Reviewerly/exHarmony huggingface dataset:
FASCINET: A Fully Automated Single-Board Computer Generator Using Neural Networks

Designing single-board computers (SBCs) is becoming more challenging given the growing number of discrete components that are made available and the rate at which this number grows. Keeping track of all available components options, revisions, and functionalities is challenging for SBC designers who are striving for faster design cycles. Moreover, the procedure of deciding peripheral components, their values, and connections of an SBC is not only difficult because of various parameters that need to be considered but also is time consuming as there exist numerous components on a typical SBC nowadays. In this article, an SBC generator tool, FASCINET, is presented that uses a neural network (NN) model to design customized peripheral circuits for SBCs. The tool creates a large commercial off-the-shelf database (COTS DB) of existing components, efficiently searches through them, and selects optimal components for both main and peripheral components based on the user’s requirements. Creating such a broad COTS DB requires processing abundant datasheets. A manual approach is time consuming, even if only a fraction of all available datasheets is considered. In order to automate this process, this article describes a novel NN-based approach for automatically categorizing datasheets and proposes an extraction technique for parsing relevant functional information from tables within. Our evaluation using a test set that contains over 770 000 components shows that the category of datasheets is identified correctly over 95% of the time. Additionally, the table extractor has a precision above 96%. Our proposed fully autonomous SBC design approach reduces the time for generating the schematic of an SBC to as little as 2 min. For validating the accuracy of our model, the netlists of 400 SBCs designed by FASCINET are compared to the human-designed versions. This evaluation shows that FASCINET is able to design SBCs that are identical to the manually designed ones except for minor differences.
Teaching Mode of Art Design Wisdom Course Based on VR Technology

With the continuous development of Internet and computer technology and the increasing popularity of mobile terminal equipment, new teaching media continue to emerge. After multimedia, a new teaching media, Virtual Reality technology, has emerged in the education field. The purpose of this article is to study the teaching mode of art design wisdom course based on VR technology. This article takes virtual reality technology as a new type of educational technology, applies it to the teaching research of “Virtual Fashion Art Design”, and summarizes a teaching model suitable for VR experiments through educational practice. This paper proposes the application design and realization of a smart classroom teaching model based on VR technology. From the perspective of ecological classroom teaching, with intelligence and student-centered learning environment as the basic concept, cultivate value orientation, student action ability, thinking quality and creative potential, and lay a solid foundation for lifelong learning and professional career. Develop life values and promote the cultivation of intelligent talents. Experimental research shows that 60% of students believe that the VR-based flipped classroom wisdom teaching model can well stimulate students' interest in learning.
Depth Pruning with Auxiliary Networks for Tinyml

Pruning is a neural network optimization technique that sacrifices accuracy in exchange for lower computational requirements. Pruning has been useful when working with extremely constrained environments in tinyML. Unfortunately, special hardware requirements and limited study on its effectiveness on already compact models prevent its wider adoption. Depth pruning is a form of pruning that requires no specialized hardware but suffers from a large accuracy falloff. To improve this, we propose a modification that utilizes a highly efficient auxiliary network as an effective interpreter of inter-mediate feature maps. Our results show a parameter reduction of 93% on the MLPerfTiny Visual Wakewords (VWW) task and 28% on the Keyword Spotting (KWS) task with accuracy cost of 0.65% and 1.06% respectively. When evaluated on a Cortex-M0 microcontroller, our proposed method reduces the VWW model size by 4.7× and latency by 1.6× while counter intuitively gaining 1% accuracy. KWS model size on Cortex-M0 was also reduced by 1.2× and latency by 1.2× at the cost of 2.21% accuracy.
Privacy-Enhanced Access Control for Providing Efficient Security in Cloud Environment

Cloud computing is the distributed collection of virtual computing devices which are used to store, process and outsource the data for the different vendors in dynamic manner. Data which is being the most valuable asset in cloud computing is vulnerable to security and privacy of data that is being shared and used in the cloud environment. Guaranteeing security and privacy for information management and query handling in the cloud is very much needed to provide privacy to user’s data which is stored in the cloud environment. Additionally, the main aim should be at protection from cyber-attacks and also preserve the information access and the authority sharing in multiuser collective cloud environments. To overcome these issues, a privacy preserving with enhanced access model has been proposed by using the Advanced Encryption Standard (AES) algorithm along with distributed key management technique which aims at securing data which is stored within a cloud environment. Moreover, a trusted third-party along with the data owners and data users is employed in the proposed system to make sure that authentication and authorization are carried out with independently without cloud server. By doing so, the proposed system can provide an efficient authentication with trusted third party and can provide privacy-enhanced access control to the data or information stored in the cloud environment. The proposed system is implemented using CloudSim with realistic simulation parameters. The simulation results justifies that the proposed system has better DoS attack detection and has better overhead in terms of computation, communication and storage.
Data-driven online traffic reconstructions: Interactively optimizing in virtual reality

