Reviewerly/exHarmony huggingface dataset:
Shadow Detection using GAN with Residual CNN Discriminator

The objective of this paper is shadow detection. For this purpose, we have proposed a Generative Adversarial Network (GAN) model with a unique generator-discriminator combination. The U-Net convolutional neural network architecture comprising of an encoder and decoder, that is popularly used for image segmentation, is used as the generator. The discriminator module is based on the ResNet pre-trained network architecture, and uses residual convolutional blocks. The residual blocks considerably improve the performance of our network. Our model was evaluated on the SBU shadow detection dataset and the results prove the efficacy of our method as compared to the state of the art.
Face Tracking Sign-in System Based on LBP Feature Algorithm

A face tracking sign-in system based on LBP algorithm was designed. The system is integrated by OPENMV module, steering engine, HMI and wireless communication module, etc. These modules cooperate with each other to construct a face tracking sign-in system based on LBP feature algorithm. Haar-cascade detection algorithm is adopted to detect face images; LBP feature algorithm is used to extract face features; SVM support vector machine is used to classify the extracted feature vectors. The HMI serial screen is adopted for human-computer interaction, and the sign-in result is fed back to the mobile phone app through the wireless communication module. The main advantage of the system is that the system can intelligently find and track the position of the face through the two-axis gimbal combined with the image data. Improve the efficiency of face recognition and optimize the characteristics of human-computer interaction. The system is easy to implement, and has the advantages of high recognition rate and efficiency.
Minerals chemistry and geothermobarometry of Dourbeh and Nari intusive bodies of W Urumieyeh, NW Iran

Minerals chemistry and geothermobarometry of Dourbeh and Nari intusive bodies of W Urumieyeh, NW Iran
A Measurement of Attitude Toward Working with Robots (AWRO): A Compare and Contrast Study of AWRO with Negative Attitude Toward Robots (NARS)

Organizations are increasingly relying on a workforce that includes humans and robots working collaboratively. Yet, many humans are reluctant to work with robots. To help identify and predict who is likely to want to work with a robot, this paper introduces a new scale called attitude toward working with a robot (AWRO). The author conducted a study to assess the AWRO scale’s construct validity and reliability along with its predictive power in relation to NARS. The AWRO scale was administered to 220 restaurant employees. AWRO demonstrated good construct validity and reliability and was also much more predictive of worker outcomes than NARS. Results of this study have implications for a workforce that includes humans and robots working collaboratively.
Smart access control system by using sparse representation features

The access control system is used in massive scenarios to protect personal property safety, such as dormitory in campus, company office, and smart apartment. The recent access control system use either RFID card or biometric features, such as fingerprint, face. The RFID card may be lost and misused by other persons. The fingerprint features must be collected by touching a sensor, which is improper during the epidemic of Covid‐19. The face identification in access control system is effected by the mask, especially under the current epidemic situation. The research about partial occlusion face recognition is helpful to improve the existing face authentication for access control system. This paper establishes a smart access control system by using partial occlusion face recognition technology. First, the face is captured by a camera. Second, the collected faces are used to establish a face library for constructing low rank sparse model by using nuclear norm to measure the error matrix. Third, the sparse features of faces are used to train a neural network. The new personal face are verified by the learnt neural network model. The experimental results on a public partial occlusion face dataset demonstrate the effectiveness of the proposed access control system.
Improving Efficiency in Neural Network Accelerator using Operands Hamming Distance Optimization

Neural network accelerator is a key enabler for the on-device AI inference, for which energy efficiency is an important metric. The datapath energy, including the computation energy and the data movement energy among the arithmetic units, claims a significant part of the total accelerator energy. By revisiting the basic physics of the arithmetic logic circuits, we show that the datapath energy is highly correlated with the bit flips when streaming the input operands into the arithmetic units, defined as the hamming distance (HD) of the input operand matrices. Based on the insight, we propose a post-training optimization algorithm and a HD-aware training algorithm to co-design and co-optimize the accelerator and the network synergistically. The experimental results based on post-layout simulation with MobileNetV2 demonstrate on average 2.85x datapath energy reduction and up to 8.51x datapath energy reduction for certain layers.
DDGNet-YOLO: A Target Detection Network For Dangerous Driving Gestures

As we all know, some bad driving behaviors such as using mobile phone, eating, drinking etc., may lead to traffic accidents. In order to reduce these potential dangers, we proposed a target detection network to detect the abovementioned dangerous driving gestures. We have improved the YOLOv4 target detection network and designed the DDGNet- YOLO target detection network, so as to reduce the detection time of dangerous driving gesture. Our algorithm have been performed on the CVRR-HANDS 3D dataset. The mAP@0.75 of dangerous driving gesture is 76.4%, and the average real-time processing speed of the algorithm was 143 frames per second.
Rational points on cubic surfaces and AG codes from the Norm–Trace curve

