Reviewerly/exHarmony huggingface dataset:
Network Pruning Using Linear Dependency Analysis on Feature Maps

Network pruning can be achieved by removing redundant channels. In this paper, we regard a channel ‘redundant’ if its output is linearly dependent with respect to those of other channels. Inspired by this, we propose an efficient pruning method, named as LDFM, by linear dependency analysis on all the feature maps of each individual layer. Specifically, for each layer, by applying the QR decomposition with column pivoting (PQR) on the matrix consisting of all feature maps, those channels corresponding to small absolute diagonal elements of the R matrix from the PQR decomposition are identified as redundant, and are pruned naturally. Although pruning these channels causes loss of information and hence degrades accuracy, the accuracy of the pruned network can be easily recovered by fine-tuning, as the lost information in the pruned channels can be recovered from that in the retained channels. Extensive experiments demonstrate that LDFM makes great improvement on accuracy with similar parameters and FLOPs as other methods, and achieves the state-of-the-art results on several different benchmarks and networks.
Classifying Speech Intelligibility Levels of Children in Two Continuous Speech Styles

Speech difficulties of children may result from pathological problems. Oral language is normally assessed by expert-directed impressionistic judgments on varying speech types. This paper attempts to construct automatic systems that help detect children with severe speech problems at an early stage. Two continuous speech types, repetitive and storytelling speech, produced by Chinese-speaking hearing and hearing-impaired children are applied to Long Short-Term Memory (LSTM) and Universal Transformer (UT) models. Three approaches to extracting acoustic features are adopted: MFCCs, Mel Spectrogram, and acoustic-phonetic features. Results of leave-one-out cross-validation and models trained by augmented data show that MFCCs are more useful than Mel Spectrogram and acoustic-phonetic features. Respective LSTM and UT models have their own advantages in different settings. Eventually, our model trained on repetitive speech is able to achieve an F1-score of 0.74 for testing on storytelling speech.
Research Status of Soil Organic Remediation - Visual Analysis of Literature Based on Web of Science Database

Abstract In order to understand the latest research progress, research trend and future research hotspot of remediation of soil organic pollutants at home and abroad, this paper uses the core database of web of science and its own literature analysis tools, vosviewer visual analysis software and origin, etc., and publishes the top 10 countries (regions) in the amount of papers and publications at different times, research institutions, research directions, journal sources and other types of published literature on remediation of soil organic pollutants were analyzed. The results showed that: with the growth of time, the number of publications increased gradually; China, the United States and Spain ranked in the top 3, among which China had 9 organizations in the top 10; the journal with the most publications was Chemosphere, while the journal with the most citations was water research, indicating that the remediation of soil organic pollution mostly drew lessons from the remediation of water organic pollution.
E-learning System Customized in the Integral Process of Teaching Learning of Engineering Students

The objective of this research work is to determine the incidence of application of the personalized e-learning system in the achievement of the integral teaching-learning process of the students of Electrical Mechanical Engineering of the National University San Luis Gonzaga 2019; basic type research, nonexperimental design, causal explanatory level, quantitative approach, non-probabilistic sample, for convenience; the instrument applied was the survey, using the inductive deductive method; With a non-probabilistic sample made up of enrolled students, the results made it possible to deduce and explain the facts or phenomena that influenced the improvement of the teaching-learning process. It was concluded that e-learning platforms increase technological speed, allowing the creation of learning spaces (LE) on the Internet, with a wide range of functionalities at the service of different types of teaching and learning.
Comparative Design Analysis of Optimized Learning Rate for Convolutional Neural Network

Learning Rate is the most critical hyper-parameter to tune while training neural networks. It determines the amount of weight that should be adjusted for loss gradient. Therefore, choosing an optimal learning rate is a challenging task for neural networks. In this paper, various learning rate schedules like constant learning rates, step decay and exponential decay algorithm are implemented. Exponential decay comes out to be the best learning method among the constant, step and exponential decays. Further, best learning rate is used to optimize Adam, RMSProp and SGDM algorithm. These are implemented on the CIFAR-10 data set. Various combinations of different parameters of exponential decay such as decay steps, decay factor, initial learning rates are used to train the model, and their optimal learning rate and losses are observed. The parameters have minimum loss considered the best one. The comparative analysis of the model is performed based on accuracy, loss function and a number of iterations. The experimental results show that Adam with exponential decay based learning method achieving higher accuracy at batch size 128, decay steps 400, and drop out rate 0.3 with minimum loss 0.5. So, it is proven the best optimization method for training convolutional neural networks.
Attention-Enabled Object Detection to Improve One-Stage Tracker

