Reviewerly/exHarmony huggingface dataset:
Automatic Detection of Word-Level Reading Errors in Non-native English Speech Based on ASR Output

Automated reading error detection has attracted a lot of interest in the area of computer-assisted language learning and auto-mated reading tutors. This paper presents preliminary experimental results on automatic detection of word-level reading errors in non-native speech. A state-of-the-art large vocabulary automatic speech recognition (ASR) system is developed to transcribe non-native speech, with performance comparable to humans in transcribing non-native read speech data. With this ASR system, we investigate the feasibility of detecting substitution, insertion and deletion errors from ASR decoding results on non-native read speech. Experimental results show that the performance of detecting substitution and insertion errors are on the low side. Several possible reasons for causing such results are discussed in this paper. Common types of reading errors occurring in non-native read speech and those that are difficult to be detected are analyzed for future investigation.
Underwater real-time target detection based on key frame and model compression

Abstract In order to improve the speed and keep high accuracy of UUVs object detection on embedded device. This paper proposes an acceleration strategy based on key frame extraction and model compression. Firstly, selecting shipwreck as target and creating a real-world underwater image dataset of the shipwreck. And this work applies both online and offline data augmentation to accelerate the model convergence and to improve the model generalization. Then, this paper builds a fusion algorithm with structural similarity, color histogram and image entropy to select the key frame of the captured video for removing the redundant information, and analyzing the reason for the complexity of the neural network and the method of model compression. Finally, this work cuts off unimportant structure of network based on channel and layer pruning to reduce model complexity and keeps the model with high accuracy by fine-tuning. The experiment shows the Billion Floating Operations of the compressed YOLOV4 model is reduced to 15.778. On the Nvidia Jetson TX2 embedded image processor, the average detection speed for video with 608 × 608 resolution can reach 15.12FPS. And the detection speed of the video composed of key frames is 2.98 times faster than the raw video.
Featured Snippets Comparison in Six European Languages

This paper provides 743,798 keywords and results with featured snippets retrieved from the Google search engine. It presents a comparison of featured snippets displayed in six languages (English, Polish, German, Spanish, Italian, and French) in terms of snippets length, search query length, type of resulted snippet as paragraph, list, or table and top resulting domains for each country. It is found that keywords triggering featured snippets are most at two or three words long and snippets are mainly presented in the form of a paragraph. In each language, the most visible domain resulting with a direct answer was wikipedia.org.
An Empirical Analysis of Integrating Feature Extraction to Automated Machine Learning Pipeline

Machine learning techniques and algorithms are employed in many application domains such as financial applications, recommendation systems, medical diagnosis systems, and self-driving cars. They play a crucial role in harnessing the power of Big Data being produced every day in our digital world. In general, building a well-performing machine learning pipeline is an iterative and complex process that requires a solid understanding of various techniques that can be used in each component of the machine learning pipeline. Feature engineering (FE) is one of the most time-consuming steps in building machine learning pipelines. It requires a deep understanding of the domain and data exploration to discover relevant hand-crafted features from raw data. In this work, we empirically evaluate the impact of integrating an automated feature extraction tool (AutoFeat) into two automated machine learning frameworks, namely, Auto-Sklearn and TPOT, on their predictive performance. Besides, we discuss the limitations of AutoFeat that need to be addressed in order to improve the predictive performance of the automated machine learning frameworks on real-world datasets.
A Quantitative Security Risk Analysis Framework for Modelling and Analyzing Advanced Persistent Threats

Advanced persistent threats (APTs) are different from other computer-based attacks in their target selection, attack technique, and malicious motive. Distinct from script kiddie attacks, these attacks target critical systems to inflict maximum damage, such as to stall critical industrial processes. Standard defenses against APT attack is to deploy security mechanisms that are typically reminiscent of enterprise defense systems such as firewalls, intrusion detection systems, etc. However, given the nature and attack potential of APT attacks, one cannot rely on these security mechanisms alone as they are susceptible to failure, false alarms, and interfere with usability. A yet another problem is to decide on which mechanisms to deploy and at which points to offer maximum coverage against attacks. We believe, given the unique characteristics of APT attacks, one needs a robust and layered defense to protect against APT by timely detection, prevention, mitigation, and emergency plan. One such objective way to determine the countermeasures' efficacy is by modeling and simulating attack behaviour.
Key Length Reconfigurable ARIA Hardware with S-box Optimization

