Reviewerly/exHarmony huggingface dataset:
Decentralized Privacy-Preserving Netting Protocol on Blockchain for Payment Systems

This paper proposes a decentralized netting protocol that guarantees the privacy of the participants. Namely, it leverages the blockchain and its security properties to relax the trust assumptions and get rid of trusted central parties. We prove the protocol to be optimal and we analyze its performance using a proof-of-concept implemented on top of Hyperledger Fabric.
Data Augmentation Analysis in Vehicle Detection from Aerial Videos

Recent growth in deep learning and computer vision has opened up many opportunities for advanced intelligent systems. While the dataset quality plays a crucial role in the training phase and also affects the performance of a model, creating a reliable and diverse dataset appears to be challenging. Therefore, data augmentation can be used as a preprocessing step with a view to tackling the problem. In this paper, we conduct a comprehensive analysis on different augmentation strategies to investigate their impact on vehicle detection in top-down videos captured by drones. Our experiments show that by adding similar data, randomly rotating and cropping images with respect to the model's input size, we have remarkably increased the accuracy of YOLOv3, one of the state-of-the-art and real-time object detection methods.
Metamath Zero: Designing a Theorem Prover Prover

As the usage of theorem prover technology expands, so too does the reliance on correctness of the tools. Metamath Zero is a verification system that aims for simplicity of logic and implementation, without compromising on efficiency of verification. It is formally specified in its own language, and supports a number of translations to and from other proof languages. This paper describes the abstract logic of Metamath Zero, essentially a multi-sorted first order logic, as well as the binary proof format and the way in which it can ensure essentially linear time verification while still being concise and efficient at scale. Metamath Zero currently holds the record for fastest verification of the set.mm Metamath library of proofs in ZFC (including 71 of Wiedijk's 100 formalization targets), at less than 200 ms. Ultimately, we intend to use it to verify the correctness of the implementation of the verifier down to binary executable, so it can be used as a root of trust for more complex proof systems.
A Multimodal Human-Computer Interaction System and Its Application in Smart Learning Environments

A multimodal human-computer interaction system is composed of the comprehensive usage of various input and output channels. For the information input, apart from the traditional keyboard typing, mouse clicking, screen touching, the latest speech and face recognition technology can be used. For the output, the traditional screen display, the latest speech and facial expression synthesis and gesture generation can be used. After literature review of related works, this paper at first presents such a system, MMISE (Multimodal Interaction System for Education), about its architecture and working mechanism, POOOIIM (Pedagogical Objective Oriented Output, Input and Implementation Mechanism) illustrated with practical examples. Then this paper introduces this system’s pilot applications in the epidemic time of novel coronavirus in 2020.
A Conflict-free Scheduler for High-performance Graph Processing on Multi-pipeline FPGAs

FPGA-based graph processing accelerators are nowadays equipped with multiple pipelines for hardware acceleration of graph computations. However, their multi-pipeline efficiency can suffer greatly from the considerable overheads caused by the read/write conflicts in their on-chip BRAM from different pipelines, leading to significant performance degradation and poor scalability. In this article, we investigate the underlying causes behind such inter-pipeline read/write conflicts by focusing on multi-pipeline FPGAs for accelerating Sparse Matrix Vector Multiplication (SpMV) arising in graph processing. We exploit our key insight that the problem of eliminating inter-pipeline read/write conflicts for SpMV can be formulated as one of solving a row- and column-wise tiling problem for its associated adjacency matrix. However, how to partition a sparse adjacency matrix obtained from any graph with respect to a set of pipelines by both eliminating all the inter-pipeline read/write conflicts and keeping all the pipelines reasonably load-balanced is challenging. We present a conflict-free scheduler, WaveScheduler, that can dispatch different sub-matrix tiles to different pipelines without any read/write conflict. We also introduce two optimizations that are specifically tailored for graph processing, “degree-aware vertex index renaming” for improving load balancing and “data re-organization” for enabling sequential off-chip memory access, for all the pipelines. Our evaluation on Xilinx®Alveo™ U250 accelerator card with 16 pipelines shows that WaveScheduler can achieve up to 3.57 GTEPS, running much faster than native scheduling and two state-of-the-art FPGA-based graph accelerators (by 6.48× for “native,” 2.54× for HEGP, and 2.11× for ForeGraph), on average. In particular, these performance gains also scale up significantly as the number of pipelines increases.
An Effective Block-Chain Based Authentication Technique for Cloud Based IoT

