Reviewerly/exHarmony huggingface dataset:
A New Native Video Filtering based on OpenGL ES for Mobile Platform

In the last five years, there have been many Android applications implementing video filter or video effect as an excellent feature. Open CV is an open source computer vision library that can be simply and easily used for video filtering in Android application. However, using OpenCV library for video filtering commonly yields a bigger size of Android application. The concept of “Develop for Billion People” has enforced the developers to optimize the size of their applications to preserve resources and size of memory—as not all Android devices come with sufficiently large memory. On the other hand, OpenGL ES does not burden the filtering process because of its smaller size when it is implemented during the application development. In this research, we present a new native video processing technique using OpenGL ES. We implement the proposed method on a native video file without decreasing its quality before video filtering process. The experiments were conducted with five different mobile devices. We compared several metrics including: quality of the resulted video, file size of the apk, power consumption, and memory usage. Based on the experimental results, OpenGL ES produces smaller file size of apk (2 MB) compared with the produced file size of apk by Open CV (20MB). The resulted file after video filtering possesses same properties as observed before video filtering. Additionally, OpenGL ES uses more efficient power with 0.1965 mAh, while OpenCV consumes 0.283 mAh. Finally, video filtering with OpenGL ES uses 29.3% lesser memory than video filtering with OpenCV. The proposed method is proven to be more appropriate with “Develop for Billion People” as it preserves more computational resources compared with the existing video filtering technique in Android.
Wild Extensions: Discovering and Analyzing Unlisted Chrome Extensions

With browsers being a ubiquitous, if not required, method to access the web, they represent a unique and universal threat vector. Browsers can run third-party extensions virtually invisibly in the background after a quick install. In this paper, we explore the abuse of browser extensions that achieve installations via suspicious methods. We scan the web for links to extension installations by performing a web crawling of the Alexa top 10,000 websites with recursive sub-page depth of 4 and leverage other tools to search for artifacts in the source code of webpages. We discover pages that have links to both listed and unlisted extensions, many times pointing to multiple different extensions that share the same name. Using this data, we were able to find 1,097 unlisted browser extensions ranging from internal directory lookup tools to hidden Google Docs extensions that pose a serious threat to their 127 million users.
Toward a Characterization of Loss Functions for Distribution Learning

In this work we study loss functions for learning and evaluating probability distributions over large discrete domains. Unlike classification or regression where a wide variety of loss functions are used, in the distribution learning and density estimation literature, very few losses outside the dominant $log\ loss$ are applied. We aim to understand this fact, taking an axiomatic approach to the design of loss functions for learning distributions. We start by proposing a set of desirable criteria that any good loss function should satisfy. Intuitively, these criteria require that the loss function faithfully evaluates a candidate distribution, both in expectation and when estimated on a few samples. Interestingly, we observe that \emph{no loss function} possesses all of these criteria. However, one can circumvent this issue by introducing a natural restriction on the set of candidate distributions. Specifically, we require that candidates are $calibrated$ with respect to the target distribution, i.e., they may contain less information than the target but otherwise do not significantly distort the truth. We show that, after restricting to this set of distributions, the log loss, along with a large variety of other losses satisfy the desired criteria. These results pave the way for future investigations of distribution learning that look beyond the log loss, choosing a loss function based on application or domain need.
A new dynamic and enhanced resource allocation algorithm in cloud computing

Abstract One of the testing issues in Cloud data centers is to take the segment and relocation of reconfigurable virtual machines into thought and moreover the joined features of facilitating physical machines. We present a dynamic resource assignment framework (DRAM) for Cloud data centers. Dissimilar to customary stack balance arranging calculations which consider only a solitary factor, for instance, the CPU stack in physical servers, this strategy treats CPU, memory and framework information transmission composed for both physical machines and virtual machines. We create joined estimation for the all out irregularity dimension of a Cloud data center and furthermore the ordinary disproportion dimension of each server. Reproduction results show that DRAM has great execution as of indicate abnormality level, normal ponderousness dimension of each server, too as in general running time.
Measurement and Evaluation in Education by Active Learning for Students in Mathematics Major Usage of Activities Base

