Reviewerly/exHarmony huggingface dataset:
The Implementation of Smart Method for The Selection Of Concentration Computer Engineering Courses At The University Potensi Utama

computer engineering courses At the University the main Potential each semester 5 held the election of concentration. In the election the Party held a study program socialization areas of concentration to students then the student must fill out the form concentration given by the election of the party's program of study. From the results of the evaluation of the courses, many students in the selection of the concentration in terms of a number of factors, including the factors of interest and ability. Therefore, the researcher will be designing a decision support system for the selection of concentration with the SMART method. In his election gave prodi's Party question form to the student and the student in question form fill values prerequisite courses from each concentration and the concentration of interest will be selected. On the process of the selection of the concentration, there are decisions that must be taken to select each student. Inside there are selection criteria-the criteria used in selecting appropriate, for its decision support system (SPK) will make it easier to use in the selection of the concentration. Simple Multi-Attribute Rating Technique (SMART) is a method that is often used in the SPK. Advantages of SMART is if doing the addition/subtraction on alternative will not affect the calculation of weighting because each assessment alternative is not interdependent, calculations on the SMART method are simpler so it's not It takes a complicated mathematical calculation with a strong mathematical understanding. By using the SMART method is expected to be an alternative solution for the Prodi professionally appropriate criteria have been determined from the study programs of the University computer engineering Major Potential.
Wavelet pointwise estimations under multiplicative censoring

In this paper, we investigate the wavelet-based estimators of a kind of censored mixture density and discuss their pointwise asymptotic convergence rates over Hölder spaces. We first consider the linear wavelet estimator and give its upper bound. However, the linear one is nonadaptive and not applicable since it is related to the unknown space parameter. Finally, we use the hard threshold method to explore adaptive nonlinear wavelet estimator and obtain the same convergence order as the linear one up to a logarithmic penalty.
Feature Enhanced Fully Convolutional Networks for Monocular Depth Estimation

In the research of monocular image depth estimation, the fully convolutional residual network structure can achieve better prediction results, but the precision of image depth prediction still has room for improvement. Therefore, this paper proposes a feature of enhanced fully convolutional networks (FE-FCN). Firstly, a feature pyramid structure is introduced to fuse multi-scale feature maps extracted from down-sampling with the result of up-sampling. By reusing feature maps, the image depth prediction maps become richer and more delicate. Secondly, the high-frequency information of the input image is extracted by a high-pass filter and fused with up-sampling results in fully convolutional networks, which further reduces the error and improves the accuracy. In addition, by preprocessing the raw data in NYU dataset, a new dataset is generated for training and testing. Finally, the experimental results prove that the proposed method improves the quality of depth estimation maps. At the same time, the rms and rms(log) of image depth prediction error were reduced by 13.96% and 10.25% respectively.
Human Gesture Controlled Car Robot

In present era of development and growth, the technology has made possible for people to operate electronic devices more conveniently. Now we are able to operate machines without giving it a touch with the help of technology called Hand Gesture Recognition. Here we have devised a gesture controlled car robot which uses PIC16F738 microcontroller and accelerometer to achieve human computer interaction. In this paper we deal with development and implementation of wireless and an accelerometer based hand gesture controlled car robot using RF transmitter and receiver. Transmitter detects the movement of hand and sends the command to the receiver by RF; receiver receives the command and moves the robot accordingly. Apart from conventional approach of controlling mechanism of car robots via buttons etc., here we have developed a definite and effective algorithm for identification of the gestures.
Autonomous Selective Parts-Based Tracking

Object tracking from videos is still a challenging task due to various changes throughout a video sequence including occlusions, motion blur, scale and other deformation changes. In this paper, we propose a selective parts-based approach, using correlation filters, that makes choices based on a consensus of the parts and global tracking. Moreover, we further enhance our parts-based approach by introducing a segmentation-assisted parts initialization. In addition, we present a genetic algorithm-based method to autonomously select various parameters of the tracking algorithm, as opposed to the common practice of manually tuning those parameters. In contrast to existing part-based methods, the proposed method does not dilute accurate tracking by averaging results over multiple parts at every frame. Instead, we take a selective approach based on the relative weight of the responses across parts. Moreover, we only make location corrections when a part diverges, and rely on these location corrections to maintain an accurate appearance model. In the case of occlusions, which are among the main reasons for using a parts-based approach, our proposed approach consistently achieves the best performance. It is due to the ability to handle occlusion and not dilute decisions with incorrect parts, that our proposed approach enables state-of-the-art performance. The proposed approach was evaluated on videos from three different challenging benchmark datasets. Our approach has resulted in better overall precision and success rates for three different base tracking approaches.
Controllability and explainability in a hybrid social recommender system

The growth in artificial intelligence (AI) technology has advanced many human-facing applications. The recommender system is one of the promising sub-domain of AI-driven application, which aims to predict items or ratings based on user preferences. These systems were empowered by large-scale data and automated inference methods that bring useful but puzzling suggestions to the users. That is, the output is usually unpredictable and opaque, which may demonstrate user perceptions of the system that can be confusing, frustrating or even dangerous in many life-changing scenarios. Adding controllability and explainability are two promising approaches to improve human interaction with AI. However, the varying capability of AI-driven applications makes the conventional design principles are less useful. It brings tremendous opportunities as well as challenges for the user interface and interaction design, which has been discussed in the human-computer interaction (HCI) community for over two decades. The goal of this dissertation is to build a framework for AI-driven applications that enables people to interact effectively with the system as well as be able to interpret the output from the system. Specifically, this dissertation presents the exploration of how to bring controllability and explainability to a hybrid social recommender system, included several attempts in designing user-controllable and explainable interfaces that allow the users to fuse multi-dimensional relevance and request explanations of the received recommendations. The works contribute to the HCI fields by providing design implications of enhancing human-AI interaction and gaining transparency of AI-driven applications.
Fast Image Deblurring Based on Dual-Exposure Prior