Nowadays, cloud computing services and applications have created plenty of storage space to meet the demand and the utility for human life. However, these cloud services face serious security challenges against various attacks. Cloud computing services rely primarily on confidential data generated by devices connected to cloud account-specific personal information. These devices typically use security in an ID-based encryption scheme. The Identity-Encryption Technique (IDET) technique is an essential public cryptosystem that uses a user’s identity information email or IP address. IDET uses signature authentication used in public-key encryption or public key infrastructure (PKI) instead of digital keys. The PKI uses a password for public key infrastructure (PKI) authentication. In contrast, the proposed model, an ID-based Block-chain Authority (IDBA) no need to maintain hash for authentication. Getting rid of Key management issues and avoiding a secure channel at the key exchange point is a functional problem.
Working with Experts with Experience: Charting Co-production and Co-design in the Development of HCI-Based Design

This chapter outlines the co-design process for ‘Let’s meet up!’, a hybrid electronic system, which combines traditional board games and digital features, created to facilitate and maintain social engagement for people living with dementia. It allows people with dementia to stay in touch with their loved ones and to remain socially and physically active by arranging joint activities for themselves through a simple, user-friendly tangible interface. Let’s meet up! is one of four solutions developed by a multidisciplinary team of researchers and people living with dementia as part of the European MinD project. The aim of MinD was to research and co-develop mindful design solutions to support people with dementia and their caregivers with self-empowerment and social engagement. Co-design with groups of experts with experience (GEE), including people with dementia, caregivers and care professionals, was used throughout the research and development process, comprising data collection, design idea development, decision-making, design concept and prototype development, to ensure the relevance and appropriateness of those ideas, concepts and prototypes for people with dementia. Co-production was increasingly used to enable GEE to co-host and co-curate the co-design sessions, and to take ownership of the process. The chapter explains the process of research and the activities undertaken and provides recommendations for this symbiotic approach, taking into account both the benefits and the limitations.
Analysis of Intelligent Machines using Deep learning and Natural Language Processing

Intelligent Machines are playing an important role in performing various activities in the industry thereby reducing the human efforts, error rate and increase the efficiency and accuracy. Artificial Intelligence is the backbone for development of intelligent machines which use natural language processing and deep learning as fundamental tools. In this paper, we present the new perspective of intelligent machines in everyday usage, along with the importance of understanding the natural language and generating machine level natural language for intelligent machines. Further, we summarize an overview of deep learning concurrent neural network and recurrent neural network models. Also, we review the significance of sentimental analysis using natural language processing and the ability of machines to take decisions and solve problems.
Indian Currency Recognition from Live Video Using Deep Learning

Foreign and Visually disable people in India often find difficulties in recognizing different currency notes. Even if some time it is also difficult for Indian healthy people to identify same amount of currency notes with different-new designs. Human eye has also some limitation so some time fake currency not identifiable by them. In this paper using deep learning technique, detection model trained with dataset and tested it with different Indian currency with good accuracy.
Multi-attention deep reinforcement learning and re-ranking for vehicle re-identification

For solving the vehicle Re-identification (Re-ID) task, we need to focus our attention on the details with arbitrary size in the image, and it’s tough to locate these details accurately. In this paper, we propose a Multi-Attention Deep Reinforcement Learning (MADRL) model to focus on multi-attentional subregions that spreading randomly in the image, and extract the discriminative features for the Re-ID task. First, we obtain multiple attentions from the representative features, then group the feature channels into different parts, then train a deep reinforcement learning model to learn more accurate positions of these fine-grained details with different losses. Unlike existing models with complex strategies to keep the patch-matching constrains, our MADRL model can automatically locate the matching patches (multi-attentional subregions) in different vehicle images with the same identification (ID). Furthermore, based on the fine-grained attention and global features we re-calculate the distance between the inter- and intra- classes, and we get better re-ranking results. Compared with state-of-the-art methods on three large-scale vehicle Re-ID datasets, our algorithm greatly improves the performance of vehicle Re-ID.
Semi-Supervised Self-Training Feature Weighted Clustering Decision Tree and Random Forest

A self-training algorithm is an iterative method for semi-supervised learning, which wraps around a base learner.It uses its own predictions to assign labels to unlabeled data.For a self-training algorithm, the classification ability of the base learner and the estimation of prediction confidence are very important.The classical decision tree as the base learner cannot be effective in a self-training algorithm, because it cannot correctly estimate its own predictions.In this paper, we propose a novel method of node split of the decision trees, which uses weighted features to cluster instances.This method is able to combine multiple numerical and categorical features to split nodes.The decision tree and random forest constructed by this method are called FWCDT and FWCRF respectively.FWCDT and FWCRF have the better classification ability than the classical decision trees and forests based on univariate split when the training instances are fewer, therefore, they are more suitable as the base classifiers in self-training.What's more, on the basis of the proposed node-split method, we also respectively explore the suitable prediction confidence measurements for FWCDT and FWCRF.Finally, the results of experiment implemented on the UCI datasets show that the self-training feature weighted clustering decision tree (ST-FWCDT) and random forest (ST-FWCRF) can effectively exploit unlabeled data, and the final obtained classifiers have better generalization ability.
Application of Computer Technology in English Translation

