Reviewerly/exHarmony huggingface dataset:
Tight Compression: Compressing CNN Through Fine-Grained Pruning and Weight Permutation for Efficient Implementation

The unstructured sparsity after pruning poses a challenge to the efficient implementation of deep learning models in existing regular architectures like systolic arrays. On the other hand, coarse-grained structured pruning is suitable for implementation in regular architectures but tends to have higher accuracy loss than unstructured pruning when the pruned models are of the same size. In this work, we propose a model compression method based on a novel weight permutation scheme to fully exploit the fine-grained weight sparsity in the hardware design. Through permutation, the optimal arrangement of the weight matrix is obtained, and the sparse weight matrix is further compressed to a small and dense format to make full use of the hardware resources. Two pruning granularities are explored. In addition to the unstructured weight pruning, we also propose a more fine-grained subword-level pruning to further improve the compression performance. Compared to the state-of-the-art works, the matrix compression rate is significantly improved from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5.88\times $ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$14.13\times $ </tex-math></inline-formula> . As a result, the throughput and energy efficiency are improved by 2.75 and 1.86 times, respectively.
AresB-Net: accurate residual binarized neural networks using shortcut concatenation and shuffled grouped convolution

This article proposes a novel network model to achieve better accurate residual binarized convolutional neural networks (CNNs), denoted as AresB-Net. Even though residual CNNs enhance the classification accuracy of binarized neural networks with increasing feature resolution, the degraded classification accuracy is still the primary concern compared with real-valued residual CNNs. AresB-Net consists of novel basic blocks to amortize the severe error from the binarization, suggesting a well-balanced pyramid structure without downsampling convolution. In each basic block, the shortcut is added to the convolution output and then concatenated, and then the expanded channels are shuffled for the next grouped convolution. In the downsampling when stride &gt;1, our model adopts only the max-pooling layer for generating low-cost shortcut. This structure facilitates the feature reuse from the previous layers, thus alleviating the error from the binarized convolution and increasing the classification accuracy with reduced computational costs and small weight storage requirements. Despite low hardware costs from the binarized computations, the proposed model achieves remarkable classification accuracies on the CIFAR and ImageNet datasets.
A Large Dataset With a New Framework for Abandoned Object Detection in Complex Scenarios

Video surveillance is a crucial part of public safety and security systems. State-of-the-art techniques either use back-tracing for owner identification or there is no provision of reporting ownership of the abandoned objects. We provide a novel object association technique which very elegantly tackles this issue. We propose a convolution neural network based framework for abandoned object localization and owner identification in video surveillance systems that performs exceptionally well on publicly available datasets and our newly developed dataset. For testing the usability and robustness of these systems, an extensive set of test cases is a prerequisite, to this end we provide an elaborate surveillance dataset covering many complex cases which are not available in existing datasets. Also, the developed dataset has been tested with various state-of-the-art techniques, showing the complexity and challenging nature of the cases. Results obtained from the proposed framework are highly motivating as it provides event detection along with the time of abandonment and ownership information, which can help further investigation and prevent unwanted incidents.
Adaptive Inference through Early-Exit Networks: Design, Challenges and Directions

DNNs are becoming less and less over-parametrised due to recent advances in efficient model design, through careful hand-crafted or NAS-based methods. Relying on the fact that not all inputs require the same amount of computation to yield a confident prediction, adaptive inference is gaining attention as a prominent approach for pushing the limits of efficient deployment. Particularly, early-exit networks comprise an emerging direction for tailoring the computation depth of each input sample at runtime, offering complementary performance gains to other efficiency optimisations. In this paper, we decompose the design methodology of early-exit networks to its key components and survey the recent advances in each one of them. We also position early-exiting against other efficient inference solutions and provide our insights on the current challenges and most promising future directions for research in the field.
Multi granular level-based IFT model for RISC-V

Tracking the information flow to mitigate the potential vulnerabilities with localization and accuracy has led to various IFT techniques with different levels of abstraction. However, each technique focusses on a particular level of granularity for information flow control which leads to limited access control or area overhead costs decreasing the precision logic of the system. This paper presents a novel approach providing both fine and coarse grain granularity by integrating Instruction level and Gate level IFT to track the data. The proposed approach translates from Instruction level to Gate level based on the user application and module instantiation.
Workload Migration across Distributed Data Centers under Electrical Load Shedding

