Reviewerly/exHarmony huggingface dataset:
Research on Personalized Recommendation Method of Intangible Social Heritage and Materials in Schools under Double Reduction Policy

Individualized recommendation of imperceptible social heritage and materials in schools under the double reduction policy is to evaluate and judge students’ learning interests and specialties and recommend suitable imperceptible social heritage and materials to students. Aiming at the problem that the collaborative filtering recommendation method does not match the individualized needs of primary and secondary school students under the double reduction policy, in this paper, we suggest a personalized recommendation system of imperceptible social heritage and materials in schools under the double reduction policy based on joint template feature matching and interest feature point mining. First, taking the information management platform of imperceptible social heritage and materials in schools as the structural model, the grading model and homomorphic distribution attribute model of imperceptible social heritage and materials for primary and secondary school students under the double reduction policy are constructed. Second, the probability density characteristic analysis method of joint template matching is used to construct the personalized recommendation model of the imperceptible social heritage and materials in schools. Third, then the personalized characteristic distribution and fitness parameter extraction of the imperceptible social heritage and materials in schools under the double reduction policy are carried out, so as to realize the reasonable matching of personalized characteristic requirements and project interest points and realize the personalized recommendation of imperceptible social heritage and materials in schools under the double reduction policy. Finally, a simulation experiment was carried out to test and evaluate the outcomes. The outcomes express that the personalized recommendation items of imperceptible social heritage and materials in schools with this method have higher scores, and the average absolute error and root mean square error are smaller, which improves the quality of dynamic and accurate matching between imperceptible social heritage and materials and students’ hobby characteristics.
Deep Learning-Assisted Solar Radiation Forecasting for Photovoltaic Power Generation Management in Buildings

Due to its advantages and the continual availability of solar energy, photovoltaic (PV) systems have become the most popular energy production equipment in various business and residential structures. This chapter proposes solar radiation forecasting to manage solar power generation in residential and commercial buildings using deep learning algorithms. Convolutional neural network (CNN) and long short-term memory (LSTM) are two proposed algorithms created to forecast solar radiation to control the energy produced at a PV plant on the roof of the University of Macau in China. Climate data collected at the university’s meteorological station are used as input variables in solar radiation forecasting. The performance of each network is assessed using a variety of performance evaluation measures. Based on the results and analysis, the LSTM technique, which forecasts solar radiation with an accuracy of R = 99.84%, outperforms the CNN technique that predicts solar radiation with an accuracy of R = 99.71%. Furthermore, the LSTM technique’s predictions exhibit a lower forecasting error than the CNN process.
Employing Tactile Visual Reproduction Technology in the Extended Educational Reality of Middle School Students

This research investigates the influence of one of the most well-known strategies for 3D visualization and modeling by using technologies that are both haptic and tactile visual in nature. Additionally, there is a system that combines visual reality with augmented reality for added functionality. A case study was conducted with students at a middle school to investigate how well this instrument was used to teach STEM (science, technology, engineering, and mathematics) courses. The students served as the case study's subjects. An investigation of the outcomes of using facilities for a STEM enrichment program over the course of a single academic year was carried out and analyzed.
IoT Security in Industry: A Threat Model of Existing and Future Network Infrastructure

While recent developments in The Internet of Things (IoT) present an infinite number of new possibilities for smart factories, the rapid expansion of this technology in industry presents an unprecedented security concern. Implementing IoT solutions in industrial environments must be accomplished smartly by creating a comprehensive threat model of existing and future network infrastructure, and then by utilizing standard information security controls followed by the application of an industry-appropriate risk management framework. Approaching the problem of IoT security methodically will allow smart factories to benefit from IoT solutions while mitigating all associated risks.
Addressing Most Common Vulnerabilities in Blockchain-Based Voting Systems

Blockchain technology is becoming the most used technology to use in offering new types of digital services. Leveraging blockchain technology in developing voting systems is becoming an emerging research topic. However, not many studies have focused on the suitability of such system. Election makes a fundamental contribution to democratic governance, and any lack of transparency or manipulation in the process could affect citizens’ trust in their democracy. In this research, we are going to study the feasibility of using blockchain technology in real elections. We will as well cite what we believe are problems with blockchain-based electronic voting systems and propose solutions to the most common vulnerabilities related to blockchain-based voting systems.
Development of a Neighborhood Based Adaptive Heterogeneous Oversampling Ensemble Classifier for Imbalanced Binary Class Datasets