Abstract With the continuous development of economy, communication is increasing between China and overseas, and business activities are becoming more and more frequent. In this process, foreign trade English plays an important role. Foreign trade English is unique, professional and practical. Under different scenes of foreign trade, English words can express different meanings. In the course of trade, foreign trade English can help both of parties to communicate, and can achieve the translation of language, the interpretation of contract, the understanding of letter and so on. In the process of language communication and reading important documents, we need a solid foundation of English language and profound foreign cultural background, and have a strong ability to deal with the professional knowledge of international trade and the use of English vocabulary. The characteristics of foreign trade English language are analyzed in terms of vocabulary, style and grammar. At the same time, in the process of language understanding and translation, we need to meet the requirements of real-time. With the frequent exchange of foreign trade between our country and other countries, a large number of English documents will be produced, the relevant departments have put forward higher requirements for the accuracy of these documents and real time of these documents, the traditional manual translation can not meet the growing demand for foreign trade translation. With the help of computer aided-translation software memoQ, we help people do auxiliary translation, therefore, it can reduce translator’s burden and improve the accuracy of translation and efficiency of translation. We select a large number of texts and adopt memoQ software to translate, so as to analyze the role of computer-aided translation software in the translation process of English documents.
Realtime Multi-Person Pose Estimation with RCNN and Depthwise Separable Convolution

Human pose estimation is a fundamental research topic in computer vision. This topic has been largely improved recently thanks to the development of the convolution neural network. This paper introduces an efficient human pose estimator based on Mask RCNN, a member of RCNN family. It uses MobileNetV3 as backbone and replaces the vanilla convolutions with the proposed expanded depthwise separable convolutions to reduce the model size, FLOPs and inference time. The model can run in realtime speed at 25 FPS with acceptable scores.
Blind copy-move forgery detection using SVD and KS test

In this paper, we present a new copy-move forgery detection method based on singular value decomposition (SVD) and the Kolmogorov Smirnov (KS) test. This work introduces a new method of detecting copy-move forgery in images with accuracy up to the pixel level using only 4 features per image block. The proposed method consists of three steps. First, an image is partitioned into blocks of size $$16 \times 16$$ . Second, image features are extracted from each block using steerable pyramid and SVD transforms. Finally, the extracted features are sorted lexicographically and matched using the KS test. The performance of the proposed method is evaluated using the CoMoFoD database. Four post-processing techniques are considered, namely brightness change, contrast adjustment, color reduction, and image blurring. This method achieved a high precision of more than 95% for 3 of the 4 post-processing techniques. The fourth post-processing (i.e., image blurring), we achieved a precision of 75% which is considerably high for such forgery. In addition, the proposed method outperformed published methods when the images were subjected to brightness change, contrast adjustment, color reduction and image blurring. Finally, the performance of the proposed algorithm shown to provide better precision using fewer features compared to several well-known techniques in the literature.
Automated bug reproduction from user reviews for Android applications

Bug-related user reviews of mobile applications have negative influence on their reputation and competence, and thus these reviews are highly regarded by developers. Before bug fixing, developers need to manually reproduce the bugs reported in user reviews, which is an extremely time-consuming and tedious task. Hence, it is highly expected to automate this process. However, it is challenging to do so since user reviews are hard to understand and poorly informative for bug reproduction (especially lack of reproduction steps). In this paper, we propose RepRev to automatically Reproduce Android application bugs from user Reviews. Specifically, RepRev leverages natural language processing techniques to extract valuable information for bug reproduction. Then, it ranks GUI components by semantic similarity with the user review and dynamically searches on apps with a novel one-step exploration technique. To evaluate RepRev, we construct a benchmark including 63 crash-related user reviews from Google Play, which have been reproduced successfully by three graduate students. On this benchmark, RepRev presents comparable performance with humans, which successfully reproduces 44 user reviews in our benchmark (about 70%) with 432.2 seconds average time. We make the implementation of our approach publicly available, along with the artifacts and experimental data we used [4].
Improving Memory Utilization in Convolutional Neural Network Accelerators

