Reviewerly/exHarmony huggingface dataset:
Gesture Recognition by Machine Learning Combined with Geometric Calculation

Human Machine Interface (HMI) in the vehicle environment is becoming more complex due to an increased number of desired user functions. These added functions increase the driver's distraction since the driver must visually identify a particular physical interface that they want to control. A gesture recognition system is one possible method for controlling HMI functionalities without visual cues. Some technical approaches to gesture recognition that exploit deep learning have been proposed. However, these methods require increased computational cost in relation to processing time. Ideally, a gesture recognition system should enable fast and accurate driver action to avoid a distracted driver and maximize safety. In this study, we propose an algorithm that combines geometric calculation and machine learning techniques to estimate gestures with low computational overhead. The algorithm is explained in detail, and results are reported including a gesture recognition accuracy of 85% across three gestures for a moving hand from eight people.
Robust Neural Network Training Using Inverted Probability Distribution

This paper presents strategies to tweak the probability distribution of the data set to bias the training process of a neural network for a better learning outcome. For a real-world problem, provided that the probability distribution of the population can be assumed, the training set can be sampled from the population in such a way that its probability distribution satisfies certain targeted characteristics. For example, if the boundary between classes is critical to the training outcome, a larger proportion of training data may be drawn from the area around the boundaries. On the other hand, if the learning outcome is aimed at resembling a common concept encoded in the training set, learning from the data near the norm may be more effective. In order to explore the effectiveness of the various strategies, the concept was applied to two problems: 3-spiral and wine quality. Experimental results suggest that, whether the problem requires an emphasis on classifying boundary or recognizing the central pattern, our novel sampling strategy – inverted probability distribution – performs exceptionally well.
Medical Image Retrieval Using Modified Directional Extrema Based Pattern

The retrieval of medical images plays a significant role specifically in medical diagnosis. The need for an effective and fast retrieval system is essential to retrieve most appropriate images from a template image to a query image for a particular case. This paper proposes a new effective image retrieval algorithm depending on local directional extrema based pattern for retrieval of CT images. The principle local binary pattern approach defines the relationship between the centre pixel and its corresponding neighbours by comparing the values of the grey level. In this article, a new feature descriptor is defined which extracts local directional extrema edge information's along 0°, 45°, 90° and 135° directions. In the proposed method, first the directions of the existing extremas are investigated in a local neighborhood within a fixed radius around the center pixel. The feature descriptor is formed taking the local directional extremas into consideration, the directions in which extrema pixels are present and where they aren't and encoded into a four bit binary pattern. The pattern value is computed by applying a uniform weighting function on the four-bit binary pattern which significantly decreases the dimension of the feature descriptor and solves the large size constraint problems. The proposed methodology is compared with local binary pattern and local diagonal extrema pattern for CT class of image database and it is observed that even with smaller feature dimension, the proposed feature descriptor gives better retrieval results outperforming both LDEP and LBP.
An improved gesture recognition algorithm for human-robot collaboration

Abstract In this paper, an improved gesture recognition algorithm framework based on the improved SSD algorithm and OpenPose model for solving the robot’s mobile grasping problem in human-robot environment is proposed. Improved SSD algorithm by optimizing the front network of SSD algorithm and adjusting the prediction box is designed to identify the position of the hand rapidly, and to reduce the time for the training model. Testing experiments are perfomed for recognizing the hand’s key points information and eight designed gestures are compared with the traditional algorithm. The results show that the method can greatly improve the recognition distance of the original model under the dense environment, and also enhance the robustness and accuracy of the model, and have improved the ability of long-distance human-robot collaboration to a certain extent.
Design of Virtual Automotive Showroom with Augmented Reality Technology Using The Smartphone

Abstract In an exhibition like an automotive showroom, it is usually not possible to display all the features of products directly to potential customers. Displaying products of cars with various colors, for example, had challenges due to several limitations. In this study, the researcher developed an application for smartphones that can interactively display a product of cars with different features in real-time. The development process was using Vuforia and 3D Unity applied in Android system. Customers can change the color of the car and see the animation through an Augmented Reality application. The system provides convenience to customers in obtaining information by displaying an animation of a certain product in real-time and gives benefits to the owner to promote their products uniquely.
Text Classification of Digital Forensic Data

