Reviewerly/exHarmony huggingface dataset:
An Iterative Classification Network for Semantic Action Recognition

Action recognition has been employed in many fields such as autonomous vehicles, surveillance, etc. A novel method for semantic action recognition through an iterative classification network is proposed in this paper. This paper assumes that action labels with noises are also beneficial for classification, the network iteratively applies the inferred classification results of previous iteration. The noisy action labels could be also corrected through multiple iterations. Specifically, the proposed iterative classification network improves the Convolutional Bi-directional Long Short-Term Memory (Bi-LSTM) network by adding iterative mechanism, which incorporates the classification probabilities in previous one iteration into the feature representation in the next one iteration. Experiments conducted on MSRC-12 and WorkoutUOW-18 datasets verify the effectiveness of the proposed method.
Statistical Measure of China's Innovation Capability in Artificial Intelligence

As the core of technological change, the ability to innovate artificial intelligence (AI) is related to a country's scientific and technological strength and competitiveness. In this paper, an indicator system was constructed for measuring technological innovation capability in 30 provinces and cities in China (2012 ~2019) from four levels - innovation subjects, environment, motivation, and benefits - based on the coefficient of variation method and TOPSIS model. The results show that China's innovation capability in AI is on the rise, but there are significant spatial differences in various regions, which are mainly manifested in a decreasing gradient from east to west. Further research found that among the four levels, innovation capability of the first echelon provinces and cities is generally higher; except for innovation benefits, the progress in innovation capability of the first echelon provinces and cities in the other three levels has been slow.
Data Acquisition System Based on FPGA/SOPC

A field-programmable logic gate array (FPGA) based data acceleration acquisition system is designed. A 100MHz clock signal is generated using a phase-locked loop, which is applied to the synchronous dynamic random memory (SDRAM) to make it work properly. A 100Hz sinusoidal signal is sent internally by the FPGA, which is stored in the SDRAM and sent to the host computer for display.
Integrating Cross-lingual Ontologies through Co-Evolutionary Algorithm

To support the collaborations among intelligent applications, it is necessary to integrate various ontologies which are developed and maintained by different organizations. One of the challenges is that different ontologies in the same application domain might use different languages to describe the same concept, which yields the cross-lingual heterogeneity problem, i.e., how to map two identical entities in different languages. To address this problem, this work proposes a problem-specific co-evolutionary algorithm (CoEA)-based matching technique. In particular, we first propose a parallel aggregating framework to aggregate different SMs and then construct a continuous optimization model for defining the problem of cross-lingual ontology integration. To better trade off the algorithm’s exploitation and exploration, we use two competitive subpopulations to, respectively, execute the exploitation and exploration. The experiment utilizes OAEI’s multifarm track for testing purpose, and the experimental results show that CoEA is able to effectively integrate various cross-lingual ontologies.
Significance and Development Direction of Internet Plus Network Teaching in Foreign Language Teaching in Post Epidemic Era

China has experienced a sudden epidemic in 2020. With the loss of time and the continuous efforts and struggle of all sectors of society, the epidemic situation in China has gradually improved, but it is still inevitable that there will be a small-scale outbreak. In view of the above situation, it is collectively referred to as the post epidemic era. This paper aims to analyze the development direction and significance of Internet plus network teaching in effective foreign language teaching in the post epidemic era. Through the statistics of teachers in the foreign language and teacher development center of our university, we can see that the mixed teaching mode of "Online + offline" has the most support rate for teachers, and the students' academic performance is relatively faster at this time. The "Online + offline" teaching mode can also avoid the transmission risk caused by the epidemic to a certain extent.
Experimental comparison of ten state-of-the-art saliency detection algorithms for segmenting leukocyte nucleus

There have been manifold dangerous health conditions such as leukemia, anemia, and polycythemia that can be diagnosed by analyzing the count and ratio of different types of leukocytes. The microscopic analysis of leukocyte morphology has a significant role in the effective diagnosis of hematological disorders. However, manual analysis under a microscope is stressful, dreary, inaccurate, and highly subjective because of the inherent complexity of leukocyte morphology. It has been recommended in digital hematology to use image segmentation as an important device for performing robust, accurate, and automatic analysis of Leukocyte nuclei. Leukocyte nuclei segmentation plays an important role in computer-aided analysis because further processing phases are highly influenced by the segmentation results. However, leukocyte nuclei segmentation is generally challenging because of its morphological diversity plus variation in illumination and contrast imbalance resulting from multifarious methods and instruments used for image acquisition. Consequently, ten state-of-the-art saliency segmentation algorithms were experimentally compared in this study using the recently published Raabin_White blood cell dataset and standard performance evaluation metrics to help decide the best algorithm for automatic segmentation of leukocyte nuclei. This study recommends a new segmentation algorithm based on color histogram clustering that has demonstrated the properties of salient objects as adequate for the segmentation of leukocyte nuclei.
Spectral Analysis of Surface Occlusion Model for Image-Based Rendering Sampling

