Reviewerly/exHarmony huggingface dataset:
Improving K-Mean Method by Finding Initial Centroid Points

The paper is concerned with Improving k-Mean Algorithm in terms of accuracy by selecting the best initial seed points based on the provided k value. This paper presents two modified k-mean method for the selection of initial centroid points. In the first method based on the calculated k value with the help of elbow method, the original sorted data based on distances calculated using Euclidean distance method is divided into k equal partitions. And the mean of each partition is considered as initial centroid points. And in the second method the number of k is chosen randomly and the mean of each partition is considered as initial centroid points. We compared within cluster distance and number of iterations. Modified k-mean methods are better than original k-mean method as the distance within the clusters are less in modified k-mean than the original k-mean and the accuracy is also better.
Abstractive Summarization using Genetic Semantic Graph for Indonesian News Articles

We propose an abstractive summarization model using genetic semantic graph for Indonesian news articles. The model is adapted from an abstractive summarization model for English news articles that employs genetic algorithm (GA) for feature weights. Three of seven components in this model are language dependent, so they have to be modified to be able to process Indonesian news articles. Predicate argument structure (PAS) extraction component is replaced with subject, verb, object, and adverbial (SVOA) extraction component. We also replace WordNet-based similarity measure in semantic similarity matrix component with cosine similarity algorithm based on word embedding. Lastly, abstractive summary generation component is replaced with heuristic rules. Experiments are held to obtain the best pretrained word embedding, mutation probability, and selection, crossover, and mutation algorithm in genetic algorithm (GA) for each type of summary, i.e. 100 words and 200 words. The evaluation shows that the proposed model has 0.320 and 0.394 average ROUGE-2 recall for 100 words and 200 words summary respectively.
Adversarial Video Compression Guided by Soft Edge Detection

We propose a video compression framework using conditional Generative Adversarial Networks (GANs). We rely on two encoders: one that deploys a standard video codec and another one which generates low-level soft edge maps. For decoding, we use a standard video decoder as well as a decoder that is trained using a conditional GAN. Recent "deep" approaches to video compression require multiple videos to pre-train generative networks that conduct interpolation. By contrast, our scheme trains a generative decoder that requires only a small number of key frames and edge maps taken from a single video, without any interpolation. Experiments on two video datasets demonstrate that the proposed GAN-based compression engine is a promising alternative to traditional video codec approaches that can achieve higher quality reconstructions for very low bitrates.
On the Generality of Facial Forgery Detection

A variety of architectures have been designed or repurposed for the task of facial forgery detection. While many of these designs have seen great success, they largely fail to address challenges these models may face in practice. A major challenge is posed by generality, wherein models must be prepared to perform in a variety of domains. In this paper, we investigate the ability of state-of-the-art facial forgery detection architectures to generalize. We first propose two criteria for generality: reliably detecting multiple spoofing techniques and reliably detecting unseen spoofing techniques. We then devise experiments which measure how a given architecture performs against these criteria. Our analysis focuses on two state-of-the-art facial forgery detection architectures, MesoNet and XceptionNet, both being convolutional neural networks (CNNs). Our experiments use samples from six state-of-the-art facial forgery techniques: Deepfakes, Face2Face, FaceSwap, GANnotation, ICface, and X2Face. We find MesoNet and XceptionNet show potential to generalize to multiple spoofing techniques but with a slight trade-off in accuracy, and largely fail against unseen techniques. We loosely extrapolate these results to similar CNN architectures and emphasize the need for better architectures to meet the challenges of generality.
Layer-Normalized LSTM for Hybrid-Hmm and End-To-End ASR

Training deep neural networks is often challenging in terms of training stability. It often requires careful hyperparameter tuning or a pretraining scheme to converge. Layer normalization (LN) has shown to be a crucial ingredient in training deep encoder-decoder models. We explore various LN long short-term memory (LSTM) recurrent neural networks (RNN) variants by applying LN to different parts of the internal recurrency of LSTMs. There is no previous work that investigates this. We carry out experiments on the Switchboard 300h task for both hybrid and end-to-end ASR models and we show that LN improves the final word error rate (WER), the stability during training, allows to train even deeper models, requires less hyperparameter tuning, and works well even without pre-training. We find that applying LN to both forward and recurrent inputs globally, which we denoted by Global Joined Norm variant, gives a 10% relative improvement in WER.
Deep Orthogonal Transform Feature for Image Denoising