Data centers are essential components in the current digital world. The number and scales of data centers have both increased a lot in recent years. The distributed data centers are standing out as a promising solution due to the development of modern applications which need a massive amount of computation resource and strict response requirement. However, compared to centralized data centers, distributed data centers are more fragile when the power supply is unstable. Power constraints or outages because of electrical load shedding or other reasons will significantly affect the service performance of data centers and damage the quality of service (QoS) for customers. Moreover, unlike conventional data centers, distributed data centers are often unattended, so we need a system that can automatically calculate the best workload schedule to maximize profit in such situations. In this paper, we closely investigate the influence of electrical load shedding in distributed data centers and construct a physical model to estimate the relationship among power, heat and workload. We then use queueing theory to approximate the tasks’ response time and aim to minimize the overall response time of tasks by migration. Our extensive evaluations show that our method can improve the response time with more than 9% reduction.
Anonymous Hidden Transaction Model for Blockchain Systems

Blockchain system is the novel decentralized trustworthy network and it is widely used for the digital asset management. In the bitcoin-like blockchain systems, the transactions are finished via the cryptography scripts. The user's public key hash code and address information are recorded in a locking script, and posted on the blockchain for verification. By analyzing all transaction records of an address, we can obtain the transaction behaviors of the address, including transaction frequency, number of transactions, and typical address for long-term usage. Sometimes, user privacy will be impacted according to the above analysis. To solve the problem, this paper proposes an anonymous hidden transaction model of the blockchain systems through modifying the original blockchain transaction script. In the proposed multi-layer model, the blockchain transaction system is divided into three layers. For top to bottom, they are shell account (under the public address) layer, hidden account (under the private address) layer and the user layer respectively. The hidden accounts can map to different shell account to complete the transactions. We combine the zero-knowledge proof mechanism so that the finished transaction can be verified by any user. The theoretical analysis shows that our proposed method can effectively protect transaction user information and realize truly anonymous transactions.
What can corpus software do?

This chapter focuses on the software tools available to researchers interested in carrying out corpus studies. First, the chapter describes the strengths and weaknesses of ready-built online and offline tools and compares them to custom-built do-it-yourself (DIY) tools that usually come in the form of programming scripts. Next, the chapter explains how online, offline, and DIY tools can be effectively used to analyze bottom-up language patterns, such as the word and keyword frequencies, clusters, n-grams, lexical-bundle patterns, and Key-Word-In-Context (KWIC) concordances. Then, the chapter looks at how corpus tools can be used in combination with dedicated tagging and annotation tools to investigate top-down language patterns, including cohesion, register variation, discourse structure, and pragmatic phenomenon. Next, the chapter explains the importance of data interoperability in corpus tools, which allows for data to be imported into a tool and the results from that tool to be exported for use in other tools. Finally, the chapter discusses cases when a researcher might consider programming their own custom corpus tools and introduces several resources to help them create their first scripts.
Peramalan Kedatangan Wisatawan Mancanegara Ke Indonesia Menurut Kebangsaan Perbulannya Menggunakan Metode Multilayer Perceptron

The beauty of tourist attractions in Indonesia has a certain attraction for foreign tourists to serve as a place for vacation. However, the number of visitors needs to be predicted to anticipate an increase or decrease in the number of visitors, so that the state can determine policies regarding changes in the number of visitors in the future. Forecasting is used to predict previous data patterns so that further data patterns can be known. Multilayer Perceptron (MLP) is a neural network development that can be used for modeling time series data. Several researchers have conducted research using the Multilayer Perceptron method in making predictions. Forecasting systems or forecasting are very helpful in the current era, forecasting aims to predict future conditions. Prediction results, obtained 82% accuracy for tourist predictions in Period 7, namely September 2020, 97% for Prediction Period 8, namely December 2020 so that the Number of Tourists for Period 9 is 7,106 people.
Antitampering Scheme of Evidence Transfer Information in Judicial System Based on Blockchain

