Reviewerly/exHarmony huggingface dataset:
Single Image Haze Removal Using Hybrid Filtering Method

The mist, smog, fog, and haze occurring in the outdoor environment produces distortion in the image when it is picked up by the imaging sensor and hence it degrades the image resolution and clear scene of the image acquired. Hence to clear the haze from the captured image a wavelet transform method combined with Guided image filter and Global guided image filter for better visibility and improved quality in the output image has been developed. In hybrid filtering, two different filters are combined as a hybrid filter (GIF & GGIF) to remove the haze present in the single image and this method also conserves the finer details of the image. The wavelet technique is used to detect the edges in the input image which gives good results when compared to other techniques. The global guided image filter (GGIF) is a filter that combines the effect of edge conserving method and guidance structure transfer method which conserves small edge structures in the dehazed output image. The dehazed output image has good preservation of edge details and also gives sharper edges which are used in real time transportation systems. The performance parameters obtained has better results such as high PSNR and low MSE.
Implementing Central Force optimization on the Intel Xeon Phi

Central Force optimization (CFO) is a fully deterministic population based metaheuristic algorithm based on the analogy of classical kinematics. CFO yields more accurate and consistent results compared to other population based metaheuristics like Particle Swarm optimization and Genetic Algorithms, but does so at the cost of higher computational complexity, leading to increased computational time. This study presents a parallel implementation of CFO written in C++ using OpenMP as implemented for both a multi-core CPU and the Intel Xeon Phi Co-processor. Results show that parallelizing CFO provides promising speedup values from 5-35 on the multi-core CPU and 1-12 on the Intel Xeon Phi.
Gerçek Zamanlı Lidar - Kamera Tümleştirme Mimarisi ve Araç Üzeri Uygulaması [Not available in English]

No English translation of this document was provided by the author or conference organizers.
Mapping the Distortion Function via Multivariate Analysis of Atomically Resolved Images

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Combinatory Connection Network for Facial Expression Classification

Facial expression classification is important to the fields of computer vision, knowledge discovery, and human-computer interaction. Improving the accuracy of facial expression classification contributes to the development of safety, traffic, and recommendation. In this study, we proposed a Combinatory Connection Network, which called ComNet. In each layer, we divide the feature maps into different areas and then use the intra combinatory connection to learn more local information. Not only improving the accuracy of expression classification, but ComNet also has better performance in the case of less labeled samples. Between each layer, we use the inter combinatory connection to optimize the propagation of the gradient, which improves the accuracy of the network and reduces the generalization error. To verify the accuracy of the network, we performed experiments on the CK+ dataset to present the performance of the ComNet on facial expression classification tasks. We also experimented on other datasets to prove that ComNet is not only effective on specific datasets. A similar phenomenon was obtained.
An Error Feedback Method to Enhance Teaching Ability of Young Teachers for Classroom Teaching Process

Teaching ability is a hot issue of teachers' team construction, and plays an important role in establishing teachers' teaching quality evaluation system, changing classroom teaching models and establishing teaching supervision and operation mechanisms. According to the main problems in teaching management and classroom teaching process, such as the difficulty in analyzing teaching data and quantitative evaluation, this paper proposes an error feedback method of teaching information based on classroom teaching process data, which is generated by teachers, students, teaching administrators and teaching supervisors. This method can generate teaching error information by comparing teaching process data and teaching evaluation data, and it is helpful for young teachers to improve teaching methods. Besides, the teaching information feedback method has important reference significance for the concrete implementation of the teaching quality monitoring system in the teaching closed loop.
Robot Path Planning–Prediction: A Multidisciplinary Platform: A Survey

In recent times, there is an impressive progress in the field of automation and robotics. Google driverless car or intelligent Unmanned Air Vehicle (UAV) is the latest in the research works aiming for a high degree of autonomy. In this research field of automation and robotics, there is a mandatory requirement of continuously improving path planning algorithms. Path planners aim in finding an optimal and collision-free path in the work environment. The purpose of path planning is to find a kinematical optimal path with the least time complexity as well as model the environment completely. In this paper, we discuss the most successful robot path planning algorithms which have been developed in recent years from different field of science, making it a multidisciplinary approach and concentrate on universally applicable algorithms which can be implemented in aerial robots, ground robots and underwater robots. The algorithms are analysed from an optimality and completeness area perspective. In our study, we have included the Dynamic Programming Planning approach also which none of the review papers have covered.
What is Happening Inside a Continual Learning Model? - A Representation-Based Evaluation of Representational Forgetting -

