Reviewerly/exHarmony huggingface dataset:
Context Modelling Using Hierarchical Attention Networks for Sentiment and Self-assessed Emotion Detection in Spoken Narratives

Automatic detection of sentiment and affect in personal narratives through word usage has the potential to assist in the automated detection of change in psychotherapy. Such a tool could, for instance, provide an efficient, objective measure of the time a person has been in a positive or negative state-of-mind. Towards this goal, we propose and develop a hierarchical attention model for the tasks of sentiment (positive and negative) and self-assessed affect detection in transcripts of personal narratives. We also perform a qualitative analysis of the word attentions learnt by our sentiment analysis model. In a key result, our attention model achieved an un-weighted average recall (UAR) of 91.0 % in a binary sentiment detection task on the test partition of the Ulm State-of-Mind in Speech (USoMS) corpus. We also achieved UARs of 73.7 % and 68.6 % in the 3-class tasks of arousal and valence detection respectively. Finally, our qualitative analysis associates colloquial reinforcements with positive sentiments, and uncertain phrasing with negative sentiments.
Joint Optimization of Quantization and Structured Sparsity for Compressed Deep Neural Networks

The usage of Deep Neural Networks (DNN) on resource-constrained edge devices has been limited due to their high computation and large memory requirement. In this work, we propose an algorithm to compress DNNs by jointly optimizing structured sparsity and quantization constraints in a single DNN training framework. The proposed algorithm has been extensively validated on high/low capacity DNNs and wide/deep sparse DNNs. Further, we perform Pareto-optimal analysis to extract optimal DNN models from a large set of trained DNN models. The optimal structurally-compressed DNN model achieves ~50X weight memory reduction without test accuracy degradation, compared to floating-point uncompressed DNN.
An OpenMP-based tool for finding longest common subsequence in bioinformatics

Finding the longest common subsequence (LCS) among sequences is NP-hard. This is an important problem in bioinformatics for DNA sequence alignment and pattern discovery. In this research, we propose new CPU-based parallel implementations that can provide significant advantages in terms of execution times, monetary cost, and pervasiveness in finding LCS of DNA sequences in an environment where Graphics Processing Units are not available. For general purpose use, we also make the OpenMP-based tool publicly available to end users.In this study, we develop three novel parallel versions of the LCS algorithm on: (i) distributed memory machine using message passing interface (MPI); (ii) shared memory machine using OpenMP, and (iii) hybrid platform that utilizes both distributed and shared memory using MPI-OpenMP. The experimental results with both simulated and real DNA sequence data show that the shared memory OpenMP implementation provides at least two-times absolute speedup than the best sequential version of the algorithm and a relative speedup of almost 7. We provide a detailed comparison of the execution times among the implementations on different platforms with different versions of the algorithm. We also show that removing branch conditions negatively affects the performance of the CPU-based parallel algorithm on OpenMP platform.
Silent Timestamping for Blockchain Mining Pool Security

Blockchain miners processing the transactions and generating blocks are rational and incentivized by financial rewards. Prior research in blockchain security studied such financial incentives for the miners and identified attacks based on withholding and delaying block submissions. Among such attacks, the fork-after-withholding (FAW) attack is the state of the art and provides practical incentives to the rational attacker by increasing its reward at the expense of the rest of the miners in the compromised pool. To defend against FAW attack, we propose Silent Timestamping, which has the mining pool manager enforce the ordering of the shares. Implemented at the mining pool manager, Silent Timestamping does not require networking nor any behavior-or implementation-changes in the miners; the lack of the implementation overheads distinguish Silent Timestamping from other schemes which have been proposed but had limited success in practice. We analyze the optimal attacker behavior against Silent Timestamping and the effectiveness of Silent Timestamping against FAW attack. Silent Timestamping effectively nullifies the reward gain of the FAW attack and reduces the optimal FAW attacker strategy to be either the suboptimal block-withholding attack (which never submits the withheld block in the compromised pool and provides limited incentives for a rational attacker) or honest mining.
A Model of External Memory for Navigation in Partially Observable Visual Reinforcement Learning Tasks

