Reviewerly/exHarmony huggingface dataset:
Implementation of An Energy-Efficient Binary Square Rooter Using Reversible Logic By Applying The Non-Restoring Algorithm

Calculation of square root will be an essential mathematical operation that will have broad applications. In Hardware, the square root will be designed in order to gain power which will be quite low. Similarly, there are also other advantages that comes with gaining of low power that is high speed and also low area. A trade-off will also occur with the three metrics which is quite natural. As we know that the present technology is very advanced so it will aim for low power and architectural modification will be required by the relative designs. This sheet represents an energy efficient square rooter by using reversible logic. (RCSM) Reversible Controlled Subtract Multiplexer will be designing and it also plays an important part in implementing the binary square rooter. Saimur Rahman Gate will also be implementing the binary square rooter in order to improve and develop it. The Improvements such as cost of the quanta, inputs given by constants and also garbage outputs. The approaches such as conventional approach and SRG are used for designing the binary square rooter and it will be completed using non-restoring algorithm. Xilinx Software will be responsible for carrying out simulations and Synopsys Design Compiler will be the factor for obtaining power. The gate count which has been 75 will be decreased to 35. There will be an improvement of 20% in terms of power which will be obtained in this paper.
Application of a Musical Robot for Adjusting Guitar String Re-Excitation Parameters in Sound Synthesis

Sound synthesis methods based on physical modelling of acoustic instruments depend on data that require measurements and recordings. If a musical instrument is operated by a human, a difficulty in filtering out variability is introduced due to a lack of repeatability in excitation parameters, or in varying physical contact between a musician and an instrument, resulting in the damping of vibrating elements. Musical robots can solve this problem. Their repeatability and controllability allows studying even subtle phenomena. This paper presents an application of a robot in studying the re-excitation of a string in an acoustic guitar. The obtained results are used to improve a simple synthesis model of a vibrating string, based on the finite difference method. The improved model reproduced the observed phenomena, such as the alteration of the signal spectrum, damping, and ringing, all of which can be perceived by a human, and add up to the final sound of an instrument. Moreover, as it was demonstrated by using two different string plucking mechanisms, musical robots can be redesigned to study other sound production phenomena and, thus, to further improve the behaviours of and sounds produced by models applied in sound synthesis.
Relative Pose Consistency for Semi-Supervised Head Pose Estimation

Human head pose estimation from images plays a vital role in applications like driver assistance systems and human behavior analysis. Head pose estimation networks are typically trained in a supervised manner. Unfortunately, manual/sensor-based annotations of head poses are prone to errors. A solution is supervised training on synthetic training data generated from 3D face models which can provide an infinite amount of perfect labels. However, computer generated face images only provide an approximation of real-world images which results in a domain gap between training and application domain. To date, domain adaptation is rarely addressed in current work on head pose estimation. In this work we propose relative pose consistency, a semi-supervised learning strategy for head pose estimation based on consistency regularization. It allows simultaneous learning on labeled synthetic data and unlabeled real-world data to overcome the domain gap, while keeping the advantages of synthetic data. Consistency regularization enforces consistent network predictions under random image augmentations. We address pose-preserving and pose-altering augmentations. Naturally, pose-altering augmentations cannot be used on unlabeled data. We therefore propose a strategy to exploit the relative pose introduced by pose-altering augmentations between augmented image pairs. This allows the network to benefit from relative pose labels during training on the unlabeled, real-world images. We evaluate our approach on a widely used benchmark (Biwi Kinect Head Pose) and outperform domain-adaptation SOTA. We are the first to present a consistency regularization framework for head pose estimation. Our experiments show that our approach improves head pose estimation accuracy for real-world images despite using only labels from synthetic images.
Sentiment Classification Against the Public Activity Restrictions Policy in Jakarta Using Machine Learning Models

To stop the spread of the COVID-19, the Indonesian government implemented community activities restrictions enforcement (in Indonesian language: Pemberlakuan Pembatasan Kegiatan Masyarakat or PPKM) starting from January 2021. The term PPKM applied are PPKM Mikro (in Indonesian language) or Micro PPKM, PPKM Darurat (in Indonesian language) or Emergency PPKM, and PPKM Level 1-4 or Level 1-4 PPKM. On the other hand, the existing research mostly used Twitter as the data source to do sentiment classification. Therefore, we aimed to classify social media comments on Facebook and YouTube on Level 1-4 PPKM policy in Jakarta. We used "PPKM Jakarta" as the keyword topic in August - September 2021 when Level 1-4 PPKM was ongoing. In addition, we compared datasets composition, machine learning models, and features extraction. Random Forest, Naive Bayes, and Logistic Regression were performed as the machine learning models due to they were the top three models on the previous research. We extracted word unigram, word bigram, character trigram, and character quadrigram as the feature extraction. The highest average F-measure was obtained with a 79.6% score of the Logistic Regression model using character quadrigram extraction. We found that comments from Facebook and YouTube were dominated by neutral sentiment (49.8%) with this setup. It means the people of Jakarta started to trust the government in handling the COVID-19 pandemic. Through word cloud analysis, it is recommended that social assistance be reviewed for those directly affected.
Elicitability of Instance and Object Ranking

