Reviewerly/exHarmony huggingface dataset:
Smart library book sorting application with intelligence computer vision technology

Purpose Book sorting system is one of specific application in smart library scenarios, and it now has been widely used in most libraries based on RFID (radio-frequency identification devices) technology. Book identification processing is one of the core parts of a book sorting system, and the efficiency and accuracy of book identification are extremely critical to all libraries. In this paper, the authors propose a new image recognition method to identify books in libraries based on barcode decoding together with deep learning optical character recognition (OCR) and describe its application in library book identification processing. Design/methodology/approach The identification process relies on recognition of the images or videos of the book cover moving on a conveyor belt. Barcode is printed on or attached to the surface of each book. Deep learning OCR program is applied to improve the accuracy of recognition, especially when the barcode is blurred or faded. The approach the authors proposed is robust with high accuracy and good performance, even though input pictures are not in high resolution and the book covers are not always vertical. Findings The proposed method with deep learning OCR achieves best accuracy in different vertical, skewed and blurred image conditions. Research limitations/implications Methods that the authors proposed need to cooperate and practice in different book sorting machine. Social implications The authors collected more than 500 books from a library. These photos display the cover of more than 100 randomly picked books with backgrounds in different colors, each of which has about five different pictures captured from variety angles. The proposed method combines traditional barcode identification algorithm with the authors’ modification to locate and deskew the image. And deep learning OCR is involved to enhance the accuracy when the barcode is blurred or partly faded. Book sorting system design based on this method will also be introduced. Originality/value Experiment demonstrates that the accuracy of the proposed method is high in real-time test and achieves good accuracy even when the barcode is blurred. Deep learning is very effective in analyzing image content, and a corresponding series of methods have been formed in video content understanding, which can be a greater advantage and play a role in the application scene of intelligent library.
Improving Banking Transactions Using Blockchain Technology

Today, the majority of banks offer many different online services to their customers and our study case will focus specifically on domestic and international banking transactions. By doing these services, these banks use enough time to conduct bank transactions from one bank account to another, some of which take more than a week, under a security that does not fully respect the privacy of operators and under the mercy of certain third party's services. Unfortunately, these banks face the limitations of payment systems (such as SWIFT, SEPA, and union pay) for international transactions and other banking exchange services. To remedy these problems of third-party trust, exaggerated latency, payment of high transaction fees, problems of theft and falsification of banking information, we will set up a storage and bank exchange platform, based on a private and confidential blockchain. In this platform, a number of authorized users will be able to hold and operate the nodes that will support the network. Nowhere in the world is there a system that directly connects banks, currencies and financial institutions without a trusted third party. In our case, these sworn users are banks. This platform will eliminate the presence of the trusted third party which is the third entity through which the various transactions and banking information must pass. To begin with, our platform will eliminate third-party trust, promote user-user transactions and then store bank transaction information in the blockchain. Our blockchain platform will allow users to make secure and confidential transactions at a lower cost and without a foreign exchange ban due to a maximum amount not to exceed as with the case of banks.
Defining Quality of Experience for the Internet of Things

The Internet of Things (IoT) brings a set of unique and complex challenges to the field of Quality of Experience (QoE) evaluation. The state-of-the-art research in QoE mainly targets multimedia services, such as voice, video, and the Web, to determine quality perceived by end-users. Therein, main evaluation metrics involve subjective and objective human factors and network quality factors. Emerging IoT may also include intelligent machines within services, such as health-care, logistics, and manufacturing. The integration of new technologies such as machine-to-machine communications and artificial intelligence within IoT services may lead to service quality degradation caused by machines. In this article, we argue that evaluating QoE in the IoT services should also involve novel metrics for measuring the performance of the machines alongside metrics for end-users' QoE. This article extends the legacy QoE definition in the area of IoT and defines conceptual metrics for evaluating QoE using an industrial IoT case study.
An accelerator for online SVM based on the fixed-size KKT window

Support vector machine (SVM), as a general and useful supervised learning tool, is facing with some challenges such as low learning efficiency, poor generalization performance, noise sensitivity, etc. when it is applied to online learning tasks. To overcome these limitations, an accelerator model based on window technology and the KKT conditions for online SVM learning is proposed in this paper. The proposed model is not an independent online algorithm but may be regarded as an accelerator for other online SVM learning algorithms, and it constructs working set of SVM by a fixed-size window with the samples which violate the KKT conditions. The relationship between Lagrangain multipliers in dual problem of SVM and KKT conditions are analyzed in the case of online learning. On this basis, a fixed-size KKT window can be constructed according to whether the samples violate KKT conditions or not. Then, it takes the samples that violate the KKT conditions as the training window, which not only makes the training samples with the same size each time, but also ensures that all samples are useful for the hyperplane updating (it means that the classifier can be updated more smoothly). Two typical and specific online SVM algorithms are used as baseline, and the corresponding speeding online SVM learning algorithms with "X+accelerator" models are proposed to testing the performance of the proposed accelerator. Comprehensive experiments clearly show that the proposed model can accelerate the online learning process effectively and has good robustness and generalization performance.
Effective Collaborative Design of Large Virtual 3D Model using Multiple AR Markers