State-of-the-art (SoTA) detection-based tracking methods mostly accomplish the detection and the identification feature learning tasks separately. Only a few efforts include the joint learning of detection and identification features. This work proposes two novel one-stage trackers by introducing implicit and explicit attention to the tracking research topic. For our tracking system based on implicit attention, we further introduce a novel fusion of feature maps combining information from different abstraction levels. For our tracking system based on explicit attention, we introduce utilization of an additional auxiliary function. These systems outperform the SoTA tracking systems in terms of MOTP (Multi-Object Tracking Precision) and IDF1 score when evaluated on public benchmark datasets including MOT15, MOT16, and MOT17. High MOTP score indicates precise detection of bounding boxes of objects, while high IDF1 score indicates accurate ID detections, which is very crucial for surveillance and security systems. Therefore, proposed systems are good choice for event-detections in surveillance feeds as we are capable of detecting correct ID and precise location.
NLP Based on Twitter Information: A Survey Report

With the further development of Natural language processing (NLP), the analysis of text has been widely applied in a variety of industries. Among them, Twitter, as an online comment publishing platform, has great research potentials. Predictions on Twitter in various industries have become increasingly relevant to scholars. However, the combination of NLP and Twitter still lacks detailed descriptions and reviews. In this paper, NLP and Twitter are studied, and their combinations in a wide range of applications are also analyzed. Twitter and its advantages as NLP corpus are introduced as well. Besides, the history of NLP development as well as its recent tendency of application and development are fully discussed. Meanwhile, the recent applications of NLP and its combination with Twitter, as well as the classification of the combination, are listed. The detailed reports have shown that the application and advantage of the combination of Twitter and NLP are very significant. In future work, the combination of Twitter data analysis and deep learning methods should be taken into consideration.
Design and Application of Product Traceability Blockchain-based Platform

Product traceability is an effective technical solution to control and address product quality and safety issues, enabling consumers to fully know the full range of aspects of product flow. At present, the mainstream product traceability platform has the risk of system centralization, data being maliciously altered or destroyed. Blockchain can be effectively used in the supply chains to solve the above problems. The platform suitable for traceability system application based on Hyperledger Fabric was selected, designed a product traceability technical solution and a product traceability system solution prototype. The program content includes product traceability business requirements, architecture of traceability system, blockchain data storage scheme, transaction method and consensus mechanism, inquiry of traceability result etc. The platform of the product traceability blockchain based system in experimental environment is built and the query and verification process of the traceability on the blockchain traceability system is realized and tested, which proves the feasibility of the system.
A Gesturally Controlled Virtual Musical Instruments for Chinese Guqin

Guqin is a kind of Chinese seven-string bridgeless zither with a history of more than 3000 years. This paper describes the design and implementation of a novel virtual Guqin application, based on Kinect sensor, aiming to arouse user`s interest in Guqin. The player's gestures are tracked, and when the system generates the data and feeds it back to the audio library, the performance is complete. Players can engage in the charm of the virtual instrument through natural interaction without being restricted by domain knowledge, equipment or site. Finally, this paper carried out a user study to prove that this system has a significant performance in enhancing the interest of players.
Securing a Vehicle Fleet Management Through Blockchain and Internet of Things

In this era of intelligent systems, automated processes are making life easier and efficient. The rapid proliferation of services empowered with the internet has made long-established legacy systems outdated in all walks of life. Companies and enterprises invest time and money into managing their vehicle fleet. New technologies can help reduce logistical and maintenance related hassles and operational costs. However, this application has received little attention to modernization. Therefore, this paper proposes a solution for efficient and automated vehicle fleet management for a permission network. Blockchain is an immutable ledger that preserves privacy and ensures security. The Internet of Things (IoT) provides tailored services with connectivity and portability. Integrating Blockchain with IoT can be a feasible solution for vehicle fleet management. However, traditional Blockchain is not feasible as it is slow, requires significant energy and computational resources. This paper proposes a system architecture for vehicle fleet management and evaluates the same in terms of latency and energy consumption. The per transaction latency is around 30 ms and energy consumed is 45 mJ which is a vast improvement from traditional solutions.
Inertial Proximal Deep Learning Alternating Minimization for Efficient Neutral Network Training

In recent years, the Deep Learning Alternating Minimization (DLAM), which is actually the alternating minimization applied to the penalty form of the deep neutral networks training, has been developed as an alternative algorithm to overcome several drawbacks of Stochastic Gradient Descent (SGD) algorithms. This work develops an improved DLAM by the well-known inertial technique, namely iPDLAM, which predicts a point by linearization of current and last iterates. To obtain further training speed, we apply a warm-up technique to the penalty parameter, that is, starting with a small initial one and increasing it in the iterations. Numerical results on real-world datasets are reported to demonstrate the efficiency of our proposed algorithm.
Polyadic Cyclic Codes over a Non-Chain Ring

