Reviewerly/exHarmony huggingface dataset:
Gator: Customizable Channel Pruning of Neural Networks with Gating

The rise of neural network (NN) applications has prompted an increased interest in compression, with a particular focus on channel pruning, which does not require any additional hardware. Most pruning methods employ either single-layer operations or global schemes to determine which channels to remove followed by fine-tuning of the network. In this paper we present Gator, a channel-pruning method which temporarily adds learned gating mechanisms for pruning of individual channels, and which is trained with an additional auxiliary loss, aimed at reducing the computational cost due to memory, (theoretical) speedup (in terms of FLOPs), and practical, hardware-specific speedup. Gator introduces a new formulation of dependencies between NN layers which, in contrast to most previous methods, enables pruning of non-sequential parts, such as layers on ResNet's highway, and even removing entire ResNet blocks. Gator's pruning for ResNet-50 trained on ImageNet produces state-of-the-art (SOTA) results, such as 50% FLOPs reduction with only 0.4%-drop in top-5 accuracy. Also, Gator outperforms previous pruning models, in terms of GPU latency by running 1.4 times faster. Furthermore, Gator achieves improved top-5 accuracy results, compared to MobileNetV2 and SqueezeNet, for similar runtimes. The source code of this work is available at: https://github.com/EliPassov/gator.
Improving Multi-Category Sentiment Classification by Semantic Dual-Granularity and Syntax-Path Encoding

Semantic features, word sequences, and syntactic structures are three key elements for human classification of multi-category sentiment texts. However, current sentiment classification neural network models focus mostly on semantic features and word sequences, thereby losing syntactic structure information in modeling and limiting classification accuracy. In this paper, we combine three key elements at different levels and propose an enhanced model with semantic dual-granularity and syntax-path encoding for multi-category sentiment classification. In particular, at the semantic level, we utilized two long short-term memories to process semantic dual-granularity units that are word and n-gram sequences; then we combined the two memory outputs via the attention mechanism to highlight keywords or key phrases in sentences. At the syntactic level, we extracted syntactic features by encoding each word's syntax path into a syntactic neural network, aiming to capture implied sentiments through an expression pattern. Finally, the two levels' models were combined for multi-category sentiment classification. We applied our model to three common and public multi-category sentiment datasets. Experimental results showed our model significantly outperforming eight strong baseline models with a maximum accuracy improvement of 48.8%; a minimum accuracy improvement of 1%; and average accuracy improvements of 20.9%, 19.3%, and 14.8% on each dataset, respectively.
Framework of EcomTDMA for Transactional Data Mining Using Frequent Item Set for E-Commerce Application

In data mining, comprehending out the common item set is an indispensable job. In statements such as participation rule mining and co-relationships, these conventional item sets are beneficial. These systems use particular algorithms to secure numerous item sets, but during unnecessary data come opposite; these are unproductive in communicating and coordinating the load. With these algorithms, automated parallelization is not feasible either. There is an inadequacy to build an algorithm to determine these difficulties with modern algorithms that will help the lacking characteristics, such as automatic parallelization, balancing and fair data configuration. We are using a new technique in this paper to discover frequent item sets by using MapReduce. With the HDFS framework, the modified Apriori algorithm is used, which is called the EcomTDMA technique. By using the disintegrate method, the MapReduce approach can manipulate individually and simultaneously in this system. The consequence of this approach to the reducers and the redactors will show the produce. Three various algorithms like base Apriori, FP-growth and our advanced development Apriori have practiced in the experiment, and the process was performed in both standalone computer and assigned environment and showed the consequences how the recommended algorithm is more substantial than standard algorithms.
Speaker Authorization for Air Traffic Control Security

The number of incidents in which unauthorized persons break into frequencies used by Air Traffic Controllers (ATCOs) and give false instructions to pilots, or transmit fake emergency calls, is a permanent and apparently growing threat. One of the measures against such attacks could be to use automatic speaker recognition on the voice radio channel to disclose the potential unauthorized speaker. This work describes the solution for a speaker authorization system in the Security of Air Transport Infrastructures of Europe (SATIE) project, presents the architecture of the system, gives details on training and testing procedures, analyses the influence of the number of authorized persons on the system’s performance and describes how the system was adapted to work on the radio channel.
Adaptive restoration of pedestrian appearance in the spatiotemporal domain

