Reviewerly/exHarmony huggingface dataset:
A Concept

Student's feedback on academics can provide valuable information for an institution for finding their current teaching practices quality and, also can provide suggestions for improving the overall teaching process. There are various kinds of student feedback systems used in different institutions and are mostly manual based process. Therefore, the study proposes a concept of computerized student feedback system (SFS) for academics. Student generally provides their feedback using open ended sentences. SFS will classify all feedback into several categories such as positive, negative, neutral. The proposed SFS uses a combination of machine learning's rule-based and lexicon based custom corpus datastore. This system will be implemented and evaluated in various educational institutions to test the effectiveness of SFS in the academic's context.
Application of Remote-Sensing Techniques for Water-Resources Investigations in the UAE

The remote-sensing (RS) technique, uniformity coefficient (Cu) and infiltration capacity (Ic) were used for the classification of Quaternary clastic sediments southwest of Al Ain City, eastern UAE. About 75 sand and gravel samples were collected from sand dunes and interdune areas for grain-size analysis and hydraulic-conductivity measurement. Measurements of infiltration rates (Ir) were carried out in 27 sites during sampling with the use of a double ring infiltrometer. The interdune areas were classified into four classes: standing water (C1), >50% moisture (C2), 5–25% moisture (C3) and <25% moisture (C4), while sand dunes were distinguished into C5 in the east, C6 in the middle and C7 in the west, with increasing sand sorting, and hydraulic properties, from east to west. Remote-sensing indicated that the evaporation rate from standing surface water and shallow groundwater in the investigated area is 6.35 Mm3/year.
Energy Consumption Analysis of Scheduling Algorithms for Cloud Computing Systems

With more and more data and service moved to different cloud computing systems, progress in the area of energy consumption and environmentally friendly practices must be made so these advancements do not plateau. The goal of this study is to provide exceptional insight regarding which scheduling algorithms will yield environmentally sustainable, commonly referred to as ‘green,’ practices for energy efficiency. This will be achieved by tracking the similarities and differences of multiple scheduling algorithms, considering various datacenter topologies and their task sizes (MIPS). Trace files produced by the simulator will be used to create a visual representation of the observed data and analyze the energy consumption of the servers and switches (core, aggregation, and access).
Generalized Secret Sharing Schemes Using N$$^\mu $$MDS Codes

Mehta et al. [11] recently proposed an $${{\,\mathrm{NMDS}\,}}$$ code-based secret sharing scheme having a richer access structure than the traditional (t, n) threshold secret sharing schemes, and is based on two mutually nonmonotonic sets of user groups of sizes t and $$t-1$$ respectively, where $$n \ge t > 1$$ corresponds to the total number of users. We give a full generalization of their scheme with complete security proofs. We propose an efficient generalized secret sharing scheme constructed using $${{\,\mathrm{N^{\mu }MDS}\,}}$$ codes with time complexity of $$O(n^3)$$. The scheme accepts an access structure constructed using $$\mu +1$$ mutually nonmonotonic sets of user groups with sizes, $$t, t-1, \dots , t-\mu $$, respectively, where $$1 \le \mu < t$$, and the parameter t defines the threshold such that all user groups of size greater than t can recover the secret. The proposed secret sharing scheme is perfect and ideal and has robust cheating detection and cheater identification features.
Identification Random Disturbances of Optomechanical Control Systems Based Neural Observers

This paper deals with the application of neural observer to estimate external random disturbance in the form of wind gust dynamic acting on the opto-mechanical control systems. First, we introduce the mathematical model of wind gust disturbances, which are considered as random processes with zero mean. The next section investigates architecture of neural observer based on dynamic recurrent neural networks (DRNN). The learning process of DRNN is described and the performances mean square error (MSE) of different learning algorithms are compared with the aim of studying quality identification and selection of the best learning algorithm for synthesis neural observer. In this work, designing, training, testing of DRNN is carried out in Matlab/ SIMULINK environment.
Enabling Performance Management During Cloud Applications Migration

Enterprises are rapidly deploying HR, Finance and other mission critical applications into off-premise SaaS (Software as a Service) and PaaS (Platform as a Service) public cloud environments. Deployment of applications into the public cloud environments is creating opportunities for developing performance management capabilities for performance testing of customized enterprise applications. As a result, many enterprises are engaging IT Services provider to do an assessment of the requirements for developing capabilities for performance testing of applications hosted in cloud environments, develop interim-final state roadmap and provide recommendations for tools and for standing up a performance management competency to address concerns related to performance and scalability of the applications hosted in the cloud environments and Develop process, methodology, and capabilities to support the objectives and mission of the performance management competency. In this paper, author has defined the operating model to complete the performance testing during migration to cloud. Author also suggested the different types of applications and its testing scenarios along with objectives for performance testing.
Complex Object Recognition using Deep Deformable Regularized Sparse Learning