Let( ) f u and ( ) g v be two polynomials of degree k and  respectively, not both linear which split into distinct linear factors over qbe a finite commutative non-chain ring.In this paper, we study polyadic codes and their extensions over the ring  .We give examples of some polyadic codes which are optimal with respect to Griesmer type bound for rings.A Gray map is defined frompreserves duality.The Gray images of polyadic codes and their extensions over the ring  lead to construction of self-dual, isodual, self-orthogonal and complementary dual (LCD) codes over q  .Some examples are also giv- en to illustrate this.
An Improved Automatic Zone Detection Algorithm based on Mechanic Flow for Complex Scene Event

Due to the objects detection and an complex background in the scene, the accuracy and stability of the tracking algorithm are often interrupt. Therefore, The accurately objects tracking and zone detection in the scene are difficult problem in object tracking applications. A robust zone detection method is proposed to solve the problem of occlusion. It adopt weight ideas in mechanism design. In order to modulate the problems of background issue, object detection is firstly divide into blocks and the different weights are related with motion direction for each blocks. In addition, we proposed the weight mechanism for keeping the object and tracking when object disappear in the scene. Additional, The experiments represent that our algorithm can be achieved the performance in handling various complex videos surveillance both vehicles and persons detection.
Feature Interactions on Steroids: On the Composition of ML Models

One of the key differences between traditional software engineering and machine learning (ML) is the lack of specifications for ML models. Traditionally, specifications provide a cornerstone for compositional reasoning and for the divide-and-conquer strategy of how we build large and complex systems from components, but these are hard to come by for machine learned components. While the lack of specification seems like a fundamental new problem at first sight, in fact, software engineers routinely deal with iffy specifications in practice. We face weak specifications, wrong specifications, and unanticipated interactions among specifications. ML may push us further, but the problems are not fundamentally new. Rethinking ML model composition from the perspective of the feature-interaction problem highlights the importance of software design.
Modeling Homophone Noise for Robust Neural Machine Translation

In this paper, we propose a robust neural machine translation (NMT) framework to deal with homophone errors. The framework consists of a homophone noise detector and a syllable-aware NMT model. The detector identifies potential homophone errors in a textual sentence and converts them into syllables to form a mixed sequence that is then fed into the syllable-aware NMT. Extensive experiments on Chinese→English translation demonstrate that the proposed method not only significantly outperforms baselines on noisy test sets with homophone noise, but also achieves substantial improvements over them on clean texts.
MCR-NET: A Multi-Step Co-Interactive Relation Network for Unanswerable Questions on Machine Reading Comprehension

Question answering systems usually use keyword searches to retrieve potential passages related to a question, and then extract the answer from passages with the machine reading comprehension methods. However, many questions tend to be unanswerable in the real world. In this case, it is significant and challenging how the model determines when no answer is supported by the passage and abstains from answering. Most of the existing systems design a simple classifier to determine answerability implicitly without explicitly modeling mutual interaction and relation between the question and passage, leading to the poor performance for determining the unanswerable questions. To tackle this problem, we propose a Multi-Step Co-Interactive Relation Network (MCR-Net) to explicitly model the mutual interaction and locate key clues from coarse to fine by introducing a co-interactive relation module. The co-interactive relation module contains a stack of interaction and fusion blocks to continuously integrate and fuse history-guided and current-query-guided clues in an explicit way. Experiments on the SQuAD 2.0 and DuReader datasets show that our model achieves a remarkable improvement, outperforming the BERT-style baselines in literature. Visualization analysis also verifies the importance of the mutual interaction between the question and passage.
Design and Implementation an Open Source Web Based Clearance System for Iraqi Educational Institutions