Video synopsis based on visual rhythm has a small amount of calculation and strong representativeness, but the drawback is that the spatiotemporal pedestrians are distorted and illegible due to the loss of their own unique spatial morphology information, which leads to poor performance of video synopsis. To solve this problem, this letter proposed an adaptive restoration method of pedestrian appearance in the spatiotemporal domain. First, a single-pixel row is taken as the sampling unit to complete the horizontal visual rhythm stitching; second, the relationship between the pedestrian spatiotemporal trajectory slope in the horizontal visual rhythm and the pedestrian movement speed is deduced; finally, the spatiotemporal trajectory slope is used as the basis for the vertical visual rhythm sampling width, and the adaptive restoration of pedestrian appearance in the spatiotemporal domain is completed. Experimental results show that the contour fluency and visual perception effect of pedestrian appearance restored by the proposed method are better, and the difference ratio of the proposed method is 0.08566, and the average running time is 421.0760 s, which has obvious advantages over the comparison method.
Alcohol and Collision Sensing in a Smart Helmet

An accident is a specific, unexpected, unusual and unintended outermost action which occurs in a particular time and location, with no apparent and deliberate cause but with marked effects. Nowadays many nations are forcing the motor cycle riders to wear the helmet and to not use the motor vehicles when the person is in drunken condition. But still the conditions are being violated by the users. The introduction of electronic stability control and some other features have made cars much ahead and safer than before. The concept of smart helmet aims to supply the same for bikes. With features like built-in recorder GPS logging which will keep track of the rider’s location and should be accessed in case of emergency. A collision sensor will alert just in case the rider met with an accident and or is drunken driving. It consists of an intelligent system embedded into the helmet and the vehicle. Helmet unit ensures that rider is wearing helmet and not under influence of alcohol through out the ride. It communicates with vehicle unit to switch off ignition system of bike if above condition is not met. Vehicle unit checks and intimates accident through geometric coordinates via SMS. By using geometric coordinates, location of the injured rider can be traced using simple GPS tracking application. Also, this system provides theft protection as helmet is also essential along with key to start bike.
Bag of Tricks for Chinese Named Entity Recognition

Named entity recognition (NER) is an important and challenging task in natural language processing. In this paper, we investigate thoroughly about the advances of Chinese NER in recent years. We explore the validity of a wide range of approaches in the literature of NLP that may benefit NER. We further employ the effective ones, such as data augmentation, adversarial learning, cross-sentence context and cost-sensitive learning to improve the performance of our BERT-based backbone model. Empirical results show that our model with this bag of tricks outperforms previous state-of-the-art on Weibo and achieves competitive performance on MSRA. Our code is publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/ccoay/bag-ner.
Harnessing Cloud Computing to Power Up HPC Applications: The BRICS CloudHPC Project

In this paper, we report the main accomplishments obtained in the context of the CloudHPC project. Accepted in response to the BRICS Pilot Call 2016, the project gathered researchers from the Federal University of Rio Grande do Sul (UFRGS), the St. Petersburg State University (SPbSU), and Beijing Normal University (BNU). Its main objective was to identify cloud computing aspects that can be explored to provide a far-reaching, flexible, and efficient environment for running HPC applications. In addition to the main results, we also discuss the lessons learned and perspectives for future research in the area .
Video Human Action Recognition with Channel Attention on ST-GCN

Abstract Action recognition based on human skeleton information is a hot research topic in the field of computer vision, and ST-GCN graph convolutional network is widely used to extract spatial and temporal features of human skeleton to represent the human skeleton structure. However, in the process of extracting features, the weights on each channel of the feature are the same, so it is difficult to effectively discriminate the useful features from the useless ones. In this paper, we propose Channel Attention module, which learns the importance of each feature channel to perform human action recognition more effectively. Experimental results on Kinetics and NTU-RGB+D datasets show that Channel Attention module can achieve better accuracy.
Multiple Granularity Network and Dynamic Label for Domain Adaptive Person Re-identification

Abstract The domain adaptive person re-identification (Re-ID) has be more popular among researchers. Because it can save a lot of resources as it only exploits the source domain knowledge and does not need the complex annotation efforts in target domain. It aims to extend a model trained on a labeled dataset to another dataset which is unlabeled. Many works reduce feature distribution gap between two different datasets to solve the problem. However, these works ignore the problem which is the variations within an unlabeled dataset. In the paper, we propose a domain adaptive person Re-ID framework based on multiple granularity network and dynamic label (MGDL). Specifically, we send the images of two different datasets into multiple granularity network at the same time for joint training to reduce feature distribution gap which is between the two different datasets. The network is trained by two different kinds of pseudo labels, namely, conservative label and radical label. The two kinds of pseudo labels are used to alternating pull and push the feature distribution in the target domain to reduce the variations within an unlabeled dataset. Experiments have shown that the MGDL achieves competitive performance in person Re-ID which is under the cross-domain setting.
Multi-view Defense with Adversarial Autoencoders

