Reviewerly/exHarmony huggingface dataset:
Deep Learning Based Hybrid Approach for Crowd Anomalous Behavior Detection

Abnormal activity detection from the video is a challenging task in day to day life. This work proposed the unsupervised approach to detect abnormal activity from video with auto indication. Here, proposed a new hybrid C-SVM deep learning based to fuse the extracted features, which integrates convolutional neural network (CNN) and SVM. Firstly, the video is preprocessed and extracted visual features by CNN. Next, SVM is used to learn the temporal features of visual features and added an attention mechanism to select important features. Finally, the video feature vector is obtained layer by layer to judge abnormal activity. An experiment is used to test the ability of the model on the standard dataset to recognize the abnormal activity, the result shows that our experiment demonstrates the high performance of recognition and outperforms the state-of-the-art algorithms.
Intelligent Traffic Signal Control System Based on Machine Learning Algorithm

Urban traffic signal control is an important part of modern intelligent transportation system. Traffic lights play an important role in regulating traffic flow at intersections and major road sections. How to use advanced control technology to achieve reasonable time allocation optimization of traffic lights, maximize the use of traffic time for traffic lights, and ensure smooth roads is one of the key contents of intelligent transportation research. This paper aims to study the intelligent traffic signal control system (ITSCS) based on machine learning algorithm. This paper analyzes the support vector machine algorithm in the machine learning algorithm, and analyzes the advantages and disadvantages of the support vector machine algorithm. It also analyzes the hardware structure of the ITSCS and the communication process between the devices of the ITSCS. Finally, the experimental results are obtained. The empirical analysis results show that the traditional ITSCS becomes more concise and convenient after the machine learning algorithm is adopted, and we conducted a questionnaire and found that more than 87% of the people feel that the design of the ITSCS applies machine learning algorithm, and found that the design efficiency has become higher.
If-This-Then-Allow-That (to Phone Home): A Trigger-Based Network Policy Enforcement Framework for Smart Homes

The Internet of Things (IoT) has become entrenched in many users’ networks due to the utility these Internet-connected objects provide. But this does not mean that users should unconditionally trust IoT devices on their networks. While several approaches exist for restricting network connectivity of IoT devices, these proposals typically identify legitimate traffic, and then permanently allow it to flow to or from the device. In this paper, we argue that this permanent access control can lead to privacy and security violations, and in many cases is not strictly required. We present If-This-Then-Allow-That (IFTAT), a framework that supports security policies that dynamically update network access control rules based on the type of access that is required at any given time. Device or environmental triggers such as motion sensors or mobile phone applications initiate the process of adding firewall exceptions, which are removed either automatically or after another trigger is activated. We describe a proof of concept implementation which shows how IFTAT can restrict the network access of untrusted IoT devices with little impact to the usability of these devices.
Syntax-Based Aspect Sentiment Quad Prediction by Dual Modules Neural Network for Chinese Comments

Aspect-Based Sentiment Analysis (ABSA) is one of the essential research in the field of Natural Language Processing (NLP), of which Aspect Sentiment Quad Prediction (ASQP) is a novel and complete subtask. ASQP aims to accurately recognize the sentiment quad in the target sentence, which includes the aspect term, the aspect category, the corresponding opinion term, and the sentiment polarity of opinion. Nevertheless, existing approaches lack knowledge of the sentence’s syntax, so despite recent innovations in ASQP, it is poor for complex cyber comment processing. Also, most research has focused on processing English text, and ASQP for Chinese text is almost non-existent. Chinese usage is more casual than English, and individual characters contain more information. We propose a novel syntactically enhanced neural network framework inspired by syntax knowledge enhancement strategies in other NLP studies. In this framework, part of speech (POS) and dependency trees are input to the model as auxiliary information to strengthen its cognition of Chinese text structure. Besides, we design a relation extraction module, which provides a bridge for the overall extraction of the framework. A comparison of the designed experiments reveals that our proposed strategy outperforms the previous studies on the key metric F1. Further experiments demonstrate that the auxiliary information added to the framework improves the final performance in different ways.
The Impact of Derivational Relatedness on Inflectional Predictions

