Reviewerly/exHarmony huggingface dataset:
FPGA Implementation of a Configurable Vocal Feature Extraction Embedded System for Dysarthric Speech Recognition

In the last few years, users have been increasingly demanding for a hands-free interaction with their digital devices. This kind of technology is even more useful if used by people with disabilities, improving their quality of life. In particular, speech-impaired users (e.g. dysarthric speakers) represent a big challenge for an Automatic Speech Recognition (ASR) system because standard approaches are ineffective with them. Therefore, new speech analysis algorithms are implemented and generally tested on off-line datasets, but their performance can differ from a real case. Hence comes the need to easily validate their performance in a real scenario. The work presented in this paper shows an implementation of a highly configurable off-line embedded system for both MFCC and Mel Filterbanks extraction equipped with an on-board microphone. The results show that our system performs well in a real scenario case in terms of both power consumption and word error rate.
A Six-Stream CNN Fusion-Based Human Activity Recognition on RGBD Data

RGBD-based activity recognition is quite an interesting task in computer vision. Inspired by the exemplary results obtained from automatic features learning from RGBD data, in this work a six-stream CNN fusion approach has been addressed, which is developed on 2D-convolution neural network (2DCNN) and spatial-temporal 3D-convolution neural networks (ST3DCNN). The proposed approach has six streams and runs in parallel, where the first and second streams are used to extract space and time features with the help of a ST3DCNN model. Similarly, the remaining four streams have been used to extract the temporal features by means of two motion templates on motion history image (MHI) and motion energy image (MEI) via a 2DCNN. Further, a support vector machine (SVM) is employed to generate the score from each stream. Finally, a decision level fusion scheme particularly a weighted product model (WPM) to fuse the scores is obtained from all the streams. The effectiveness of the proposed approach has been tested on popular benchmark public datasets, namely UTD-MHAD, and gives promising results.
Research on Automatic Generation Method of Innovative Enterprise Evaluation Text

The evaluation text of innovative companies results from natural language processing. However, there are two shortcomings in the generation of evaluation texts: rough processing and failure to consider the evaluation index system. This paper proposes a model using BiLSTM+Attention+p value, using BiLSTM+Attention to obtain the relevant information of each indicator from the knowledge graph to make the sentence accurate and considering the p-value to combine the corpus and the knowledge graph make sentences more fluent. The experimental results show that compared with the basic model that does not use the knowledge graph to connect many indicators and the coarse-grained processing that does not consider the coverage of each indicator, our framework has improved scores on ROUGE-1, ROUGE-2, and ROUGE-L.
Investing with Cryptocurrencies Between Risk and Profit

In the international stock market, the possibility of investment with “cryptocurrencies” has its appearance, which has been gaining strength in the Colombian credit and operating scenario, especially with young financiers who adhere to this language with the so-called Blockchain technology. From this comes the risk and the benefit in the absence of a possible regulation. On these situations, in this text, an analysis is made on the identification and characterization of a problem of financial and public ethics in the scene of cryptocurrencies, in addition to the relationship and comparison of their behavior with other currencies and the position of entities International such as NYSE (New York Stock Exchange, in Spanish, New York Stock Exchange), CFTC (Commodity Futures Trading Commission, in Spanish, Commodity Futures Trading Commission in the USA) and Banco de la República in Colombia. The fundamental purpose of this text is the clear and theoretical rationalization around cryptocurrencies as an “investment alternative” and their ethical costs in correspondence to their operation in the Colombian financial state and their risk status in exchange rate normalization. The characterization of how Bitcoin managed to become a desired asset and belonging in the market and accredited in the North American economy was deployed and its characteristics were leveled, events which have led to the spread of the cryptocurrency. In the same way, how the practice of crypto-currencies in Colombia is not legislated and can offer an economic risk.
New Family of Polyphase Sequences with Low Correlation from Galois Rings

In this letter, we give a new construction of a family of sequences of period pk-1 with low correlation value by using additive and multiplicative characters over Galois rings. The new constructed sequence family has family size (M-1)(pk-1)rpkr(e-1) and alphabet size Mpe. Based on the characters sum over Galois rings, an upper bound on the correlation of this sequence family is presented.
Using Corpus Linguistics to Better Prepare Students for the IELTS Reading Exam