Recently, many continual learning methods have been proposed, and their performance is usually evaluated based on their final output such as the class they predicted. However, this output-based evaluation cannot tell us anything about how representations the model learned from given tasks are forgotten during learning process inside the model although understanding it is important to devise a robust algorithm to catastrophic forgetting that is an intrinsic problem in continual learning. In this work, we propose a representation-based evaluation framework and demonstrate it can help us better understand the representational forgetting through intensive experiments on three benchmark datasets, which eventually brought us the following findings: 1) non-negligible amount of representational forgetting appears at shallow layers of a deep neural network model, and 2) which tasks are more accurately learned when representational forgetting occurred depends on the depth of the layer at which the representational forgetting is observed.
A hands-on guide to distributed computing paradigms for evolutionary computation

tutorial Share on A hands-on guide to distributed computing paradigms for evolutionary computation Authors: Rui Wang Uber AI Uber AIView Profile , Jiale Zhi Uber AI Uber AIView Profile Authors Info & Claims GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference CompanionJuly 2020 Pages 1055–1074https://doi.org/10.1145/3377929.3389880Published:08 July 2020Publication History 0citation133DownloadsMetricsTotal Citations0Total Downloads133Last 12 Months8Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Effective Text Comment Classification Using Novel ML Algorithm—Modified Lazy Random Forest

Machine learning (ML) algorithms are methods used to classify data. The various patterns or classes can be classified with the help of these various ML algorithms. There are numerous areas where these algorithms can be used. One such area is to detect whether the comment, sms or text message is SPAM or Normal message. So the aim of this work is to identify the best machine learning algorithms to detect SPAM text message on two different dataset. The first dataset is collected from YouTube comment dataset and second is the SMS dataset. The Random Forest (RF) is the ensemble learning method for classification, regression and other tasks that operates by developing a multitude of decision trees at learning phase and outputting the class. Its one variant which performs well as compare to normal RF is Lazy RF, as the study shown and is the base for this research work. In this work, we have proposed one more novel variant of LRF and the different machine learning algorithms are compared in terms of accuracy with proposed Modified Lazy Random Forest. The results are compare with two techniques, first is the simple hold out, and second is the K-fold cross validation. For both cases the proposed algorithm performs well to detect the SPAM messages for the both datasets.
Citizen Data in Distributed Computing Environments

Data security is paramount in the increasingly connected world. Securing data, while in transit and rest, and while under usage, is essential for deriving actionable insights out of data heaps. Incorrect or wrong data can lead to incorrect decisions. So, the confidentiality and integrity of data have to be guaranteed through a host of technology-inspired security solutions. Organizational data is kept confidentially by the businesses and governments, often in distant locations (e.g., in cloud environments), though more sensitive data is normally kept in house. As the security mechanisms are getting more sophisticated, cyber security attacks are also becoming more intensive, so there is a constant battle between the organisations and the hackers to be one step ahead of the other. In this chapter, the aim is to discuss various mechanisms of accomplishing citizens' data confidentiality and privacy and to present solution approaches for ensuring impenetrable security for personal data.
A Survey on Methodologies and Algorithms for Mutual Authentication in IoT Devices

Authentication refers to the process of proving the identity of the IoT device or simply, it is an act of verifying the identity of the system. The scenario where two communicating parties authenticate each other at nearly the same time is called two-way authentication or mutual authentication. Mutual authentication provides trusted communication between remote IoT devices. This paper is aimed at performing a survey on various algorithms used for mutual authentication of IoT devices. The classification has been done based on the parameters used for authentication, its simplicity, optimality, and efficiency of the algorithm. An abstract analysis of the methodologies is illustrated.
Speaker Identification and Verification System for Arabic and Berber Language

The purpose of this work is to explore the advantage of Mel Frequency Cepstral Coefficients and Linear Predictive Coding combined with Principal Component Analysis, for speaker identification system applied for Arabic and Algerian Berber language. For this aim, the multilayer perception classifier is applied where different training functions are used for recognition process to improve the global efficiency. Dynamic time wrapping (DTW) is also applied for speaker verification. A Comparative analysis with some methods reveals that the proposed method provide good efficiency for verification and identification.
Research on Cloud Resource Optimization Based on Tabu Search and Improved Wolf Pack Algorithm