In this chapter, we will investigate how the picture of interpredictability between paradigm cells changes when considering not only the phonotactic shape of inflected wordforms, but also additional information of a different kind, namely the derivational relatedness of the lexemes involved. In Sect. 6.1, we will use some examples to illustrate the question and to stress the potential relevance of knowing whether two lexemes are ultimately derived from a same base or whether they are formed by means of the same derivational process. We will propose a method to take this information into account and briefly discuss the difference with the standard procedure for entropy computation. We will start from verbal lexemes that ultimately derive from the same base in Sect. 6.2, proposing a working definition of the notion of derivational-inflectional family and showing how our data were coded so as to include a classification in such families in Sect. 6.2.1, briefly providing a qualitative picture of the inflectional behaviour of verbs that belong to the same derivational-inflectional family in Sect. 6.2.2 and presenting our results in Sect. 6.2.3. The same line of reasoning will be followed in Sect. 6.3 for nouns that are formed by means of the same derivational process—i.e., that belong to the same derivational-inflectional series, as defined in Sect. 6.2.1. We will then present in Sect. 6.4 some of the results obtained on the same topic with a different methodology by Bonami and Pellegrini (2022), where the issue of inflectional predictions is framed as a classification problem: a classifier is trained to predict various correlates of the inflectional behaviour of a lexeme from several derivational predictors. We discuss how these results can integrate those of the other sections of this chapter by avoiding some of the problems of the entropy-based approach to the PCFP and by allowing for a quantitative evaluation of the role of many more facets of derivational history. Lastly, in Sect. 6.5, we will discuss the theoretical and methodological implications of our results, also highlighting some problems that we leave to further research.
BN-GEPSO: Learning Bayesian Network Structure Using Generalized Particle Swarm Optimization

At present Bayesian Networks (BN) are being used widely for demonstrating uncertain knowledge in many disciplines, including biology, computer science, risk analysis, service quality analysis, and business. But they suffer from the problem that when the nodes and edges increase, the structure learning difficulty increases and algorithms become inefficient. To solve this problem, heuristic optimization algorithms are used, which tend to find a near-optimal answer rather than an exact one, with particle swarm optimization (PSO) being one of them. PSO is a swarm intelligence-based algorithm having basic inspiration from flocks of birds (how they search for food). PSO is employed widely because it is easier to code, converges quickly, and can be parallelized easily. We use a recently proposed version of PSO called generalized particle swarm optimization (GEPSO) to learn bayesian network structure. We construct an initial directed acyclic graph (DAG) by using the max-min parent’s children (MMPC) algorithm and cross relative average entropy. This DAG is used to create a population for the GEPSO optimization procedure. Moreover, we propose a velocity update procedure to increase the efficiency of the algorithmic search process. Results of the experiments show that as the complexity of the dataset increases, our algorithm Bayesian network generalized particle swarm optimization (BN-GEPSO) outperforms the PSO algorithm in terms of the Bayesian information criterion (BIC) score.
Blockchain-Enabled Secure and Privacy-Preserving Data Aggregation for Fog-Based ITS

As an essential component of intelligent transportation systems (ITS), electric vehicles (EVs) can store massive amounts of electric power in their batteries and send power back to a charging station (CS) at peak hours to balance the power supply and generate profits. However, when the system collects the corresponding power data, several severe security and privacy issues are encountered. The identity and private injection data may be maliciously intercepted by network attackers and be tampered with to damage the services of ITS and smart grids. Existing approaches requiring high computational overhead render them unsuitable for the resource-constrained Internet of Things (IoT) environment. To address above problems, this paper proposes a blockchain-enabled secure and privacy-preserving data aggregation scheme for fog-based ITS. First, a fog computing and blockchain co-aware aggregation framework of power injection data is designed, which provides strong support for ITS to achieve secure and efficient power injection. Second, Paillier homomorphic encryption, the batch aggregation signature mechanism and a Bloom filter are effectively integrated with efficient aggregation of power injection data with security and privacy guarantees. In addition, the fine-grained homomorphic aggregation is designed for power injection data generated by all EVs, which provides solid data support for accurate power dispatching and supply management in ITS. Experiments show that the total computational cost is significantly reduced in the proposed scheme while providing security and privacy guarantees. The proposed scheme is more suitable for ITS with latency-sensitive applications and is also adapted to deploying devices with limited resources.
Adaptive Noise Detector and Partition Filter for Image Restoration