Clearance is a mandatory process in almost every university. Either graduated or undergraduate students must undertake the procedure to grant the permission to participate in the final exams. In addition to this, students need stamped clearance paper to receive their final exam results. Over the last decades, clearance has been carried out manually. With the manual clearance system, students endure a long and exhausting process of signatures and stamps. In the Iraqi educational system, the process involves visiting many departments including library, registrar, accounting, accommodation, laboratory, storage, sport and artistic activity departments, and the dean's office; only to have the clearance certified. The aim of this project is to propose an open source clearance system. In simple words, the proposed system will have several advantages over the old system for instance time and effort, cost, speed, and data safety. Languages such as HTML, CSS, and JavaScript are used to design and implement the front-end side. On the other hand, its back-end is created by the help of PHP scripts and MySQL for the database. Generally, the system has four equally important major tasks to be fulfilled in order to have the clearance. The tasks are registration, unit administrator (to confirm a clearance status as Cleared/Not Cleared), Exam committee (to check whether student's clearance status), and finally, a system administrator (to manage the entire system). Furthermore, system admin can add or remove any unit administrator or any student to/from the system. This proposed system is available online and can be found at the following link: shorturl.at/hvwNY
A Neural Text-to-Speech Model Utilizing Broadcast Data Mixed with Background Music

Recently, it has become easier to obtain speech data from various media such as the internet or YouTube, but directly utilizing them to train a neural text-to-speech (TTS) model is difficult. The proportion of clean speech is insufficient and the remainder includes background music. Even with the global style token (GST). Therefore, we propose the following method to successfully train an end-to-end TTS model with limited broadcast data. First, the background music is removed from the speech by introducing a music filter. Second, the GST-TTS model with an auxiliary quality classifier is trained with the filtered speech and a small amount of clean speech. In particular, the quality classifier makes the embedding vector of the GST layer focus on representing the speech quality (filtered or clean) of the input speech. The experimental results verified that the proposed method synthesized much more high-quality speech than conventional methods.
Teachers’ perceptions in optimizing elearning software to enhance the world elearning model cheapest in covid-19 (a case study in Darunnajah kindergarten school East Java)

Abstract To finding about the best, simple, cheapest and legal of elearning software which was forced by Corona. Before Covid-19 Pandemic, many people used many various kind of online learning (elearning) software, both proprietary software and free software. But these are nothing effect. Since Covid-19 Pandemic happen big economic crisis in the world, meaning many rich people become poor because lost of job, bankruptcy because close of companies and lock down to avoid spreaded of this desease. Because of that, this was needed focus on finding or optimalizing of free elearning software so can be using on all the world in Covid-19 Pandemic. There were main software : proprietary or close and free softwares. Using free software meaning was can be most cheapest using in the world. The meaning of free software were : the first was free of the charge. The second was free to download and using. The other benefit would not breaking copyright and avoid using stolen software, which was latest very much to using in globalization era, would not be cared. This research used a descriptive study with qualitative approach to explore teachers’ perceptions in optimizing elearning software in covid-19 era. Participants of the study were 1 principal and 6 teachers. Method of data collection was observations, interviews, documents. Data extraction and assessment of quality. Three themes emerged were: [1] Many difficulties in using WhatsApp, [2] Lack of funding and internet network, [3] Recommendations in using Moodle elearning. The Moodle elearning software is very common using the world because the simplest, best and free to download. Lately, many provider opened free hosting, direct to made as simple as blog made in Moodle. Findings suggested that choice to free elearning software because more accordance with Covid-19 situation, world crisis economy happen and to improve online learning especially in kindergarten level which most little attention in Covid-19 Pandemic.
Internet of Things Computer Network Security and Remote Control Technology Application

As China gradually enters the age of network information, technologies such as the Internet of Things based on computer networks have been more and more widely used, and they have also greatly improved the convenience of people's production and life. The development and promotion of the Internet of Things not only need to further strengthen the application of remote control technology, but also put forward higher requirements for network security. The Internet of Things must reduce the security risks in the computer network based on the realization of remote automatic control, so as to ensure the safety of data and information and ensure the normal operation of the Internet of Things. This can ensure that the Internet of Things can give full play to its due functions, thereby promoting the modern development of my country's social economy.
Developing Vietnamese Sentiment Lexicon from Social Reviews Corpus Based on Support Measurement

The sentiment dictionary plays an important role in analyzing or identifying opinion of users. A Sentiment dictionary is widely applicable to many different domains. Therefore, many researchers are interested in and building sentiment dictionaries. However, most of these dictionaries were built based on Vietnamese lexicon, when applied to social reviews often have low accuracy, because of the way social media is used different from Vietnamese lexicon. In this paper, we present a methodology of constructing Vietnamese sentiment dictionary with scoring for analyzing opinion of social reviews. We used training set with 5,200 labeled sentences that were collected from customer’s reviews about electronic product domain on electronic commerce websites. After that, we extracted nouns, adverbs and adjectives and then applied support measurement to calculate weight of them. The experimental results with 249 sentences have an accuracy of approximately 92% compared to 87% of dictionary that is developed based on Vietnamese lexicon, showing that our dictionary has a higher accuracy when applied to social sentiment analysis.