In view of the vulnerability of multi-view deep models to adversarial perturbation, this paper designs two kinds of multi-view adversarial autoencoders (MAAEs). We first propose the MAAE1 defense, in which each view is used to train the corresponding single-view adversarial autoencoders separately, and then the reconstructed output is fed into the target model for classification. Based on the consistency and complementary principle, we further integrate the scheme of adjusting the weights of different views adaptively into the design of multi-view defense, referred to it as MAAE2. Experimental results verify that the proposed multi-view adversarial defense effectively enhances the adversarial robustness of the deep multi-view model without negatively affecting the classification accuracy in the original examples. Besides, MAAEs show superior adversarial robustness to unseen perturbations.
THE STRUCTURE OF LEARNING MOTIVES OF FRESHMEN IN TERTIARY EDUCATION

Today, the main problem in higher education is the decrease in students' interest in learning and obtaining new knowledge. This factor reduces the effectiveness of education process. Thus, the problem of formation and preservation of motivation for learning among higher school students becomes relevant. The article offers a solution to the problem by studying the structure of the main motives for learning among freshmen, that are necessary to maintain interest in acquiring knowledge and developing professionally significant qualities. To determine the main motives for learning, the method of reviewing scientific literature and the method of diagnosing students' educational motivation, proposed by A.A. Rean and V.A. Yakunin, were used. The communicative, professional, educational, cognitive, social motives, as well as the motives of creative selfrealization, avoidance of failure and prestige were studied, the levels of motivation and the average score of motivation for educational activities among students were determined. The ranking procedure applied allowed us to determine not only the significance of each motive for students, but to describe a motives’ structure, which allows us to establish the main factors that determine the attitudes of students to learning. Experimental data show that the most valuable and significant learning motive is “to become a highly qualified specialist”. This motive determines the main tasks both for freshmen and their teachers, namely, for students the main motive is to obtain knowledge that they will be able to apply in their further professional activities and for academic staff it is to organize educational environment in which professional competencies will be formed and where the professional formation of future specialists will take place.
Debiased Prototype Network for Adversarial Domain Adaptation

Domain adaptation is an important and challenging task. Existing adversarial domain adaptation methods explore the relationship between the source and target domains, with the knowledge learned in the source domain supporting the target domain task. The quality of the knowledge will affect the task performance of the transfer, i.e., the higher the quality of the knowledge, the better the transfer task performance. To obtain better domain-invariant knowledge, we extract domain-invariant semantic information over the unit sphere via the prototype network. With the help of geometric constraints from the hypersphere, the features can be more tightly clustered with the estimated prototype (representatives of each class). Adaptation is achieved by adversarial learning to align the domain distribution, which enhances the transferability of the learned features and obtains the basic prototype. Since the basic prototypes dominantly computed from the source domain are biased against the expected domain-invariant prototype, a debiased method is further proposed to obtain the domain-invariant prototypes. Specifically, our method diminishes the intra- and inter- class bias to achieve the class-level alignment. Extensive experiments demonstrate that our model achieves state-of-the-art performance on several domain adaptation benchmark datasets. Our code is available at https://github.com/Chunweiwu-source/DPN.
Optimal Control via Combined Inference and Numerical Optimization

Derivative based optimization methods are efficient at solving optimal control problems near local optima. However, their ability to converge halts when derivative information vanishes. The inference approach to optimal control does not have strict requirements on the objective landscape. However, sampling, the primary tool for solving such problems, tends to be much slower in computation time. We propose a new method that combines second order methods with inference. We utilise the Kullback Leibler (KL) control framework to formulate an inference problem that computes the optimal controls from an adaptive distribution approximating the solution of the second order method. Our method allows for combining simple convex and non convex cost functions. This simplifies the process of cost function design and leverages the strengths of both inference and second order optimization. We compare our method to Model Predictive Path Integral (MPPI) and iterative Linear Quadratic Gaussian controller (iLQG), outperforming both in sample efficiency and quality on manipulation and obstacle avoidance tasks.
Sense Prediction for Explicit Discourse Relations with BERT