Accurate object recognition in complex transformation and multi-modal is a challenging problem in computer vision tasks. An efficient complex object recognition method using Deep Deformable Regularized Sparse Learning (DDRSL) is proposed, which not only can handle the deformable and multi-modal problems, but also can leverage datasets, features, attributes, classes and their high-level semantic descriptions to learn about their relations with unsupervised transfer learning. Extensive qualitative and quantitative experiments are performed, and the results show the proposed method outperforms the several state-of-the-art methods.
Activity Recognition by Delving deeper using CNN and RNN

Video content has a protagonist role in this age of data revolution. These days, computer vision research community is fascinated towards application of convolutional neural networks for various image and video analysis tasks. Recurrent Neural Networks are also used in various computer vision tasks. Introduction of residual connections in traditional CNN model to design very deep architectures known as Residual Networks are very efficient for computer vision tasks. To exploit capabilities of both CNN and RNN the proposed model is based on CRNN which is trained from scratch as well as using ResNet 152 which is pre trained on ImageNet dataset. The architecture is trained and validated on popular UCF-101 dataset on the basis of accuracy and average loss. From results, it can be observed that proposed approach provides better results than state of art methods.
The performance identification of gripper by isocline analysis

In this work, a way to identify the performance and adjustment of the gripper was proposed by isocline analysis. This isocline analysis method is processed in three steps. Initially, the performance of the original gripper was tested, followed by adjusting the size factors of the gripper, and finally the boundary curve of the catch range was translated inside. Through these three steps, three results were obtained. First, the original performance of the gripper by step 1 was understood, then the difference between the adjusted gripper and the original gripper by step 2 was noted, and finally, the best position or boundary for this gripper by step 3 was identified. This is a feasible performance test, and it has three advantages. Most important of all, the working range of the gripper is understood. Moreover, the most appropriate working position of the gripper is identified. The results showed that the obtained gripper has the potential to improve performance.
Automated Anomaly Detection Assisted by Discrimination Model for Time Series

As more and more sensor data have been recorded, automated anomaly detection of high reliability is urgently needed to lessen the increasing monitoring burden and reduce the risk of system faults. To this end, this paper proposes a method named Anomaly Detection Assisted by Discrimination Model (ADADM). In ADADM, the anomaly score for each point in sequences is calculated using an LSTM-based Encoder-Decoder model. In the mean time, a discrimination model based on ALSTM-FCN distinguishes the subsequences containing the Known Type Anomalies (KTAs) from others. Finally, we set two different thresholds for above two types of subsequences respectively and determine the anomalous points according to the anomaly scores and the corresponding threshold. Experiments on four real datasets of periodicity/aperiodicity and univariable/multivariable demonstrate that not only ADADM can improve the overall performance significantly, but also can improve the precision and recall of the anomaly detection simultaneously.
FPGA Chaotic Memory Indexing Image Steganography

In this paper, two lossy image steganography techniques are introduced based on generalized chaotic logistic maps. The chaotic maps are used for memory indexing employing two different methods. The first method uses only one chaotic map for one dimensional memory indexing, while the second method uses two maps for two dimensional memory indexing. The maps' iterative outputs are pseudorandom numbers used for pixel's position selection. The generalization of logistic maps adds extra degrees of freedom in designing different chaotic behaviors enhancing the security levels of the proposed methods. Several performance metrics are employed to compare between both methods. The hardware designs of the two methods are presented and implemented on Xilinx Virtex 7 FPGA, achieving frequency of 350MHz.
Theoretical and Experimental Analysis of Photovoltaic Module under Clouds Effects

Abstract Photovoltaic (PV) module is one of the most useful, sustainable and non-harmful product in the field of renewable energy therefore, it increased rapidly. Photovoltaic performance affected inversely by Partial shading which occurs due to clouds, birds, buildings, trees and dust deposition etc,. This paper investigates the performance of monocrystalline photovoltaic module under cloud effect theoretically and experimentally. The experiments are conducted in Baghdad during November, December and January in 2018and 2019. PV parameters are simulated by using MATLAB to determine this effect. The measured and simulated data are compared to verify the effectiveness and robustness of the proposed approach. The results show that the cloud has a significant impact on the performance of photovoltaic module. The clouds decrease the amount of direct radiation reaching to the surface of PV module. Therefore, output power of PV module in cloudy days is less than the power output in sunny days. The power output in the sunny days was 69.7W, and by comparing this value with measured values in (November, December and January), it is found that the measured values are reduced by 15.5, 59.65 and 62.26% respectively.
Exobiology Extant Life Surveyor (EELS)