It is important to realize three-dimensional (3D) online traffic reconstructions based on the video data. Existing methods always use structured data to generate 3D traffic reconstructions and do not process unstructured pixel data. In this paper, we propose a 3D online traffic reconstruction method driven by video data and virtual traffic simulations. There are mainly two steps in our method. The first step is data preprocessing. We extract vehicles’ trajectories from video data and transform them into the geodetic coordinate system. The transformation parameters are optimally solved in multiple human–computer interactions. The second step is trajectory reconstructions. We use the traffic simulation method to predict vehicles’ trajectories and use the Kalman filter to optimize vehicles’ trajectories according to the extracted trajectories and the predicted trajectories. We also optimize the traffic simulation parameters iteratively according to the optimized trajectories when needed. The experimental results show that our method can provide realistic and smooth traffic reconstructions. It has nearly the same efficiency as that of existing online multiple object tracking methods.
Image Encryption Using Chaotic Neural Network

In this paper, we will introduce a new concept; an algorithm is used to encrypt and decrypt the images suggested using a neural network concept. In an anarchic system, giving you a similar output where input data is the same, it is the unexpected, in the sense that no one can tell in advance how the system will change with a slight change in the data that will be entered into the system. This paper aims to use artificial neural networks to the chaos in the crypto sector.
Embodying an artificial hand increases blood flow to the investigated limb

The autonomic nervous system is the main determinant of the blood flow directed towards a body part, and it is tightly connected to the representation of the body in the brain; would the experimental modulation of the sense of limb ownership affect its blood perfusion?In healthy participants, we employed the rubber hand illusion paradigm to modulate limb ownership while we monitored the brachial artery blood flow and resistance index within the investigated limb.In all conditions with brush-stroking, we found an initial drop in the blood flow due to tactile stimulation. Subsequently, in the illusion condition (where both the rubber and real hand synchronous brush-stroking were present), the blood flow rose significantly faster and reached significantly higher values. Moreover, the increase in blood flow correlated with the extent of embodiment as measured by questionnaires and correlated negatively with the change of peripherical vascular resistance.These findings suggest that modulating the representation of a body part impacts its blood perfusion.
Development of a Sign Language Glove Translator Using Microcontroller and Android Technology for Deaf-Mute

There are about 70 million deaf and mute people in the world using sign language, but apart from them, only a few people know how to communicate with them using sign language. The goal of the project is to develop a sign language glove translator that can convert American Sign Language gestures into text and speech. The prototype can translate within 3 seconds numbers one to ten, alphabet, and 50 basic words/phrases using flex sensors and accelerometer. The output of the translation can be seen through an Android application. The glove translator will be connected through Bluetooth to allow wireless activity. The prototype has an accuracy rate of 95%. The aim of this study is to help the deaf-mute community talk to those that do not understand sign language.
Comparing deep and shallow neural networks in forecasting call center arrivals

Abstract Forecasting volumes of incoming calls is the first step of the workforce planning process in call centers and represents a prominent issue from both research and industry perspectives. We investigate the application of Neural Networks to predict incoming calls 24 hours ahead. In particular, a Machine Learning deep architecture known as Echo State Network, is compared with a completely different rolling horizon shallow Neural Network strategy, in which the lack of recurrent connections is compensated by a careful input selection. The comparison, carried out on three different real world datasets, reveals better predictive performance for the shallow approach. The latter appears also more robust and less demanding, reducing the inference time by a factor of 2.5 to 4.5 compared to Echo State Networks.
Characteristics of Field Operation Data for Optical Transceivers in Hyperscale Data Centers

We collect and analyze field operation data of optical transceivers in hyperscale data centers, including temperatures, infant mortality and causes of failures, to shed some light on reliability of optical transceivers in data centers.
Multilevel Traceability Links Establishments Between SOFL Formal Specifications and Java Codes Using Multi-dimensional Similarity Measures