These The goals are: to develop and discover efficiency of textbook on the subject of Measurement and Evaluation in Education by active learning for students in mathematics major usage of activities base to meet with criteria at 80/80 and to study satisfaction level of students by using the sufficiency economy philosophy textbook for sustainable learning .The target group herein was 40 students who studied in Academic year of 2014 and interested in online registration.Target group was determined by using purposive sampling.Tools used in this research were 20 items of post-test contained in textbook, student's satisfaction evaluation form towards textbook usage.Data analysis was conducted to find efficiency of textbook as defined by criteria at 81.50 / 82.00 and student's satisfaction level towards textbook usage of 40 students.The obtained mean was 4.52 and standard deviation was 0.61.The results showed that the efficiency of textbook on the subject of Measurement and Evaluation in Education was at 81.50 / 82.00 that was higher than defined criteria at 80/80.In addition, Overall satisfaction of target group was in the highest level with the mean of 4.52 and S.D. at 0.61.
Transwarp Conduit: Interoperable Blockchain Application Framework

Transwarp-Conduit (TWC) is a protocol for message transfers between two smart-contract enabled blockchains. Furthermore, we specify an application framework (leveraging the TWC protocol) that enables developers to define arbitrarily complex cross-blockchain applications, simply by deploying framework-compliant smart contracts and hosting a TWC node (daemon process).The TWC protocol is implementable without additional effort on part of the base blockchain protocol.
Multimodal Logical Inference System for Visual-Textual Entailment

A large amount of research about multimodal inference across text and vision has been recently developed to obtain visually grounded word and sentence representations. In this paper, we use logic-based representations as unified meaning representations for texts and images and present an unsupervised multimodal logical inference system that can effectively prove entailment relations between them. We show that by combining semantic parsing and theorem proving, the system can handle semantically complex sentences for visual-textual inference.
Content Adaptive Optimization for Neural Image Compression

The field of neural image compression has witnessed exciting progress as recently proposed architectures already surpass the established transform coding based approaches. While, so far, research has mainly focused on architecture and model improvements, in this work we explore content adaptive optimization. To this end, we introduce an iterative procedure which adapts the latent representation to the specific content we wish to compress while keeping the parameters of the network and the predictive model fixed. Our experiments show that this allows for an overall increase in rate-distortion performance, independently of the specific architecture used. Furthermore, we also evaluate this strategy in the context of adapting a pretrained network to other content that is different in visual appearance or resolution. Here, our experiments show that our adaptation strategy can largely close the gap as compared to models specifically trained for the given content while having the benefit that no additional data in the form of model parameter updates has to be transmitted.
Photo-Geometric Autoencoding to Learn 3D Objects from Unlabelled Images

We show that generative models can be used to capture visual geometry constraints statistically. We use this fact to infer the 3D shape of object categories from raw single-view images. Differently from prior work, we use no external supervision, nor do we use multiple views or videos of the objects. We achieve this by a simple reconstruction task, exploiting the symmetry of the objects' shape and albedo. Specifically, given a single image of the object seen from an arbitrary viewpoint, our model predicts a symmetric canonical view, the corresponding 3D shape and a viewpoint transformation, and trains with the goal of reconstructing the input view, resembling an auto-encoder. Our experiments show that this method can recover the 3D shape of human faces, cat faces, and cars from single view images, without supervision. On benchmarks, we demonstrate superior accuracy compared to other methods that use supervision at the level of 2D image correspondences.
Exploiting Sentential Context for Neural Machine Translation

In this work, we present novel approaches to exploit sentential context for neural machine translation (NMT). Specifically, we first show that a shallow sentential context extracted from the top encoder layer only, can improve translation performance via contextualizing the encoding representations of individual words. Next, we introduce a deep sentential context, which aggregates the sentential context representations from all the internal layers of the encoder to form a more comprehensive context representation. Experimental results on the WMT14 English-to-German and English-to-French benchmarks show that our model consistently improves performance over the strong TRANSFORMER model (Vaswani et al., 2017), demonstrating the necessity and effectiveness of exploiting sentential context for NMT.
Organization of Pedagogical Relationships between Learners and Teachers in the Framework of Inclusive Education

Nowadays at the process of globalization the system of value orientations and educational traditions are undergoing noteworthy modifications represented in the Concept of Inclusive Education Development (2010) in Ukraine. The research demonstrates the conception of organization of pedagogical relationships of the participants of art-therapeutic process as a component of inclusive education. The problem of the investigation defined the choice of the empirical research methods such as observation, interview, testing, study of the products of the activities of the subjects, study, and generalization of pedagogical experience. As a framework for the research it was used the model of five different aspects of the therapeutic relationship described by Clarkson (1995). One of the most significant finding to emerge from the study is that inclusive education is much wider than the opportunity for everybody to visit neighbourhood schools, deeper than deletion of distinctions between "general education" and "special education" programs. Findings of this study suggest that elements of art therapy should become an integral part of work with both the disabled and displaced (refugees or immigrants) students. The results allow singling out some means for the modern teacher to organize pedagogical relationships as a dialogue of the equal partners in the art-therapy-oriented education process. Application of “Role-play” refers here to the special technique used at the art-oriented activity for studying interpersonal behavior of the participants. This promoted a better understanding between participants, empathy, and frankness that removed psychological and emotional barriers in their verbal and non-verbal communication, and stimulated the process of self-actualization.
Scan-flood Fill(SCAFF): an Efficient Automatic Precise Region Filling Algorithm for Complicated Regions