Recently, CNN-based image denoising has been investigated and shows better performance than conventional vision based techniques.However, there are still a couple of limits that are weak partly in restoring image details like textured regions or produce other artifacts.In this paper, we introduce noise-separable orthogonal transform features into a neural denoising framework.We specifically choose wavelet and PCA as an orthogonal transform, which achieved a good denoising performance conventionally.In addition to spatial image signals, the orthogonal transform features (OTFs) are fed into a denoising network.For the guide of the denoising process, we also concatenate OTFs from the image denoised by the existing method.This can play a role of prior for learning a denoising process.It has been confirmed that our proposed multi-input network can achieve better denoising performance than other single-input networks.
Utilising Information Foraging Theory for User Interaction with Image Query Auto-Completion

Query Auto-completion (QAC) is a prominently used feature in search engines, where user interaction with such explicit feature is facilitated by the possible automatic suggestion of queries based on a prefix typed by the user. Existing QAC models have pursued a little on user interaction and cannot capture a user's information need (IN) context. In this work, we devise a new task of QAC applied on an image for estimating patch (one of the key components of Information Foraging Theory) probabilities for query suggestion. Our work supports query completion by extending a user query prefix (one or two characters) to a complete query utilising a foraging-based probabilistic patch selection model. We present iBERT, to fine-tune the BERT (Bidirectional Encoder Representations from Transformers) model, which leverages combined textual-image queries for a solution to image QAC by computing probabilities of a large set of image patches. The reflected patch probabilities are used for selection while being agnostic to changing information need or contextual mechanisms. Experimental results show that query auto-completion using both natural language queries and images is more effective than using only language-level queries. Also, our fine-tuned iBERT model allows to efficiently rank patches in the image.
Feature Selection Under Orthogonal Regression with Redundancy Minimizing

Various supervised embedded methods have been proposed to select discriminative features from original ones, such as Feature Selection with Orthogonal Regression (FSOR) and Robust Feature Selection. Compared with embedded methods based on the least square regression, FSOR, utilizing orthogonal regression, can preserve more discriminative information in the subspace and have better performance on feature selection. However, the embedded approaches have scarcely considered the dependency among the selected feature subset. To address the defect, in this paper, we propose a two-stage (filter-embedded) feature selection technique based on Maximum Relevance Minimum Redundancy and FSOR, termed as Orthogonal Regression with Minimum Redundancy (ORMR). We compared the feature selection performance between ORMR and nine other state-of-the-art supervised feature selection methods on six benchmark datasets. The results demonstrate the advantage of ORMR method over others in choosing discriminative features with considering the redundant information among the selected feature subset.
RISC-V Graphics Rendering Instruction Set Extensions for Embedded AI Chips Implementation

In this paper, we present graphics rendering instruction extension to RISC-V ISA. In order to achieve high performance at low power consumption, the fragment rendering process is designed to be fixed-pipeline. The proposed system on a chip (SOC) is fabricated into a 3.5 × 3.5 mm2 chip using 110-nm CMOS technology. The RISC-V core with programmable vertex engine consumes 1.10 mm2 and runs at 102 MHz. The fragment engine consumes 7.44 mm2 and runs at 200 MHz. Experiment Results show that the whole chip only consumes 20 mW at full speed, and the performance per milliwatt is over 200 times more than modern MCU chip with GPU core.
Synchronous n-Step Method for Independent Q-Learning in Multi-Agent Deep Reinforcement Learning

Experience replay memory (ERM) is an effective tool for sampling decorrelated data to train the policy network and improving data utilization in off-policy deep reinforcement learning. However, ERM introduces fluctuations in the training processes of independent Q-learning (IQL) in multi-agent deep reinforcement learning (MA-DRL) because its stored experiences may become obsolete as the agents in IQL update their policies in parallel. Reducing the influence of obsolete experiences while extensively exploring the potential of data in training poses a huge challenge in IQL. Therefore, we propose the synchronous n-step method that totally eliminates obsolete experiences but suffers from the low data utilization problem. Then, we propose the ERM-helped synchronous n-step method that makes a balance between reducing the influence of obsolete experience and enhancing data utilization. We apply these methods to lenient deep reinforcement learning and propose the LSnDQN and LESnDQN algorithms, which we subsequently test in some extended variations of the coordinated multi-agent object transportation problem. Results show that LSnDQN has a great advantage on training with less iterations but no advantage on computation time over LDQN, while LESnDQN has a great advantage on computation time over both LDQN and LSnDQN.
Experimental Analysis of a Visual-Recognition Control for an Autonomous Underwater Vehicle in a Towing Tank