In the new era of industry, collaboration amongst partners will be the most challenging part of design and manufacturing. There are many ways for companies to collaborate with remote experts or partners in designing, manufacturing, or distributing the new product, depending on how complicated the product is. With the progress in information technology, many techniques can be used for supporting collaborative work. For the design that needs specific or complicates visual appearance, Augmented Reality (AR) is the most promising one. The virtual 3D model can be distributed to the remote partners for evaluation and providing comments. In this research, the technique in generating a stable large 3D model for remote design collaboration using multiple markers was developed. Users from different locations can work on the same virtual model. The platform for the mobile application was also developed. The results from the experiment with this setup confirmed that the system could be used for real collaboration.
Confidence Estimation for Black Box Automatic Speech Recognition Systems Using Lattice Recurrent Neural Networks

Recently, there has been growth in providers of speech transcription services enabling others to leverage technology they would not normally be able to use. As a result, speech-enabled solutions have become commonplace. Their success critically relies on the quality, accuracy, and reliability of the underlying speech transcription systems. Those black box systems, however, offer limited means for quality control as only word sequences are typically available. This paper examines this limited resource scenario for confidence estimation, a measure commonly used to assess transcription reliability. In particular, it explores what other sources of word and sub-word level information available in the transcription process could be used to improve confidence scores. To encode all such information this paper extends lattice recurrent neural networks to handle sub-words. Experimental results using the IARPA OpenKWS 2016 evaluation system show that the use of additional information yields significant gains in confidence estimation accuracy. The implementation for this model can be found online <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
JIT Leaks: Inducing Timing Side Channels through Just-In-Time Compilation

Side-channel vulnerabilities in software are caused by an observable imbalance in resource usage across different program paths.We show that just-in-time (JIT) compilation, which is crucial to the runtime performance of modern interpreted languages, can introduce timing side channels in cases where the input distribution to the program is non-uniform.Such timing channels can enable an attacker to infer potentially sensitive information about predicates on the program input.We define three attack models under which such side channels are harnessable and five vulnerability templates to detect susceptible code fragments and predicates.We also propose profiling algorithms to generate the representative statistical information necessary for the attacker to perform accurate inference.We systematically evaluate the strength of these JIT-based side channels on the java.lang.String, java.lang.Math, and java.math.BigInteger classes from the Java standard library, and on the JavaScript built-in objects String, Math, and Array.We carry out our evaluation using two widely adopted, open-source, JIT-enhanced runtime engines for the Java and JavaScript languages: the Oracle HotSpot Java Virtual Machine and the Google V8 JavaScript engine, respectively.Finally, we demonstrate a few examples of JIT-based side channels in the Apache Shiro security framework and the GraphHopper route planning server, and show that they are observable over the public Internet.
A De-Identification Face Recognition Using Extracted Thermal Features Based on Deep Learning

Facial recognition based on visible-light RGB image is a mature technique. With the continuous progress of civilization, the protection against personal privacy and the effectiveness of the system is more stringent. In the past research, the distance from facial features was used as the de-identified biological feature, but this could not solve the problem of using a photo to deceive the system. Therefore, we used thermal image to convert into features by using the proposed feature extraction method, and uses deep learning, random forest, and ensemble learning to build a face prediction model. The proposed feature extraction method cuts the facial image (RGB and thermal image) into 12 and 48 blocks respectively as well as regenerates the feature image and the feature matrix. Based on the experimental results, accuracy of RGB image only with 0.834, feature image with 0.953, and feature matrix with 0.967. The feature images and feature matrices produced by the proposed feature extraction method can achieve better prediction performance. High accuracy of thermal imaging can solve the problem of fake photos and the issue of personal privacy de-identification could be solved.
25.3 A 65nm Edge-Chasing Quantizer-Based Digital LDO Featuring 4.58ps-FoM and Side-Channel-Attack Resistance

