Reviewerly/exHarmony huggingface dataset:
Real-Time Human Action Recognition Using Deep Learning Architecture

In this work, efficient human activity recognition (HAR) algorithm based on deep learning architecture is proposed to classify activities into seven different classes. In order to learn spatial and temporal features from only 3D skeleton data captured from a “Microsoft Kinect” camera, the proposed algorithm combines both convolution neural network (CNN) and long short-term memory (LSTM) architectures. This combination allows taking advantage of LSTM in modeling temporal data and of CNN in modeling spatial data. The captured skeleton sequences are used to create a specific dataset of interactive activities; these data are then transformed according to a view invariant and a symmetry criterion. To demonstrate the effectiveness of the developed algorithm, it has been tested on several public datasets and it has achieved and sometimes has overcome state-of-the-art performance. In order to verify the uncertainty of the proposed algorithm, some tools are provided and discussed to ensure its efficiency for continuous human action recognition in real time.
Mobile Based Offline Handwritten Signature Forgery Identification using Convolutional Neural Network

A signature is one of the official biometric identification media that has special characteristics that are difficult to distinguish just by comparing with existing signatures. This causes irresponsible people to use signatures as objects of forgery. In addition, handwritten signature identification is a challenge because it has less information when compared to digital signatures. This research proposes a new approach to identify handwritten signature forgeries using a convolutional neural network (CNN) using mobile-based. Two model architectures are compared with the same testing scenario. The first architecture is a simplification of the Siamese convolutional network namely adjusted CNN. The second architecture is a standard CNN with 6 convolution layers which in this study is called general CNN. CNN is used for both feature extraction and classification processes. The dataset is built with a limited number of individuals consisting of genuine and forged signatures. Based on the experiments, the accuracy of each network reached 88.89% for adjusted CNN and 84.45% for general CNN. Adjusted CNN then embedded in a mobile-based application to identify the signature directly. The application that is built makes the process of identifying signatures more ease.
An Improved Lightweight Traffic Sign Detection Model for Embedded Devices

The target detection algorithm based on the YOLO-v4 model can realize real-time traffic signs because of high detection accuracy and fast detection speed. Owing to the lack of sufficient computing resources and storage space in vehicles and other embedded devices, it is difficult to deploy this kind of computing model with high requirements for power consumption. With the continuous development of intelligent transportation technology and the rise of advanced driving assistance systems. This paper proposes an improved lightweight traffic sign detection model called M-Yolo (micro-Yolo) to solve this problem. First, the feature extraction network of this model is changed from CSPDarknet53 to MobileNetv3-large network structure. Then, the SiLU activation function is used instead of the ReLU activation function in the MobileNetv3 shallow network, which further improves the detection accuracy of the algorithm and optimizes the convergence effect of model training. Finally, use the Gaussian loss function as the positioning loss of the bounding box. Experimental results show that the algorithm can guarantee real-time performance and detection accuracy and perform real-time detection tasks for embedded devices with low computing power and low storage capacity.
Hybrid Cloud Data Protection Using Machine Learning Approach

In today’s digital world, information created by Internet of Things (IoT) devices has expanded drastically. This expansion is due to an increase in the number of IoT devices associated with the internet. Hybrid cloud computing provides enormous support to these emerging IoT devices in processing vast data. However, security is a challenging issue because of the integration of IoT and hybrid cloud. To achieve a sufficient level of hybrid cloud IoT security, a combination of Enhanced C4.5 machine learning algorithm and Dynamic Spatio Role-Based Access Control Algorithm is introduced. In this approach, the data users are classified using the Enhanced C4.5 algorithm and the user’s level of cloud data access is restricted using the Dynamic Spatio Role-Based Access Control Algorithm. As a result, the major security issues pertaining to IoT cloud are addressed. The security framework also uses a deduplication algorithm for eliminating redundant data and significantly improving IoT data storage requirements.
Automatic Algorithm for Fractal Plant Art Image Similarity Feature Generation