Assessing the quality of a forecasting model crucially depends on a proper scoring rule or suitable loss function. As for point forecasts, the existence of a strictly consistent loss function that allows for a fair comparison of competing forecast models has to be guaranteed, which means that the corresponding statistical functional has to be elicitable. We consider instance and object ranking problems that intend to correctly predict the ordering of instances in a data set. A ranking prediction is naturally identified with a point forecast in the respective symmetric group, that is, the forecaster predicts one single permutation of the row indices. We show that, in the presence of ties, this strategy does not allow for strictly consistent scoring functions because of multiple true permutations. Those multiple optima cannot be entirely covered by a single point forecast, which causes all corresponding optima to be minimizers of standard scoring functions that operate on symmetric groups, so these scoring functions are not strictly consistent. As a remedy, we consider accurately accounting for ties. This is done by treating each configuration of clear orderings and ties as an additional category, which induces extended decision spaces with a clearly defined single optimum. Because these decision spaces are still finite, each type of instance ranking problem that we consider in this work and corresponding ranking functional, mapping into a symmetric group, can be identified with a certain classification problem and corresponding classification functional, mapping into one of our extended decision spaces, which is elicitable.
Blockchain-based Electronic Evidence Storage and Efficiency Optimization

Electronic evidence is the electronic data generated based on electronic technology and stored in digital form. Aiming at the problems of data authenticity and integrity in the traditional storage methods of electronic evidence, and the existing blockchain storage schemes fail to give good consideration to the storage cost and efficiency. This paper proposes a model for electronic evidence storage, which uses directed acyclic graph to optimize the efficiency of on-chain storage of electronic evidence. Experimental results show that the proposed model is effective and practical.
Attention-aware Multi-encoder for Session-based Recommendation

In session-based recommendation, the user&#x0027;s next possible click can solely be predicted based on historical interaction behavior in the ongoing session. Previously representative works mainly use sequence models and graph neural networks to model user&#x0027;s behaviors of the session. These works have achieved promising results, but each also has certain defects. In view of the shortcomings of the previous works, we propose a multi-encoder framework, under which the advantages of each encoder are retained. Different encoders are used to mine different session features and finally generate a more powerful session representation to improve the recommendation result. Furthermore, in order to improve the performance of recommendation, we introduce the inter-session collaboration information by designing a Inter-session Collaboration Module. Extensive experiments on two real-world datasets demonstrate the superiority of our method over state-of-the-art algorithms.
Diagnoses of reformed responses in curative applications using wireless sensors with dynamic control

The major objective of the exploration work is to detect various emotions of an individual for protective measures and to avoid shudder activities. In day-to-day life, many people are facing high thwarting and it can be only realized from their sensations. It is observed on average that a maximum number of individuals is bountiful to different emotions at periodic time intervals. Therefore, by empathetic rudiments, the proposed method protects many individuals by avoiding the risk of a lifetime. For this appliance wireless sensors are integrated with dynamic management protocol which avoids high energy conventions and in turn, a Binary Coded Artificial Neural Network (BCANN) is applied for observing the output in two different states. Meanwhile, if the false positivity rate is higher it indicates that emotions are not detected in periodic times where dimensionality scores will be reduced. Further for perceiving the outcomes, real-time simulation is executed in two diverse modes and the consequences under four scenarios proves that the projected method can be able to provide improved results for an average of 52 per cent than existing methods in the absence of cognitive radio.
Generative Adversarial Networks

Before the invention of the generative adversarial network (GAN), the variational autoencoder was considered to be theoretically complete and simple to implement. It is very stable when trained using neural networks, and the resulting images are more approximate, but the human eyes can still easily distinguish real pictures and machine-generated pictures.
BitCluster: Fine-Grained Weight Quantization for Load-Balanced Bit-Serial Neural Network Accelerators