The random-value impulse noise (RVIN) detection approach in image denoising, which is dependent on manually defined detection thresholds or local window information, does not have strong generalization performance and cannot successfully cope with damaged pictures with high noise levels. The fusion of the K-means clustering approach in the noise detection stage is reviewed in this research, and the internal relationship between the flat region and the detail area of the damaged picture is thoroughly explored to suggest an unique two-stage method for gray image denoising. Based on the concept of pixel clustering and grouping, all pixels in the damaged picture are separated into various groups based on gray distance similarity features, and the best detection threshold of each group is solved to identify the noise. In the noise reduction step, a partition decision filter based on the gray value characteristics of pixels in the flat and detail areas is given. For the noise pixels in flat and detail areas, local consensus index (LCI) weighted filter and edge direction filter are designed respectively to recover the pixels damaged by the RVIN. The experimental results show that the accuracy of the proposed noise detection method is more than 90%, and is superior to most mainstream methods. At the same time, the proposed filtering method not only has good noise reduction and generalization performance for natural images and medical images with medium and high noise but also is superior to other advanced filtering technologies in visual effect and objective quality evaluation.
Error Detection and Correction of High-Resolution Remote Sensing Images Using Cyclic Code

High-resolution images are generally used in remote sensing applications like land mapping, deforestation, urban and rural growth, weather prediction and monitoring of crops, etc. In all these applications, we need a series of high-resolution images for proper analysis. There is a possibility of being affected by error when they are transmitted over a long distance. So, for proper analysis, it is mandatory to eliminate errors from these images. In this paper, we have proposed an error control method for high- resolution remote sensing images using a cyclic code. Here, the entire process is divided into two phases. In the first phase, the image is encoded with generation polynomial and remainder to the generate codeword and in the last phase, the original image is retrieved using the same generator polynomial and syndrome table. To measure the output of extracted images, we have used the conventional PSNR method. It is a single error detection and checking system. The error of each pixel can be detected as well as corrected. We have analyzed the result using simulation software and proved the effectiveness of our proposed method. Cyclic coding is already used to detect errors in images in a few previous types of research but the main drawback of that research work is that either the resolution of the selected images is low or the images are binary images. In this communication, we have worked with high-resolution images.
Cyber safety and security awareness initiatives in india - a systematic review

Cyberspace-related risks have increased massively due to the absence of awareness and self-resistance among Internet users to safeguard themselves. One of the vital measures to cultivate knowledge and understanding is the holistic intervention of cyber safety and security programs. Creating awareness through cyber safety and security initiatives is such a great way to address the issue in detail. It ultimately strengthens the discourse about online safety. There are a large number of awareness initiatives available worldwide that meet the requirements of the online community. This paper tries to highlight such initiatives in India to promote awareness about cyber safety and security.
Non-Zero Grid for Accurate 2-Bit Additive Power-of-Two CNN Quantization

Quantization is an effective technique to reduce the memory and computational complexity of CNNs.Recent advances utilize additive powers-of-two to perform non-uniform quantization, which resembles a normal distribution and shows better performance than uniform quantization.With powersof-two quantization, the computational complexity is also largely reduced because the slow multiplication operations are replaced with lightweight shift operations.However, there are serious problems in the previously proposed grid formulation for 2-bit quantization.In particular, these powers-of-two schemes produce zero values, generating significant training error and causing low accuracy.In addition, due to improper grid formulation, they also fallback to uniform quantization when the quantization level reaches 2-bit.Due to these reasons, on large CNN like ResNet-110, these powers-of-two schemes may not even train properly.To resolve these issues, we propose a new non-zero grid formulation that enables 2-bit non-uniform quantization and allow the CNN to be trained successfully in every attempt, even for a large network.The proposed technique quantizes weight as power-of-two values and projects it close to the mean area through a simple constant product on the exponential part.This allows our quantization scheme to closely resemble a non-uniform quantization at 2-bit, enabling successful training at 2-bit quantization, which is not found in the previous work.The proposed technique achieves 70.57% accuracy on the CIFAR-100 dataset trained with ResNet-110.This result is 6.24% higher than the additive powers-of-two scheme which only achieves 64.33% accuracy.Beside achieving higher accuracy, our work also maintains the same memory and computational efficiency with the original additive powers-of-two scheme.
Lightweight Storage Framework for Blockchain-Enabled Internet of Things Under Cloud Computing

