Reviewerly/exHarmony huggingface dataset:
High Throughput Hardware/Software Heterogeneous System for RRPN-Based Scene Text Detection

Rotation Region Proposal Networks (RRPN) are used to generate rotated proposals with the information of text angle for arbitrary oriented scene text detection (STD). However, the computational complexity of RRPN inference is relatively high compared with other methods, which makes it difficult for massive deployment. In this paper, the first full-stack FPGA-CPU heterogeneous system design of RRPN-based STD algorithm is proposed. A hardware/software partition method is presented to analyze and split the tasks to enhance the computation efficiency of hardware. The fast 2D Winograd algorithm and block floating point are utilized to reduce computation complexity while maintaining a relatively high precision. The implementation results show that the peak performance of MAC arrays in the proposed architecture reaches 655.4 GOPS and the energy efficiency achieves 64.9 GOPS/W. By fully exploiting the parallel and pipelined merits in the algorithms, the first hardware architectures for skew non-maximum suppression (S-NMS) layer and rotation region-of-interest (RRoI) polling layer are proposed. The throughput of the proposed hardware/software heterogeneous system achieves 40 times and 1.4 times improvements compared with CPU and GPU, respectively. Moreover, the comprehensive operating expense ratio of pure CPU, GPU, and the proposed system is 80.7:2.5:1, which indicates that it is suitable for massive deployment.
FFNSL: Feed-Forward Neural-Symbolic Learner

Abstract Logic-based machine learning aims to learn general, interpretable knowledge in a data-efficient manner. However, labelled data must be specified in a structured logical form. To address this limitation, we propose a neural-symbolic learning framework, called Feed-Forward Neural-Symbolic Learner (FFNSL) , that integrates a logic-based machine learning system capable of learning from noisy examples, with neural networks, in order to learn interpretable knowledge from labelled unstructured data. We demonstrate the generality of FFNSL on four neural-symbolic classification problems, where different pre-trained neural network models and logic-based machine learning systems are integrated to learn interpretable knowledge from sequences of images. We evaluate the robustness of our framework by using images subject to distributional shifts, for which the pre-trained neural networks may predict incorrectly and with high confidence. We analyse the impact that these shifts have on the accuracy of the learned knowledge and run-time performance, comparing FFNSL to tree-based and pure neural approaches. Our experimental results show that FFNSL outperforms the baselines by learning more accurate and interpretable knowledge with fewer examples.
ADGAS: An Advanced Data Generation for Anomalous Signals

Anomaly detection using deep learning approaches is still challenging, especially in the case of data limitations. A small number of samples for training deep learning models typically result in poor performance of detection and classification. Previously, data augmentation was one of the methods used to solve this problem. The data augmentation with rotation, permutation, time warping, and their combination can increase the performance of anomaly classification. However, this method is still limited and does not guarantee that the generated output data have adequate varieties and keep original characteristics of data. Our recent work, data augmentation and generation for anomalous time series signals (DAGAT) was proposed to expand the space of possible augmented data by implementing vanilla augmentation on various domains in conjunction with variational autoencoder (VAE). Nonetheless, the DAGAT still has barriers, which are an uncontrollable number of target results, a missed opportunity of integrating multiple augmentation characteristics in latent space, and a possibility of including any bad data for training in VAE. To overcome these limitations, this paper proposed an advanced data generation for anomalous signals (ADGAS). By focusing on the quality of generated data, one more quality classifier (QC) was added as a prepossessing step of VAE. In this way, the experimental results showed that convolutional neural networks (CNNs), used as a performance tester, trained with the generated datasets of ADGAS achieved better accuracy in classifying anomalous events when compared to models trained with a combination of rotation, permutation, and time warping data augmentation methods.
Multi-resolution quality-based video coding system for DASH scenarios

Today, more than 85% of Internet traffic has a multimedia component. Video streaming occupies a large part of this percentage mainly because this type of content is provided by the most used applications on the Internet (e.g. Twitch, TikTok, Disney+, YouTube, Netflix, etc.). Most of these platforms use HTTP Adaptive Streaming (HAS) to send this media content to end users in order to ensure a good quality of experience (QoE). But, this QoE should be guaranteed from the video to be transmitted, i.e., the video should have an adequate quality by minimizing the bitrate before transmission. In order to solve this issue, we present a system capable of encoding a video in several resolutions given the desired value of an objective metric. Our system includes the objective metric in the encoding loop in order to maintain the quality in all segments. This system has been tested with three video and five resolutions for each video. Our proposal provides improvements of more than 10% in terms of video size and with similar coding times when compared with a fixed Constant Rate Factor (CRF) encoding. A visual comparison between our proposal and a fixed CRF encoding can be seen at: https://links.uv.es/jgutierr/multiresQ
Rollback Edit Inconsistencies in Developer Forum