We present experiments in predicting a discourse sense for explicit inter-sentential discourse relations in Czech and English, using embedding and deep learning (fine-tuned BERT) to predict the senses, and annotation projection from English to Czech to increase the size of training data for Czech. We show that strengthening information about the explicit connective improves the results and, on the other hand, that annotation projection from an annotation system with a different set of senses may not help if used in a straightforward way, which we discuss in detail.
User Mapping Strategy in Multi-CDN Streaming: A Data-Driven Approach

Using content delivery networks (CDNs) for video distribution has become the normalem <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">de facto</i> approach for video streaming today, because they are easy to use (e.g., video chunks can be delivered as files via HTTP) and have good scalability. Today, it has become the norm rather than the exception for video providers to hire multiple CDNs for their video services in a pay-per-use manner—not only to serve users at different locations, but also to reduce operational costs. Given that multiple CDNs and their peering servers exist at many different locations, selecting different CDNs for different users in the same online video system has become a critical decision that can significantly affect the users’ Quality of Experience (QoE). Conventional strategies are generally rule based, e.g., assigning users to CDNs according to their locations or ISPs, but cannot guarantee any particular QoE level because QoE is affected by a combination of complicated factors. In this article, we propose using a data-driven approach to study the factors determining users’ QoE, including both Quality of Service (QoS) and user factors. Our findings indicate that QoE is affected by both the QoS provided by the CDNs and user preferences for the video content. We design a machine learning-based predictive model to capture the “utility” of a video for a user given a particular QoS guarantee. Based on that, we strategically assign CDNs to users to maximize the overall QoE. One month trace-driven experiments are used to demonstrate the effectiveness and efficiency of our design.
Hybrid Evolutionary Algorithm for Optimizing the Control of a Multi-mode Moving Object

The article deals with the problem of development and research of a hybrid evolutionary algorithm for multi-mode moving objects’ control optimization on the example of electric railway rolling stock. This class of objects is characterized by an optimal-terminal control, with restrictions on control and phase coordinates under conditions of parametric uncertainty, variations of external environment and the controlled object’s parameters. Despite a large number of studies in this subject area, the problem of synthesizing optimal control of objects of this class is not completely solved due to its overall complexity and a large number of specific features of specific control objects. This is confirmed, in particular, by the analysis of the operating results of the existing locomotive fleet. One of the promising directions for solving this problem is the development of optimal-terminal control computational methods. The article represents an algorithm based on random search, and proposes a software complex for calculation the energy-optimal program-reference trajectory of a train moving on a given section in accordance with the schedule. The proposed method of moving object’s control optimization differs from existing ones: it allows to identify a set of local extremums of the objective function, and – as a result – to find the global extremum. Computational experiments confirm high quality of control and good convergence of the algorithm in terms of accuracy. Speaking of limitations, the method requires significant computational resources for its implementation.
The Semantics of Plurals

Abstract Plural logic provides an appealing framework for the regimentation of natural language plurals. However, the choice of a regimenting language leaves wide open the semantic question of how this language should be interpreted. One option is to interpret a plural term as denoting a non-empty set. Another is to embrace plurals in the metalanguage and take a plural term to have plural reference. A detailed comparison of the options reveals that there is no simple solution to the problem of choosing among them.
Object-Centric Anomaly Detection Using Memory Augmentation

Video anomaly detection is becoming of increased interest as surveillance is becoming more widespread. We propose an object-centric method with memory augmentation (ObjMemAE) for video anomaly detection. Recently, object-centric approaches is seen at the top of the leaderboards, where we take the novel approach of combining an object-centric approach with memory augmentation using a long term memory bank storing prototypical objects. The memory module also allows the use of additional object-centric features. The proposed method is shown to outperform the baseline by 4.5%, with an AUC score of 98.3% on the UCSD-Ped2 dataset achieving state-of-the-art.
Comprehension of Reading Content Using Skimming Techniques to Improve Comprehension of Narrative Text Reading Content in Elementary School Students

This study aims to determine the effect of using skimming techniques and describe the use of skimming techniques in understanding the reading content of elementary students. This study uses the System Literature Review (SLR) research method. From the results of several studies that have been successfully reviewed, it shows that the use of skimming techniques in understanding reading content is the right technique to use in understanding reading content because skimming techniques can increase students' knowledge of reading more. The use of skimming techniques can be done in the first way, having questions to look for, reading book titles and subtitles, reading with rapid eye movements, understanding sentences in the reading, then being able to draw conclusions. Based on the analysis that has been done, it can be concluded that the use of skimming techniques has a very good effect on students 'reading comprehension and can be an alternative or method to help improve reading content comprehension and broaden students' knowledge.