Class imbalance prevails in many real-word datasets. In this paper, a Neighborhood based Adaptive Heterogeneous Oversampling Ensemble Classifier method is proposed to handle class imbalance in datasets. The proposed method adopts an oversampling approach to create a set of balanced representative training datasets. Several base classifiers are built based on those training datasets, and an adaptive heterogeneous ensemble classifier is created. The proposed method is examined with five datasets, and examination results are compared with popular oversampling algorithms. The comparison revealed that proposed method is able to achieve better performance results.
Topic Identification and Prediction Using Sanskrit Hysynset

The topic model implementation is not a new concept for English corpus due to the availability of plenty of resources, but developing a topic model for Sanskrit is comparatively an untouched area. The proposed approach is a 4 phased. The first phase constructs Hysynset followed by building a topic model that acts as a second phase. In the third phase, clustering is applied and the approach completes with classification and prediction that is the fourth phase. Hypernyms-hyponyms and synonyms are grouped in the first phase to reduce the dimensions and creates semantic space. The topic model is built using Latent Dirichlet Allocation (LDA) which shows very specific and informative topics as it uses Hysynset vector space model for Sanskrit (HSVSMS). The dataset belongs to more than 1100 Sanskrit stories. The documents’ wise topics are presented using dendrogram obtained after applying HAC and then supervised model that is random forest is used to predict the topic of the test/new document and evaluated using classification error and accuracy. In the absence of the availability of standard experiments, current work could not be compared with other existing work in case of prediction of stories. Comparative analysis of topic identification using existing technique Vector Space Model for Sanskrit (VSMS) proves that betterment of the proposed technique that is (HSVSMS) in the form of the accuracy, misclassification error of classification, coherence score, entropy and purity and topic titles.
Collaborative Approach Toward Information Retrieval System to Get Relevant News Articles Over Web: IRS-Web

Effectual information retrieval from collection of document available over the Web, such as news articles, is a tedious task. The objective of this research work is to introduce an Information Retrieval System for retrieval of relevant news articles available over the Web, incorporating a unified framework based on machine learning techniques. The most prominent newsgroup of India, Times of India archive has been taken into consideration to perform experimental setup under this research work. The MongoDB operational database platform has been used to build a cloud-based data warehouse. The collaboration of different machine learning techniques has been used to perform constraint-based user’s query to get the optimized resultant news articles. The resultant news articles have been ranked corresponding to their scoring values to sort in a required manner.
PSTN Enterprise Collaboration Systems

Both intranet and extranet can be interchanged in Enterprise Collaboration System (ECS). However, it still has some limitations and challenges are faced by the organization due to connection issues. System consistency, safety, and security issues are the main concerns in this discussion on how the system can ensure information protection in secured status. To solve these issues, it is suggested that ECS should use the other connection which is called PSTN connectivity that is used to connect collaboration systems with the network. Especially in this pandemic period, almost 90% of the employees are required to work from home; this is indirectly challenging the connection to the company ECS, considering the stability of the system. Email is one of the most popular communication tools used in the enterprise. The company will create a unique email address for their employee used in ECS. However, problems such as message overloading, spam, and so on may occur. Therefore, LightWeight Semantics (LWS) approach is suggested to plug into an email to further process email messages. This is indirectly helping the searching process to become more direct to obtain the expected search results via semantic search. Legacy in LWS is connected to avoid any data loss and to study the previous data storing style.
Blockchain Technology Adoption for General Elections During COVID-19 Pandemic and Beyond

Blockchain technology is one of the fourth industrial revolution technologies that ensure traceability and transparency in recording electronic transactions. Though this technology has been applied in different domains such as finance, logistics and many more, its use in electoral system is yet to be fully realized. An electoral system hinges on fairness and transparency of voting processes. The traditional voting method involves the use of paper and ink system, manual vote counting and irregularities such as multiple voting, over-voting and delay in declaring election results. This results in a lack of trust and transparency in the voting processes. COVID-19 outbreak increased the pace of digitization in most fields, economic sectors and institutions. In this paper, we review the use of blockchain technology because it is a foundational technology that can ensure fairness in voting during general elections across the globe. We look at the case of Ghana’s electoral irregularities during the 2020 general elections. Ghana was chosen because it served as benchmark for most developing nations. Recent data were collected from an online data repository using keywords including blockchain technology, COVID-19 pandemic and conventional voting methods. The findings of the review suggest that during the year 2020 which witnessed the COVID-19 pandemic, no country in Africa adopted or implemented the blockchain technology in voting despite its ability to facilitate remote voting and curb the spread of the Coronavirus. This could be attributed to the fact that the COVID-19 outbreak took most nations by surprise and countries which have scheduled elections in 2020 did not budget for its use. The paper also provides shortcomings of the conventional voting methods and the capabilities of blockchain technology to address those shortcomings. These findings are imperative to form the electoral process of nations because of the underlying benefits of blockchain: ensuring transparency, auditability, traceability in processes, immutability and fairness.
Getting Around the Semantics Challenge in Hateful Memes