The success of developer forums like Stack Overflow (SO) depends on the participation of users and the quality of shared knowledge. SO allows its users to suggest edits to improve the quality of the posts (i.e., questions and answers). Such posts can be rolled back to an earlier version when the current version of the post with the suggested edit does not satisfy the user. However, subjectivity bias in deciding either an edit is satisfactory or not could introduce inconsistencies in the rollback edits. For example, while a user may accept the formatting of a method name (e.g., getActivity()) as a code term, another user may reject it. Such bias in rollback edits could be detrimental and demotivating to the users whose suggested edits were rolled back. This problem is compounded due to the absence of specific guidelines and tools to support consistency across users on their rollback actions. To mitigate this problem, we investigate the inconsistencies in the rollback editing process of SO and make three contributions. First, we identify eight inconsistency types in rollback edits through a qualitative analysis of 777 rollback edits in 382 questions and 395 answers. Second, we determine the impact of the eight rollback inconsistencies by surveying 44 software developers. More than 80% of the study participants find our produced catalogue of rollback inconsistencies to be detrimental to the post quality. Third, we develop a suite of algorithms to detect the eight rollback inconsistencies. The algorithms offer more than 95% accuracy and thus can be used to automatically but reliably inform users in SO of the prevalence of inconsistencies in their suggested edits and rollback actions.
Hashcashed Reputation with Application in Designing Watchtowers

We propose a novel reputation system to stimulate well-behaviour, and competition in online markets. Our reputation system is suited for markets where a publicly-verifiable "proof-of-misbehaviour" can be generated when one party misbehaves. Such markets include those that provide blockchain services, such as monitoring services by watchtowers. Watchtowers are entities that watch the blockchain on behalf of their offline clients to protect the clients' interests in applications such as payment networks (e.g., the Lightning network).In practice, there may be no trust between clients and watchtowers, and it is challenging to incentivize watchtowers to well-behave (e.g., to refuse bribery). To showcase our reputation system, in this work, we create an open market of watchtowers, where watchtowers are motivated to not only deliver their promised service but also reduce their service fees in competition with each other.
Determination of Optimal Leadership Styles Through Knowledge Management: A Case from the Automotive Industry

This study proposed a model to examine the impact of various leadership styles on knowledge management. This case study identified the best leadership style, which can affect the key factors of the knowledge management success. The statistical population was composed of all staff of the Pars Khodro Company headquarters in Iran, in which 226 people were accordingly chosen as the sample. The required data for examining research hypotheses were also collected using questionnaire. Results revealed the impact of leadership styles on knowledge management. The transformational leadership style was consequently found to have a positive and significant impact on the knowledge management key success factors, in contrast with other leadership styles including self-management, pragmatic, conservative and structure-based.
Security Analyses of Misbehavior Tracking in Bitcoin Network

Because Bitcoin P2P networking is permissionless by the application requirement, it is vulnerable against networking threats based on identity/credential manipulations such as Sybil and spoofing attacks. The current Bitcoin implementation keeps track of its peer's networking misbehaviors through ban score. In this paper, we investigate the security problems of the ban-score mechanism and discover that the ban score is not only ineffective against the Bitcoin Message-based DoS attacks but also vulnerable to a Defamation attack. In the Defamation attack, the network adversary can exploit the ban-score mechanism to defame innocent peers.
Robust Pose Transfer With Dynamic Details Using Neural Video Rendering

Pose transfer of human videos aims to generate a high-fidelity video of a target person imitating actions of a source person. A few studies have made great progress either through image translation with deep latent features or neural rendering with explicit 3D features. However, both of them rely on large amounts of training data to generate realistic results, and the performance degrades on more accessible Internet videos due to insufficient training frames. In this paper, we demonstrate that the dynamic details can be preserved even when trained from short monocular videos. Overall, we propose a neural video rendering framework coupled with an image-translation-based dynamic details generation network (D 2 G-Net), which fully utilizes both the stability of explicit 3D features and the capacity of learning components. To be specific, a novel hybrid texture representation is presented to encode both the static and pose-varying appearance characteristics, which is then mapped to the image space and rendered as a detail-rich frame in the neural rendering stage. Through extensive comparisons, we demonstrate that our neural human video renderer is capable of achieving both clearer dynamic details and more robust performance even on accessible short videos with only 2 k ∼ 4 k frames, as illustrated in Fig. 1.
Automotive Safety Systems