As smart devices are developed, cryptography is one of the key points when designing the devices. ARIA has been developed in substitution for AES as a light-weight hardware implementation. As ARIA has four types of S-box, S-box hardware cost is high compared to AES. In this paper, we proposed key length reconfigurable ARIA architecture which operates on three kinds of key length 128/192/256-bit in one module. It is based on 8-bit datapath, thereby sharing multiplication inverse is possible that four kinds of S-box can be optimized. The proposed key length reconfigurable ARIA has been implemented using 65nm CMOS technology and it showed 16,822 gate counts. This result showed 23% area reduction compared to previous work.
Using Organ Specific and Circulatory Biofluids to Screen Individuals at High Risk for Breast Cancer Presents Unique Challenges and Opportunities

Abstract Intraductal assessment of the breast holds the potential to provide useful information regarding breast cancer risk assessment, early diagnosis, and/or response to therapy. Intraductal assessment can be through imaging (ductography), direct visualization (mammary ductoscopy), or evaluation of the intraductal fluid collected. The most common nonradiologic approaches to intraductal assessment that provide intraductal fluid for evaluation include breast nipple aspiration fluid (NAF), spontaneous nipple discharge (SND), mammary ductoscopy, and ductal lavage. The first two approaches are entirely noninvasive while the latter are considered minimally invasive. Nipple aspiration is performed both on women with and without evidence of possible disease in the breast. On the other hand, unilateral SND suggests the presence of a lesion in the incident breast, while bilateral SND is most often physiologic. The focus of the report by Patuleia and colleagues is on challenges, lessons learned, and recommended solutions in the identification of women with increased breast cancer risk who are more likely to develop in situ or invasive breast cancer based on sequential collection and subsequent analysis of biofluids (NAF and serum). The lessons learned that are discussed can also be applied to other types of biofluid studies for cancer early detection and response to treatment. See related article by Patuleia et al., p. 441
Feature Combination to Alleviate Hubness Problem of Source Code Representation for Bug Localization

Deep learning-based bug localization (DLBL) can effectively reduce software maintenance costs. However, the inherent hub ness problem of the high-dimensional vector of the source code file used in DLBL leads to inaccurate bug localization. To solve this problem, we analyzed 10,359 defects and found that the call graph and flow of the program can distinguish buggy files from non-buggy files, and provide functional semantic information for bug localization. Based on our observations, we propose a feature combination to alleviate the hubness problem of the source file representation by using functional semantic information. Our proposed method models the functional semantics with the call graph and program flow based on the raw abstract syntax tree. We evaluated the effectiveness of the proposed approach on 19 widely used projects and conducted an ablation study. The experimental results show that the proposed method can improve the current approaches by 12 % to 45 %, with differentiating buggy files and non-buggy files. In our ablation study, functional information shows its significance as the absence of functional semantics deteriorates performance by 8.5 %.
Node Switching Method in Power Distribution Internet of Things based on Blockchain

With the rapid development of communication and network technology, the Internet of Things for power distribution has become an increasingly important hot topic. Meanwhile, the security at the terminal level of the power distribution Internet of Things is relatively weak. As terminals with different security zones share the same frequency, the adversary may steal the privacy-sensitive information by controlling one of the terminals, particularly in node switching. In this paper, we use block chain and trusted computing technology to realize the secure node switching.
An Image processing Player Acquisition and Tracking System for E-sports

Studying one's enemy has been the key to winning a war for centuries. This can be seen in the success of the historical success of the Chinese, Greek, and Mongolian empires. Over the years, these dogmas of studying one's enemy before the battle have now made its way into our modern lives as well, not only in real battle but also on the electronic battlefield. In E-Sports, teams will often study one another to find the strengths and weaknesses that can be exploited and avoided in the next encounters. The manner in which this is done is through the observing of prior matches to determine patterns in the manner in which they operate, play and react. This process is extremely time-consuming as one match is a minimum of an hour in duration. This paper demonstrates a program able to acquire and track player positions throughout a match in order to simplify and automate this process. This tracking data can be used with machine learning and or neural networks as part of a professional E-Sport prediction model.
Traffic Safety Analysis of Highway Plane Intersection

As a meeting point of motor vehicles, non-motor vehicles, pedestrians, etc., road intersections are prone to traffic safety accidents, which in turn seriously affect traffic order. This paper studies the causes of frequent occurrences of road intersections from the aspects of people, vehicles, road traffic environment and management, analyses the existing road junction accident prevention measures, and bases on the intersection accidents and existing accident prevention measures, proposes several measures for the government and related enterprises. The effective prevention and control of traffic accidents at highway intersections is a complex system project, which requires the participation and efforts of all parties. This study can also provide reference for this.
Floor-Volleyball Motion Feedback System for Visually Impaired Players