Occlusion is a common and important problem in light field rendering (LFR) technology. We present a signal-processing framework to study the occlusion phenomenon of object surfaces. Our framework combines the occlusion phenomenon with object surface construction information. Based on the geometry of a given scene, we establish an accurate closed-form expression describing object surface occlusion for LFR. In this manner, scene occlusion and spectral support of the light field are analyzed in the frequency domain. LFR predictions rooted in the frequency domain then serve as controlling factors of scene sampling and rendering. This extends previous work on light field sampling(LFS) theory proposing no occlusion in scenes, and it offers a comprehensive treatment of both self-occlusion and no occlusion. Extensive experimental evaluation of synthetic and actual scenes demonstrates that our LFS theory notably outperforms competing algorithms.
A Lévy-Inspired Kinodynamic A* Algorithm for Quadrotor Fast Path Searching

The advance in research on path searching has enabled quadrotor to navigate autonomously in unknown environments. However, high-speed path searching still remains a significant challenge. Given very limited time, existing path searching methods has no strong guarantee on the feasibility of the solution. This paper proposed a Lévy-inspired kinodynamic A*(LIK-A*) algorithm for quadrotor path searching application. Search strategy with Lévy random walk is introduced in the kinodynamic A* update formula, which helps to increase the perturbation and the diversity of candidate path solution. The search step length is dynamically adjusted during the searching process, making the trajectory to achieve reasonable time allocation and be away from obstacles. To demonstrate the effectiveness of the proposed algorithm, this paper executes simulated flight test. The experiment results show that, comparing to the kinodynamic A* algorithm, the proposed LIK-A* algorithm has better convergence accuracy, stability, and stronger path searching capability, using significantly less memory and time.
QOSCR: Quantification of Source Code Resemblance

Discovering coping is a serious problem at dissimilar level and had become a serious issue for intellectuals. Finding out similarity in code that may be in same programming languages or converted into different programing language increases the problem complexity, and this problem can be faced in different fields like software development, educational institute, etc. For finding out plagiarism among programming codes, in this paper, we developed the QOSCR software. With the help of this software, we can find out the likeness among two code at procedure’s level. By using this software, reviewers of programming language can give the results to the scholars in a task of software design topic.
Who Watches the Watchmen? Social Media and Election Securitisation

In the wake of the 2020 presidential election, incumbent Donald Trump voiced dubious allegations of vote fraud. In response to that, social media giants Twitter and Facebook made a dramatic step of attaching corrective statements to the U.S. president’s posts. By doing so, they highlighted their power over core political speech. In his 2014 article ‘Old-School/New-School Speech Regulation’, influential American legal scholar Jack Balkin argued that today speech is often suppressed by private actors, controlling the digital infrastructure, rather than directly by governments. In a follow-up article ‘Free Speech in Algorithmic Society’, Balkin suggests that there is a triangular relationship between governments, corporations and end-users. My contribution will discuss this triangulation in the context of electoral speech. In recent years, the threat of foreign interference in elections became a widely discussed issue in established democracies. These discussions specifically highlighted the role of social media. They have also led to an increasingly aggressive policing of election-related content on major platforms such as Facebook and Twitter. This chapter suggests that securitisation of elections in established democracies occurs primarily as cooperation between private actors and governments. The apparent exclusion of end-users is a worrying tendency. It obscures the distinction between established democracies and competitive authoritarian regimes, ultimately undermining the very democratic legitimacy that needs to be secured. I argue that, given the role of social media companies in the current environment, their obligations go beyond the traditional notions of corporate social responsibility. These companies have de facto assumed the role of a regulator effecting election securitisation in online speech. I suggest that such (self-) regulation could only be proportionate, if internationally recognised standards of free elections are considered.
Opto-Video Encryption Based on Logistic Adjusted Sine map in FrFT