Convolutional neural network (CNN) has demonstrated great success in pattern recognition scenarios at the cost of nearly billions of parameters and consequent convolution operations. Various dedicated hardware designs are proposed to accelerate the CNN computation in more energy-efficient manners. Especially, the bit-serial accelerator (BSA) is one of the most effective approaches on resource-limited platforms by eliminating zero-bit computations. However, the irregular distribution and varying number of effectual (nonzero) bits in weights significantly cause hardware underutilization, impeding further performance improvement of state-of-the-art BSAs. To address this issue, BitCluster, a hardware-friendly quantization method, is proposed to make each weight with the identical number of effectual bits for load-balanced computation. Considering distinct sensitivities to weight precision in different neural layers, layer-level BitCluster is proposed to design further for fine-grained weight quantization. It systematically determines the layerwise quantization configurations, which significantly improve the overall performance with <1% accuracy loss. BitCluster is comprehensively evaluated on a BitCluster-compatible BSA design by taking six mainstream CNN models as benchmarks. The experimental results show that the BitCluster-based BSA achieves <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 hardware utilization and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.4\times $ </tex-math></inline-formula> speedup on average than state-of-the-art BSAs, with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5\times $ </tex-math></inline-formula> better energy efficiency on average.
A Novel Dictionary Management Scheme of LZW Compression Algorithm Based on Insignificant Dictionary Area

In order to avoid the possible degradation of the compression ratio, the dictionary management scheme is employed when the dictionary of Lempel-Ziv-Welch (LZW) algorithm is filled. Dictionary reset (DR) and least recently used (LRU) are two dictionary management schemes widely used. However, DR trades compression ratio for less time overhead and LRU obtains a high compression ratio while time and memory are sacrificed. This paper proposes a novel insignificant dictionary area LZW (IDA-LZW) which has a high and stable compression ratio while maintaining acceptable compression time and excellent decompression time. The proposed dictionary management scheme called IDA consists of dictionary partition and impact factor (IF) which returns the dictionary area should be cleared for new entries. Three variants of IDA-LZW and LZW with DR and LRU are implemented and show that the proposed scheme achieves the best average compression while consuming reasonable resources. Furthermore, comparisons are made with the results in the paper recently published to prove the proposed scheme is valuable.
Aerial target recognition method based on improved sensor credibility

In order to achieve reliable aerial target recognition under complex situation, an aerial target recognition method based on improved sensor credibility was proposed. Firstly, the basic theories of evidence theory and intuitionistic fuzzy sets are introduced. Then, intuitionistic fuzzy entropy is used to correct and fuse the evidence information. Then, on the basis of measuring the weight of sensors with the degree of evidence conflict, a sensor weight determination method based on index allocation was proposed, and the two methods were combined to correct and fuse the evidence information. Finally, an example is given to verify the rationality and reliability of the proposed method.
HyperKRP: A Kernel Runtime Security Architecture with A Tiny Hypervisor on Commodity Hardware

The large body of kernel code provides broad attack surfaces to exploitable bugs or misconfigurations. Current mitigations are difficult to be integrated together or have a non-trivial performance or code size impact. Thus, systematical protection for the kernel is of critical importance and is required. In this paper, we propose a kernel runtime security architecture, called HyperKRP, to provide systematical protection for kernel code, critical kernel data, and efficient kernel page tables. We have implemented a fully working prototype for a recent Linux kernel running on the Intel x86 processor. Our prototype is compromised of three protection engines based on a small size hypervisor. The evaluation shows that HyperKRP effectively ensures kernel runtime security with acceptable overhead.
Investigate the Ensemble Model by Intelligence Analysis to Improve the Accuracy of the Classification Data in the Diagnostic and Treatment Interventions for Prostate Cancer

Class imbalance problem become greatest issue in data mining, imbalanced data appears in daily application, especially in the health care.This research aims at investigating the application of ensemble model by intelligence analysis to improving the classification accuracy of imbalanced data sets on prostate cancer.The primary requirements obtained for this study included the datasets, relevant tools for pre-processing to identify the missing values, models for attribute selection and cross validation, data resembling framework, and intelligent algorithms for base classification.Additionally, the ensemble model and meta-learning algorithms were acquired in preparation for performance evaluation by embedding feature selecting capabilities into the classification model.The experimental results led to the conclusion that the application of ensemble learning algorithm on resampled data sets provides highly accurate classification results on single classifier J48.The study further suggests that gain ratio and ranker techniques are highly effective for attribute selection in the analysis of prostate cancer data.The lowest error rate and optimal performance accuracy in the classification of imbalanced prostate cancer data is achieved using when Adaboost algorithm is combined with single classifier J48.
Research on Recommendation Algorithm Based on E-commerce User Behavior Sequence