This research aims to propose a model to classify text messages that extracted from the smart phone using forensic software and several machine learning algorithms. The data analysis procedure subdivided into physical extraction, relevant partitions, logical extraction, digital forensic analysis, and text classification. In the text classification step, the final result derived by applying sentiment analysis and k-means clustering algorithm under the control of python application. Through this model, we were able to classify most of the messages correctly as either being positive or negative.
Tissue Evaluation, Detection and Measuring Morphological Characteristics of Cell Regions in Histological images

This paper discusses about the accurate, automatic method which differentiates between cancerous and noncancerous images. The images considered here are uterus microscopic images, microscopic images are called histological images. As thousands of samples are taken every day, it is difficult for the medical team to evaluate each and every samples manually. The aim of this project is to help medical team to differentiate between cancerous and non-cancerous images automatically. To differentiate between cancerous and non-cancerous images, the model is built using convolution neural network (CNN). This model classifies between cancerous and non-cancerous images automatically.
Application of an algorithm for determining permissible displacement deviations in J-integral measurements by the digital image correlation method

The paper is devoted to testing an algorithm for determining the permissible displacement deviations in J-integral calculations based on constructing vector fields through digital image correlation. A comparative check is carried out for J-integral values calculated through DIC without preprocessing by the algorithm, with such a processing and by experimental data obtained according to the ASTM E 1820 Standard Test Method for Measurement of Fracture Toughness. It is demonstrated that the algorithm improves the results of J-integral calculation by the optical method and that it can be applied to cases where the standard method of J-integral measurement cannot be used.
Thwarting Control Plane Attacks with Displaced and Dilated Address Spaces

To maintain the control-flow integrity of today's machines, code pointers must be protected. Exploits forge and manipulate code pointers to execute arbitrary, malicious code on a host machine. A corrupted code pointer can effectively redirect program execution to attacker-injected code or existing code gadgets, giving attackers the necessary foothold to circumvent system protections. To combat this class of exploits, we employ a Displaced and Dilated Address Space (DDAS), which uses a novel address space inflation mechanism to obfuscate code pointers, code locations, and the relative distance between code objects. By leveraging runtime re-randomization and custom hardware, we are able to achieve a high-entropy control-flow defense with performance overheads well below 5% and similarly low power and silicon area overheads. With DDAS in force, attackers come up against 63 bits of entropy when forging absolute addresses and 18 to 55 bits of entropy for relative addresses, depending on the distance to the desired code gadget. Moreover, an incorrectly forged code address will result in a security exception with a probability greater than 99.996%. Using hardware-based address obfuscation, we provide significantly higher entropy at lower performance overheads than previous software techniques, and our re-randomization mechanism offers additional protections against possible pointer disclosures.
Transfer Learning using Transformation: Is Large Unlabeled Data Helpful at Segmentation?

We propose a simple method of transfer learning for image segmentation. Creating labeled data for deep neural network training in image segmentation is particularly expensive than other tasks. Hence, practically, the labeled data is much less than the unlabeled data. So, we introduce a method that is helpful for segmentation by using unlabeled data. Our key is the RGB-to-HSV transformation and we use it in two ways. The first way is to pre-train a network to work as an RGB-to-HSV transformer which can extract useful features, and transfer the pre-trained weights to another network for segmentation, which is one of the most common transfer learning method. The second way is to provide additional information to the segmented network by providing HSV, the output of the pre-trained network, as additional input. We performed several experiments about our proposal using Cityscapes dataset.
pacSCA: A Profiling-Assisted Correlation-based Side-Channel Attack on GPUs

GPUs which serve as graphic-oriented computation platform are now gradually used to solve a range of compute-intensive and data-parallel scientific computing problems which can be perfectly parallelized for performance speedups. Particularly, GPUs have recently become popular to host the encryption/decryption algorithms due to its high-throughput computing capability. However, the security issues of moving the cryptographic algorithms onto GPUs have not been studied adequately. Consequently, with absence of any protection strategy, the potential vulnerabilities of GPUs to side-channel attacks may expose the confidential information with high risk. In this paper, we propose a Profiling-Assisted Correlation-based Side-Channel Attack (pacSCA) to demonstrate that ignoring security issues and naively moving security services onto GPUs can offer adversaries fatal vulnerabilities to thieve critical information. The results show that the proposed SCA can rebuild the secure key of the AES-128 algorithm in less than 6 seconds, revealing the urgency of protecting GPUs against side-channel threats.
MASA-Net