Cassini confirmed Enceladus’s habitability for life as we know it. Enceladus plumes provide a unique pathway to sample from and potentially travel to an alien ocean. Enceladus offers many mobility challenges. At 1/87th Earth gravity the unconsolidated plume ejecta likely fluidizes when disturbed. This creates a buoyancy and swimming problem. Sintering of the ice grains challenges this and must be accounted for. The various models of how the plumes are driven provide a wide range of environments that may need to be traversed. Finally, fluid mobility is necessary. The exobiology extant life surveyor (EELS) architecture is developed to address this. It is composed of serially-replicated segments with encapsulated locomotion and bending. The concept uses a screw propulsion configuration that acts as tracks, gripping mechanisms, and propelling units especially under water. The resulting design is an adaptable solution to ocean world inspired terrains, fluidized granular media, enclosed labyrinthian environments, and liquids. This paper will cover an adaptable mobility system concept, current development, modeling, and experimental testing.
Exploring Characteristics of Word Co-occurrence Network in Translated Chinese

The translation activity involves both the source language and the target language. Compared to the standard texts in the two language, translated texts show unique language characteristics. In order to explore them from the perspective of integrality and complexity, we introduce complex network method into the study on translated Chinese. Firstly, selected the experimental texts from The ZJU Corpus of Translational Chinese (ZCTC) and its corresponding six sub-corpora, such as Press reportage and Popular lore. And then removed the punctuation and did word segmentation. Secondly, constructed a word co-occurrence network of translated Chinese. After analyzing and counting the parameters, such as their shortest path lengths, degree distributions and clustering coefficients in these networks, we verify that the word co-occurrence network of translated Chinese has small world effect and scale-free property. Finally, by constructing co-occurrence networks of standard Chinese and calculating their network parameters, we compare and verify the differences between translated Chinese and standard Chinese: “simplification” and the more usage of common words. Our work expands the application of complex network in translation studies, and provides a feasible approach for studying translated Chinese based on complex networks.
Improving Efficiency of Automatic Labeling by Image Transformations on CPU and GPU

Automatic image labeling is a process which detects and creates labels on various objects of an image. This process is widely used for autonomous vehicles, when original images collected by cameras placed in the car are sent to the artificial neural network (ANN). Improving the efficiency of labeling is vital for further development of such vehicles. In this study, three main aspects of image labeling are analyzed and tested, namely, (i) time required for labeling, (ii) accuracy, and (iii) power consumption. In this paper, an approach is proposed to improve the efficiency of the serial and parallel implementation of labeling, performed respectively on CPU and GPU. One of transformations used in this approach is converting an original image to its monochrome equivalent. Other transformations, such as sharpening and change of colors intensity, are based on using the image histogram. The testing and validation of image transformations are performed on a test dataset containing frames of proprietary videos collected by an onboard camera. For both serial and parallel labeling, the same ANN is used. Preliminary results of these tests show promising improvements when considering the above-mentioned aspects of automatic labeling.
An Information Theoretic Approach for Assessing the Performance of Vehicle Kinematic Tracking

Estimating the position, velocity and orientation of a vehicle is an extremely important aspect of highly assisted and autonomous driving scenarios. As a result of decades of research into this topic, there exist many tracking algorithms, each with different operating principles driven from different statistical frameworks. However, due to the complexity of the applications with which they are applied to, no algorithm has sufficient generality to be applied in all circumstances. While the topic of assessing the performance of algorithms has been investigated in the past, there exists no standardized framework for comparing the performance of different algorithms. In this paper we introduce an information theoretic framework which uses the Kullback Leibler Divergence to consider the relative information gain between different fusion algorithms. This framework is independent of the sensor systems and trajectories and considers only the technical operation of the algorithms. The results presented in this paper illustrate the utility of this approach and provide valuable insight for the development of algorithmic methodologies for real world vehicle dynamics estimation.
On Developing the Model of Blockchain Technology for Logistic Services in Indonesia

Blockchain technology was early used for financial application which is using for accounting method of the virtual currency. In the past year, the attention of business practitioner and researcher moved from financial applications to other domain, one of them is logistics. We propose the function model of blockchain for logistic services in Indonesia, focusing on logistic and decentralized in industry. Overall, blockchain can optimize the process of logistic process and increase transparency, reliability and efficiency for logistic services in Indonesia.
Agile Development Process of a By-Wire Electric Vehicle for Intelligent Ground Vehicle Competition Self-Driving Challenges