With the popularity of smart devices and the Internet, the volume of multimedia data is growing rapidly, and content-based image retrieval (CBIR) can search for similar images from large-scale images to realize the utilization of the data. For data owners, outsourcing the management and maintenance of image data to cloud service providers can effectively reduce costs, but there is a privacy leakage problem. In this paper, we focus on image feature extraction, index design, and image similarity recognition methods under a dual server model with content-based image security similarity recognition as the research topic, the work done such as proposing a BOVW (Bag of Visual Word) feature-based image security similarity recognition scheme. The scheme combines SIFT (scale-invariant feature transform) feature secure extraction and locally sensitive hashing algorithm to achieve secure extraction of BOVW features of images. To protect the BOVW features of images, an inverted index based on word frequency division is designed, the index is stored in chunks, and an image secure similarity recognition scheme based on CNN (convolutional neural networks) features is proposed. The scalable hash index based on dimensional division is designed based on the image CNN features secure extraction algorithm. The security and performance of the proposed scheme are theoretically analyzed and experimentally verified. Based on different image datasets, the impact of different parameters on the performance of the scheme is tested, and optimized parameters are given. The experimental results show that the proposed scheme in this paper can effectively improve the efficiency of analyzing the similarity of plant botanical art images compared to the existing schemes.
The Method for Optimum Design of Water Rocket Flight Stability Performance Conditions Using CAE with T Method and Robust Parameter Design

A water rocket is a rocket system that obtains thrust by injecting water with compressed air of up to about 8 atmospheres.It is usually manufactured using a pressure-resistant PET bottle.The mechanical elements and principles contained in the water rocket have much in common with the actual small rocket system, and are suitable as educational and research teaching materials in the field of mechanics.Especially in the field of disaster prevention and mitigation, the use of water rockets is being researched and developed as a rescue tool in the event of a flood or earthquake as a disaster countermeasure.However, since the water rocket is a flying object based on the mechanical principle, it is important to ensure the accuracy and stability of the flight path.In this paper, a mechanical simulator is developed with a numerical calculation program based on the mechanical consideration of water rocket flight performance.In addition, the correlation between the flight distance obtained in the simulation and the estimated flight distance is analyzed by applying a multivariate analysis method and verifying the validity of the flight distance calculated from the result.Based on the verification results, we will apply a statistical optimization method to approach the optimization of flight stability performance conditions for water rockets.
ascCAPTCHA: an Invisible Sensor CAPTCHA for PCs Based on Acoustic Side Channel

Our growing reliance on the digital world has caused a similar growth in the sophistication of bots trying to impersonate humans. The most classic tool to tell human and computers apart is the CAPTCHA, however CAPTCHAs based on cognitive challenges are becoming either insecure or very difficult to be solved by humans too. A possible solution is leveraging the rich sensor set of modern mobile devices to capture the physical nature of humans while they are interacting with the system, however, traditional PCs do not have the same opportunity. In this paper we describe ascCAPTCHA, a CAPTCHA based on an acoustic side-channel that leveraging a simple microphone is compatible with PCs lacking the rich sensor set of smart devices.
Leveraging HW Approximation for Exploiting Performance-Energy Trade-offs Within the Edge-Cloud Computing Continuum

Today, the need for real-time analytics and faster decision making mechanisms has led to the adoption of hardware accelerators, such as GPUs and FPGAs, within the edge-cloud computing continuum. Moreover, the need for energy-, yet performance-efficient solutions both in the edge and cloud has led to the rise of approximate computing as a promising paradigm, where “acceptable errors” are introduced to error-tolerant applications, thus, providing significant power-saving gains. In this work, we leverage approximate computing for exploiting performance-energy trade-offs of FPGA accelerated kernels with faster design time though an extended source-to-source HLS compiler based on Xilinx Vitis framework. We introduce a novel programming interface that operates at a high level of abstraction, thus, enabling automatic optimizations to the existing HLS design flow supporting both embedded and cloud devices through a common API. We evaluate our approach over three different application from DSP and machine learning domains and show that a decrease of 27% and 28% in power consumption, 61% and 69% in DSP utilization and 7% in clock period is achieved for Alveo U200 and ZCU104 FPGA platforms, on average.
AnswerSumm: A Manually-Curated Dataset and Pipeline for Answer Summarization

Community Question Answering (CQA) fora such as Stack Overflow and Yahoo!Answers contain a rich resource of answers to a wide range of community-based questions.Each question thread can receive a large number of answers with different perspectives.One goal of answer summarization is to produce a summary that reflects the range of answer perspectives.A major obstacle for this task is the absence of a dataset to provide supervision for producing such summaries.Recent works propose heuristics to create such data, but these are often noisy and do not cover all answer perspectives present.This work introduces a novel dataset of 4,631 CQA threads for answer summarization curated by professional linguists.Our pipeline gathers annotations for all subtasks of answer summarization, including relevant answer sentence selection, grouping these sentences based on perspectives, summarizing each perspective, and producing an overall summary.We analyze and benchmark state-of-the-art models on these subtasks and introduce a novel unsupervised approach for multi-perspective data augmentation that boosts summarization performance according to automatic evaluation.Finally, we propose reinforcement learning rewards to improve factual consistency and answer coverage and analyze areas for improvement.
Performance Evaluation of a Hybrid Dual-Axis Solar Tracking System