In this paper we give a complete characterization of the intersections between the Norm-Trace curve over $\mathbb{F}_{q^3}$ and the curves of the form $y=ax^3+bx^2+cx+d$, generalizing a previous result by Bonini and Sala, providing more detailed information about the weight spectrum of one-point AG codes arising from such curve. We also derive, with explicit computations, some general bounds for the number of rational points on a cubic surface defined over $\mathbb{F}_{q}$.
Vehicle Tracking and Controlling Based on Roadside Cameras

In this paper, we design a vehicle target tracking system based on roadside cameras. This system contains vehicles and devices on roadside including computation units. The motion of vehicles is captured by cameras, and then is sent to computation units, in which the YOLO-V3 algorithm is used to identify the target vehicles. In particular, the Kalman filter is utilized to estimate the position and velocity of vehicles we are interested in. Moreover, we design a PID control law to drive the target vehicles to the designed sites. Experimental results show that the system we design can achieve vehicle target tracking by using roadside cameras.
Modeling and Supervisory Control of Blockchain Forks

Blockchain is a distributed ledger technology for recording transactions. When two or more miners create different versions of the blocks at almost the same time, blockchain forks occur. We model the mining process with forks by a discrete event system and design a supervisor controlling these forks.
CNN-BPSO approach to Select Optimal Values of CNN Parameters for Software Requirements Classification

Software requirement analysis plays a vital role in Software Development Life Cycle (SDLC). Users requests are transformed into structured software requirements. It is required to know the class of requirements that each request belongs to. Manual classification of these requirements is time consuming . In this work, Convolutional Neural Network (CNN) model is proposed to classify software requirements into functional and non functional. The performance of CNN is affected by model architecture, embedding input word vectors, filter region size and number of filters. In this work, Binary particle swarm optimization (BPSO) is used to optimize the above parameters of CNN (CNN-BPSO) to improve the performance of CNN for software requirements classification. The proposed model is evaluated on PROMISE corpus data set which contains a set of functional and non-functional requirements. The experimental results of proposed CNN-BPSO model is able to provide better prediction accuracy than CNN model.
An Integrated Detection and Treatment Recommendation Framework for Breast Cancer Using Convolutional Neural Networks and TOPSIS

Breast cancer is the most prevalent type of cancer, accounting for 14 percent of all cancers among women in India, according to National Health Portal. Various machine learning including deep learning methods find extensive applications in the field of medicine i.e. Computer Aided Diagnosis (CAD) and one of its major examples is detection of cancerous cells. While many such systems explore the idea of classifying the region of abnormality as benign or malign, traditional treatment recommendation solely depends on the knowledge of the physician examining. This paper explores regression using Convolutional Neural Networks (CNN) to pin-point the probable abnormal region in mammograms and calculate several morphological, texture and histogram features associated with it. These features are then leveraged to recommend the next probable treatments. The recommendation is based on the extracted features and therefore constitutes a Multi-Criteria Decision Making (MCDM) problem. To implement this type of decision making rightfully, the paper makes use of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) where the identified features are used as criteria and experts' opinion are used as alternatives to obtain ranked recommendations. The methodology proposed in the paper is capable of recommending the correct treatment with an accuracy of 81.5%. The proposed methodology would make treatment recommendation reachable even at the most remote places where advanced facilities including consultation with specialists are not easily available.
Depth Distortion Score Estimation in 3-D Image Retargeting using Disparity Map

Depth distortion in an image yields some geometric errors which leads certain image quality degradation. Therefore, it is important to enhance the depth information in the left as well as right stereo images and achieve them in a substantial way. The Disparity Map Acquisition (DMA) algorithm gives rise to the depth distortion with improved disparity matrix. In this paper, we emphasis on depth score enhancement in 3D stereo images retargeting to accomplish the acceptable 3D images with improved depth distortion score. The experimental results show the stereo seam carving which deconsideres the unwanted image patches in order to generate an acceptable 3D stereo images. The obtained 3D stereo images are widely used in the applications of 3D animated movies by abolishing the blurriness in the stereo images and generate the images where the users can relish with the better visual effects. This may lead to the non-usability of 3D sterilize googles and eventually helps and reduces the burden on Indian economy.
Learning Aberrance Repressed and Temporal Regularized Correlation Filters for Visual Tracking