Preparing students for the reading component of the Academic IELTS exam can be a particularly daunting task. Of the four components of the exam, the reading is considered to be the most difficult, and this is due to the amount of unknown lexical items. With that in mind, I conducted a study to ascertain the possibility of generating a wordlist from a sizeable selection of previous IELTS readings. I downloaded readings from the three parts of the reading section and generated a wordlist from them using the concordance program WordSmith Tools. I filtered out all function words and concentrated on lexical words. I chose the first 100 lexical words from the wordlist and grouped them according to the CEFR scale and the Academic Wordlist. This study showed that it is indeed possible to generate focussed vocabulary lists that will potentially help students perform better on the IELTS reading sections.
Co-design In High Performance Computing Systems

Hardware/Software co-design has been pursued in the High-Performance Computing (HPC) community as a methodology for application scientists, software, and hardware communities to work together for designing future computing environments. In this paper, we first summarize an industry experience, namely the journey in designing and building two supercomputers -Summit and Sierra- delivered to the Department of Energy Labs (DoE) in 2018, developed through collaboration among teams from IBM and NVIDIA, as well as from DOE Labs. We then transition to the Cloud environment, as HPC users are increasingly considering using Cloud infrastructure due to its scale and flexibility. We describe an internal EDA case wherein users have extended their on-premises cluster to the Cloud and operate in a hybrid-cloud environment, bursting the workload into the Cloud when on-premises data center capacity is strained, enabled by advanced system software management solutions.
Design and Implementation of Identifying Points on Elliptic Curve Efficiently Using Java

Internet of Things (IoT) is a field which is growing enormously in day to day life. Every second there are billions of devices getting connected to internet world wide. With advancement in growth of this technology has made a wider scope for research in this domain. However authentication, authorization, data security and data confidentiality is still a huge threat for an IoT device. In securing IoT devices elliptic curve cryptography happens to be computationally feasible than earlier algorithms like Rivest, Shamir, Adleman (RSA). In this paper we have worked with design and implementation to identify the points on elliptic curve using java programming language on raspberry pi.
Image-Text Matching with Intra-Modal Knowledge

In this paper, we study the problem of image-text matching. Image-text matching aims to train an model that learns to identify whether or not an (image, caption) input pair constitutes an aligned pair. To boost image-text matching, the soft label is introduced by knowledge distillation. Its benefit is generally attributed to the privileged information on similarities among image-text pairs provided by the teacher model. However, training a stronger teacher is computationally heavy and inefficient. In this paper, we propose a novel parameter-free Soft Labels Generation(SLG) method for image-text matching which can produce soft labels with privileged information without the need of training a heavy teacher model. We propose to use intra-modal knowledge to generate the soft label for each image-text pair, which provides an alternative to knowledge distillation. Our method is especially applicable to cases where a stronger teacher model is not available, or only limited computation resources are provided. Systematic experiments conducted on public benchmarks demonstrate that our SLG is empirically effective. Our SLG achieves about 2%-3% recall@1 boosting than the baseline.
Dual-channel BERT-DBLCA Based on Attention Mechanism for News Category Label Classification Model

The accuracy of classification often requires contextual information, and there is a large amount of redundant information that interferes with the accuracy of classification. In response to the above problems, a two-channel BERT-DBLCA news category label classification model based on the attention mechanism is proposed. First, the input text is represented by vector through the embedding layer of BERT, the CLS is output to the dual-channel BiLSTM-CNN layer, the BiLSTM channel is used to extract the contextual information in the CLS, the CNN channel is used to extract the local feature information of the CLS, and the two. The feature vector of the channel performs feature fusion, and then enters the Attention layer, and distributes according to the weight of the feature vector, extracts keywords, and performs more accurate classification. The performance of this model is evaluated on the public data set of THUCNews and compared with other neural network models. The classification accuracy of the model used in this article is 97.80%, which has a better classification effect.
Innovation of Audio-visual Teaching Modes of TCSOL Based on “Tik Tok”

In the era of big data, the great development of internet invades every aspect of daily life, and changes the developing direction of various industries. With the fast development of digital media, it influences the booming developing career of Chinese international teaching under the field of education as well. Recently, short video Apps, such as Tik Tok, Wesee and kuaishou, come into vogue, which has become an important power in the field of new-media and teaching by short video apps also greatly affects the filed of teaching Chinese to speakers of other languages. This passage will set about from the example of teaching Chinese to other language speakers on “Tik Tok”, and talk about the pros and coins of this new prevailed Chinese teaching modes. Then it will put forward some suggestions about optimizing and improving this modes in order to facilitate the innovation and development of international Chinese teaching under the background of “digital economy”.
RTS Game AI Robots Winner Prediction Based on Replay Data by using Deep Learning