In a photo-voltaic solar energy system, solar tracking can harness more energy compared to a fixed system. This paper presents a design of a Manual/Automatic Hybrid Dual Axis Solar Tracking System that tracks the sun's daily and seasonal motions. The hybrid tracker tries to ensure that the solar panel is always positioned perpendicular to the solar incident radiation by operating the tracker along two axes, of east to west (daily motion) and north to south (seasonal motion). The automatic tracking includes Time based Chronological Tracking and Light Dependent Resistor (LDR) based Active Tracking. Daily tracking is operated by a stepper motor while seasonal tracking is done manually by a gear system once a month. Results of the study show that the proposed hybrid dual-axis solar tracking system is capable of improving the overall energy efficiency by around 35%, over a fixed installation, in an economical manner.
Building a Vietnamese Dataset for Natural Language Inference Models

Natural language inference models are important resources for many natural language understanding applications. These models are possibly built by training or fine-tuning using deep neural network architectures for state-of-the-art results. This means high-quality annotated datasets are important for building state-of-the-art models. Therefore, we propose a method of building Vietnamese dataset for training Vietnamese inference models which work on native Vietnamese texts. Our method aims at two issues: removing cue marks and ensuring the writing-style of Vietnamese texts. If a dataset contains cue marks, the trained models will identify the relation between a premise and a hypothesis without semantic computation. For evaluation, we fine-tuned a BERT model on our dataset and compared it to a BERT model which was fine-tuned on XNLI dataset. The model which was fine-tuned on our dataset has the accuracy of 86.05% while the other has the accuracy of 64.04% when testing on our Vietnamese test set. This means our method is possibly used for building a high-quality Vietnamese natural language inference dataset.
A Hybrid Data Manipulation Approach for Energy and Latency-Efficient Vision-Aided UDNs

The combination of deep learning (DL) and computer vision (CV) is shaping the future of wireless communications by supporting the operations of ultra-dense networks (UDNs). However, vision-aided wireless communications (VAWC) are highly dependent on DL algorithms that rely on a wide range of multimodal data stored at a central location. Although the performance of the DL model is improved when the model becomes deeper, the need for a large number of datasets for model training incurs more computational complexity in terms of model training time and storage size. Hence, the energy efficiency of the network will become worse due to the higher energy costs associated with model training and transmitting a large amount of data over wireless links. Therefore, a crit-ical challenge is to reduce the computational complexity and bandwidth utilisation of DL-based vision-aided UDNs without compromising their performance. In this paper, we adopt single-channel (SICH) images, joint photographic expert group (JPEG) image compression (COMP), and object detection (ODET) to form a hybrid data manipulation technique. This technique can reduce the model computation cost and data storage volume, as well as alleviate the transmission burden on the wireless links to make future wireless networks more reliable and energy efficient. Specifically, this technique is used to manipulate datasets before using them in model training. Compared to reference datasets, simulation results show that our hybrid technique achieves the best performance in reducing the model computation by 34%, a significant reduction of 86% in memory size for data storage, reducing data transmission time by 83%, and 82.5% more energy efficient networks.
Energy and Resource Consumption in Cryptocurrency Mining: A Detailed Comparison

Cryptocurrency can be a relatively new combination of cryptocurrency and currency in the cash space and is widely used around the globe. However, the entity of the blockchain program area is expected to reshape the renewable energy market for the usability of modern virtual currencies. Therefore, this study makes a specialty of various experiments on mining potency, especially for dogecoin mining. A targeted assessment of carried out a mathematics evaluation of information in an exceptional benchmark and test outcomes of Dogecoin mining is conducted. The goal of this paper is to illustrate the energy consumption and performance of the mining of various cryptocurrencies. The renewable electricity zone could be restructured in blockchain applications. However, research on the use of electricity in digital currencies is lacking. Therefore, nine types of cryptocurrencies and 10 algorithms were examined in this take a look at for mining effectiveness. A comparison was made of statistical data analysis in a Dogecoin mining benchmark and experimental results. This research subsequently provided an estimate of the Dogecoin mining activity's gross electricity consumption. The results show that the hashing algorithm defines primarily the mining performance.
Implementation of Web-based Interactive Learning Platform for User Interface Design in Android Programming Learning Assistance System