Visual reinforcement learning implies that, decision making policies are identified under delayed rewards from an environment. Moreover, state information takes the form of high-dimensional data, such as video. In addition, although the video might characterize a 3D world in high resolution, partial observability will place significant limits on what the agent can actually perceive of the world. This means that the agent also has to: (1) provide efficient encodings of state, (2) store the encodings of state efficiently in some form of memory, (3) recall such memories after arbitrary delays for decision making. In this work, we demonstrate how an external memory model facilitates decision making in the complex world of multi-agent 'deathmatches' in the ViZDoom first person shooter environment. The ViZDoom environment provides a complex environment of multiple rooms and resources in which agents are spawned from multiple different locations. A unique approach is adopted to defining external memory for genetic programming agents in which: (1) the state of memory is shared across all programs. (2) Writing is formulated as a probabilistic process, resulting in different regions of memory having short- versus long-term memory. (3) Read operations are indexed, enabling programs to identify regions of external memory with specific temporal properties. We demonstrate that agents purposefully navigate the world when external memory is provided, whereas those without external memory are limited to merely 'flight or fight' behaviour.
End-to-end single image enhancement based on a dual network cascade model

A single-exposure image may lose details because of the imaging dynamic range limitations of single camera sensor. Multi-image fusion techniques are often used to improve the image quality, but if there are moving objects in the scene, the fused images may result in ghost artifacts. In order to avoid this problem and enhance single-exposure images, this paper proposes a dual network cascade model for single image enhancement, including exposure prediction network and exposure fusion network. First, the exposure prediction network generates two under-/over-exposure images that differ from the input normal-exposure image so as to recover the lost details of the under-exposed/over-exposed regions. Then, the exposure fusion network fuses the input image and the generated under-/over-exposure images to generate the final enhanced image. The loss function constructed by a structural dissimilarity index is used to alleviate chessboard artifacts in the generated image. Further, through three-phase training, the model robustly generates enhanced images without any post-processing. The experimental results demonstrate that the proposed method can effectively improve the image contrast and reconstruct details of under-exposed/over-exposed regions in the original image.
Two-B-real Net: Two-branch Network for Real-time Salient Object Detection

As a hot topic in computer vision, recent researches on salient object detection (SOD) have focused on using the over-designed deep convolutional neural networks (CNNs) to improve the detection accuracy. However, these complex architectures constraint themselves to low speed and drag them on wide-ranging applications. In this paper, we simplify the over-designed networks and propose the Two-Branch Network for Real-time Salient Object Detection (Two-B-Real Net). Particularly, the Perceptual Branch and the Objectness Branch in our network can efficiently capture detailed information and distinctive objectness simultaneously. And we also design novel attention mechanisms to guide the network to focus on most saliency-related features and generate more accurate results. Extensive evaluations show that the proposed algorithm achieves the leading accuracy performance with real-time speed (125fps) which is significantly faster than the existing methods.
Efficient Low-Complexity Decoding of CCSDS Reed–Solomon Codes Based on Justesen’s Concatenation

Forward error correction (FEC) is a key capability in modern satellite communications that provide the system designer with the needed flexibility to comply with the different applications' requirements. Reed-Solomon (RS) codes are well known for their ability to optimize between the system power, bandwidth, data rate, and the quality of service. This paper introduces an efficient decoding scheme for decoding the RS codes adhering to the Consultative Committee for Space Data Systems (CCSDS) standards based on Justesen's construction of concatenation. To maintain the standard output size, the proposed scheme first encodes every m - 1 bits using the single-parity-check (SPC) code, while the RS code encodes K SPC codewords into N symbols that are of the same size as CCSDS standard. Decoding on the inner SPC code is based on maximum-likelihood decoding Kaneko algorithm, while for the proposed coding scheme, the reduced test-pattern Chase algorithm is adapted for decoding the outer RS code. The simulation results show the coding gains of 1.4 and 7 dB compared with the algebraic decoding of RS codes over the AWGN and Rayleigh fading channels, respectively. Moreover, the adopted reduced test-pattern Chase algorithm for decoding the RS code achieves an overall complexity reduction of 40% compared with the conventional Chase decoding algorithm.
Learning for Detection: A Deep Learning Wireless Communication Receiver Over Rayleigh Fading Channels

The evolution of data driven optimization has been shown advantageous in many applications. In this paper, we propose a deep learning architecture for the wireless communications receiver to optimize detection in terms of Bit Error Rate (BER), which enables reliable communication over AWGN and multipath Rayleigh fading channels with bandwidth constraints. Our approach uses a deep autoencoder to estimate the received signal along with an additional layer for symbol detection, which are jointly trained and fine-tuned. Significant improvement in terms of BER is achieved in simulation compared to the theoretical baseline.
Interpolation and Denoising of Graph Signals Using Plug-and-play Admm

