Reviewerly/exHarmony huggingface dataset:
ESSA: An energy-Aware bit-Serial streaming deep convolutional neural network accelerator

Over the past decade, deep convolutional neural networks (CNN) have been widely embraced in various visual recognition applications owing to their extraordinary accuracy. However, their high computational complexity and excessive data storage present two challenges when designing CNN hardware. In this paper, we propose an energy-aware bit-serial streaming deep CNN accelerator to tackle these challenges. Using ring streaming dataflow and the output reuse strategy to decrease data access, the amount of external DRAM access for the convolutional layers is reduced by 357.26x when compared with that of no output reuse case on AlexNet. We optimize the hardware utilization and avoid unnecessary computations using the loop tiling technique and by mapping the strides of the convolutional layers to unit-ones for computational performance enhancement. In addition, the bit-serial processing element (PE) is designed to use fewer bits in weights, which can reduce both the amount of computation and external memory access. We evaluate our design using the well-known roofline model. The design space is explored to find the solution with the best computational performance and communication to computation (CTC) ratio. We can reach 1.36x speed and reduce energy consumption by 41% for external memory access compared with the design in [1]. The hardware implementation for our PE Array architecture design can reach an operating frequency of 119 MHz and consumes 68 k gates with a power consumption of 10.08 mW using TSMC 90-nm technology. Compared to the 15.4 MB external memory access for Eyeriss [2] on the convolutional layers of AlexNet, our method only requires 4.36 MB of external memory access to dramatically reduce the costliest portion of power consumption.
Twenty-six years of LIS research focus and hot spots, 1990–2016: A co-word analysis

The purpose of this research is to map and analyse the conceptual and thematic structure of library and information science (LIS) research from the perspective of the co-word analysis. The bibliographical records consist of all the research papers published in the LIS core journals between 1990 and 2016 and indexed in Web of Science. ‘CiteSpace’ was used to visualise the co-word network of LIS studies. The frequency of co-occurrence and centrality scores in the overall structure of the field showed that the word ‘Science’ is the most significant and pivotal keyword among the nodes in the co-word network of LIS literature, and in this respect, the word ‘Library’ is in the second place. However, the results of the social network analysis uncovered that in spite of the high frequency of the word ‘library’, the pivotal role of the term has been exposed to decline over the time. The results of the analysis of co-word clusters showed that ‘information seeking and retrieval’ is the most important research focus in the intellectual structure of LIS literature during 1990–2016. Also, analysis of the hot spots of the LIS research based on Kleinberg algorithm indicated that the words ‘Internet’ and ‘World Wide Web’ have attracted the most attention by LIS scholars during the years under study.
Image captioning with semantic-enhanced features and extremely hard negative examples

Image captioning is a task to generate natural descriptions of images. In existing image captioning models, the generated captions usually lack semantic discriminability. Semantic discriminability is difficult as it requires the model to capture detailed differences in images. In this paper, we propose an image captioning framework with semantic-enhanced features and extremely hard negative examples. These two components are combined in a Semantic-Enhanced Module. The semantic-enhanced module consists of an image-text matching sub-network and a Feature Fusion layer which provides semantic-enhanced features of rich semantic information. Moreover, in order to improve the semantic discriminability, we propose an extremely hard negative mining method which utilize the extremely hard negative examples to improve the latent alignment between visual and language information. Experimental results on MSCOCO and Flickr30K show that our proposed framework and training method can simultaneously improve the performance of image-text matching and image captioning, achieving competitive performance against state-of-the-art methods.
Defect Prediction Framework using Neural Networks for Business Intelligence Technology Based Projects

So far, researchers in field of defect prediction have published multiple approaches, but none of these publications have identified the Business Intelligence project life cycle. In this paper, we have taken data from 60 BI projects from a Data Analytics organization and have used the real project data to design a prediction model based on artificial neural networks. Results are evaluated with comparison of three different training algorithms, i.e., Lavenberg-Marquardt, Scaled Conjugate Gradient and Bayesian Regularization backpropagation algorithms, in the view of their ability to perform defect prediction. The objective is to design a prediction framework, which is expected to be effective and acceptable for predicting the defects in multiple phases across Business Intelligence projects.
The effects of a virtual learning environment compared with an individual handheld device on pharmacology knowledge acquisition, satisfaction and comfort ratings