The success of the AlphaGo series of artificial intelligence (AI) programs in the game of Go has injected new vitality into AI research. The research of real-time strategy (RTS) games using deep learning algorithms provides an ideal method for AI planning, state evaluations, and human behavior research. Our work focused on winner prediction for RTS game AI robots based on states and actions information of replay dataset generated by μRTS simulator by using deep learning. Since previous winner prediction methods only encoded states information without actions information, the game information is incomplete and the accuracy of prediction is low. In the present work, we used one-hot encoding to encode both “states” elements and “actions” elements in the attributes of each sampled time point and corresponding “winner” attribute, which can achieve higher winner prediction performance. We used the encoded datasets to train (validate) and test five deep learning algorithms: CNN, MSCNN, CNP, LSTM, and BNN. The experimental results showed that all five deep learning methods can achieve an accuracy greater than 77% (often greater than 80%) using this new encoding on a task comparing with around 60% accuracy of previous algorithms. The Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) value can achieve greater than 0.85 (often greater than 0.9). Finally, we sampled 20 time points from the beginning to the end of each match's replay data to illustrate the feasibility of our methods.
Privacy and Other Legal Concerns in the Wake of Deepfake Technology

The world is filled with technology-driven systems and instruments in the present era. Artificial intelligence (hereinafter referred to as “AI”) is increasingly dominating our everyday life. Deepfakes is one such AI-based technological innovation that is leading to huge concerns amongst legal and scientific experts. There have been recent instances wherein videos of statements never made and acts never done by public figures became viral on the internet. Face swap in videos is easily done for the purpose of cyber bullying and harassment of innocent victims. Since creation of these synthetic contents called deepfakes is becoming easier by the day, the growing concerns surrounding them demand immediate attention. This chapter shall specifically probe into the deepfake technology with dual objectives (i.e., to understand the causes of concern surrounding deepfakes related AI-technology and to compare effective ideas and regulatory measures of nations abroad such as US and China) in order to arrive at possible solutions to appropriately tackle this issue in India.
Bookending ARFIS: The Development, Final Results, and Current State of the Academic Reading Format International Study

The Academic Reading Format International Study (ARFIS) is the largest study conducted to date on tertiary students’ reading format preferences and behaviors. It was born at the European Conference on Information Literacy (ECIL) and has been tied intimately with it throughout its formation and development. From 2014–2017, forty-three researchers in 33 countries participated in ARFIS, many of whom first connected and networked through attendance at ECIL. As of this writing, Google Scholar shows over 30 articles and reports on results of individual ARFIS participant countries and the amalgamated ARFIS results, 15 of which appear in various ECIL proceedings. The project has generated much interest from many academic fields. This paper will present its background and highlights from the findings, a list of ARFIS and ARFIS-related publications and researchers, discussions of the current status, and future directions. We will also discuss the impact of ARFIS on future scholarship, especially in the wake of potential changes in educational practices as a result of the COVID-19 pandemic.
Instrumental Variables Zipf Regression

The standard Zipf regression involves a linear relation between the natural logs of the rank and the natural logs of a score on a certain variable. If there are measurement errors in the scores, then the regressor is endogenous and renders Ordinary Least Squares inappropriate. Instrumental Variables estimation alleviates the problem, where instruments are easily found. Five diverse illustrative cases emphasize the empirical relevance.
Spark Performance Optimization Analysis with Multi-Layer Parameter using Shuffling and Scheduling in Different Data Caching Options

As social networking services and e-commerce are growing rapidly, the number of online users also dynamically growing which facilitates the contribution of huge content to the digital world. In such a dynamic environment, meeting the demand of computing is very challenging special with the existing computing model. Although Spark is recently introduced to alleviate the problems with the concept of in-memory computing for big data analytic with many parameters configuration that allow to conFigure and improve its performance, still it has a performance bottleneck which requires to investigate peformance improvement mechanism by focusing on the combinations of Scheduling and Shuffle Manager with intermediate data caching options. The standalone cluster computing model was selected as an experimental methodology with submit command line for data submission. Three Spark applications such as WorkCount, TeraSort, and PageRank were selected and developed for the experiment.As a result, 2.45% and 8.01% performance improvement are achieved in OFFHEAP and Memory Only Ser data caching option, respectively.
Research on Entity Relationship Joint Extraction Technology Based on Multitask Learning