Signals defined on a network or a graph are often prone to errors due to missing data and noise. In order to restore the graph signal, interpolation and denoising are two necessary steps along with other graph signal processing procedures. However, existing graph signal interpolation and denoising methods are largely decoupled due to the opposite objectives of the two tasks and the inherent high computational complexity. The goal of this paper is to integrate graph interpolation and denoising using the Plug-and-Play (PnP) ADMM, a recently developed technique in image processing. When using the subsampling process as the forward model and graph filter as the denoiser, we show that PnP ADMM is equivalent to interpolating a bandlimited signal. Preliminary results are demonstrated via experiments, where the proposed method shows significantly better performance over existing methods.
Contextualizing Research Evidence through Knowledge Translation in Software Engineering

Usage of software engineering research in industrial practice is a well-known challenge. Synthesis of knowledge from multiple research studies is needed to provide evidence-based decision-support for industry. The objective of this paper is to present a vision of how a knowledge translation framework may look like in software engineering research, in particular how to translate research evidence into practice by combining contextualized expert opinions with research evidence. We adopted the framework of knowledge translation from health care research, adapted and combined it with a Bayesian synthesis method. The framework provided in this paper includes a description of each step of knowledge translation in software engineering. Knowledge translation using Bayesian synthesis intends to provide a systematic approach towards contextualized, collaborative and consensus-driven application of research results. In conclusion, this paper contributes towards the application of knowledge translation in software engineering through the presented framework.
Introduction: Sharing Data in a Medical Information Commons

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Enhancing Hybrid Self-attention Structure with Relative-position-aware Bias for Speech Synthesis

Compared with the conventional "front-end"-"back-end"- "vocoder" structure, based on the attention mechanism, end-to-end speech synthesis systems directly train and synthesize from text sequence to the acoustic feature sequence as a whole. Recently, a more calculation efficient end-to-end architecture named transformer, which is solely based on self-attention, was proposed to model global dependencies between the input and output sequences. However, although with many advantages, transformer lacks position information in its structure. Moreover, the weighted sum form in self-attention may disperse the attention to the whole input sequence other than focusing on the more important neighbouring positions. In order to solve the above problems, this paper introduces a hybrid self-attention structure which combines self-attention with the recurrent neural networks (RNNs). We further enhance the proposed structure with relative-position-aware biases. Mean opinion score (MOS) test results indicate that by enhancing hybrid self-attention structure with relative-position-aware biases, the proposed system achieves the best performance with only 0.11 MOS score lower than natural recording.
Factors Affecting Enf Based Time-of-recording Estimation for Video

ENF (Electric Network Frequency) oscillates around a nominal value (50/60 Hz) due to imbalance between consumed and generated power. The intensity of a light source powered by mains electricity varies depending on the ENF fluctuations. These fluctuations can be extracted from videos recorded in the presence of mains-powered source illumination. This work investigates how the quality of the ENF signal estimated from video is affected by different light source illumination, compression ratios, and by social media encoding. Also explored is the effect of the length of the ENF ground-truth database on time of recording detection and verification.
Research and Analysis of the Front-end Frameworks and Libraries in E-Business Development

With web technology rapidly expands out in recent years, there is a significant trend that Hypertext Markup Language(HTML)5 turns into a worldwide web consortium and leads the front-end development to stand on the front stage of internet history. However, there are numerous front-end development frameworks and libraries such as React, Angular and Vue. How to select a suitable framework or library to establish the e-Business and reach out to maximize the user experience becomes a priority operation in web development. This paper starts with introducing an overview of the leading frameworks and libraries in the field of front-end development and examine each performance in web services. By analyzing the research data on several aspects, this paper will list the pros and cons of each framework and library under separate commercial criteria. Finally, the paper summarizes the contributions and concludes with some possible future of front-end development in e-Business.
Random Infinite Tree and Dependent Poisson Diffusion Process for Nonparametric Bayesian Modeling in Multiple Object Tracking

Recent methods for tracking multiple objects have addressed important issues such as time-varying cardinality, unordered sets of measurements, and object labeling. Another challenge is how to robustly associate objects on a new scene with previously estimated objects. We propose a new method to track a dynamically varying number of objects using information from previously tracked ones. Our method is based on nonparametric Bayesian modeling using diffusion processes and random trees. We use simulations to demonstrate the performance of the proposed algorithm and compare it to a labeled multi-Bernoulli filter based tracker.
Handwritten Isolated Bangla Compound Character Recognition

Bangla compound characters are a part of Bangla alphabet. Without developing a better recognizer for BangIa isolated compound characters Bangla OCR is incomplete. Whereas most of the researches have been conducted for Bangla basic characters and numerals, little has been done for handwritten Bangla compound characters. From this motivation, a convolutional neural network (CNN) based model has been developed to recognize handwritten isolated Bangla compound characters. The performance of the proposed model has been analyzed by training it over CMATERdb 3.1.3.3 and comparing the result over test dataset with other existing methods for handwritten Bangla compound character recognition. The result of the network showed 95.5% accuracy on the test dataset which is better than some current approaches.
Sequential Matching Model for End-to-end Multi-turn Response Selection