Due to its decentralized, tamper-proof, and trust-free characteristics, blockchain is used in the Internet of Things (IoT) to guarantee the reliability of data. However, some technical flaws in blockchain itself prevent the development of these applications, such as the issue with linearly growing storage capacity of blockchain systems. On the other hand, there is a lack of storage resources for sensor devices in IoT, and numerous sensor devices will generate massive data at ultra-high speed, which makes the storage problem of the IoT enabled by blockchain more prominent. There are various solutions to reduce the storage burden by modifying the blockchain’s storage policy, but most of them do not consider the willingness of peers. In attempt to make the blockchain more compatible with the IoT, this paper proposes a storage optimization scheme that revisits the system data storage problem from a more practically oriented standpoint. Peers will only store transactional data that they are directly involved in. In addition, a transaction verification model is developed to enable peers to undertake transaction verification with the aid of cloud computing, and an incentive mechanism is premised on the storage optimization scheme to assure data integrity. The results of the simulation experiments demonstrate the proposed scheme’s advantage in terms of storage and throughput.
Neural Machine Translation Models with Attention-Based Dropout Layer

In bilingual translation, attention-based Neural Machine Translation (NMT) models are used to achieve synchrony between input and output sequences and the notion of alignment. NMT model has obtained state-of-the-art performance for several language pairs. However, there has been little work exploring useful architectures for Urdu-to-English machine translation. We conducted extensive Urdu-to-English translation experiments using Long short-term memory (LSTM)/Bidirectional recurrent neural networks (Bi-RNN)/Statistical recurrent unit (SRU)/Gated recurrent unit (GRU)/Convolutional neural network (CNN) and Transformer. Experimental results show that Bi-RNN and LSTM with attention mechanism trained iteratively, with a scalable data set, make precise predictions on unseen data. The trained models yielded competitive results by achieving 62.6% and 61% accuracy and 49.67 and 47.14 BLEU scores, respectively. From a qualitative perspective, the translation of the test sets was examined manually, and it was observed that trained models tend to produce repetitive output more frequently. The attention score produced by Bi-RNN and LSTM produced clear alignment, while GRU showed incorrect translation for words, poor alignment and lack of a clear structure. Therefore, we considered refining the attention-based models by defining an additional attention-based dropout layer. Attention dropout fixes alignment errors and minimizes translation errors at the word level. After empirical demonstration and comparison with their counterparts, we found improvement in the quality of the resulting translation system and a decrease in the perplexity and over-translation score. The ability of the proposed model was evaluated using Arabic-English and Persian-English datasets as well. We empirically concluded that adding an attention-based dropout layer helps improve GRU, SRU, and Transformer translation and is considerably more efficient in translation quality and speed.
Teaching with QR Codes: Accessible Technology for the Novice Educator

Quick response (QR) codes allow for quick and easy accessibility of just-in-time training (JiTT) for a variety of online materials. They are becoming more widely utilized to share educational materials, with potential vast applications for health professions education. In this article, we describe the development of a novel video-based equipment skills training curriculum incorporating QR codes and discuss the broader implications for educators.
Identification of a Printed Anti-Counterfeiting Code Based on Feature Guidance Double Pool Attention Networks

The authenticity identification of anti-counterfeiting codes based on mobile phone platforms is affected by lighting environment, photographing habits, camera resolution and other factors, resulting in poor collection quality of anti-counterfeiting codes and weak differentiation of anti-counterfeiting codes for high-quality counterfeits. Developing an anti-counterfeiting code authentication algorithm based on mobile phones is of great commercial value. Although the existing algorithms developed based on special equipment can effectively identify forged anti-counterfeiting codes, the anti-counterfeiting code identification scheme based on mobile phones is still in its infancy. To address the small differences in texture features, low response speed and excessively large deep learning models used in mobile phone anti-counterfeiting and identification scenarios, we propose a feature-guided double pool attention network (FG-DPANet) to solve the reprinting forgery problem of printing anti-counterfeiting codes. To address the slight differences in texture features in high-quality reprinted anti-counterfeiting codes, we propose a feature guidance algorithm that creatively combines the texture features and the inherent noise feature of the scanner and printer introduced in the reprinting process to identify anti-counterfeiting code authenticity. The introduction of noise features effectively makes up for the small texture difference of high-quality anti-counterfeiting codes. The double pool attention network (DPANet) is a lightweight double pool attention residual network. Under the condition of ensuring detection accuracy, DPANet can simplify the network structure as much as possible, improve the network reasoning speed, and run better on mobile devices with low computing power. We conducted a series of experiments to evaluate the FG-DPANet proposed in this paper. Experimental results show that the proposed FG-DPANet can resist high-quality and small-size anti-counterfeiting code reprint forgery. By comparing with the existing algorithm based on texture, it is shown that the proposed method has a higher authentication accuracy. Last but not least, the proposed scheme has been evaluated in the anti-counterfeiting code blurring scene, and the results show that our proposed method can well resist slight blurring of anti-counterfeiting images.