With the rapid development of information technology in recent years, entity relationship extraction as a key step of information extraction has been widely concerned. In the past practice relationship extraction technologies, entity identification and relationship extraction were mostly realized as two independent tasks, ignoring the relationship between the two tasks. In this paper, an entity relationship extraction model is proposed. Based on the pre-training model BERT, an entity identification and multi-head selection task model is constructed to jointly extract entity relationships. The model realizes entity identification through CRF, and classifies relationships by taking relationship extraction as a multi-head selection task. The joint extraction model proposed in this paper improves the tasks of entity recognition and relationship extraction. This paper integrates the entity relationship extraction model into the knowledge map management system, labels the entity relationship through the model, verifies the data through domain experts, and establishes the domain knowledge map. At the same time, the verified data is used as training data for incremental training of the model, so as to continuously optimize the model.
A New Handwritten Number Recognition Approach Using Typical Testors, Genetic Algorithms, and Neural Networks

In this paper, a method combining three techniques is proposed in order to reduce the amount of features used to train and predict over a handwritten data set of digits. The proposal uses typical testors and searches through evolutionary strategy to find a reduced set of features that preserves essential information of all the classes that compose the data set. Once found it, this reduced subset will be strengthened for classification. To achieve it, the neural network prediction accuracy plays the role of fitness function. Thus, when a subset reaches a threshold prediction accuracy, it is returned as a solution of this step. Evolutionary strategy makes this intense search of features viable in terms of computing complexity and time. The discriminator construction algorithm is proposed as a strategy to achieve a smaller feature subset that preserves the accuracy of the overall data set. The proposed method is tested using the public MNIST data set. The best result found a subset of 171 features out of the 784, which only represents 21.81% of the total number of characteristics. The accuracy average was 97.83% on the testing set. The results are also contrasted with the error rate of other reported classifiers, such as PCA, over the same data set.
ENSOCOM: Ensemble of Multi-Output Neural Network’s Components for Multi-Label Classification

Multitasking and multioutput neural networks models jointly learn related classification tasks from a shared structure.Hard parameters sharing is a multitasking approach that shares hidden layers between multiple taskspecific outputs.The output layers' weights are essential in transforming aggregated neurons outputs into tasks labels.This paper redirects the multioutput network research to prove that the ensemble of output layers prediction can improve network performance in classifying multi-label classification tasks.The network's output layers initialized with different weights simulate multiple semi-independent classifiers that can make non-identical label sets predictions for the same instance.The ensemble of a multi-output neural network that learns to classify the same multi-label classification task per output layer can outperform an individual output layer neural network.We propose an ensemble strategy of output layers components in the multi-output neural network for multi-label classification (ENSOCOM).The baseline and proposed models are selected based on the size of the hidden layer and the number of output layers to evaluate the proposed method comprehensively.The ENSOCOM method improved the performance of the neural networks on five different multi-label datasets based on several evaluation metrics.The methods presented in this work can substitute the standard labels representation and predictions generation of any neural network.
Real-Time Semantic Segmentation with Local Spatial Pixel Adjustment

Abstract—With the rapid development of deep learning and the urgent need for real-time tasks such as autonomous driving, the research of semantic segmentation network has achieved a significant breakthrough. However, most methods are difficult to make full use of the information generated in each stage of the network, resulting in pixel value dislocation and blurred boundary of small-scale objects. To overcome these challenges, a local spatial pixel adjustment network (LSPANet) is proposed in this paper, which mainly consists of a dual-branch decoding fusion (DDF) module and spatial pixel cross-correlation (SPCC) block. Specifically, the DDF module takes the high-level and low-level feature maps with different stages as the input, and gradually eliminates the difference in the information of the feature map to fuse a variety of information extracted in the encoder stage. The SPCC block adopts the horizontal spatial pixel adjustment (HSPA) module and the vertical spatial pixel adjustment (VSPA) module to capture the relationship of each pixel value in the local horizontal and vertical space respectively, and then adjust the importance of all values based on this relationship. LSPANet is evaluated on Cityscapes and Camvid datasets. The experimental results show that our network achieves 74.7% mIoU with 2M parameters on the challenging Cityscapes dataset and the inference speed is 60.3FPS in a single GTX 2080 Ti GPU.
Beaver Triple Generator from Multiplicatively Homomorphic Key Management Protocol

SPDZ, the nickname of secure multi-party computation (MPC) protocol of Damgård et al. from Crypto 2012, is an elegant secret value splitting technique which enjoys highly scalable and efficient multiplications with the help of off-line Beaver (multiplicative) triples. Since an arithmetic circuit defined over a finite field can be implemented by addition and multiplication gates, and also since an implementation of addition gate is almost cost free within the SPDZ framework, a design of efficient secure multi-party computations within the SPDZ framework is reduced to that of Beaver (multiplicative) triples. This paper studies Beaver (multiplicative) triple generators and our contributions are two-fold: