high resolution (HR) content from low resolution (LR) content, which is a hotspot in image and video processing. Since there is strong correlation between video sequence, how to effectively exploit spatio-temporal information among serial frames is significant for video super-resolution (VSR). In this paper, we propose a recurrent multi-column 3D convolution network to fuse inter-frame and intra-frame information for VSR. Specifically, we first introduce motion compensation by optical flow to align the reference frame and supporting frames. Different from the previous methods, our supporting frames is originated from the previous reconstructed SR frame, instead of front and back low resolution frames of reference frame. This is more conducive to generating visual consistent video sequence. Then, 3D multi-column block (3DMB), which is composed with different separable 3D convolutions by reducing the parameters without performance penalties, is performed to fuse spatio-temporal features and recover missing details of reference frame. Finally, consecutive HR video sequence is obtained by reconstruction module. Comparative experiments are carried out to demonstrate our proposed method outperforms other advanced methods.
An Architecture for the Management of Virtual Machine Migrations

Virtual Machine (VM) migrations became a defacto technology adopted in the management of cloud resources. However, the expansion (in number and complexity) of cloud Datacenters (DCs) requires management capabilities specifically dedicated to migrations. In fact, the increasing number of resource remappings is hardly managed by the Cloud Management Systems (CMS) that usually handle migrations as a stage of the VM life cycle. As a consequence, migrations are potentially planned based on outdated or missing information. In addition, VM migrations are usually under the control of a human, increasing the likelihood of fails and delays. This work proposes an architecture and an approach to automate the management of VM migrations. Our proposal was compared with a solution not based on management premises, both considering interDC migration scenarios. The evaluation shows the effectiveness of the management approach, confirming that the number of unnecessary migrations decreases, and hot spots are less frequent.
Design and Development of Bayesian Optimization Algorithms for Big Data Classification Based on MapReduce Framework

The handling of big data refers efficient management of processing and storage requirements of very large volume of structured and an unstructured data of association. The basic approach for big data classification using naïve Bayes classifier is extended with correlation among the attributes so that it becomes a dependent hypothesis, and it is named as correlative naïve Bayes classifier (CNB). The optimization algorithms such as cuckoo search and grey wolf optimization are integrated with the correlative naïve Bayes classifier, and significant performance improvement is achieved. This model is called as cuckoo grey wolf correlative naïve Bayes classifier (CGCNB). The further performance improvements are achieved by incorporating fuzzy theory termed as fuzzy correlative naïve Bayes classifier (FCNB) and holoentropy theory termed as Holoentropy correlative naïve Bayes classifier (HCNB), respectively. FCNB and HCNB classifiers are comparatively analyzed with CNB and CGCNB and achieved noticeable performance by analyzing with accuracy, sensitivity and specificity analysis.
Unsupervised Training of Denoising Networks

This work explore approach for image denoising of received by CMOS sensor. Proposed pipeline solves the problem of unsupervised training neural network architectures for image denoising which uses datasets without clean data. This approach bases on theoretical background about image restoration proposed by Nvidia researchers. We implemented custom denoising neural network architectures using specifics of noise distribution. Networks are trained on custom images collection.
Data Collection Using Wireless Sensor Networks and Online Visualization for Kitui, Kenya

Abstract Kenya is a developing country with a population of 47,213,282 people this comprises of 56% low-income earners. Small businesses and crop production represent 23% of the income within the country, which is at risk as soils become less productive. Various factors have led to this, climate change and land overuse being leading causes. Without adaptation, the rural to urban migration will continue to increase. Through Internet of Things (IoT) and specifically wireless sensor networks, we can change how we obtain and consume information. Small-scale farmers can collect data and in exchange receive useful information about their soils, temperature, humidity, and moisture content hence make better choices during crop production. Connected end devices bring in data, which is currently sparse in relation to small-scale farming. IoT will enable analysis and informed decision-making including crop selection, support equipment, fertilizers, irrigation, and harvesting. The cloud-based analysis will provide information useful for policy making and improvement. This chapter presents a wireless sensor network (WSN) in mesh topography using XBee communication module, communication, and raspberry pi, combined with a cloud-based data storage and analysis. We successfully set up a proof of concept to test a sensor node that sends information to a RPi and onto an online visualization platform.
Sobolev gradients for segmentation of vector-valued texture images

In this article, we proposed a new variational approach to smooth and segment vector-valued texture images. The given image F0 in first stage before segmentation, we smooth texture it through L0 norm in order to get smooth image I. Then in the segmentation model, the internal energy/curve length is minimized using Sobolev gradients while the external enrgy/fidelity term is minimized through L2 norm/gradient. The use of Sobolev gradients helps in fast convergence of the model. Semi-Implicit (SI) method is used for the solution of the partial differential equation that arises from the proposed model. Numerical results of the proposed model are compared with state-of-the-art L2 gradients. Experimental results illustrate better performance of the proposed method compared to two other PDE-based methods.
Multi Algorithmic Approach to Galactic Swarm Optimization (MAGSO)

Abstract Multi population algorithms have noted advantages over single population algorithms when optimizing over multimodal function space. The existence of multiple attractors allows exploration on multiple optimality regions which in turn provide more exploration compared with single population algorithms. This paper is a novel utilization of Galactic Swarm Optimization (GSO) framework in which the subpopulations are evolved with multiple variants of Particle Swarm Optimizers (PSOs) rather than single PSO. The proposed algorithm is called Multi Algorithmic Galactic Swarm Optimization (MAGSO). MAGSO utilizing multivariant PSOs in subswarm level combined with the unique superswarm tailored for exploitation has shown significant improvement of performance over GSO. Experiments done on Congress on Evolutionary Computation 2013 (CEC 2013) and other standard general benchmark suite indicate that the proposed MAGSO algorithm outperforms the previously proposed state-of-the-art swarm algorithms such as Bollinger bands approach on boosting ABC (ABCBB) and Dynamic Neighbourhood Learning Particle Swarm Optimizer (DNLPSO) besides GSO. Detailed statistical evidence over 51 independent trials on the benchmark functions indicate the best performance of MAGSO. The main research goal is to propose a superior performing algorithm by exploiting the GSO framework. In order to show its efficacy, the algorithm is compared with state-of-the-art swarm algorithms such as ABCBB, DNLPSO and unmodified GSO. The method proposed here is quite general and demonstrates that more ability and extension work possible from the basic GSO framework.