In this study, underwater recognition technology and a fuzzy control system were adopted to adjust the attitude and revolution speed of a self-developed autonomous underwater vehicle (AUV). To validate the functionality of visual-recognition control, an experiment was conducted in the towing tank at the Department of Systems and Naval Mechatronic Engineering, National Cheng Kung University. An underwater lighting box was towed by a towing carriage at low speed. By adding real-time contour approximation and a circle-fitting algorithm to the image-processing procedure, the relationship between the AUV and the underwater lighting box was calculated. Both rudder plane angles and propeller revolution speeds were determined after the size and location of the lighting box was measured in the image. Finally, AUV performance with visual-recognition control was verified by controlling the target object in the image center during passage.
Malaria Diagnosis with Dilated Convolutional Neural Network Based Image Analysis

Malaria is a worldwide health issue. Traditionally, microscopic visual examination of blood smears to detect parasite infected red blood cells has been the standard procedure for diagnosing malaria. This manual method is prone to human errors and time consuming too. To automate the diagnosis, machine learning based image recognition has been applied in the past. But the performance was below par specially in case of big datasets. This paper proposes a kernel dilation based new and robust convolutional neural network (CNN) to automatically classify infected and uninfected red blood cells. Three different dilation approaches were used among which Fibonacci series-wise dilated CNN model performed best in all metrics such as accuracy (96.05%), precision (95.80%), recall (96.33%) and F1 score (96.06%) while working with a dataset of 27,558 cell images.
Creating Collaborative Library Spaces Through Partnerships with Campus Organizations

In 2018, the Merrill-Cazier Library at Utah State University tasked their Space Planning Committee with assessing if the Library was meeting user needs through physical spaces. Capitalizing on their existing relationship, the Committee initiated a joint space survey with Campus Information Technology to identify how they could cooperatively meet student needs to provide students with a more holistic experience in the Library. This article discusses the survey results and provides academic librarians a roadmap to establish valuable cross-campus collaborations that contribute to student success by using cooperative needs assessments. The process illuminated the possibilities and strengths with collaborative space design.
Regional Covariance Matrix-Based Two-Dimensional PCA for Face Recognition

Two-dimensional principal component analysis (2DPCA) is widely used in many applications, especially, face recognition. A key factor to improve the performance of the 2DPCA method comes from the efficiency of the covariance matrix. This paper believes that the effective eigenvector can be extracted when the effective covariance matrix is given. Therefore, the computing covariance matrix is a focus point in this paper. The set of the covariance matrix in the 2DPCA and its extensions is usually represented with a single directional correlation, which is then used to obtain a mean covariance matrix by using the average technique. This causes in obtaining the ineffective eigenvector since the covariance matrix is ineffective. In order to obtain the effective eigenvector, a regional covariance matrix-based on 2DPCA method (RCM-2DPCA) is proposed here. The contribution of this paper consists of two main parts including (i) regional matrix calculation for computing the two directional correlations and (ii) ELSSP conversion for extracting the effective representation of the covariance matrix. The experimental results show that the performance of the proposed method is higher than the baseline methods including 2DPCA, I-2DPCA, Bi2DPCA, 2D2PCA and ILM-2DPCA methods on a basis of three well- known datasets-ORL Face, Yale Face, and Yale Face extended B+ datasets.
QOS-Aware Flow Control for Power-Efficient Data Center Networks with Deep Reinforcement Learning

Reducing the power consumption and maintaining the Flow Completion Time (FCT) for the Quality of Service (QoS) of applications in Data Center Networks (DCNs) are two major concerns for data center operators. However, existing works either fail in guaranteeing the QoS due to the neglect of the FCT constraints or achieve a less satisfying power efficiency. In this paper, we propose SmartFCT, which employs Software-Defined Networking (SDN) coupled with the Deep Reinforcement Learning (DRL) to improve the power efficiency of DCNs and guarantee the FCT. The DRL agent can generate a dynamic policy to consolidate traffic flows into fewer active switches in the DCN for power efficiency, and the policy also leaves different margins in different active links and switches to avoid FCT violation of unexpected short bursts of flows. Simulation results show that with similar FCT guarantee, SmartFCT can save 8% more of the power consumption compared to the state-of-the-art solutions.
An Approach to Knowledge Base Completion by a Committee-Based Knowledge Graph Embedding

Knowledge bases such as Freebase, YAGO, DBPedia, and Nell contain a number of facts with various entities and relations. Since they store many facts, they are regarded as core resources for many natural language processing tasks. Nevertheless, they are not normally complete and have many missing facts. Such missing facts keep them from being used in diverse applications in spite of their usefulness. Therefore, it is significant to complete knowledge bases. Knowledge graph embedding is one of the promising approaches to completing a knowledge base and thus many variants of knowledge graph embedding have been proposed. It maps all entities and relations in knowledge base onto a low dimensional vector space. Then, candidate facts that are plausible in the space are determined as missing facts. However, any single knowledge graph embedding is insufficient to complete a knowledge base. As a solution to this problem, this paper defines knowledge base completion as a ranking task and proposes a committee-based knowledge graph embedding model for improving the performance of knowledge base completion. Since each knowledge graph embedding has its own idiosyncrasy, we make up a committee of various knowledge graph embeddings to reflect various perspectives. After ranking all candidate facts according to their plausibility computed by the committee, the top-k facts are chosen as missing facts. Our experimental results on two data sets show that the proposed model achieves higher performance than any single knowledge graph embedding and shows robust performances regardless of k. These results prove that the proposed model considers various perspectives in measuring the plausibility of candidate facts.
A Self-Attentive Emotion Recognition Network