In order to further realize the optimization of resource scheduling in cloud computing environment, this paper proposes an improved wolf pack algorithm (TS_WPA) based on tabu search. The main improvement strategy is to add a tabu list to the traditional wolf pack algorithm, integrate the tabu search algorithm into the wolf pack algorithm, and dynamically modify the step size factor in the algorithm. The simulation experiment results show that the improved wolf pack algorithm has improved convergence accuracy and speed. The problems that the ordinary WPA algorithm is easy to fall into the local optimal solution and the excessive calculation resource consumption have been solved to a certain extent, which has been greatly improved. The ability of resource scheduling in the cloud environment is realized, and the effective utilization of resources is realized.
Identification, Tracking and Impact: Understanding the Trade Secret of Catchphrases

Understanding the topical evolution in industrial innovation is a challenging problem. With the advancement in the digital repositories in the form of patent documents, it is becoming increasingly more feasible to understand the innovation secrets - 'catchphrases' - of organizations. However, searching and understanding this enormous textual information is a natural bottleneck. In this paper, we propose an unsupervised method for the extraction of catchphrases from the abstracts of patents granted by the U.S. Patent and Trademark Office over the years. Our proposed system achieves substantial improvement, both in terms of precision and recall, against state-of-the-art techniques. As a second objective, we conduct an extensive empirical study to understand the temporal evolution of the catchphrases across various organizations. We also show how the overall innovation evolution in the form of introduction of newer catchphrases in an organization's patents correlates with the future citations received by the patents filed by that organization. Our code and data sets will be placed in the public domain.
Obstacle Avoidance System of Substation Inspection Robot Based on the Dual-mode Controller of Bumpless Switching

The design of obstacle avoidance system for substation inspection robot is widely known as a challenging task. This paper addresses this issue by exploiting a novel approach to combine the fuzzy logic control and fuzzy PID control into the controller design with bumpless switching between these two control modes. Such dual-mode design approach can significantly promote the real-time performance of the obstacle avoidance system and effectively eliminate the blind spots and enables the system to reach the equilibrium points faster. The moving velocity and rotation angle of robots are proactively controlled via the fuzzy-PID dual-mode controller to avoid obstacles with graceful path and velocity changes. The suggested design approach is evaluated through a set of simulation experiments for realistic scenarios. The numerical results have demonstrated its effectiveness which can be adopted in the practical deployment.
Enhancement of Degraded CCTV Footage for Forensic Analysis

Vinay, A. Lokesh, Aditya Kamath, Vinayaka R. Murty, K. N. B. Natarajan, S.Forensic analysis has proved to be one of the most utilitarian tool in investigating crime. Forensic analysis provides evidence/basic information of the said crime through analysis of physical evidence. In this paper, we present a scintillating technique to enhance the image quality of CCTV video to assist in the investigation of criminal cases. This paper, address the issue of lack of a budget surplus for a high-quality image/video capturing device. The problem of enhancing images is addressed by pure image processing methods and machine learning techniques. The paper analyzes both techniques and further concludes that the machine learning approach produces a more efficient result. The applications of this technique can range from simple image filtering/beautification to forensic image processing.
Sistem Penunjang Keputusan Penentuan Biaya Investasi Tanaman Hidroponik Menggunakan Fuzzy Tsukamoto

Decision support system is a system used to support and help management make decisions in semi-structured and unstructured conditions. Hydroponics is a term used to explain some ways to grow crops without using soil as a medium for planting. The problem that occurs is that many people demand for vegetables to be consumed which causes farmers to be overwhelmed to offset these needs. Lack of public knowledge about hydroponic farming causes a lack of people who run this business. The design of the decision support system for determining the investment cost of hydroponic plants uses the Tsukamoto Fuzzy Logic method because the formulation and calculation of the Tsukamoto fuzzy logic system are very easy to understand, accurate and weighty in determining the value of the investment costs sought. The decision support system design method used is prototype. The modeling technique used in this decision suppot system is UML (Unified Modeling Language) because it is an object-oriented modeling method and focuses on defining static structures and dynamic information system models. The results of application testing can produce the optimal value of investment costs which is almost close to the Tsukamoto fuzzy interest. The weakness of this application is the level of accuracy of the system is only 35.8%, and the lack of determinants.
Analysis of The Characteristics of Similar Words Computed by Word Embeddings