While the accuracy of convolutional neural networks has achieved vast improvements by introducing larger and deeper network architectures, also the memory footprint for storing their parameters and activations has increased. This trend especially challenges power- and resource-limited accelerator designs, which are often restricted to store all network data in on-chip memory to avoid interfacing energy-hungry external memories. Maximizing the network size that fits on a given accelerator thus requires to maximize its memory utilization. While the traditionally used ping-pong buffering technique is mapping subsequent activation layers to disjunctive memory regions, we propose a mapping method that allows these regions to overlap and thus utilize the memory more efficiently. This work presents the mathematical model to compute the maximum activations memory overlap and thus the lower bound of on-chip memory needed to perform layer-by-layer processing of convolutional neural networks on memory-limited accelerators. Our experiments with various real-world object detector networks show that the proposed mapping technique can decrease the activations memory by up to 32.9%, reducing the overall memory for the entire network by up to 23.9% compared to traditional ping-pong buffering. For higher resolution de-noising networks, we achieve activation memory savings of 48.8%. Additionally, we implement a face detector network on an FPGA-based camera to validate these memory savings on a complete end-to-end system.
Expression Packing: As‐Few‐As‐Possible Training Expressions for Blendshape Transfer

Abstract To simplify and accelerate the creation of blendshape rigs, using a template rig is a common procedure, especially during the creation of digital doubles. Blendshape transfer methods facilitate copy and paste functionality of the blendshapes from the template model to the digital double. However, for adequate personalization, such methods require a set of scanned training expressions of the original actor. So far, the semantics of the facial expressions to scan have been defined manually. In contrast, we formulate the semantics of the facial expressions as an integer optimization of the blendshape weights. By combining different blendshapes of the template model, our method creates facial expressions that serve as semantic references during scanning. Our method guarantees to compute as‐few‐as‐possible training expressions with minimal overlap of activated blendshapes. If the number of training expressions is limited, blendshapes are selected based on their power to personalize the resulting blendshapes compared to generic blendshape transfer methods.
ParaLabel: Autonomous Parameter Learning for Cross-Domain Step Counting in Wearable Sensors

Wearable step counters, also referred to as activity trackers, have been developed for health and activity monitoring, as well as for step tracking. These trackers, however, produce unreliable measurements during slow walking and when walking with assistive devices (i.e., aided walking). To address this challenge, in this article, we introduce, ParaLabel, a filter-based step counting algorithm that is reliable against various walking velocities and intensities. ParaLabel addresses this problem by learning a filter cut-off frequency autonomously in a new domain without the need for collecting sensor data and manually tuning the algorithm parameter for a different velocity and/or pattern of walking. We formulate this problem as a transfer learning problem in which the new filter cut-off frequency is transferred from a bank containing previously fine-tuned parameters from different domain(s). Our extensive analysis using real data collected from 15 participants while wearing an accelerometer sensor on their chest, wrist, or left pocket demonstrates the superiority of ParaLabel to two commercially available trackers worn on the same body location, and state-of-the-art techniques. ParaLabel achieves 96.3% - 99.9% accuracy during walking on a treadmill at three different velocities, 98.2% - 99.9% accuracy during walking with a shopping cart, and 89.3% - 97.3% accuracy while walking with the aid of a walker.
A Multi-sensor School Violence Detecting Method Based on Improved Relief-F and D-S Algorithms

School bullying is a common social problem, and school violence is considered to be the most harmful form of school bullying. Fortunately, with the development of movement sensors and pattern recognition techniques, it is possible to detect school violence with artificial intelligence. This paper proposes a school violence detecting method based on improved Relief-F and Dempster-Shafe (D-S) algorithms. Two movement sensors are fixed on the object's waist and leg, respectively, to gather acceleration and gyro data. Altogether nine kinds of activities are gathered, including three kinds of school violence and six kinds of daily-life activities. After wavelet filtering, 39 time-domain features and 12 frequency-domain features are extracted. To reduce computational cost, this paper proposes an improved Relief-F algorithm which selects features according to classification contribution and correlation. By drawing boxplots of the selected features, the authors find that the frequency-domain energy of the y-axis of acceleration can distinguish jumping from other activities. Therefore, the authors build a two-layer classifier. The first layer is a decision tree which separates jumping from other activities, and the second layer is a Radial Basis Function (RBF) neutral network which classifies the remainder eight kinds of activities. Since the two movement sensors work independently, this paper proposes an improved D-S algorithm for decision layer fusion. The improved D-S algorithm designs a new probability distribution function on the evidence model and builds a new fusion rule, which solves the problem of fusion collision. According to the simulation results, the proposed method has increased the recognition accuracy compared with the authors' previous work. 89.6% of school violence and 95.1% of daily-life activities were correctly recognized. The accuracy reached 93.6% and the precision reached 87.8%, which were 29.9% and 2.7% higher than the authors' previous work, respectively.