With the rapid rise and development of the Internet, e-commerce has become one of the most promising and fastest-growing industries. we propose an architecture of a personalized recommendation system based on offline mining, real-time mining, and deep learning technology. First, through the Flume + Kafka + Spark Streaming system, user behavior data is collected and stored in the business database after data preprocessing First, through the Flume + Kafka + Spark Streaming system, user behavior data is collected and stored in the business database after data preprocessing, which is the work preparing for the next step of data mining. Data mining includes offline mining and real-time mining. Offline mining mainly runs MapReduce on the Hadoop platform, there are also some calculations on the Spark platform, and the calculation results are stored in the business database; The implementation of real-time mining mainly is based on the message subscription of the Kafka cluster, and get real-time consumption statistics through Spark clusters; Deeplearning4j, a deep learning framework, runs on a multi-GPU Spark distributed cluster, which can be optimized the algorithm model online and generate recommendation results to push to the real-time business database and give feedback to the users.; It can also extract features from these data for training automatically, which is conducive to improving the quality and efficiency of users' shopping decisions, and improve the platform's cross-selling capabilities, shorten the user's shopping path, increase traffic conversion rates.
Multi-paths Deep Convolutional Neural Network Based on Knowledge Distillation

Recent years have seen deep convolutional neural networks present poor robustness which hinders its popularization and applications. Focusing on this issue, our paper proposes multi-paths deep convolutional neural network based on knowledge distillation, which enhances the low robustness of deep convolutional neural network to a certain extent while maintain its accuracy. The main contributions of our paper are listed as follows. Firstly, adopting internal ensemble to improve the utilization of network internal parameters; Secondly, designing lightweight model without increasing parameter scale; Thirdly, using as many features as possible to make decisions for robustness improvement. Experiments show that the algorithm is better than defensive knowledge distillation in robustness improvement by increasing the features used in model decision-making.
Code Vision: Learning Mobile Application

Technology is becoming a major part of each individual&#x2019;s life. The modern society is saturated with technology and cannot be separated from technology. Now a days, every person is using many number of devices and systems. Code Vision is a mobile application which is created for e-learning purposes. So we are emerging entertainment and education together for students to learn programming language. The aim of this article is to illustrate the leaning mobile application which is based on animated dry run of programming language C++ and Object-Oriented Programming. It is based on the basic and advance concepts of OOP and C++. Mostly people face problems like they could not be able to understand the working of programming when they initially start learning, they won&#x2019;t be able to create the logic of their own program. This application reduces the confusion factor and make all the concepts clear in people&#x2019;s mind. In this article, the development process of this application has been described, and also the functionalities and significance of requirements of this application is defined. This article will be helpful in further researches and development in the field of mobile application development.
Malicious URL Classification Using Deep Neural Network

One of the most serious cybersecurity threats has been discovered as malicious URL classification. People are duped into believing they are visiting a respectable website, and they are persuaded to provide their credentials by malicious websites. Many cyber crimes, such as phishing, cyberbullying, spamming, and malware, are founded on the hosting of dangerous URLs. The existing methods are slow and rely on manual feature engineering. In this research, we look at Convolutional Neural Networks, Long Short Term Memory, and a hybrid model of CNN followed by LSTM, and we propose a new strategy based on the results. Character embedding, in which each character is treated as a token, and word embedding, in which each word is treated as a token, are both used in our technique. In comparison to the word embedded hybrid mode1(0.988), the character embedded hybrid model has a higher accuracy (.996).
Multi-vendor IoT-based Resource Sharing using OAuth and Blockchain

Scarcity of resources is a major problem associated with growing urbanization across the globe. A natural way to address the issue is to share the resources. This solution is not only cost-effective but also sometimes environment friendly. However, sharing resources among people in a trustless environment (in big cities, people do not know each other) and preserving the participant’s privacy are important issues to be addressed. Internet-of-Things and Blockchain together are being considered by the researchers to develop solutions for different smart city applications. In this paper, we propose a privacy preserving blockchain-based framework for multi-vendor IoT-based resource sharing for smart cities. OAuth is integrated with the proposed framework to authorize the blockchain to make the payment from the requester to the service provider. The proposed solution is implemented on the EOS blockchain instead of Ethereum because EOS does not require a fee for executing the smart contract and can conduct millions of transactions per second.