Recently, instant level labeling for supervised machine learning requires a considerable number of filled masks. In this paper, we propose an efficient automatic region filling algorithm for complicated regions. Distinguishing between adjacent connected regions, the Main Filling Process scans through all pixels and fills all the pixels except boundary ones with either exterior or interior label color. In this way, we succeed in classifying all the pixels inside the region except boundary ones in the given image to form two groups: a background group and a mask group. We then set all exterior label pixels to background color, and interior label pixels to mask color. With this algorithm, we are able to generate output masks precisely and efficiently even for complicated regions as long as boundary pixels are given. Experimental results show that the proposed algorithm can generate precise masks that allow for various machine learning tasks such as supervised training. This algorithm can effectively handle multiple regions, complicated `holes' and regions whose boundaries touch the image border. By testing the algorithm on both toy and practical images, we show that the performance of Scan-flood Fill(SCAFF) has achieved favorable results.
Exploring Phoneme-Level Speech Representations for End-to-End Speech Translation

Previous work on end-to-end translation from speech has primarily used frame-level features as speech representations, which creates longer, sparser sequences than text. We show that a naive method to create compressed phoneme-like speech representations is far more effective and efficient for translation than traditional frame-level speech features. Specifically, we generate phoneme labels for speech frames and average consecutive frames with the same label to create shorter, higher-level source sequences for translation. We see improvements of up to 5 BLEU on both our high and low resource language pairs, with a reduction in training time of 60%. Our improvements hold across multiple data sizes and two language pairs.
Interpretability of Recurrent Neural Networks Trained on Regular Languages

We study the ability of recurrent neural networks to model and recognize simple regular languages. Training the networks under different levels of noise and regularization, we analyze their response in terms of accuracy and interpretability using a complete set of validation data. Our results show that a small noise level improves the generalization of the networks, while regularization provides a higher interpretability. Under proper levels of noise and regularization, the networks are able to obtain a high accuracy, and the hidden units display activation patterns that could be related to discrete states in a deterministic finite automaton.
A Novel Study of Application of Information &amp; Communication Technology in Library Classification

In this research paper, we have carried out a study onto the applications of Library Classification and the use of Information and Communication Technology in it. We have seen that how the growing advancements in this area has affected the growth of the Library Automation w.r.t Classification and has revolutionized the field of Library Sciences.
Penerapan Algoritma Secure Hash Algorithm (SHA) Keamanan Pada Citra

Current information exchange is not only in the form of text, it can be an image or video. All of that can be done using computer network technology in the form of the internet. Through an internet connection, you can connect with many people. Cryptography aims for messages or images that cannot be seen by other parties who have no interest in the information. Messages or secured images can be data stored in secure computer memory or sent through computer networks. And can protect the confidentiality of images from various threats that arise. To maintain data security, SHA-256 is used when transforming data bytes into string hashes. The system built has been able to implement the SHA 256 algorithm which can change the original image file in the form of unknown files and encrypted images that are difficult to know the original image, unless read using an application that has been built. the image when encrypted and used again at the time of decryption with as many as 45 characters and may be in the form of numbers or letters. The SHA 256 process when encrypted in only one round, can be played with decryption.
Accuracy vs. Efficiency: Achieving Both through FPGA-Implementation Aware Neural Architecture Search

A fundamental question lies in almost every application of deep neural networks: what is the optimal neural architecture given a specific dataset? Recently, several Neural Architecture Search (NAS) frameworks have been developed that use reinforcement learning and evolutionary algorithm to search for the solution. However, most of them take a long time to find the optimal architecture due to the huge search space and the lengthy training process needed to evaluate each candidate. In addition, most of them aim at accuracy only and do not take into consideration the hardware that will be used to implement the architecture. This will potentially lead to excessive latencies beyond specifications, rendering the resulting architectures useless. To address both issues, in this paper we use Field Programmable Gate Arrays (FPGAs) as a vehicle to present a novel hardware-aware NAS framework, namely FNAS, which will provide an optimal neural architecture with latency guaranteed to meet the specification. In addition, with a performance abstraction model to analyze the latency of neural architectures without training, our framework can quickly prune architectures that do not satisfy the specification, leading to higher efficiency. Experimental results on common data set such as ImageNet show that in the cases where the state-of-the-art generates architectures with latencies 7.81x longer than the specification, those from FNAS can meet the specs with less than 1% accuracy loss. Moreover, FNAS also achieves up to 11.13x speedup for the search process. To the best of the authors' knowledge, this is the very first hardware aware NAS.
Reproducibility Evaluation of SLANT Whole Brain Segmentation Across Clinical Magnetic Resonance Imaging Protocols