Abstract Background Virtual reality is reported to improve post-intervention knowledge and skills outcomes of health professionals compared to traditional teaching methods or digital online media. However, providing equitable access to high quality virtual reality resources for large, diverse nursing and midwifery student cohorts within multi-campus settings remains challenging. Objectives This study compared the effect on student learning, satisfaction and comfort following exposure to a three-dimensional pharmacology artefact in a virtual facility (CAVE2™) 1 with viewing of the same artefact using a mobile handheld device with stereoscopic lenses attached. Design The study used a pretest-posttest design. Setting School of Nursing and Midwifery in a regional university in Southeast Queensland, Australia. Participants Two hundred and forty-nine second year undergraduate nursing and midwifery students. Methods Online multiple choice tests were deployed to measure knowledge acquisition. Self-reported satisfaction scores and comfort ratings were collected using questionnaires. Results Participants were not disadvantaged in terms of knowledge acquisition by using either CAVE2™ or the mobile handheld visualisation mode (P = 0.977). Significant differences in favour of the CAVE2™ environment were found in between students' satisfaction scores for clinical reasoning (P = 0.013) and clinical learning (P  Conclusion Three-dimensional artefacts using mobile devices is promising in terms of cost-effectiveness and accessibility for students with restricted access to on-campus teaching modes.
The Design of Hierarchical Consensus Mechanism Based on Service-Zone Sharding

The byzantine agreement protocol has a disadvantage that there are many limitations on node scalability, because the performance degradation may occur due to a large amount of traffic. Hence, this classical byzantine agreement algorithm seems to be infeasible to achieve scale-out performance with the same level of security as a Bitcoin. In order to improve the performance degradation due to a large amount of traffic, we propose the hierarchical consensus mechanism based on service-zone sharding rather than how all nodes participate in the consensus process. In the proposed consensus mechanism (SZHBFT), a disjoint set of transactions is locally processed by a secure consensus subgroup or globally processed between consensus subgroups. Thus, the transactions that occur related to multiple Service-Zone Consensus Groups are updated and maintained in the Inter-Service Zone Public Ledger, whereas service transactions occurring locally are processed in parallel by each Service-Zone Consensus Group and then distributed into a local Service-Zone Private Ledger. The scheme of the proposed SZHBFT mechanism provides the hierarchical agreement solution along with distributed multiledger structure for the scalable byzantine resilient agreement by forming secure consensus subgroups in order to minimize the overhead of overall communication messages.
Mining Temporal Evolution of Knowledge Graphs and Genealogical Features for Literature-based Discovery Prediction

Literature-based discovery process identifies the important but implicit relations among information embedded in published literature. Existing techniques from Information Retrieval (IR) and Natural Language Processing (NLP) attempt to identify the hidden or unpublished connections between information concepts within published literature, however, these techniques overlooked the concept of predicting the future and emerging relations among scientific knowledge components such as author selected keywords encapsulated within the literature. Keyword Co-occurrence Network (KCN), built upon author selected keywords, is considered as a knowledge graph that focuses both on these knowledge components and knowledge structure of a scientific domain by examining the relationships between knowledge entities. Using data from two multidisciplinary research domains other than the bio-medical domain, and capitalizing on bibliometrics, the dynamicity of temporal KCNs, and a recurrent neural network, this study develops some novel features supportive for the prediction of the future literature-based discoveries - the emerging connections (co-appearances in the same article) among keywords. Temporal importance extracted from both bipartite and unipartite networks, communities defined by genealogical relations, and the relative importance of temporal citation counts were used in the feature construction process. Both node and edge-level features were input into a recurrent neural network to forecast the feature values and predict the future relations between different scientific concepts/topics represented by the author selected keywords. High performance rates, compared both against contemporary heterogeneous network-based method and preferential attachment process, suggest that these features complement both the prediction of future literature-based discoveries and emerging trend analysis.
Application of Blockchain Technology in the Cultural and Creative Industries

Nowadays blockchain technology is a subject of continuous interest both of scientists and practitioners in order to expand its implementation in other sectors of economy and finance. Still the most popular application of blockchain technology rests bitcoins. The present paper proposes several possible applications of blockchain in the sphere of Cultural and Creative Industries based on the proven and accepted so far strengths of the technology.
Applying Deep Learning for Discovery and Analysis of Software Vulnerabilities: A Brief Survey

Singh, Shashank Kumar Chaturvedi, AmritaMachine learning has been proved very successful in analyzing and detecting software vulnerabilities. One of the limitations of machine learning is that it heavily depends on human expertise for feature selection. Deep learning can be used to reduce this burden of manual feature selection. The objective of this paper is to survey the feasibility and advantages of applying deep learning techniques for the analysis and detection of software vulnerability. However, deep learning has been developed for a very different class of problems so it needs to be tailored in such a way that it can easily fit in the application of software vulnerability. This paper emphasizes on all such modifications required for deep learning approaches to efficiently accommodate the problem of detection of software vulnerabilities. We have also discussed the various vulnerability databases/resources and some of the recent successful applications of deep learning in predicting vulnerabilities present in the software.
A new image encryption algorithm based on optimized Lorenz chaotic system