In the last few years, videos became the most common form of information transmitted over the internet, and a lot of the traffic is confidential and must be protected and delivered safely to its intended users.This introduces the challenges of presenting encryption systems that can encode videos securely and efficiently at the same time.This paper presents an efficient opto-video encryption system using Logistic Adjusted Sine map (LASM) in the Fractional Fourier Transform (FrFT).In the presented opto-video LASM-based FrFT scheme, the encoded video is split into distinct frames and transformed into optical signals utilizing an optical supply.Each of the developed optical video frames is ciphered by utilizing the LASM in optical FrFT system using two-phase modulation forms on the video frame, the first in the time-domain and the second in the FrFT domain.In the end, the ciphervideo frame is spotted utilizing a CCD digital camera and transformed into a digital structure that can be managed using a computer.We test the proposed opto-video LASM-based FrFT scheme using various security tools.The outcomes demonstrate that the presented scheme can effectively encrypt and decrypt video signals.In addition, it encrypts videos with a high level of encryption quality without sacrificing its resistance to noise immunity.Finally, the test outcomes demonstrate that the presented scheme is immune to known attacks.
Threshold-Based Analysis of the Code Quality of High-Performance Computing Software Packages

Many popular metrics used for the quantification of the quality or complexity of a codebase (e.g. cyclomatic complexity) were developed in the 1970s or 1980s when source code sizes were significantly smaller than they are today, and before a number of modern programming language features were introduced in different languages. Thus, the many thresholds that were suggested by researchers for deciding whether a given function is lacking in a given quality dimension need to be updated. In the pursuit of this goal, we study a number of open-source high-performance codes, each of which has been in development for more than 15 years—a characteristic which we take to imply good design to score them in terms of their source codes' quality and to relax the above-mentioned thresholds. First, we employ the LLVM/Clang compiler infrastructure and introduce a Clang AST tool to gather AST-based metrics, as well as an LLVM IR pass for those based on a source code's static call graph. Second, we perform statistical analysis to identify the reference thresholds of 22 code quality and callgraph-related metrics at a fine grained level.
Cloud Supported Secure e-Card College Management System

As the term, smart campus attracts professionals and academics from multiple disciplines, and the technology keeps intervening in every aspect of life; it becomes inevitable for the smart campus to take place and deploy the future vision of smart cities. As a first step to achieving this vision, it is very important to develop a clear understanding of a smart campus. The student e-card management system (SEMS) is a barcode-based cutting-edge technology and innovative software deployed in the AWS cloud environment. It uses the core infrastructure of the AWS cloud, which includes AWS DynamoDB, AWS EC2 cluster, AWS S3, and the REST API. There are many manual activities in the existing college management system and lead to problems as they are time-consuming and expensive. The proposed idea aims to design a student smart card that configures a bar code with each user’s ID card, and that bar code can be read through the web, mobile app, and card reader. With that said, the user’s ID card will be multi-functioned now and will serve many purposes. The users of this application can be anyone who is associated with the college. The ID card is used for financial transactions within the college’s boundary, which ultimately serves the biggest commitment of having cashless transactions across the country. The same ID card can be used to have books from the library and many more activities. As this paper is a full-fledged cloud computing-based system, we are also proposing an OTP-based transaction to provide the end-to-end reliability of the financial transaction. This feature shows the perspective and influence of SEMS and its versatility, practicality, and usability. This research opens the doors for future studies to gain a deeper insight into the type of decisions needed to transform a traditional campus into a smart campus.
Corpus-Based Quantitative Analysis on Hedges and Shifting

This chapter deals with corpus-based quantitative analysis on hedges and shifting. In the first place, it briefly introduces how to run the self-built corpus in Jupyter Notebook where Python code is written beforehand according to our research purpose. Then, an overview is given of the basic information in CECIC, such as token, type, type/token ratio, and standardized type/token ratio in both the Chinese corpus and the English corpus respectively. In what follows, efforts are made to investigate the vertical distribution of hedges in both Chinese and English corpuses, that is, the total number of hedges, and the number of hedges in the four different categories. Then, Paired-sample T tests are carried out to seek data regularities and to check for significant differences between corresponding hedges in Chinese and English from each category. In addition, the horizontal observation from Chinese to English is described, especially how hedges are processed by interpreters. It has been observed that interpreters may have four alternatives to render hedges, namely, addition, abridgement, substitution and equivalence. Lastly, this chapter examines whether there are some hedges shifting regularities in additions, abridgements, substitutions, and equivalences.
Human Detection in the Depth Map Created from Point Cloud Data