Recently, in visual tracking field, discriminative correlation filter (DCF) methods have achieved competitive performances. However, the target template of the DCF may be polluted by the deformation, illumination variation, and scale variation, resulting in drift and tracking failure. To solve this problem, we propose an aberrance repressed and temporal regularized correlation filter. A novel tracking algorithm is presented by introducing a temporal regularization into ARCF tracker, which can not only let the filter template retain the historical information for filter learning, but also achieve a long-time and high precision model, compared with ARCF, which happened in large complex variations. Furthermore, hand-crafted and deep features are combined to achieve superior feature representation. Finally, the extensive experiments are conducted on OTB2015, VOT2018, and VOT2016. Specifically, compared with popular trackers, our final tracker algorithm performs well and obtains AUC score of 69.6% and DP score of 92.2% on OTB2015, besides, our tracker also works well compared with other related methods and achieves EAO score of 0.422 on VOT-2016.
Socio-Aware Recommendations Under Complex User Constraints

In this work, we consider the joint behavior of an information diffusion process integrated with a recommender system (RecSys) over an online social network (OSN), where the typical users' resilience to information varies, leading to potential information overloads. We assume that each user has a threshold over the information that she can process in a meaningful way, and exceeding it could lead to user dissatisfaction or the user remaining idle. In order to efficiently tackle this issue, while considering complex user constraints, we consider two types of users' capacity to information, that is, capacity for distinct items and capacity for duplicate items. In this setting, we aim to allocate the items in the OSN in order to maximize users' total relevance to the former while ensuring that no user exceeds any type of capacity. We show that this problem is NP-complete and present various heuristic methods to address it. A novel framework, called socially constrained recommendations (SCoRe), is developed for the final assignment of items to users, consisting of a two-step procedure. We present and evaluate two different approaches for each step and discuss the usability of SCoRe for the efficient diffusion of items in the network while respecting the users' constraints.
Genetic Interpretation of Neurosemantics and Kinetic Approach for Studying Complex Nets: Theory and Experiments

The neurosemantic approach allows the formation of information management systems based on incoming information flows. This paper explores the possibilities of molecular genetic algorithms of multiscale information analysis for displaying neurosemantic networks in various parametric spaces. The practical application of such a combined approach for tasks of molecular diagnostics of genetic diseases using Walsh’s orthogonal functions is proposed. Issues of interaction between semantics and semiotics in the context of the phenomenon of consciousness and brain are considered. A kinetic approach is also used to construct random graphs, which in a geometric version allows obtaining complex structures; after a percolation transition, a complex cluster can be compared with elements of consciousness. The importance of the study is due to the fact that scientific progress requires new methods to optimize the perception of the results of neural network analysis, as well as the perception of big data with the possibility of analysis at various scales of visualization. Also, of interest to science are model experiments in the field of the theory of the origin of consciousness.
Semantic Frustum Based VoxelNet for 3D Object Detection

In this work, we propose a new method termed Semantic Frustum Based VoxelNet (SFB-VoxelNet) for 3D object detection. Since previous frustum-based methods are greatly affected by the accuracy of 2D image proposal, we directly alleviate this influence by reducing the probability of omissions and reinforcing the discriminating ability of the 3D detector. Specifically, first, we use semantic map, generating by the method of semantic segmentation, to keep up semantic information and stuff of interest. Second, by projecting the semantic map to LIDAR space, the frustum is generated and then sent to our detector to estimate 3D object classification and bounding box location. Our method not only cuts down most of search space but also assigns a reasonable arrangement for the two steps of frustum-based methods. In addition, our method is evaluated on KITTI 3D object detection benchmark to verify its superiority while maintaining a real-time inference speed, comparing to other state-of-the-art literatures.
Multi-Stage Feature Interaction Model with Abundant Semantic Information for Image Captioning

The transformer has achieved great success in vision and language tasks. The transformer makes that the unit in any position can obtain global information. However, it ignores multistage interaction among features in image captioning. In this paper, we propose a multi-stage feature interaction model for image captioning that can generate features with richer semantic information. The multi-stage feature interaction module utilizes top-down interaction, bottom-up interaction, and self-interaction methods to obtain fine-grained features. The fine-grained feature information represents different contexts and semantics through different stages. Then the fine-grained feature is fed into the transformer. We conducted experiments on the COCO dataset. The experimental results show that our proposed method is competitive with the state-of-the-art.
The Aesthetic Experience in Business English Translation under the Background of Internet