Low-Dropout Regulators (LDOs) are commonly desired for fine-grained power management in SoCs because of their compact area, high current efficiency, and small output ripple. Digital LDOs (DLDOs) are increasingly adopted in recent years thanks to voltage and process scalability. However, achieving a fast response to a load change requires a conventional synchronous DLDO to either increase its sampling frequency with a large power overhead, or include a large output capacitor (COUT) with increased chip area and cost. Various control schemes have been proposed to mitigate these drawbacks. In synchronous designs, attempts have been made to employ more complex control algorithms to achieve faster response [1], and to replace the PMOS switch array with switched capacitors for reduced dynamic energy consumption [2]. On the other hand, asynchronous DLDOs with event-driven [3] and beat-frequency VCO-based structures [4] show promise across the key performance, power and area metrics. Recently, voltage regulators are also found to be useful in enhancing the resistance of cryptographic engines and processors against power and EM side-channel attacks (SCAs) [5]-[7], which are physical attacks representing a severe threat to mobile and embedded devices. Employing regulators for SCA defense is promising because they are already used in most systems and require no modifications to existing computing architectures and algorithms like other circuit-level defenses [8]-[9]. In this paper, we present a high-performance and SCA-aware DLDO leveraging the unique properties of the Edge-Chasing Quantizer (ECQ). The 65nm DLDO prototype achieves: 1) a 101.7mV droop and 506ns settling time after a 20mA, 0.1ns step load change, with only a 0.1nF capacitor; 2) a 0.018mm2 active area and 99.4% peak current efficiency; and, 3) more than 14000× improvement in power SCA resistance on a 128b AES engine, with 27.5% area, 19.4% power, and 4.54% performance overheads over a standalone AES, and negligible overhead when compared to an AES design integrated with conventional DLDOs.
PatronuS: A System for Privacy-Preserving Cloud Video Surveillance

Privacy has become one of the major concerns in cloud video surveillance. Privacy protection of the surveillance videos strive to protect users' privacy information without hampering regular security tasks of the surveillance, meanwhile retains the system's high accuracy and efficiency. The current state of the art in protecting the video privacy is mainly realized through Privacy Region Protection, which only protects the privacy regions while keeps the non-privacy regions visually intact so that processing in the cloud is still feasible. However, the problem of determining the privacy regions has been ignored and not properly addressed. In this paper, we propose a novel notion - concept graph, and with the aid of that, we develop our system - PatronuS to determine the privacy regions subject to satisfying both privacy and security requirements. We further propose an event distilling model and a privacy inference model to assist in determining specific privacy regions. And we evaluate PatronuS in real-world settings and demonstrate its efficiency in privacy protection without degrading system's surveillance functionality.
Two-factor authentication for voice assistance in digital banking using public cloud services

This paper presents the results of a research on using voice assistants to enable financial and commercial operations which require authentication with increased level of security. Several prototypes of authentication service with two-factor authentication for the purpose of voice-controlled digital banking and online payments have been developed at the Cyber Security Research Centre of London Metropolitan University. They utilize the power of the cloud technology and the attractiveness of the devices equipped with voice assistant software to provide secure method for two-factor authentication, which meets the recent standards for security of the operations in digital banking.
Smart Manufacturing and Continuous Improvement and Adaptation of Predictive Models

Predictive models are an important success factor for smart manufacturing. Accordingly, purely data-driven models as well as hybrid models are increasingly deployed within manufacturing environments for optimal control of plants. However, long-term monitoring and adaptation of predictive models has not been a focus of studies so far but will likely become increasingly more important as more and more predictive models are deployed. We give a number of recommendations for effectively managing predictive models in smart manufacturing environments.
A Comparative Study on Effective Approaches for Unsupervised Statistical Machine Translation

Although Machine Translation has historically trusted on huge amounts of parallel corpora, the latest analysis has accomplished to prepare each Neural and Statistical Machine Translation system using monolingual corpora only. In spite of the prospective of this methodology for low-resource settings, obtainable structures square measure way outstanding their supervised counterparts, restraining their concrete interest. In this paper, Sect. 1 contains numerous deficiencies of existing unsupervised SMT approaches by exploiting subword information. Section 2 consists of another methodology established on phrase-based statistical machine translation that significantly cessations the gap with supervised structures. Principled Unsupervised Statistical Machine Translation in Sect. 3. Results and discussions in Sect. 4 and conclusion in Sect. 5.
Learning Geometric Features with Dual–stream CNN for 3D Action Recognition