In the process of handling criminal cases, it is crucial to avoid evidence tampering and ensure the integrity, consistency, and nonrepudiation of evidence transfer records, which is highly related to the fairness and credibility of the judiciary. To address this problem, we propose a consortium blockchain network to record evidence transfer events among different departments of China's judicial system. We design the format of a transaction and a block. In addition, the smart contracts for three types of transactions are also proposed. The Raft consensus algorithm is adopted to accomplish the consensus process. A security analysis shows that the proposed scheme can achieve the design goal (the integrity, consistency, and nonrepudiation of evidence transfer records stored in blockchain). Furthermore, a set of experiments were conducted to analyse the performance of the proposed scheme. The experiments results show that the throughput of the system is proportional to the send rate within a certain threshold. The latency decreases with increasing send rate if the send rate is within a certain threshold. Peer nodes in the system consume the most storage and communication cost. The values of block size and block generation interval time have a slight influence on the performance of the system.
Some Types of Computer Crime and Cybercrime in Ukraine

In this note, the most frequent types of computer crime on the Internet in Ukraine are analysed. It is suggested that international experience in computer crime investigation should be used to combat this because cybercrime has become an international problem, causing enormous damage to governments, commercial entities, and computer systems of individual users. Governments and commercial entities spend significant funds to prevent losing information and ensure its protection. Cybercrime is based on technical knowledge, which is difficult to detect or prevent. This type of criminal activity has a high latency because of the difficulty of determining the qualification of a criminal offence.
Automatic Bug Triage Using Hierarchical Attention Networks

Bug triage, which plays a critical role in software maintenance, mainly refers to the process of assigning a bug report to an appropriate developer who could fix it. Manual bug triage, especially in open-source projects, is quite a burden to developers. To address this problem, many methods have been proposed for automatic or semi-automatic bug triage, which can be observed as a task under text classification in natural language processing. In this article, we present an end-to-end approach using hierarchical attention networks to establish an automatic bug triage system. Considering syntactic and semantic information in bug reports, two methods, Word2Vec and GloVe, are provided to pre-train word vector presentation on untriaged reports to attain high speed and accuracy, respectively. Fine-tuned word vectors achieve better results during training. We validate the performance on five large-scale public datasets. From the results of the experiments, our approach achieves higher accuracy amongst existing methods based on deep neural networks.
An improved YOLOv5s for protective gear detection

Live working refers to a method of maintenance and test on high voltage electrical equipment without power failure, which requires direct contact with wires above 10kV. At present, relying on manual inspection of protective gear is time-consuming and laborious, and there are still many deaths caused by negligence at work. In this paper, we applies YOLOv5s to this task. However, due to the similar color of the detected targets and the small targets, many false detections and missed detections occurred in the results. Aiming at these problems, we propose an improved YOLOv5s-CSBN(CAM, SAM, BiFPN, New detection scale). First, We propose a new method of embedding SAM(spatial attention module) and CAM(channel attention module) to the network, with the best detection performance improvement; Second, we add a new detection scale and introduce BiFPN instead of PANet. Finally, experimental data show that the mAP@0.5% of YOLOv5S-CSBN is 2.2% higher than that of Yolov5s in our data set, with significantly reducing the false detection and missed detection caused by similar colors and small targets.
Performance evaluation of bifacial solar PV modules under different climatic regions in Nigeria

Nigeria's annual solar radiation is estimated to be between 1400 and 2500 kWh/m 2 . This has made the use of solar energy to generate power in the country feasible. We investigated the performance of a bifacial solar PV system in Nigeria under various climatic regions in this study because bifacial PV modules are known to be location-dependent. The In-Plane solar radiation received by tilted monofacial and bifacial PV modules was calculated and compared using an analytical model. In all climatic regions, the bifacial PV system receives more in-plane solar irradiance. The systems were simulated on PVsyst to determine the energy yield, and the results show that under natural ground (vegetation and sand) of the various regions and optimization of the tilt angle, the bifacial PV system yielded more energy than the monofacial system. The bifacial gain varies depending on location, and system parameters must be optimized to improve the bifacial energy gain.
Adaptive multi-histogram reversible data hiding with contrast enhancement

Unlike existing reversible data hiding with contrast enhancement (RDHCE) methods, which excessively improve the image contrast for achieving the required capacity, the proposed method improves the image contrast appropriately while providing satisfactory embedding capacity. To this end, an adaptive multi-histogram RDHCE method is proposed in this study to improve the local and global contrast by considering the local properties of the histograms. On the one hand, fuzzy C-means clustering combining multiple features that are deliberately designed for contrast enhancement is employed to generate seven sharply-distributed prediction error histograms (PEHs). Subsequently, the genetic algorithm is utilized to adaptively select the optimal pairs achieving the best embedding performance for each PEH according to the local characteristics of PEH distribution, resulting in improving the local contrast adaptively and embedding significant amount of data. Additionally, two-sided histogram shifting (HS) is utilized to improve the global contrast appropriately while embedding reasonable amount of data. The experimental results demonstrate that the proposed method achieves better local and global contrast while providing a high embedding capacity compared with other existing RDHCE methods.
Not All Samples Are Born Equal: Towards Effective Clean-Label Backdoor Attacks