According to the popularity of Android smartphone devices, the demand for Android application programmers has been increasing rapidly. Due to this fact, the subject of mobile application programming based on Android smartphones has become one of the important subjects in software development fields. In Android applications, the user interface takes an important role to make it interactive. Based on previous studies, the Android Programming Learning Assistance System (APLAS) has been used as a self-learning platform for Android programming studies. In this paper, we propose a web-based platform in APLAS for learning user interface design interactively using XML code. The 15 learning topics as learning materials cover the learning of UI layout and application resources. Students will get an assignment to be solved in each topic and the answer can be validated automatically on the server. To evaluate the effectiveness of this platform, we asked 40 students in an IT department in Indonesia to solve all topics. As the result, all students have successfully solved the 15 provided topics on schedule. Also, they gave positive comments regarding the ease of the learning process, which has confirmed the feasibility of our proposal to support learning UI design using XML.
Visualization Co-Design with Prostate Cancer Survivors who have Limited Graph Literacy

Visualizing patient-reported outcomes overtime has become a common strategy to help patients track their health. However, traditional line graphs and bar charts might be less accessible to people with limited numeracy and graph literacy. Although patient-reported outcomes (PRO) among prostate cancer survivors may be collected with extant validated instruments, such as the Expanded Prostate Cancer Index (EPIC-26), survivors with limited numeracy and graph literacy may not find traditional charts and graphs accessible. The Graphical Representation of Symptoms of Prostate Cancer (GRASP) research team embarked on a 4-stage design process with prostate cancer survivors with limited graph literacy to investigate design considerations for timeline visualizations, including preferences, acceptability, and understandability. Through focus groups, surveys, and the recurring engagement of a community-based Patient Advisory Board, we identified 3 design approaches that reflect the needs of low graph literacy patients as well as show promise to increase acceptance and comprehension. We are developing these designs into interactive prototypes for evaluation through user testing. Findings from our design process provide insight into effective strategies for engaging vulnerable patients for visualization co-design using in-person and remote methods. Finally, the design considerations we identified for prostate cancer survivors with limited graph literacy that may provide insight for visualizations of PROs for other vulnerable groups.
An Implicit Approach for Visual Data: Compression Encryption via Singular Value Decomposition, Multiple Chaos and Beta Function

This paper proposes a digital image compression-encryption scheme based on the theory of singular value decomposition, multiple chaos and Beta function, which uses SVD to compress the digital image and utilizes three way protections for encryption viz. logistic and Arnold map along with the beta function. The algorithm has three advantages: First, the compression scheme gives the freedom to a user so that one can select the desired compression level according to the application with the help of singular value. Second, it includes a confusion mechanism wherein the pixel positions of image are scrambled employing Cat Map. The pixel location is shuffled, resulting in a cipher text image that is safe for communication. Third the key is generated with the help of logistic map which is nonlinear and chaotic in nature therefore highly secured. Fourth the beta function used for encryption is symmetric in nature which means the order of its parameters does not change the outcome of the operation, meaning faithful reconstruction of an image. Thus, the algorithm is highly secured and also saving the storage space as well. The experimental results show that the algorithm has the advantages of faithful reconstruction with reasonable PSNR on different singular values.
Civil engineering process file information management system

Construction techniques and specifications are an important and indispensable part of the construction process, and the archiving and management of each process is also an important part of enterprise data management. Along with the widespread promotion and application of engineering information technology, the traditional enterprise process and standardized management methods can no longer meet the needs of enterprise development. Therefore, this paper develops a B/S and C/S combined civil engineering process file information management system based on computer application technology. Through this system, it meets the requirements of enterprises for storage and retrieval of engineering construction processes and engineering specifications, and solves the drawbacks of traditional control systems.
Faster RCNN Hyperparameter Selection for Breast Lesion Detection in 2D Ultrasound Images

Breast cancer is one of the leading forms of cancer. Breast lesion detection is a prerequisite for description of its characteristics and ultimately correct diagnosis of the lesion type. Compared to object detection in natural images, lesion detection in ultrasound images is a challenging task due to the inherent nature of the ultrasound images. This paper is concerned with adopting Faster Regions with Convolutional Neural Network (Faster RCNN) method for detecting breast lesions in 2D ultrasound images. Faster RCNN shows great promise in this application with very few misses of the actual lesion leading to high recall. However, the overall performance of this method suffers from high number of false positives. Therefore, we investigate different modelling hyperparameters of Faster RCNN to find an optimal configuration to improve the overall performance by essentially reducing false positives without significant increase in the number of misdetected lesions. Our empirical study using a total of 1183 images in three datasets shows that the optimal detection model outperformed original Faster RCNN due to significant reductions in false positives, resulting in 15% to 28% higher precision with only 3% to 11% drop in recall. Our optimal model also outperformed an existing breast lesion detection method by 3.4% in F1-score and provides better balance in precision and recall. Our study demonstrates that the optimal hyperparameter selection for Faster RCNN is a promising direction for breast lesion detection in ultrasound images.