Recently, regarding several beneficial properties of depth camera, numerous 3D action recognition frameworks have studied high-level features by exploiting deep learning techniques, but nevertheless they cannot seize the meaningful characteristics of static human pose and dynamic action motion of a whole sequence. This paper introduces a deep network configured by two parallel streams of convolutional stacks for fully learning the deep intra-frame joint associations and inter-frame joint correlations, wherein the structure of each stream is learned from Inception-v3. In experiments, besides the compatibility verification with various backbone networks, the proposed approach achieves the state-of-theart performance in battle with several deep learning-based methods on the updated NTU RGB+D 120 dataset..
Recognition of Signed Expressions in an Experimental System Supporting Deaf Clients in the City Office

The paper addresses the recognition of dynamic Polish Sign Language expressions in an experimental system supporting deaf people in an office when applying for an ID card. A method of processing a continuous stream of RGB-D data and a feature vector are proposed. The classification is carried out using the k-nearest neighbors algorithm with dynamic time warping, hidden Markov models, and bidirectional long short-term memory. The leave-one-subject-out protocol is used for the dataset containing 121 Polish Sign Language sentences performed five times by four deaf people. A data augmentation method is also proposed and tested. Preliminary observations and conclusions from the use of the system in a laboratory, as well as in real conditions with an experimental installation in the Office of Civil Affairs are given.
Electrosense+: Crowdsourcing radio spectrum decoding using IoT receivers

Web spectrum monitoring systems based on crowdsourcing have recently gained popularity. These systems are however limited to applications of interest for governamental organizations or telecom providers, and only provide aggregated information about spectrum statistics. The result is that there is a lack of interest for layman users to participate, which limits its widespread deployment. We present Electrosense+ which addresses this challenge and creates a general-purpose and open platform for spectrum monitoring using low-cost, embedded, and software-defined spectrum IoT sensors. Electrosense+ allows users to remotely decode specific parts of the radio spectrum. It builds on the centralized architecture of its predecessor, Electrosense, for controlling and monitoring the spectrum IoT sensors, but implements a real-time and peer-to-peer communication system for scalable spectrum data decoding. We propose different mechanisms to incentivize the participation of users for deploying new sensors and keep them operational in the Electrosense network. As a reward for the user, we propose an incentive accounting system based on virtual tokens to encourage the participants to host IoT sensors. We present the new Electrosense+ system architecture and evaluate its performance at decoding various wireless signals, including FM radio, AM radio, ADS-B, AIS, LTE, and ACARS.
An Overview of Extreme Learning Machine

Extreme Learning Machine (ELM), as a new learning framework of Single Hidden Layer Feedforward Neural Network (SLFN), has become one of the hottest research directions in the field of artificial intelligence in recent years. It has been widely used in multiclass classification, human action recognition and other fields. ELM provides an efficient and unified learning framework for regression, classification, feature learning, and clustering. At the same time, ELM theories and algorithms have been improved for specific applications. This paper aims to provide a comprehensive review of existing research related to ELM. We first give an overview of the standard ELM. Then we discuss and analyze the typical variants of ELM, which are improved from different aspects, including models, strengths and weaknesses. Then we compare ELM with traditional methods in classification field and list the performance comparison between ELM and other deep network algorithms. Furthermore, the latest applications of ELM and its variants are summarized. Last but not least, we conclude the research trends of ELM variants mentioned above.
Xpsnr: A Low-Complexity Extension of The Perceptually Weighted Peak Signal-To-Noise Ratio For High-Resolution Video Quality Assessment

The objective PSNR metric is known to correlate quite poorly with subjective assessments of video coding quality. Thus, a number of alternative VQA measures such as (MS-)SSIM and VMAF have been proposed. These, however, are often algorithmically complex and difficult to use for visually motivated encoder optimization tasks, especially subjectively optimized bit allocation. In this paper we show that, by way of low-complexity enhancements of our previous work on a perceptually weighted PSNR (WPSNR) metric, addressing shortcomings with video and ultra high-definition content, the prediction of human judgments of video coding quality by the WPSNR can be improved. In fact, the resulting XPSNR seems to match the performance of the aforementioned state-of-the-art methods.
Neural Attentive Multiview Machines

An important problem in multiview representation learning is finding the optimal combination of views with respect to the specific task at hand. To this end, we introduce NAM: a Neural Attentive Multiview machine that learns multiview item representations and similarity by employing a novel attention mechanism. NAM harnesses multiple information sources and automatically quantifies their relevancy with respect to a supervised task. Finally, a very practical advantage of NAM is its robustness to the case of dataset with missing views. We demonstrate the effectiveness of NAM for the task of movies and app recommendations. Our evaluations indicate that NAM outperforms single view models as well as alternative multiview methods on item recommendations tasks, including cold-start scenarios.