Over the past century, after the slew of technological revolutions and advancements, the need for safe transportation of goods and people over a distance at higher speed has become an important part of the living and in particular, with the progression. With major advancements in the automotive industry, from the very first car that clocked just 10 miles per hour to the latest electric and autonomous vehicles, personal transportation has seen an exponential growth. With the number of vehicles and users increasing day by day, the safety and security aspects of the vehicles have become inevitable. Every step towards safer and secure practices requires many technological changes based on the past study and observations; thus, keeping in mind the needs and demands of the modern population, automakers all over the world are focusing on these features of the vehicles.
Dynamic Task Offloading in Power Grid Internet of Things: A Fast-Convergent Federated Learning Approach

With the unprecedented development of power grid Internet of Things (IoT) devices, such as the smart meter and the smart substation, various electrical applications have emerged to provide intelligent load-monitoring services. However, these computation-intensive applications significantly raise security concerns and have energy-efficient computation requirements. To tackle the problem, this article introduces a novel prediction-assisted task offloading scheme for power grid IoT to determine the optimal offloading decisions without compromising privacy. First, a federated learning approach is designed to train the task prediction model with fast training speed by selecting local servers intelligently. Then, according to the traffic loads of computation tasks at edge computing nodes and electrical users' behavioral features, a dynamic prediction-assisted task offloading method is proposed. The evaluation results demonstrate that the proposed prediction-assisted approach could efficiently ensure the power grid IoT network's loading balance and reduce the network overhead.
Experiences on Managing Technical Debt with Code Smells and AntiPatterns

Technical debt has become a common metaphor for the accumulation of software design and implementation choices that seek fast initial gains but that are under par and counterproductive in the long run. However, as a metaphor, technical debt does not offer actionable advice on how to get rid of it. To get to a practical level in solving problems, more focused mechanisms are needed. Commonly used approaches for this include identifying code smells as quick indications of possible problems in the codebase and detecting the presence of AntiPatterns that refer to overt, recurring problems in design. There are known remedies for both code smells and AntiPatterns. In paper, our goal is to show how to effectively use common tools and the existing body of knowledge on code smells and AntiPatterns to detect technical debt and pay it back. We present two main results: (i) How a combination of static code analysis and manual inspection was used to detect code smells in a codebase leading to the discovery of AntiPatterns; and (ii) How AntiPatterns were used to identify, characterize, and fix problems in the software. The experiences stem from a private company and its long-lasting software product development effort.
Imminence Monitoring of Critical Events: A Representation Learning Approach

Complex event monitoring is an important problem in data streams that has drawn much attention. Most previous work assumes that the user knows and provides a complex event pattern for the system to continuously monitor. However, we observe that in many real applications, such as healthcare, security, and businesses, there are heterogeneous substreams and a diverse set of attributes. Often there is no simple uniform pattern prior to a critical event; nor is there clean simple language to describe the pattern leading to the critical event. People often only know it after the fact -- e.g., when something undesirable happens. We propose a novel approach based on relational machine learning and representation learning. We propose and learn probabilistic state machine patterns, which are used to monitor and predict the imminence of critical events. Our experiments demonstrate the efficiency and effectiveness of our approach, as well as its clear superiority over the closest previous approaches such as IL-Miner and LSTM based early prediction.
Analysis on the Construction of Innovation and Entrepreneurship Education Model of Computer Major in Higher Vocational College

Innovation and entrepreneurship education has become one of the important contents in the education and teaching of higher vocational colleges. Through the cultivation of students' sense of innovation, it can encourage students to actively create. Especially for computer majors, with the rapid development of information technology, computer majors must keep pace with the times. Only by continuously incorporating new content and new models in computer science can the modernization of China be promoted. This article analyzes the importance of innovation and entrepreneurship education, explores the current status of innovation and entrepreneurship education in computer science, and proposes strategies for constructing innovation and entrepreneurship education models.
DAMSL: Domain Agnostic Meta Score-based Learning

In this paper, we propose Domain Agnostic Meta Score-based Learning (DAMSL), a novel, versatile and highly effective solution that delivers significant out-performance over state-of-the-art methods for cross-domain few-shot learning. We identify key problems in previous meta-learning methods over-fitting to the source domain, and previous transfer-learning methods under-utilizing the structure of the support set. The core idea behind our method is that instead of directly using the scores from a fine-tuned feature encoder, we use these scores to create input coordinates for a domain agnostic metric space. A graph neural network is applied to learn an embedding and relation function over these coordinates to process all information contained in the score distribution of the support set. We test our model on both established CD-FSL benchmarks and new domains and show that our method overcomes the limitations of previous meta-learning and transfer-learning methods to deliver substantial improvements in accuracy across both smaller and larger domain shifts.
Neuro-fuzzy Approach for the Calibration of High-Precision Embedded Systems