With the rapid development of convolutional neural network, salient object detection has achieved satisfied visual performance. However, most previous method chooses to focus on either accuracy or speed. We propose a multi-level aggregated and self-attentive Network for fast salient object detection with a great improvement and balance of both metrics. Accordingly, we solve the scale variation problem by adopting multi-level aggregated modules which aggregate the neighboring features and enhance the feature in different resolutions. To enhance the representative feature in each layer, a self-attentive module with astrous convolution is proposed in decoder stage. In order to maintain object consistency and boundary clarity, a multi-level fusion loss which combine the first layer's boundary loss and five-layer binary cross-entropy loss is proposed. Experimental results on five widely used datasets demonstrate that our proposed method performs favorably against the state-of-the-art methods in both accuracy and speed without any pre-processing and post-processing.
CAMDet: CAM-based objection detection for non-crowded views from moving IoT devices

The integration of Internet of Things (IoT) and Artificial Intelligence (AI) brings us AIoT that delivers the capabilities of object detection, device localization, object tracking and re-identification on moving IoT devices to control robots/drones for smart human-machine interactions. Among the tasks, efficient objection detection plays an importance role since it acts as the foundation of many other vision-based IoT applications. One main challenge is to locate target objects fast and accurate. The paper presents the CAMDet technology that utilizes Class Activation Map (CAM) to reduce the convolution blocks and the enormous candidate bounding boxes in the detection-head stage. We have designed CAMDet and integrate it with other backbone networks. CAMDet is shown to be 2.1-2.7 times faster than the popular Tiny-YOLO/SSD methods in non-crowded scenarios when using the same backbone and feature pyramid structure. The performance study shows that our proposed methods are very attractive for real time object detection on moving IoT devices.
Office and industrial information systems and networks in the training of modern specialists

Abstract Article discusses an interdisciplinary optional course “Office and industrial information systems and networks”. Course provides skills in preparing of hardware-software platform formation of a workplace. This course is developed for students whose field of study is not related to information technologies, but whose professional activities will be carried out in the modern enterprises in the context of Industry 4.0 development.
Comprehensive Electrical Betweenness Model of DC Grid Based on Robustness Contribution and Electrical Distance

With the further development of multi-terminal DC system, the coexistence of DC and AC grid will be formed in the near future. Currently, in the field of network key component identification, many research have been done in AC grid, but a few research in DC grid. In order to solve this problem, this paper purposes a comprehensive electrical betweenness model based on complex network theory, which can be used for identifying key branch and node of DC grid. First, this model takes the influence of electrical distance between different nodes into account, and reflects its effects by introducing weight correction factor into calculation process of electrical betweenness. In addition, the concept of natural connectivity in graph theory is introduced into the model to depict the contribution of lines and nodes to the robustness of the DC grid. Finally, a simulation based on a six-terminal DC grid model is implemented to verify the feasibility and applicability of the purposed model.
On Using Composite Word Embeddings To Improve Biomedical Term Similarity

Representation learning has gained prominence over last few years and has shown that for all underlying learning algorithm, results are vastly improved if feature representation of input is improved to capture the domain knowledge. Word embedding approaches like Word2Vec or sub-word information technique like Fast-text have shown to improve multiple NLP tasks in biomedical domain. These techniques mostly capture indirect relationships but often fail to capture deeper contextual relationships. This can be attributed to the fact that such techniques capture only short-range context defined via a co-occurrence window. In this paper we propose a novel contextual embedding for a “wide sentential context” We then generate composite word embedding achieving a multi-scale word representation. We further prove that the composite embedding performs better than the present individual state of art techniques on both intrinsic and extrinsic evaluations.
A Design of ID Sorting Module Based on Quick Sorting Algorithm

Based on the quick sorting algorithm, this paper uses verilog language to implement the RTL design of the ID sorting module applied to the CAN bus controller, and performs functional simulation on the module. Simulation results show that the ID sorting module designed in this paper can quickly sort up to 64 different ID numbers and output correct sequence to meet the design requirements. Using a parametric design strategy, the ID sorting module designed in this paper can configure parameters to achieve the function of sorting a larger number of random sequences to meet different needs, and has high scalability and practicality.
Learning Convolution Feature Aggregation via Edge Attention Convolution Network for Person Re-Identification