Word2vec is a popular word embedding technique and has also gained a lot of attention in the NLP field. But word embedding based on distributed representation is deficient in the semantics of distribution. This defect often occurs when we use word similarity to find similar words of a seed word. This article analyzes these similar words based on this deficiency. We propose a novel classification criterion to effectively classify similar words into 7 categories. Finally, we listed the future research directions, hoping to solve the problem of word confusion effectively.
Empirical Analysis of Classification Algorithms in Data Stream Mining

Data stream mining has taken over as a new field of research during past few years. It has gained lot of attention recently due to its challenging characteristics like dynamic nature, huge data size and continuous flow, temporal, etc. Processing and classifying these types of data confront many issues in terms of storage and analysis both. Moreover, existing traditional classification algorithms do not fit well with data stream, as they process over the data which is stored in memory for once and all. Data streams if taken up for mining can render very crucial information for any non-stationary system from which it is generated. Also, storing data streams is not feasible as storage cost increases with the increasing data size. But the algorithm designed for data streams should have characteristics which address incremental and multi-pass approach to deal with new data and to analyze exiting at the same time. Data stream classification aims at labeling data, and it is nearly impossible to do in real life due to the characteristics of data which act as challenges. Traditional data mining algorithm fits limited number of instances, and this model would not work with data stream. In this paper, we have focused discussing data stream classification algorithms and simulated the same with real and synthetic dataset to understand performance parameters of discussed algorithms.
Open Source Hardware: An Opportunity For Critical Systems

Dependable systems currently undergo a series of transformations, notably the shift to multi-core processors and the rise of concerns previously limited to the IT domain such as cybersecurity or cloud-like versatility. In this position paper we summarize the key challenges, and some promising solutions. In addition to software-based techniques devised for COTS multi-processors, emerging Open-Source processing platforms provide the ability to experiment domain-specific mitigation techniques that were previously deemed not economically feasible.
A Proposal of a Ground Surface Information Collection System using a 9-axis Motion Sensor for self-propelled Wheelchair Users' Exercise Promotion

In this study, the authors are working on the development of the Sound Wheelchair to increase the activity of wheelchair users. The Sound Wheelchair is a self-propelled wheelchair equipped with a 9-axis motion sensor module to convert the wheelchair user's operations to the digital media data. We aim to create a map that can be used for self-propelled wheelchair users' exercise promotion according to wheelchair users' disability grade by making a digital library of the ground surface information and activity. In this paper, we proposed a method for discriminating ground surface information of the Sound Wheelchair and we made a simple collection game with ground surface information for wheelchair users' as a method of exercise promotion.
Data Processing Using Python in DigitalMicrograph

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Image Acquisition with Wide-angle Camera for Sun Location Tracking

Solar energy has a very high potential as a renewable energy source. Besides, it is observed that the desired levels are not achieved in the efficiency of the systems based on solar energy (especially the systems set-up with photovoltaic solar panels). Therefore, the studies on solar cell technology and other solutions related to efficiency have been continuing. As a preferred solution to increase efficiency of energy production systems based on solar energy is solar tracking. In this study, an embedded system with a wide-angle camera is used to define the sun trajectory. With this system, image data are recorded in the defined date and time range, and improvement studies are carried out to create the geometric form of the sun based on these images. Obtained images will be the basis for the progressive studies to identify the location of the sun in the sky angular manner.
Study on Three-Dimension Design Management of Power Grid Projects Centered on Quality Improvement

Abstract Based on the current status of transmission and transformation project design management, the concept of lean design is highly integrated with the means of three-dimension design so as to innovate the new mode for the full-process design of transmission and transformation project, as well as construct the three-dimension design of power grid project with the features of Southern Hebei Power Network. Through the standardization construction of three-dimension design technology, lean construction of design quality and profound application construction of engineering practice, it is able to unify the design standard, enhance the design quality, rebuild the business process and deepen the design practice. This article realize the precise development of budge, as well as reach the targets of strengthening the engineering intrinsic safety and obtaining the good economic benefit.