Summary In this article, an improved Lorenz system was constructed, in the first stage, a nonlinear term in general Lorenz system was replaced by the sum of term of exponential function and term of the square of single variable, then the dynamic characteristics of the improved Lorenz system were analyzed through the analysis on the phase diagrams and its Lyapunov exponents, and it is proved that the new system has good chaotic characteristics. In the second stage, to improve the security and anti‐attack ability of image encryption algorithm, a new image encryption algorithm was proposed based on this improved Lorenz chaotic system, which was used to generate two groups of key sequences which were used in scrambling encryption and alternative encryption. In the scrambling encryption stage, the gray values of image pixels were sorted in ascending order, and combining with the sequence of scrambled encryption key, the pixel position scrambling was realized. In the phase of the pixel value replacement encryption stage, the gray values of the pixels were changed by the method of cipher text feedback. In the third stage, the quantitative analysis of the effect of encryption was carried out by using histogram, information entropy, correlation coefficient of adjacent pixels. The experimental results show that the improved Lorenz formula is a nonlinear chaotic system, the encrypted algorithm based on this chaotic system can effectively resist chosen plaintext attack, in the encrypted image, the grey value shave random‐like distribution behavior and the adjacent pixels have zero correlation. The algorithm has a 299 bit key space and it is very sensitive to the key.
A Stereo Perception Framework for Autonomous Vehicles

Stereo cameras are crucial sensors for self-driving vehicles as they are low-cost and can be used to estimate depth. It can be used for multiple purposes, such as object detection, depth estimation, semantic segmentation, etc. In this paper, we propose a stereo vision-based perception framework for autonomous vehicles. It uses three deep neural networks simultaneously to perform free-space detection, lane boundary detection, and object detection on image frames captured using the stereo camera. The depth of the detected objects from the vehicle is estimated from the disparity image computed using two stereo image frames from the stereo camera. The proposed stereo perception framework runs at 7.4 Hz on the Nvidia Drive PX 2 hardware platform, which further allows for its use in multi-sensor fusion for localization, mapping, and path planning by autonomous vehicle applications.
Making Air Quality Data Meaningful

Air pollution causes several million deaths every year. Increasing public awareness through the deployment of devices that sense air quality may be a promising step in addressing the problem; however, these wholly objective device measurements may not capture the nuanced and lived experiences people have with the air, which are often colored by perceptions, histories, imaginations, and the sociopolitical context in which people live. The gap between objective environmental realities and individuals' subjective experiences of the environment may make it difficult to form meaning from data, hindering the positive policy outcomes that they are intended to produce. To bridge this gap, we conducted a two-phase design fieldwork to obtain an empirical understanding of the rich contours of experiences people have with the air and outline design strategies in making air quality data meaningful.
NMTLAT: A New robust mobile Multi-Target Localization and Tracking Scheme in marine search and rescue wireless sensor networks under Byzantine attack

After a shipwreck (ship collision, explosion, disappearance, etc.), people rely on the marine search and rescue wireless sensor networks (MSR-WSNs) as one key weapon to locate and track drowning targets to save lives and assets. However, due to the complex and dynamic ocean environment, marine sensors could be captured by malicious attackers and then become Byzantine sensors. Byzantine sensors may tamper with the actual sensor data at a fixed or time-varying probability and transmit it to the data fusion center (DFC), which renders the normal localization and tracking function of the search and rescue system downgrade or even fail. To address this issue, we develop a New robust marine mobile Multi-Target LocAlization and Tracking scheme called NMTLAT by eliminating abnormal measurement data from the initial measurement data. NMTLAT works in several steps. By analyzing and mining sensors data and behavior, we firstly employ the information entropy of the system composed of a single sensor and their neighbor sensors to develop an efficient dynamic threshold based Byzantine node identification method. After migrating Byzantine sensors, the DFC utilizes sensor-aware and preprocessed marine data of the beacon or honest sensors to complete target localization and trajectory tracking. Specifically, NMTLAT consists of a novel distributed and cooperative multi-target localization and tracking algorithm using both received signal strength indication (RSSI) and priori information of sensors location. NMTLAT employs the importance sampling method to approximate the posterior probability distribution of the sensors and targets location. Furthermore, when the marine target is outside MSR-WSNs coverage, a piecewise function is utilized to characterize the likelihood of finding a drowning target in the search and rescue sea area. Finally, the Lyapunov’s second stability theorem is adopted to measure the stability of the NMTLAT. Our extensive simulation experiments validate that the NMTLAT performance is superior to existing solutions in various marine search and rescue scenarios.
A modular memory framework for time series prediction