Linking the components in a formal specification to those in the corresponding program is a prerequisite for formal specification-based program fault detection. Existing traceability link techniques for reducing manpower and time cost suffer from the limitation in effectiveness due to over dependency of textual similarity. Unlike the existing work, this paper presents an automatic method for constructing traceability links between SOFL formal specifications and Java codes, taking semantical, structural, functional, and relational similarities measures into account. It operates at multiple levels of a formal specification, such as data flows, processes, and modules, to establish finegrained link relationships between artifacts. Further, a comparative evaluation of the proposed method, using two selected modules of the SOFL formal specification of a critical ATM system and its Java implementation with 951 code of lines, demonstrates an improvement in precision and more generality than existing latent semantic indexing that is an information retrieval-based method.
GraSP

Nearest Neighbor Search (NNS) has recently drawn a rapid growth of interest because of its core role in high-dimensional vector data management in data science and AI applications. The interest is fueled by the success of neural embedding, where deep learning models transform unstructured data into semantically correlated feature vectors for data analysis, e.g., recommending popular items. Among several categories of methods for fast NNS, graph-based approximate nearest neighbor search algorithms have led to the best-in-class search performance on a wide range of real-world datasets. While prior works improve graph-based NNS search efficiency mainly through exploiting the structure of the graph with sophisticated heuristic rules, in this work, we show that the frequency distributions of edge visits for graph-based NNS can be highly skewed. This finding leads to the study of pruning unnecessary edges to avoid redundant computation during graph traversal by utilizing the query distribution, an important yet under-explored aspect of graph-based NNS. In particular, we formulate graph pruning as a discrete optimization problem, and introduce a graph optimization algorithm GraSP that improves the search efficiency of similarity graphs by learning to prune redundant edges. GraSP enhances an existing similarity graph with a probabilistic model. It then performs a novel subgraph sampling and iterative refinement optimization to explicitly maximize search efficiency when removing a subset of edges in expectation over a graph for a large set of training queries. The evaluation shows that GraSP consistently improves the search efficiency on real-world datasets, providing up to 2.24X faster search speed than state-of-the-art methods without losing accuracy.
Reference Points Based RNS Reverse Conversion for General Moduli Sets

Residue Number System (RNS) is a non-positional number system which can lead to parallel arithmetic operations, thereby achieving high performance in hardware implementation. However, the complexity of the reverse conversion (from RNS to binary) significantly limits the efficiency of the RNS for most modern tasks. The article proposes a new approach to the implementation of one of the most difficult and important tasks of RNS – reverse conversion. A mixed approach based on both arithmetic calculations and a Look-up-Table (LUT) implementation is proposed. The advantage of the method lies in its flexibility, since it allows one to vary the proportion of computations performed in LUTs. This is achieved by recursively implementing LUTs using the proposed reference points method. The proposed approach results in ability to create the best solution for the given performance criteria and hardware constraints. Experimental results show that the proposed method significantly outperforms the method based on the Chinese Remainder Theorem in terms of speed at the expense of moderate hardware costs. At the same time, it is possible to create more compact solutions due to the loss in timing. The issues of optimization and design automation of reverse converters based on the reference points method require further more detailed study.
Few-Shot Multihop Question Answering over Knowledge Base

KBQA is a task that requires to answer questions by using semantic structured information in knowledge base. Previous work in this area has been restricted due to the lack of large semantic parsing dataset and the exponential growth of searching space with the increasing hops of relation paths. In this paper, we propose an efficient pipeline method equipped with a pretrained language model. By adopting beam search algorithm, the searching space will not be restricted in subgraph of 3 hops. Besides, we propose a data generation strategy, which enables our model to generalize well from few training samples. We evaluate our model on an open-domain complex Chinese question answering task CCKS2019 and achieve F1-score of 62.55% on the test dataset. In addition, in order to test the few-shot learning capability of our model, we randomly select 10% of the primary data to train our model, and the result shows that our model can still achieves F1-score of 58.54%, which verifies the capability of our model to process KBQA task and the advantage in few-shot learning.
Gold Dataset for the Evaluation of Bangla Stemmer

Stemming is a preprocessing task for Natural Language Processing(NLP) that involves normalizing inflected words representing the same concept of the original word. Steaming is a process of text normalization that transforms a slated word into its root form. It has a great impact in different applications in NLP and Natural Language Understanding. One of the biggest challenges in Bangla stemming is to collect the rules that are associated with Bangla word stemming and standard dataset for testing the accuracy. This paper presents a summery of rules associated with Bangla stemming and a gold standard dataset corpus which will helps to testify the stemming algorithm. We verify the rules and using our rules and corpus we tested with existing Bangla stemming algorithms and found that the proposed corpus makes a difference with existing techniques.
PRShare: A Framework for Privacy-preserving, Interorganizational Data Sharing