Social media has evolved into a forum for people to express their thoughts and ideas on a wide range of topics. With this, there has been a correlated rise in hate and inflammatory speeches against individuals and organisations—sometimes with severe consequences. Methods to classify ‘hate’ that is propagated through multi-modal media, such as memes (contains image and text), fail to capture the meaning by comprehending both the modes(i.e. image and text). While the text itself may not be hateful, images are utilised to lend additional context to the words to subtly imply and convey hatred. On the Facebook Hate Meme Dataset, specifically curated for conveying hate implicitly in memes (further complicated by the use of ‘benign confounders’), the baseline established by Facebook with a state-of-the-art visual-linguistic model such as VilBERT is 64.73%. On the same dataset, our work beats the state-of-the-art baseline models by nearly 5% using an effective fusion of the semantics of both the text and image.
Preference of Sludge Treatment Plan in IPA II Pejompongan Water Treatment Plant

Water treatment is inseparable from the processing of sludge resulting from the deposition process. IPA II Pejompongan currently has no sludge treatment, where the treated sludge is discharged directly into the surrounding water bodies. This research was conducted to determine the suitable sludge treatment system for existing conditions. This study was conducted using data on the quantity of sludge and the quality of the resulting sludge, which was then discussed descriptively. This study also uses a literature review to find various appropriate treatments for the Pejompongan II IPA. The alternative is selected with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The highest mud generation in 2019 is during the rainy season, namely January-June. In terms of quality, the parameters of suspended solids, iron, nickel, lead, manganese, organic (KMnO4), BOD, and COD do not meet Jakarta City’s quality standards government. There are two alternatives: the belt filter press and the screw press, where the decision analysis results show a better screw press.
Attacking the Privacy of Approximate Membership Check Filters by Positive Concentration

Approximate membership check filters are increasingly used to speed up data processing in many applications.Also, privacy is becoming a key design objective for many systems and thus, the privacy of filters needs to be carefully considered.Previous works have shown that an attacker that knows the implementation details of the filter and has access to its content, may be able to extract some information about the elements stored in the filter.This attack is, however, specific to Bloom filters and requires that the universe of elements must be small.In this paper, we show that in many practical settings, an attacker that has only a black-box access to the filter, can extract information about the elements stored in the filter regardless of the specific filter type and the universe size.This is possible based on the key observation that in many applications, the elements stored in the filter are not randomly chosen, but they are concentrated in one or more parts of the universe of elements.To identify these parts, the positive probability can be measured on different parts of the universe; the parts having significantly larger values than the average positive probability for the filter are the ones on which the filter elements are concentrated.This approach is formalized and applied to several case studies showing the process by which the attacker can get additional information about the elements stored for the filters in a wide range of scenarios.
Network Pruning for Bit-Serial Accelerators

Bit-serial architectures (BSAs) are becoming increasingly popular in low-power neural network processor (NNP) designs for edge scenarios. However, the performance and energy efficiency of state-of-the-art BSA NNPs heavily depends on both the proportion and distribution of ineffectual weight bits in neural networks (NNs). To boost the performance of typical BSA accelerators, we present Bit-Pruner, a software approach to learn BSA-favored NNs without resorting to hardware modifications. Bit-Pruner not only progressively prunes but also restructures the nonzero bits in weights so that the number of nonzero bits in the model can be reduced and the corresponding computing can be load-balanced to suit the target BSA accelerators. On top of Bit-Pruner, we further propose a Pareto frontier optimization algorithm to adjust the bit-pruning rate across network layers and fulfill diverse NN processing requirements in terms of performance and accuracy for various edge scenarios. However, an aggressive Bit-Pruner can lead to nontrivial accuracy loss, especially for lightweight NNs and complex tasks. To this end, the alternating direction method of multipliers (ADMMs) is adapted to the retraining phase in Bit-Pruner to smooth the abrupt disturbance due to bit-pruning and enhance the resulting model accuracy. According to the experiments, Bit-Pruner increases the bit-sparsity up to 94.4% with negligible accuracy degradation and achieves an optimized tradeoff between NN accuracy and energy efficiency even under very-aggressive performance constraints. When pruned models are deployed onto typical BSA accelerators, the average performance is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.1\times $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.6\times $ </tex-math></inline-formula> higher than the baseline networks without pruning and those with classical weight pruning, respectively.
AdamB: Decoupled Bayes by Backprop With Gaussian Scale Mixture Prior