Floor-volleyball is one of the common sports for visually impaired people, including the blind in Japan. Its history started from the 1960s and most of young students in the special schools for the blind have experience of this sport in a class of physical education or a club activity. However, there is not an appropriate way to recognize motions of players by their own, because of their visual impairments. Therefore, sensing the motion of the Floor-volleyball player and developing a tactile device which shows the motion was carried out in this research. Using a three-dimensional motion capture system, four visually impaired players' data were collected and analyzed. The results showed that each player has each character of the motion. The significant difference was observed in the back-swing speed. To display the difference to the blind players, a small wireless tactile display device was successfully developed. It equips 384 refreshable tactile pins which are arranged in 36 by 12. These pins are driven by piezo actuators same as the common braille display. The main feature of the device is that the controller equips ESP-WROOM-02 Wi-Fi module and AAA batteries. The device is perfectly wireless and suitable for the physical education field.
Vehicle Detection Counting Algorithm Based on Background Subtraction Algorithm and SORT

At present, the deep neural network model is commonly used to detect the vehicle in the video, and the detection accuracy is relatively high. However, the neural network model requires high computing performance and high demand for network transmission bandwidth. In many cases, the edge computing device used is of small computing power, so the neural network model is not applicable. However, background subtraction algorithm is easy to realize because of its low requirement on hardware calculation force and fast and accurate detection speed. Using SORT algorithm to track with accurate detection results can improve the speed again and reduce the consumption of computing resources. Therefore, this paper proposes an algorithm that uses the background subtraction algorithm to detect the vehicles in the video, and then uses the SORT algorithm to track the detected vehicles. The vehicle counter will automatically count when the vehicle in the video passes the traffic flow counting line. The accuracy of traffic flow counting results in this paper is 88%, which proves the feasibility and effectiveness of vehicle detection counting method based on background subtraction algorithm and SORT.
On the Effectiveness of Low-Cost Face Recognition with Deep Learning

This paper adopts a deep learning approach for face recognition and provides empirical evidence about its efficiency and effectiveness within low-cost equipment in terms of video camera and graphical process unit (GPU). We implemented a state-of-the-art deep learning algorithm for face recognition, and we performed an in-lab user study (n=30) aiming to investigate whether deep learning algorithms can effectively and efficiently identify a person, with five images of references in low-cost settings. Analysis of results yielded that using deep metric learning techniques have a high accuracy and are trustworthy, even within low cost computational settings, pointing towards a valid direction of embracing deep learning algorithms for face recognition within ubiquitous computing realms. By doing so, we add external validity to existing similar research attempts suggesting that deep learning approaches are a reliable solution for face recognition even with low-cost apparatus.
Design of Vedic Multiplier Using Reversible Logic Gates

Multiplier is one among the essential hardware blocks in present-day communication and digital signal processing (DSP) systems. The primary advantage of Vedic multiplier is that delay increases slowly with the increase of input bits. Vedic multiplier has supreme advantage when compared with other multipliers over regularity of structures and gate delays. In this paper, the different designs of 8 * 8 bit Vedic multiplier are implemented with different adders, and these multipliers and adders are structured using suitable reversible logic gates, that is, Feynman gate, Peres gate, Toffoli gate, DPG gate and modified Fredkin gate. The different designs of Vedic multipliers designed with the help of different adders, that is, ripple carry adder, carry skip adder and carry select adder, and hence compared their implementation time as well as area required for the circuitry. This comparison will help designers to choose the preferable multiplier according to the application specific demands.
Sentiment Analysis on Adventure Movie Scripts

Sentiment analysis is the process of mining subjective information in different kinds of sources for playing an important role in making decisions. Movie scripts are an interesting text-domain including their diverse expressions for sentiment mining. In this paper, we have done sentiment analysis by applying Minimum Viable Product (MVP), Exploratory Data Analysis (EDA), and TextBlob sentiment analysis techniques. We compute the sentiment polarity and show the fluctuation of sentiments over time of a specified movie genre. We collected a dataset consisting of 20 adventure genre's movie scripts for sentiment analysis on movie scripts. The experimental results show that the fluctuations of the sentiment of the adventure genre's movie reflect the viewer's mindset as public opinion for social impact.
Application research of the voiceprint imaging technology in substation patrol