Whole brain segmentation on structural magnetic resonance imaging (MRI) is essential for understanding neuroanatomical-functional relationships. Traditionally, multi-atlas segmentation has been regarded as the standard method for whole brain segmentation. In past few years, deep convolutional neural network (DCNN) segmentation methods have demonstrated their advantages in both accuracy and computational efficiency. Recently, we proposed the spatially localized atlas network tiles (SLANT) method, which is able to segment a 3D MRI brain scan into 132 anatomical regions. Commonly, DCNN segmentation methods yield inferior performance under external validations, especially when the testing patterns were not presented in the training cohorts. Recently, we obtained a clinically acquired, multi-sequence MRI brain cohort with 1480 clinically acquired, de-identified brain MRI scans on 395 patients using seven different MRI protocols. Moreover, each subject has at least two scans from different MRI protocols. Herein, we assess the SLANT method's intra- and inter-protocol reproducibility. SLANT achieved less than 0.05 coefficient of variation (CV) for intra-protocol experiments and less than 0.15 CV for inter-protocol experiments. The results show that the SLANT method achieved high intra- and inter- protocol reproducibility.
Fractional-order Backpropagation Neural Networks: Modified Fractional-order Steepest Descent Method for Family of Backpropagation Neural Networks

This paper offers a novel mathematical approach, the modified Fractional-order Steepest Descent Method (FSDM) for training BackPropagation Neural Networks (BPNNs); this differs from the majority of the previous approaches and as such. A promising mathematical method, fractional calculus, has the potential to assume a prominent role in the applications of neural networks and cybernetics because of its inherent strengths such as long-term memory, nonlocality, and weak singularity. Therefore, to improve the optimization performance of classic first-order BPNNs, in this paper we study whether it could be possible to modified FSDM and generalize classic first-order BPNNs to modified FSDM based Fractional-order Backpropagation Neural Networks (FBPNNs). Motivated by this inspiration, this paper proposes a state-of-the-art application of fractional calculus to implement a modified FSDM based FBPNN whose reverse incremental search is in the negative directions of the approximate fractional-order partial derivatives of the square error. At first, the theoretical concept of a modified FSDM based FBPNN is described mathematically. Then, the mathematical proof of the fractional-order global optimal convergence, an assumption of the structure, and the fractional-order multi-scale global optimization of a modified FSDM based FBPNN are analysed in detail. Finally, we perform comparative experiments and compare a modified FSDM based FBPNN with a classic first-order BPNN, i.e., an example function approximation, fractional-order multi-scale global optimization, and two comparative performances with real data. The more efficient optimal searching capability of the fractional-order multi-scale global optimization of a modified FSDM based FBPNN to determine the global optimal solution is the major advantage being superior to a classic first-order BPNN.
Sentiment Analysis on Naija-Tweets

Examining sentiments in social media poses a challenge to natural language processing because of the intricacy and variability in the dialect articulation, noisy terms in form of slang, abbreviation, acronym, emoticon, and spelling error coupled with the availability of real-time content.Moreover, most of the knowledgebased approaches for resolving slang, abbreviation, and acronym do not consider the issue of ambiguity that evolves in the usage of these noisy terms.This research work proposes an improved framework for social media feed pre-processing that leverages on the combination of integrated local knowledge bases and adapted Lesk algorithm to facilitate pre-processing of social media feeds.The results from the experimental evaluation revealed an improvement over existing methods when applied to supervised learning algorithms in the task of extracting sentiments from Nigeria-origin tweets with an accuracy of 99.17%.
Multi-Level Matching and Aggregation Network for Few-Shot Relation Classification

This paper presents a multi-level matching and aggregation network (MLMAN) for few-shot relation classification. Previous studies on this topic adopt prototypical networks, which calculate the embedding vector of a query instance and the prototype vector of each support set independently. In contrast, our proposed MLMAN model encodes the query instance and each support set in an interactive way by considering their matching information at both local and instance levels. The final class prototype for each support set is obtained by attentive aggregation over the representations of its support instances, where the weights are calculated using the query instance. Experimental results demonstrate the effectiveness of our proposed methods, which achieve a new state-of-the-art performance on the FewRel dataset.