This paper deals with human detection in the LiDAR data using the YOLO object detection neural network architecture. RGB-based object detection is the most studied topic in the field of neural networks and autonomous agents. However, these models are very sensitive to even minor changes in the weather or light conditions if the training data do not cover these situations. This paper proposes to use the LiDAR data as a redundant, and more condition invariant source of object detections around the autonomous agent. We used the publically available real-traffic dataset that simultaneously captures data from RGB camera and 3D LiDAR sensors during the clear-sky day and rainy night time and we aggregate the LiDAR data for a short period to increase the density of the point cloud. Later we projected these point cloud by several projection models, like pinhole camera model, cylindrical projection, and bird-view projection, into the 2D image frame, and we annotated all the images. As the main experiment, we trained the several YOLOv5 neural networks on the data captured during the day and validate the models on the mixed day and night data to study the robustness and information gain during the condition changes of the input data. The results show that the LiDAR-based models provide significantly better performance during the changed weather conditions than the RGB-based models.
Sentiment Analysis in polished Product Based Inspections data using existing supervised machine learning approach

It’s believed that this online review has a significant impact on brand and sales promotions. Over the internet, different places and communities persons are connected and they share their experiences related to products and services. This information can be synchronized in a proper format and analyzed to achieve some results beneficial for business profit. Sentiment Analysis is a widely applicable technique for the same. Though in recent years many researchers have suggested ML and DL based techniques collaborating NLP for sentiment analysis, it has been still a challenging research task due to the involvement of active social communities and reviews with modified linguistics. . This paper proposes the Polished Product-Based Reviews (PPBR) technique with the aim to improve the accuracy of the end result of sentiment analysis for product reviews. The PPBR extracts the opinions written by customers in a more expressive as well as emotional manner. Going beyond the linguistic model, it accepts informal writing contents and converts it into formal form for further analyzing customer feelings with accuracy. The integrated method of cleaning the data and supervised machine learning leads to 82% accuracy as demonstrated when implemented with NLTK.
Association Rule Mining on Crime Pattern Mining

Recognizing similar offenses during a offender inquiry is a major position of offense analysts. Through the help of pattern finding technique, crime specialists discover new pattern types from dataset. Over the past few years, association rule mining is implemented to analyze crime data from real datasets to find exact trends in crime. The indicated paper has worked with the (FP Growth algorithm) to determine similar crime patterns. Observational results are presented that will help crime experts predict crime and determine the maximum chances of corruption in a particular area.
Isomer: Transfer enhanced dual-channel heterogeneous dependency attention network for aspect-based sentiment classification

Aspect-based sentiment classification aims to predict the sentiment polarity of a specific aspect in a sentence. However, most existing methods attempt to construct dependency relations into a homogeneous dependency graph with sparsity and ambiguity, which only considers one type of node and one type of edge, thus cannot cover the comprehensive contextualized features of short texts or consider any additional node types or semantic relation information. To solve those issues, we present a sentiment analysis model, Isomer, which performs dual-channel attention on heterogeneous dependency graphs incorporating external knowledge to integrate additional information effectively. Specifically, a transfer-enhanced dual-channel heterogeneous dependency attention network is designed in Isomer for modeling short texts by heterogeneous dependency graphs. These heterogeneous dependency graphs not only consider different types of information but also incorporate external knowledge. Experiments studies show that Isomer outperforms the-state-of-arts on diverse datasets. Furthermore, the results suggest that Isomer captures the importance of various information features to focus on informative contextual words.
RIM: A Reputation-Based Incentive Mechanism using Blockchain

Blockchain is secure, decentralized, and immune to attackers because of digital encryption without any trusted third party. In blockchain, list of transactions are recorded into a block, which is added to the blockchain ledger is known as mining. In mining process miners are involved in block validation and add new block to the existing blockchain. Miners crypto currencies stored in a mining pool, in public blockchain, mining pool is access by all the users in the network, which makes mining pool vulnerable to block with holding attack. This attack is done by malicious miner in order to either earning higher amount of incentive or waste honest miner computation power. To solve this issue, we propose Reputation-based Incentive Mechanism (RIM) based on Proof-of-Improved (PoIP) consensus process, which is implemented using blockchain technology. RIM provides a high incentive for legitimate miner and punishes the withholding-based irrelevant miner. The simulation results showed that proposed approach can discover the optimal option for distributing incentive and computing power for each mining pool, as well as punishing block withholding attackers.