Under the background of "Internet ", international cooperation between enterprises becomes more convenient and efficient. In the Internet platform, both enterprises can not only realize long-distance "face-to-face" communication, but also realize information exchange through various communication platforms. Compared with the traditional business model, business English under the background of "Internet" is more professional and targeted, so business English translation should not only accurately and effectively transmit business information, but also pay attention to the exchange of aesthetic experience between the two sides. By analyzing the practical application of business English translation, this paper puts forward three aesthetic experiences: harmonious beauty, overall beauty and cultural beauty.
Research on Construction of Regulatory Sandbox for Safety Supervision on Intelligent Connected Products

In the context of the explosive development of the global intelligent connected industry, the implementation of inclusive and prudential supervision on intelligent connected products may help to maximize the dividends of innovation-driven development strategies. As an innovation of the supervision system by government departments in the process of balancing innovation and risk, regulatory sandbox provides a forward-looking supervision mode. This paper studies and proposes the ideas for construction of China’s regulatory sandbox for safety supervision on intelligent connected products by analyzing the rise, creation and role of regulatory sandbox as well as the challenges faced in the course of safety supervision on intelligent connected products.
Nurturing M-Learning for Professional Development Through Online Digital Communicational Channels During COVID-19 Pandemic

Development of the technology has its impact on every aspect of human life. 21st century being called the "Information Era" underlines the importance of the technology and online communicational channels. As stated by Trique et al. (2018) after emerging the smartphones a lot of other technological things have moved to the shade of the history [1]. Mobile devices being easily accessible and usable makes learning process more interesting and desirable. Besides, internet marketing and online communicational channels being developed continuously have been supporting and motivating people interested in professional development. The world now is digital. Digital transformation created monumental opportunities for all businesses globally. Digital world, becoming faster and easier, gives society numerous possibilities to benefit from it. Using digital online communicational channels increase brand's online visibility. Social media marketing channels such as: Facebook, Instagram and LinkedIn assist companies to reach their potential customers. Being active on social media increases customer engagement, build and maintain strong relationship toward customers. There are various organic and paid online channels that stimulate customer involvement in the offered campaigns. Demand on M-learning has been dramatically increased under conditions of the world lockdown caused by the novel coronavirus. Furthermore, so-called "network society" and organizations have identified the significance of M-learning, that was less demanding previously. The current article tried to investigate, how have the online communicational channels stimulated M-learning during world pandemic COVID-19.
Recycling Construction Waste Material with the Use of AR

Abstract This paper aims to present a methodology for reusing and recycling scrap timber from building sites using augmented reality and flexible digital models. The project we present describes a process that enables existing material to be reused and repurposed such that the designed model is updated by the digital inventory of digitised offcuts/waste elements.
Frequent Itemsets Mining Algorithm based On Differential Privacy and FP-Tree

Mining algorithm for frequent patterns is applies in many fields. Itemsets may be contain user sensitive information. And attacker with strong knowledge can steal user privacy information by analyzing mined itemsets. So, in this paper, a novel algorithm is designed to obtain K itemsets whose support are more than the other itemsets and guarantee security of user privacy in itemsets. The novel algorithm firstly applies exponential mechanism to truncate original transaction dataset. And then the algorithm constructs FP-tree for truncated dataset, and utilizes Laplace mechanism to obtain noise support of itemsets. Finally, according to noise support, the algorithm can obtain K itemsets whose support are more than the other itemsets. In the end, two public datasets are applied to evaluate utility of the algorithm. The experimental result show that the utility of the algorithm is better than the other frequent itemsets mining based on differential privacy.
Automatic Indication Recognition of Dual Pointer Meter in Thermo-Hygrometer Calibrator

In order to make the thermo-hygrometer calibrator more intellectualized, an automatic indication recognition algorithm of dual-pointer meter is proposed. Firstly, Hough circle transform algorithm is used to segment the image and intercept the dial; secondly, the color feature of the image is used to detect the datum point and correct the image; then, the image of pointer is extracted by the improved Zhang thinning algorithm and progressive probabilistic Hough transform algorithm; finally, the algorithm is evaluated by the images of meter captured in an experimental platform of the thermo-hygrometer calibrator. The results show that the algorithm proposed in this paper can recognize the indication of the meter with high accuracy and robustness.
Text Classification Based on Title Semantic Information

With the rapid development of big data technology, text classification plays an important role in practical application, its applications span a wide range of activities such as sentiment analysis, spam detection, etc. Traditionally, we model the relationship between document and label. However, in many scenarios, document have specific relationship with corresponding title. Inspired by this, a text classification model based on title Semantic Information is proposed in this study. In our model, long short-term memory(LSTM)is used to learn title embedding, document embedding is obtained by using promoted LSTM(TS-LSTM) which take into account the title information. The experimental results on the standard text classification datasets show that its performance is better than the existing state-of-the-art text classification algorithms.