Recent studies demonstrated that deep neural networks (DNNs) are vulnerable to backdoor attacks. The attacked model behaves normally on benign samples, while its predictions are misled whenever adversary-specified trigger patterns appear. Currently, clean-label backdoor attacks are usually regarded as the most stealthy methods in which adversaries can only poison samples from the target class without modifying their labels. However, these attacks can hardly succeed. In this paper, we reveal that the difficulty of clean-label attacks mainly lies in the antagonistic effects of ‘robust features’ related to the target class contained in poisoned samples. Specifically, robust features tend to be easily learned by victim models and thus undermine the learning of trigger patterns. Based on these understandings, we propose a simple yet effective plug-in method to enhance clean-label backdoor attacks by poisoning ‘hard’ instead of random samples. We adopt three classical difficulty metrics as examples to implement our method. We demonstrate that our method can consistently improve vanilla attacks, based on extensive experiments on benchmark datasets.
DPP: Data Privacy-Preserving for Cloud Computing based on Homomorphic Encryption

Cloud computing has been widely used because of its low price, high reliability, and generality of services. However, considering that cloud computing transactions between users and service providers are usually asynchronous, data privacy involving users and service providers may lead to a crisis of trust, which in turn hinders the expansion of cloud computing applications. In this paper, we propose DPP, a data privacy-preserving cloud computing scheme based on homomorphic encryption, which achieves correctness, compatibility, and security. DPP implements data privacy-preserving by introducing homomorphic encryption. To verify the security of DPP, we instantiate DPP based on the Paillier homomorphic encryption scheme and evaluate the performance. The experiment results show that the time-consuming of the key steps in the DPP scheme is reasonable and acceptable.
Designing a Mobile Technology Capacity Building Framework for Workplace Learning

Although workplace learning (WPL) and mobile learning are both university priorities, surprisingly they rarely intersect in practice. This chapter reports on a multi-university project that explored how to enhance WPL with mobile technology on placement and resulted in the development of the mobile technology capacity building (MTCB) framework for WPL. We first outline our method for gathering data. We then discuss our findings, which provided evidence that there is value in using mobile technology to enhance WPL and helped us identify seven themes that underpin a productive use of mobile technology for WPL and the five learning dimensions to foster student agency. Finally, we discuss how the seven themes are related to the five learning dimensions.
Supporting tourism with public interactive displays

Interactive displays are common in public and semi-public areas, such as museums, shopping malls, train stations, and even on streets. Especially with the emergence of new ways of interaction, interactive displays could be introduced to popular tourist attractions to support tourism. The aim of this tutorial is to prepare its participants for designing such interactive public displays. The tutorial will first introduce participants to state of the art in interactive public displays. This will be followed by a city tour where a number of local attractions are visited. Finally, the participants will brainstorm and create concepts for novel interactive public displays that aim to improve the experience of visiting tourist attractions.
Cloud Based Platform Design for Court File Sharing

Courts are very important organization over the whole world they have a vital roles for countries to make this organization secure reliable and flexible this organization must be related with information technology (IT).Information technology has many alternatives like we can make desktop application for the court or we can make web based application these all technologies can make the organization modernize but they have some drawback they need maintenance that can be complex for user, need very skilled person, and there will be other problems so to be over came over this problems information technology gives us another new technology that is cloud computing.In courts there are a lot of file all this files are shared by different employers in the institution.inthese time the manual file sharing system is not more secure, not fast, user cannot get the file when they needed and there are a lot of complain that raised by user sometime file cannot available or there is a duplication of file so, for making this file sharing more secure, fast, on time and having a file at any time when they need the study use cloud computing technology.Open Shift Online 3.5 is used for doing the application.
Anomalies in the Behaviour of a Modularity Inducing Problem Domain

Espinosa-Soto and Wagner (2010) introduced a domain with weak assumptions on biology and environment, where modular structures emerge under simple evolutionary processes. We found a number of anoma...