In order to make full use of the noise information of substation power equipment and to deeply explore the value of noise, a voiceprint imaging technology in substation patrol was proposed in this paper which replace the the noise monitoring by human ear. The basic principle, algorithm, array selection and other technical details of the voiceprint imaging technology were introduced in detail. The effectiveness of the technical measure in substation patrol was proved by an application case. The voiceprint imaging technology in substation patrol is safe, fast and accurate. It fills the technical gap of power equipment noise patrol and greatly improves the efficiency of substation patrol.
Regularization with Multiple Feature Combination for Few-Shot Learning

Few-shot learning solves problems with a limited amount of labeled examples. Our analysis shows the existing metric-based methods concentrate on highly discriminative features while not fully utilizing whole capacity. In this work, we propose a novel regularization technique that constrains the model to exploit whole capacity by distinguishing data with multiple feature combinations. Our approach achieves state-of the-art performance in several public benchmarks compared to the existing metric-based methods.
A Systems Approach Toward Addressing Anonymous Abuses: Technical and Policy Considerations

Can we prevent the abuses of anonymous communication networks without affecting their ability to enhance privacy and evade censorship? We evaluate approaches for balancing the need for anonymity with the desire to mitigate anonymous abuses.
Secure and Efficient Exponentiation Architectures Using Gaussian Normal Basis

Exponentiation in finite fields is an essential operation used in many applications ranging from error control coding to cryptographic computations, while representation in Gaussian normal basis (GNB) offers low complexity arithmetic, especially in hardware architectures. In this article, we propose several new hardware architectures for binary exponentiation in GNB. The proposed architectures make use of different levels of precomputation and the efficient digit-level parallel-in parallel-out, and hybrid-double multipliers. We support our new designs with novel countermeasures against side-channel analysis that require minimal implementation overhead. Moreover, we obtain implementation results for all architectures and provide realistic and fair comparisons with the previously known works available in the literature. It is shown that our newly proposed architectures outperform the existing hardware architectures in terms of area and time complexities and security.
Secure and efficient revocable key-aggregate cryptosystem for multiple non-predefined non-disjoint aggregate sets

Revocation and renewal of access rights of users are desirable requirements of a practical access control solution. Recently, key-aggregate cryptosystems have attracted significant attention of the research community, due to their elegance and efficiency, as a tool for access control enforcement. However, key-aggregate encryption schemes proposed so far in the literature are suitable only for enforcing static predefined access control policies. This paper proposes a novel key-aggregate encryption scheme that efficiently handles dynamic access control policies. The proposed scheme not only has all key-aggregate characteristics, but can also efficiently revoke/add any data class from/to a given aggregate set. Further, unlike conventional key-aggregate cryptosystems, the proposed scheme can introduce a new data class in the cryptosystem without having to initialize it all over again. The proposed scheme requires constant length master-secret to be stored by the data owner and is proved IND-CPA secure under standard model assumption. We define forward security for the proposed key-aggregate cryptosystem and formally prove that the proposed construction is secure under the definition of forward security. Performance analysis in a practical dynamic hierarchical access control scenario further confirms suitability of the proposed scheme for enforcing dynamic access control policies.
Flexible Network Slicing Assisted 5G for Video Streaming with Effective and Efficient Isolation

Network slicing assisted 5G is driven by providing a wide range of services that aims to satisfy various pre-service requirements. There is consensus that network slicing is a key enabler for the service-oriented 5G, that aims to cope with the increasing complexity of these networks. One of the major objectives of network slicing is to provide a different level of resource isolation, through resource abstraction and virtualization and the ability to efficiently share network resources. In this paper, we focus on video streaming services in the presence of other services with different QoS requirements. We propose a novel approach for resource sharing that provides interslice protection, flexibility, load-driven elasticity, and network efficiency. In particular, we design two-level multi-scale allocation schedulers for an efficient and low complexity RAN slicing by exploiting the characteristic of adaptive traffic such as video streaming service. Our mathematical analysis and simulation results confirm the benefits of resource abstraction and exhibit the added value of our solution.
Preliminary Comparison of K-12 Computing Education in China and the United States

The thesis starts with the background of computing education in today's K-12 education development, and then compares Chinese and American computer science K12 education based on frameworks and curriculum standards. The authors compared the framework design of computer science K-12 education in two countries from the perspectives of concept definition, structure, content, etc. To compare the curriculum design standards, the authors selected from the content, curriculum structure, curriculum implementation, curriculum teaching evaluation of curriculum and in-depth analysis and discussion were conducted from this perspective. The thesis summarizes the enlightenment of current Chinese education researchers on American K-12 computing education to Chinese education, and puts forward the author's own views.