An agile process is a frequent redesign technique that is suitable for quick adaptation of various tasks, in contrast to a more time-consuming high-level design. In this paper, we describe the concept of an agile development process to convert a by-wire electric vehicle to meet Intelligent Ground Vehicle Competition (IGVC) Self-Drive Challenges. We converted a commercial electric vehicle into a basic self-driving by-wire electric vehicle within a limited development time to satisfy the rule. A key factor for enabling agile development is the Robot Operating Systems (ROS) environment for integrating multiple microcontrollers with sensor and actuator modules. The approach allows the rapid installation of hardware components implemented as nodes, and rapid debugging of software programs implemented as topics. Employment of ROS allows frequent reassessments that enable rapid troubleshooting and debugging. This paper illustrates how a team of eight students with three advisors were able to successfully integrate hardware and software to meet specific autonomous driving challenges in a matter of one and a half months. The advantages of the agile development process were confirmed in the team meeting the IGVC self-driving challenges.
Track-before-Detect Short Range Radar System for Industrial Applications

This paper presents a multiplexed short range frequency modulated continuous wave (FMCW) radar system and an algorithmic approach for the detection of targets. The algorithm uses a heuristic particle filter to track potential targets before they are actually detected. The detection algorithm is compared to a well-known constant false alarm rate (CFAR) detector. The whole setup is tested on real-life data in an industrial scenario.
Study on Principles and Characteristics of New Type Digital Hydraulic Transformation

The hydraulic transformer is usually composed of a pair of pump-motor units. When inlet pressure is stable, the outlet pressure can be controlled by changing the displacement ratio between the pump unit and motor unit. Theoretically, there is no throttling and overflow loss, so it attracts much attention in fluid transmission field. However, most of the existing hydraulic transformers are controlled by the displacement-varying mechanism, with slow response and small range of transformer ratio. Therefore, a new type of digital hydraulic transformer was proposed. The main body is a group of gear pump motor units, which are installed in coaxial direction, and the displacement ratios between these units is arranged according to 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> in every inlet and outlet of these gear pump motor units, there are digital switch valves to realize the digital control of pressure change. Using simulation software AMESIM, the conversion process of digital hydraulic transformer is simulated and analyzed, the characteristics of pressure, flow and transformation ratio are verified during the working process. The characteristics of transformer under constant voltage network were tested by building an experimental platform. By changing the pressure and load of constant voltage network, the working characteristics of digital hydraulic transformer under different working conditions are studied. The feasibility of the variable pressure principle is verified, and the energy saving characteristics of the digital hydraulic transformer are proved.
Watermark Embedding and Extraction Based on LSB and Four-Step Phase Shift Method

A method of color watermark embedding and extraction based on LSB and four-step phase shift method is proposed for information hiding. The method firstly decomposes the color watermark into three primary colors, and decomposes them into grayscale images of R, G, and B channel image. The four-step phase shift method respectively generates four computer-generated hologram (CGH) for each grayscale images, and one of the CGH will be embedded into the corresponding channel image of the color carrier image by the LSB algorithm; the watermark carrier image is decomposed into the R, G, and B channel images, the CGH is extracted from the channel image and The channel images are reconstructed from the CGH by the four-step phase shift method, the original watermark image is synthesized by the channel images, and the watermark recovery is completed. This watermark embedding extraction method based on LSB and the four-step phase shift improves the security during the watermark embedding process.
Low Cost Hand Gesture Control in Complex Environment Using Raspberry Pi

This article focuses on implementation in an embedded system with Raspberry Pi to a standalone machine for controlling electronic devices which wirelessly controlled by a hand gesture in the complex environment background. This system uses the RGB camera in combination with Raspberry Pi, a popular device today due to the inexpensive price and reliable performance. The hand gesture detection in each frame uses the radian fingertip analysis technique, a new technique presented which does not require any data training. This technique provides a good robust for light effect and complex environment. The experiment had been tested with the America Sign Language fingerspelling 12 gestures, the results found that 90.83%.
Speaker Clustering with Penalty Distance for Speaker Verification with Multi-Speaker Speech

Speaker verification in a multi-speaker environment is an emerging research topic. Speaker clustering, that separates multiple speakers, can be effective if a predetermined threshold or the number of speakers present in a multi-speaker utterance is given. However, the problem in practice does not provide the leverage for either of the factors. This work proposes to handle such a problem by introducing a penalty distance factor in the pipeline of traditional clustering techniques. The proposed framework first uses traditional clustering techniques to form speaker clusters for a given number of speakers. We then compute the penalty distance based on Bayesian information criterion that is used for merging alike clusters in a multi-speaker utterance. The studies are conducted on speakers in the wild (SITW) and recent NIST SRE 2018 databases that contain multi-speaker conversational speech in noisy environments. The results show the effectiveness of the proposed penalty distance based refinement in such a scenario.