Overfitting of neural networks to training data is one of the most significant problems in machine learning. Bayesian neural networks (BNNs) are known to be robust against overfitting owing to their ability to model parameter uncertainty. Bayes by Backprop (BBB), a simple variational inference approach that optimizes variational parameters by backpropagation, has been proposed to train BNNs. However, many studies have encountered challenges in terms of variational inference for large-scale models, such as deep learning. Thus, this study proposed Adam with decoupled Bayes by Backprop (AdamB) to stabilize the training of BNNs through the application of the Adam estimator evaluation to the gradient of the neural network. The proposed approach stabilized the noisy gradient of the BBB and mitigated excess changes in the parameters. In addition, AdamB combined with a Gaussian scale mixture as a prior distribution can suppress the intrinsic increase in variational parameters. The proposed AdamB exhibited superior stability compared to training using Adam with vanilla BBB. Further, the covariate shift benchmark using image classification tasks indicated the higher reliability of AdamB than deep ensembles in the case of noise-type covariate shifts. The considerations for stable learning of BNNs by AdamB shown in image classification tasks are expected to be important insights for application to other domains.
Folk Music Audio Enhancement Method Combining Internet of Things and GA

The current methods of music sound enhancement mainly use the way of enhancing the intensity of the sound source, which ignores the spatial noise cancellation of the human ear, resulting in poor sound enhancement effect. Therefore, this paper proposes an audio enhancement method for folk music combining Internet of Things and GA. After acquiring folk music audio by Internet of Things technology, the postprocessing algorithm of wavelet packet decomposition combined with Wiener filter is used to suppress the noise in music. On the basis of HRTF, genetic algorithm is used to reduce the output error of left and right sound channels and realize the enhancement of music sound effect. The simulation experiment verifies the method of application research. The reverberation is established at 65 ms and lasts for a long time, which has good effect of sound enhancement.
Scene Retrieval in Soccer Videos by Spatial-temporal Attention with Video Vision Transformer

This paper presents a scene retrieval method in soccer videos with video vision Transformer (ViViT). In soccer coaching, it is difficult for the training staff to find the required scenes efficiently from the large number of soccer videos. We tackle this problem with a simple yet effective method. We train ViViT and obtain the output token features of the soccer scene by the pre-trained ViViT model. The output tokens of the pre-trained ViViT contain spatio-temporal information of soccer scenes. We then transform a query scene and candidate scenes into output token features using the pre-trained ViViT and calculate the similarity between the tokens with cosine similarity. We conducted experiments on SoccerNet-V2dataset. The experimental results show that the proposed method achieves outstanding retrieval accuracy compared to the previous methods.
Object Detection Inference System with Deep Learning NVDLA Accelerator

Nowadays, when Artificial Intelligence (AI) is getting more advanced, many applications are following its footsteps. For example, in the fields of object recognition and speech processing, there are many different DNN (deep neural network) structures. By combining these DNNs and collecting a large library of images or audio. It uses GPUs for training to achieve high accuracy and meet its application requirements. This paper focuses on object recognition in store applications and uses DNN hardware accelerators for real-time inference. It maps to NVDLA hardware using YOLOv2-tiny. NVDLA can perform YOLOv2-tiny with 256 MAC@150 MHz frequency. Also, it can reach up to 5 FPS.
A New Light Weight Convolutional Neural Network for Mobile Devices

In this paper, we propose a light-weight convolutional neural network based on depth-wise separable convolutions and cross stage partial (CSP) network. Dissimilar to MobileNetV3, the proposed network is composed of some CSP blocks to reduce the model size and computational operations. For performance evaluation, Cifar10 and Cifar100 are used for testing. Compared to MobileNetv3, the model size and execution time of the proposed network in PC and mobile device are smaller. Therefore, the experimental results show that the proposed light-weight network can effectively extract visual features for image classification compared with MobileNetV3.
A Quanvolution Architecture for Image Classification

This paper proposes a hybrid quantum classic neural network with a fixed quantum circuit structure on image classification, which uses Quanvolution composed of quantum circuit with the super cube structure and randomized single qubit gates. Incorporating quantum circuits into convolutional neural net-works can introduce randomness to training through perturbing images with quantum circuits, acting as data augmentation to increase the model's accuracy. The experiment shows that the designed convolution neural network with quantum circuits has a 2% performance gain against the model without the Quanvolution.