Person Re-Identification (Re-ID) is a challenging task of matching pedestrian images collected from nonoverlapping multiple camera views due to huge variations from pose changes, occlusions, varying illumination and clutter background. Recently, graph convolution network or graph neural network increasingly gains a lot of research attention in person Re-ID. However, the existing methods have not fully exploit the available features on the graph. In this paper, we propose an efficient and effective end-to-end trainable framework, termed Edge Attention Convolution Network (EACN), to perform convolution feature learning and attentive feature aggregation for person Re-ID, in which the learned convolution features on vertex and its edges are attentively aggregated on a dynamic graph. We conduct extensive experiments on two large benchmark datasets, Market-1501 and DukeMTMC. Experimental results validate the efficiency and effectiveness of our proposal.
Robust resource demand estimation using hierarchical Bayesian model in a distributed service system

Robust resource demand prediction is crucial for efficient allocation of resources to service requests in a distributed service delivery system. There are two problems in resource demand prediction: firstly to estimate the volume of service requests that come in at different time points and at different geo-locations, secondly to estimate the resource demand given the estimated volume of service requests. While a lot of literature exists to address the first problem, in this work, we have proposed a data-driven statistical method for robust resource demand prediction to address the second problem. The method automates the identification of various system operational characteristics and contributing factors that influence the system behavior to generate an adaptive low variance resource demand prediction model. Factors can be either continuous or categorical in nature. The method assumes that each service request resolution involves multiple tasks. Each task is composed of multiple activities. Each task belongs to a task type, based on the type of the resource it requires to resolve that task. Our method supports configurable tasks per service request, and configurable activities per task. The demand prediction model produces an aggregated resource demand required to resolve all the activities under a task by activity sequence modeling; and aggregated resource demand by resource type, required to resolve all the activities under a service request by task sequence modeling.
Decision making for multi-armed bandit problem utilizing two parallel uncorrelated wideband complex chaos

We propose a solution to multi-armed bandit problem by utilizing two simultaneously-generated uncorrelated wideband complex chaotic signals. It is experimentally demonstrated indicates that any number-armed bandit problem can be successfully solved.
Implementation of Hybrid Method of Eckenrode and DIA for the Determination Customer Class

Abstract Determination of customer class at the Municipal Waterworks (PDAM) of Balangan Regency so far has been done by calculating each indicator value and criteria that have been surveyed by PDAM officers for potential drinking water customers and the calculation is processed only beside the number of measures assessed, so it takes time and the assessment process. So to avoid dissatisfaction with potential drinking water customers, decision support systems should be used. The purpose of this research is to develop a decision support system for determining the class of drinking water customers using the Eckenrode and DIA methods. The test was conducted as many as 75 data samples to determine the level of accuracy and sensitivity of the system developed, and the results of the experiment showed that the accuracy value was 91%, and the sensitivity value was 93%. The results of this research conclude that the Eckenrode and DIA methods can be applied both in the development of decision support systems for customer classifications.
Predict New App Quality By Using Machine Learning

Abstract Increasingly, individuals and companies are developing applications and selling them online. Competition between different companies has led to tens of thousands of applications being put on the market for users to choose from. There are numerous functions on the market to give app evaluation. For users, they do not need to contemplate how to select an app for a long time, because user comments and ratings can give them a clear instruction for user to select an app they need from abundant apps. As a result, we would like to explore the possibility. Hence, we think that we can make a prediction function for the prospect of the app market of whether we can make a prediction function for the app market, akin to a weather forecast or stock market forecast. Our group not only wants to know what the future of the app market is, but also want to find out why some high-quality apps are not getting the downloads they should. With the development of AR and VR technology, the future application will combine with these technology to bring user a immersive experience. In addition, high quality application without AR or VR are less competitive. We plan to answer these questions by using machine learning techniques.
Target Locating and Grabbing Algorithm for Robot Prosthesis Based on PSO-RBF

Abstract Aiming at the target locating and grabbing problem for a five-degree-of-freedom intelligent robot prosthesis worn by the disabled, an algorithm is proposed. Firstly, the laser rangefinder and attitude sensor installed at the head of the wearer are used to simulate the motion of the human eye to realize the position of the target in the prosthesis workspace. Secondly, the laser ranging information is obtained by the 3D attitude sensor installed at the wearer’s shoulder to obtain the position coordinates of the target relative to the prosthesis shoulder, so as to obtain the position coordinates of the target in the prosthesis workspace. Finally, the PSO-RBF neural network is used to train the samples to obtain the nonlinear mapping relationship between the position coordinates and the joint angle, thus predicting the joint angle and grabbing the target. The simulation and experimental results show that this method can locate the target accurately and calculate the angle of each joint of the prosthesis in real-time, so as to complete the action of target grabbing.