Multi-turn conversation understanding is an important challenge for building intelligent dialogue systems, and end-to-end multi-turn response selection is one of the major tasks. Previous state-of-the-art models used hierarchy-based (utterance-level and token-level) neural networks to explicitly model the interactions among the different turns' utterances for context modeling. In this paper, we demonstrate that the potentials of sequential matching approaches have not yet been fully exploited in the past for multi-turn response selection. We investigate a sequential matching model based only on chain sequence for multi-turn response selection. The proposed model outperforms all previous models, including previous state-of-the-art hierarchy-based models, and achieves new state-of-the-art performances on two large-scale public multi-turn response selection benchmark datasets.
Head-movement interface for wheelchair driving based on inertial sensors

Powered wheelchairs provide the only means of mobility for many people with severe motor disabilities. For those with both lower and upper limbs impairment, available interfaces may be either impossible or very difficult to use, as well as not very efficient. In this paper we propose an egocentric interface based on inertial sensors placed on the user’s head. This interface is based on head movements that provide continuous direction and speed commands to steer the wheelchair, and allows an initial null-position of the head according to the natural posture of the user. However, the development of an inertial interface for driving a wheelchair presents two main challenges, namely, 1) the simultaneous movements of the head and the wheelchair, each one with its own coordinate system, and 2) the free unrestricted movement of the head. Therefore, the two coordinate systems need to be combined and several safety features are required to only ensure admissible commands. In this paper we describe the overall implementation and preliminary experiments that show the effectiveness of the proposed solution.
PyHTK: Python Library and ASR Pipelines for HTK

This paper describes PyHTK, which is a Python-based library and associated pipeline to facilitate the construction of large-scale complex automatic speech recognition (ASR) systems using the hidden Markov model toolkit (HTK). PyHTK can be used to generate sophisticated artificial neural network (ANN) models with versatile architectures by converting a compact configuration file defining the ANN, into the form used by HTK tools, as well as supporting a range of capabilities to train and test ANN models. The ASR pipeline is divided into multiple steps, which can be arranged and customised for different ASR data sets, and allows for both step-by-step and fully automatic end-to-end operation. PyHTK is integrated with HTK 3.5.1 which includes an expanded range of ANN layer types and very flexible ways to connect them, together with capabilities for ASR training and testing. Some example systems are included to illustrate the flexibility and performance achievable.
Comparison of AR and physical experiential learning environment in supporting product innovation

This article compares how virtual and physical learning aids enhance learners’ product innovation capability, that is, design experiences and domain knowledge. The virtual aid utilises augmented reality (AR) allowing learners to experience a range of animated mechanisms using smart devices. The legacy physical aid mechanisms were made using three-dimensional printers. We studied the effects of both manipulatives on learners’ understanding of mechanical movements, for example, rack and pinion, and Geneva mechanism. To investigate learning impact of each aid, we compared the experimental results derived from two learners groups (13 participants each). This study provides a case to support product innovation education under an experiential learning environment. The outcomes showed that both aids were useful in enhancing design experiences and domain knowledge. Pre-and-post attention, relevance, confidence and satisfaction motivation of both aids was found to be similar. However, distinctive differences were observed in terms of divergent search for ideation, suggesting for further research in combining both aids. We also found that learners’ learning motivation is lower in AR-based aid.
A Neighbor-aware Approach for Image-text Matching

Image-text matching has received a large amount of interest since it associates different modalities and improves the understanding of image and natural language. It aims to retrieval semantic related images based on the given text query, and vice versa. Existing approaches have achieved much progress by projecting the image and text into a common space where data with different semantics can be distinguished. However, they process all the data points uniformly, while neglecting that data in a neighborhood are harder to distinguish due to their visual similarity or syntactic structural similarity. To address this issue, we propose a neighbor-aware network to image-text matching where an intra-attention module and neighbor-aware ranking loss are proposed to jointly distinguish data with different semantics, more importantly, semantic unrelated data in a neighborhood can be distinguished. The intra-attention attends to discriminative parts by comparing data with different semantics and magnifying difference between them, especially subtle difference between data in a neighborhood. The neighbor-aware ranking loss function utilizes the magnified difference to explicitly and effectively discriminate data in a neighborhood. We conduct extensive experiments on several benchmarks and show that the proposed approach significantly outperforms the state-of-the-art.