We consider the task of interorganizational data sharing, in which data owners, data clients, and data subjects have different and sometimes competing privacy concerns. One real-world scenario in which this problem arises concerns law-enforcement use of phone-call metadata: The data owner is a phone company, the data clients are law-enforcement agencies, and the data subjects are individuals who make phone calls. A key challenge in this type of scenario is that each organization uses its own set of proprietary intraorganizational attributes to describe the shared data; such attributes cannot be shared with other organizations. Moreover, data-access policies are determined by multiple parties and may be specified using attributes that are not directly comparable with the ones used by the owner to specify the data. We propose a system architecture and a suite of protocols that facilitate dynamic and efficient interorganizational data sharing, while allowing each party to use its own set of proprietary attributes to describe the shared data and preserving the confidentiality of both data records and proprietary intraorganizational attributes. We introduce the novel technique of Attribute-Based Encryption with Oblivious Attribute Translation (OTABE) , which plays a crucial role in our solution. This extension of attribute-based encryption uses semi-trusted proxies to enable dynamic and oblivious translation between proprietary attributes that belong to different organizations; it supports hidden access policies, direct revocation, and fine-grained, data-centric keys and queries. We prove that our OTABE-based framework is secure in the standard model and provide two real-world use cases.
An improved object detection algorithm based on YOLO*

Abstract Accuracy and speed have always been a measure of the performance of object detection algorithms. The current algorithms have reduced the detection speed on the basis of increasing a certain accuracy due to their complex structure. In response to this problem, this paper uses the RepVGG network to improve the original YOLOv3 structure, which uses a diversified branch structure to enhance the network feature extraction ability during training and transforms the training model into an equivalent VGG-like topology network model during inference. In addition, we use ASFF to deal with the problem of mutually restricted scales. Experiments show that compared with the original algorithm, the improved algorithm increases mAP by 0.51 on the VOC data set, and at the same time the speed increases by 10%.
Indian Currency Classification and Fake Note Identification using Feature Ensemble Approach

Visually challenged people face a lot of difficulty in currency recognition and counterfeit notes identification. The change of currency notes during different time frames also adds more difficulty in recognizing features in currency notes manually by the visually impaired. Further, the number of counterfeit notes in circulation in India is increasing every year. Computer-aided currency recognition and fake note detection can help people in the identification of currency denomination without any dependency on manual intervention. In this work, we have proposed a currency recognition algorithm that can classify an Indian currency and help in identifying the fake notes without manual intervention and thereby preventing their circulation in the country. In this work, Chan Vese Segmentation is used to segment the security features of a note and then an ensemble of classifiers is used for classification and fake note identification. Experimental results show promising results even on the small dataset. The method can be extended to currency recognition for different countries.
A Study of Underwater Image Pre-processing and Techniques

Submerged pictures are procured with the assistance of submerged vehicles, which are utilized for the study of acoustic sensors in the deep waters as they have the capacity of far-off detection. These kinds of vehicles are distantly or remotely handled by people. The investigation of the information is done, and it experiences the low quality of pictures as the spread of light underwater differs. Thus, the pictures which are submerged underwater experience the ill effects of low differentiation, reduced tones, obscuring, and non-uniform lighting. In this way, it is expected to pre-measure these pictures prior to picture handling. Light constriction is the primary issue, which prompts obscuring, shade lessening, and alteration of shading. These aspects are accountable for the introduction of noise, color cast, lower contrast together with lower brightness. Presenting disparate pre-processing techniques for solving the noisy image issue is the main motivation of this work. To manage submerged picture handling, the fundamental physical science of light spread should be perceived. Assimilation and dissipating occur because of water, and the segments are called gliding particles. In the preparation of picture upgrade and highlight, extraction is done, where the improvement alludes to improve the picture quality and highlight extraction alludes to estimating the picture quality through various boundaries. Various calculations and techniques are utilized to extemporize the nature of the picture. This paper analyzes disparate pre-processing methods and analyzed the methods centered upon the PSNR metrics. Therefore, the analysis exhibits that the WMB achieves better PSNR contrasted with the other prevailing methods for underwater image amelioration.