Attention networks constitute the state-of-the-art paradigm for capturing long temporal dynamics. This paper examines the efficacy of this paradigm in the challenging task of emotion recognition in dyadic conversations. In this work, we introduce a novel attention mechanism capable of inferring the immensity of the effect of each past utterance on the current speaker emotional state. The proposed self-attention network captures the correlation patterns among consecutive encoder network states, thus enabling the robust and effective modeling of temporal dynamics over arbitrary long temporal horizons. We exhibit the effectiveness of our approach considering the challenging IEMOCAP benchmark. We show that, our devised methodology outperforms state-of-the-art alternatives and commonly used approaches, giving rise to promising new research directions in the context of Online Social Network (OSN) analysis tasks.
Research on covert communication channel based on modulation of common compressed speech codec

As is well known, multimedia has been widely used in VoIP and mobile communications. Research on how to establish covert communication channel over the above popular public applications has been flourishing in recent years. This paper tries to present a novel and effective method to construct a covert channel over common compressed speech stream by embedding sense information into it. In our method, after analysing the characteristic features of the excitation pulse positions of the ITU-T G.723.1 and G.729A speech codec, we design a novel and effective covert communication channel by finely modulating the codes of excitation pulse positions of the above two codecs in line with the secret information to be hidden. To improve the embedding capacity of the proposed method, we also use all the odd/even characteristics of pulse code positions to conduct information hiding. To test and verify the proposed approach, experiments are conducted on several different scenarios. Experimental results show that our methods and algorithms perform a higher degree of secrecy and sound information embedding efficacy compared with exiting similar methods.
Compare Learning: Bi-Attention Network for Few-Shot Learning

Learning with few labeled data is a key challenge for visual recognition, as deep neural networks tend to overfit using a few samples only. One of the Few-shot learning methods called metric learning addresses this challenge by first learning a deep distance metric to determine whether a pair of images belong to the same category, then applying the trained metric to instances from other test set with limited labels. This method makes the most of the few samples and limits the overfitting effectively. However, extant metric networks usually employ Linear classifiers or Convolutional neural networks (CNN) that are not precise enough to globally capture the subtle differences between vectors. In this paper, we propose a novel approach named Bi-attention network to compare the instances, which can measure the similarity between embeddings of instances precisely, globally and efficiently. We verify the effectiveness of our model on two benchmarks. Experiments show that our approach achieved improved accuracy and convergence speed over baseline models.
Fast and Accurate Embedded DCNN for Rgb-D Based Sign Language Recognition

In this paper, fast and accurate two paths CNN architecture was designed in hardware-oriented manner. Our proposed network is composed of RGB and depth path for gesture recognition by fusing RGB and depth features, following the pre-defined constraints on dedicated hardware. The RTL simulation results indicate it only takes 0.171 milliseconds to infer a single pair of RGB image and depth maps at the operational frequency of 250MHz. Compared with running the same model at Intel i7 and GTX 1080, the speedups are 593.92x and 7.68x respectively. Besides, to increase the recognition accuracy under the diversity of the circumstance, a new RGB-D dataset, captured from Kinect, with complex background was built. Moreover, the number of parameters in our model is only 0.17M and it achieves 99.79% accuracy on the ASL Finger Spelling dataset. Compared with the Gao's CNN gesture recognition architecture, the number of parameter of our model is 2.9 times less and the accuracy is 6.49% higher. Demonstration video for sign language recognition is provided : https://youtu.be/DvO8mI7IZ5Q.
Research on the Rules of Electronic Evidence in Chinese Criminal Proceedings

As a new object in judicial practice, electronic evidence is of great practical significance. To locate the probative force of electronic evidence, which can be used to prove the facts of the crime, judging the electronic evidence validity, and how to establish scientific rules of electronic evidence, which not only effectively contains crime, but also protects civil rights from illegal infringement of state power becomes very important. This article outlines the definition of electronic evidence and rules and establishes a suitable electronic evidence system of China's criminal procedure system based on the analysis of problems in each link of judicial proof in judicial practice and the four aspects of judicial proof.