Tangled Program Graphs (TPG) is a framework for genetic programming which has shown promise in challenging reinforcement learning problems with discrete action spaces. The approach has recently been extended to incorporate temporal memory mechanisms that enable operation in environments with partial-observability at multiple timescales. Here we propose a highly-modular memory structure that manages temporal properties of a task and enables operation in problems with continuous action spaces. This significantly broadens the scope of real-world applications for TPGs, from continuous-action reinforcement learning to time series forecasting. We begin by testing the new algorithm on a suite of symbolic regression benchmarks. Next, we evaluate the method in 3 challenging time series forecasting problems. Results generally match the quality of state-of-the-art solutions in both domains. In the case of time series prediction, we show that temporal memory eliminates the need to pre-specify a fixed-size sliding window of previous values, or autoregressive state, which is used by all compared methods. This is significant because it implies that no prior model for a time series is necessary, and the forecaster may adapt more easily if the properties of a series change significantly over time.
RL-OPRA: Reinforcement Learning for Online and Proactive Resource Allocation of crowdsourced live videos

With the advancement of rich media generating devices, the proliferation of live Content Providers (CP), and the availability of convenient internet access, crowdsourced live streaming services have witnessed unexpected growth. To ensure a better Quality of Experience (QoE), higher availability, and lower costs, large live streaming CPs are migrating their services to geo-distributed cloud infrastructure. However, because of the dynamics of live broadcasting and the wide geo-distribution of viewers and broadcasters, it is still challenging to satisfy all requests with reasonable resources. To overcome this challenge, we introduce in this paper a prediction driven approach that estimates the potential number of viewers near different cloud sites at the instant of broadcasting. This online and instant prediction of distributed popularity distinguishes our work from previous efforts that provision constant resources or alter their allocation as the popularity of the content changes. Based on the derived predictions, we formulate an Integer-Linear Program (ILP) to proactively and dynamically choose the right data center to allocate exact resources and serve potential viewers, while minimizing the perceived delays. As the optimization is not adequate for online serving, we propose a real-time approach based on Reinforcement Learning (RL), namely RL-OPRA, which adaptively learns to optimize the allocation and serving decisions by interacting with the network environment. Extensive simulation and comparison with the ILP have shown that our RL-based approach is able to present optimal results compared to heuristic-based approaches.
Assimilating Second-Order Information for Building Non-Negative Latent Factor Analysis-Based Recommenders

A non-negative latent factor analysis (NLFA)-based recommender can make precise recommendations by correctly representing the non-negative characteristic of industrial data. It commonly relies on a nonconvex and bilinear optimization process, where the effects of first-order solvers maybe significantly reduced. Higher order solvers like a Newton-type method are expected to make a breakthrough; however, its computation efficiency and scalability are greatly limited due to the numerous parameters involved in a Hessian matrix. To address this issue, this article proposes an approach for assimilating second-order information for building NLFA-based recommenders. The key idea is an inner second-order solver that employs a Hessian-free method for avoiding the highly expensive manipulations of a Hessian matrix. Empirical studies on eight data cases emerging from real industrial applications indicate that the proposed approach outperforms state-of-the-art models in prediction accuracy with affordable computational burden.
Physical Modeling, Algorithms, and Sound Synthesis: The NESS Project

Abstract Synthesis using physical modeling has a long history. As computational costs for physical modeling synthesis are often much greater than for conventional synthesis methods, most techniques currently rely on simplifying assumptions. These include digital waveguides, as well as modal synthesis methods. Although such methods are efficient, it can be difficult to approach some of the more detailed behavior of musical instruments in this way, including strongly nonlinear interactions. Mainstream time-stepping simulation methods, despite being computationally costly, allow for such detailed modeling. In this article, the results of a five-year research project, Next Generation Sound Synthesis, are presented, with regard to algorithm design for a variety of sound-producing systems, including brass and bowed-string instruments, guitars, and large-scale environments for physical modeling synthesis. In addition, 3-D wave-based modeling of large acoustic spaces is discussed, as well as the embedding of percussion instruments within such spaces for full spatialization. This article concludes with a discussion of some of the basics of such time-stepping methods, as well as their application in audio synthesis.