A production of embedded systems is a multi-step process. One of the most essential steps is a calibration of the device. In many cases, the calibrating process is performed manually. This paper presents a neuro-fuzzy approach to automate the calibrating process. The approach takes advantage of neural networks and fuzzy systems - one of the most well-known concepts of an artificial intelligence.
Experimental study the mechanical properties of nano composite materials by using multi-metallic nano powder/epoxy

The mechanical properties of two commercial epoxides have been investigated in both unfilled and filled with varying the weight fraction of Nano Aluminum powder and Nano Aluminum with Nano Titanium powder. The values of Young’s modulus, modulus of rigidity, yield stress, tensile strength, flexural strength have been studied at room temperature to identify the specific influence of Aluminum powder (the particle size for Aluminum powder is less than or equal to 50 nm (d ≤ 50). The weight fraction has different values (equals to 10%, 20% and 30%), and Nano Aluminum with Nano Titanium powder for Aluminum powder, the particle size (d) is less than or equal to 50 nm (d ≤ 50). The weight fraction has different values (equals to 10%, 20% and 30%). Also for Titanium powder, the particle size is less than or equal to 50 nm (d ≤ 50), and the weight fraction is 1% for each different weight fractions of Aluminum powder to epoxy resin. The experimental results obtained from laboratory have been analyzed in order to achieve the best result. It is concluded that Young’s modulus and modulus of rigidity increase when the weight fraction for Nano Aluminum powder and Nano Aluminum with Nano Titanium powder increase.
Sentiment Analysis of an Online Sentiment with Text and Slang Using Lexicon Approach

Sentiment analysis is a technique that helps data analysts to analyze the various online users’ opinions about a particular product or service. There are various approaches for performing sentiment analysis. Lexicon-based approach uses sentiment lexicons to calculate the polarity of the sentences. It is observed that nowadays online lexicons consist of text and online slang as well. The proposed approach calculates the polarity of the sentence by evaluating a polarity score of sentiments with text and slang. The sentiment polarity of tweets is also evaluated using the machine learning classification techniques like SVM, Random Forest, and Linear regression with accuracy, recall, precision, and F-score parameters. The accuracy of the proposed approach has been evaluated to 96% for Twitter dataset containing 17,452 tweets and 97% for other social media sentiments.
Graphic Cipher of Nostradamus

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Comparison of the effects of 1:1 and 1:m CSCL environments with virtual manipulatives for scientific inquiry-based learning: a counterbalanced quasi-experimental study

Digital instructional tools, such as virtual manipulatives, have been widely adopted as an enhanced approach to inquiry-based learning. However, the optimal ratio of mobile devices to students in instructional settings remain controversial. This research introduced a counterbalanced quasi-experiment to compare the learning performance and group-process satisfaction (GS) of primary school students under various settings of mobile device-student ratios (1:1 or 1:m) and external scripts (ES, with or without). A total of 128 sixth graders (11 drop-outs in the second-round experiment) in four elementary-school classes adopted physics simulations on tablet PCs as virtual manipulatives (VMs). Group worksheets and posttests were administered during and after the inquiry process to assess students' inquiry performance and knowledge acquisition. The analyses revealed that students under the 1:m condition significantly outperformed their peers in the 1:1 condition in both subterms, concept understanding (CU) and problem-solving (PS), but no significant difference was found in knowledge acquisition (KA). ES showed no effect on students' learning performance. VMs that students used in collaborative tasks operated separately on each tablet PC, which may have distracted learners' joint attention. Hence, the collaboration patterns were observed and discussed. Four potential collaboration patterns are discussed at the end of this paper.
Multi-Dimensional Interpretations for Termination of Term Rewriting

Abstract Interpretation methods constitute a foundation of termination analysis for term rewriting. From time to time remarkable instances of interpretation methods appeared, such as polynomial interpretations, matrix interpretations, arctic interpretations, and their variants. In this paper we introduce a general framework, the multi-dimensional interpretation method, that subsumes these variants as well as many previously unknown interpretation methods as instances. Employing the notion of derivers, we prove the soundness of the proposed method in an elegant way. We implement the proposed method in the termination prover and verify its significance through experiments.