Image deblurring is an important task for practical applications. However, it remains a challenge due to the serious camera shake. In this paper, we propose a prior named Dual-Exposure Prior (DEP) according to the observation that image captured with a relatively short exposure time may achieve sharp edges. Based on the DEP, the blur kernels of the blurry images can be accurately estimated. An effective optimization between blur kernel estimation and intermediate image restoration is established by using the L 2 -regularized DEP in the maximum a posterior framework. Each sub-problem of the optimization is convex, it can be solved in close-form with fast Fourier transforms acceleration. The experiments based on real and synthesized blurry images show that the proposed image deblurring algorithm can outperform the state-of-the-art methods in terms of subjective and objective quality. Moreover, the proposed algorithm can be implemented with significantly low computational complexity.
Improving Person Search by Adaptive Feature Pyramid-based Multi-Scale Matching

Within person search problem, existing pedestrian detectors usually generate person bounding boxes with different sizes, which will degrade the performance of person re-identification in matching process. The built-in feature pyramid hierarchy to address the multi-scale matching problem adopted in previous studies, is prone to be effective for reducing the semantic gaps among different level features. Therefore, in this paper we propose an Adaptive Feature Pyramid (AFP)-based multi-scale matching method for person search. The network adaptively learns a set of adjusting vectors to make the low-level feature distributions approximate the high-level. Moreover, we integrate the AFP structure into a ResNet-50 based person search framework to search a target person from a gallery of whole scene images. Extensive experimental results show that our approach performs superiority over the state-of-the-art person search methods on CUHK-SYSU dataset.
Extended Formulation Lower Bounds for Refuting Random CSPs

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2020 ACM-SIAM Symposium on Discrete Algorithms (SODA)Extended Formulation Lower Bounds for Refuting Random CSPsJonah Brown-Cohen and Prasad RaghavendraJonah Brown-Cohen and Prasad Raghavendrapp.305 - 324Chapter DOI:https://doi.org/10.1137/1.9781611975994.19PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract Random constraint satisfaction problems (CSPs) such as random 3-SAT are conjectured to be computationally intractable. The average case hardness of random 3-SAT and other CSPs has broad and far-reaching implications on problems in approximation, learning theory and cryptography. In this work, we show subexponential lower bounds on the size of linear programming relaxation for refuting random instances of constraint satisfaction problems. Formally, suppose P: {0,1}k → {0,1} is a predicate that supports a t — 1-wise uniform distribution on its satisfying assignments. Consider the distribution of random instances of CSP P with m = Δn constraints. We show that any linear programming extended formulation that can refute instances from this distribution with constant probability must have size at least for all v > 0. For example, this yields a lower bound of size exp(n1/3) for random 3-SAT with a linear number of clauses. We use the technique of pseudocalibration to directly obtain extended formulation lower bounds from the planted distribution. This approach bypasses the need to construct Sherali-Adams integrality gaps in proving general LP lower bounds. As a corollary, one obtains a self-contained proof of subexponential Sherali-Adams LP lowerbounds for these problems. We believe the result sheds light on the technique of pseudocalibration, a promising but conjectural approach to LP/SDP lower bounds. Previous chapter Next chapter RelatedDetails Published:2020eISBN:978-1-61197-599-4 https://doi.org/10.1137/1.9781611975994Book Series Name:ProceedingsBook Code:PRDA20Book Pages:xxii + 3011
Implementation of Trifid Cipher Algorithm in Securing Data

In the digital era, data transmission is mostly done by using internet media such as sending via email and social media. In sending information using the internet there are many threats that can occur such as corruption, tapping, modification, and fabrication. In securing information there are several ways including using data disguise techniques called cryptography. The Trifid Cipher algorithm uses polybial cubes to convert letters to numbers and write numbers vertically under the message with sequences, columns, and rows. Then the numbers are read horizontally and then grouped into 3 parts then the cube is used again to convert numbers to ciphertexts.
1-D/2-D/3-D Hopfield Associative Memories

Abstract In this research paper storage as well as retrieval of 1-D/2-D/3-D information using Hopfield type Associative Memories (AMs) is discussed. Various Artificial Neural Networks(ANN) architectures are proposed. Also, implementational issues associated with those Associative Memories are discussed. Cascade connection of AM and Convolutional Neural Network is proposed for noise immunity.
Spline-Orthogonal Extended Neo-Fuzzy Neuron

The development of successful human-computer interaction means depends on the possibilities of automatic recognition of signals that are natural or familiar to people. These signals include speech, handwritten texts, gestures, facial expressions, and so on. To develop interfaces taking into account such signals, artificial intelligence methods, in particular, artificial neural networks, are being widely used now. One of the vast classes of neural networks are hybrid neuro-fuzzy systems, which are characterized by universal approximation capabilities and considerable computational simplicity. Such systems allow the implementation of speed-optimal learning algorithms also. These qualities make hybrid neuro-fuzzy systems very useful in solving classification problems, especially for online mode. This paper proposes an architecture and learning algorithm for spline-orthogonal extended neo-fuzzy neuron, which allows simplifying the network learning process when new data is received. For this purpose, the usage of nonlinear synapses based on Chebyshev orthogonal polynomials for neo-fuzzy neuron is proposed. B-splines are used as fuzzy membership functions. The effectiveness of the developed architecture and learning algorithm is studied on the example of the problem of basic person emotional states recognition in real time.
Unpaired Images based Generator Architecture for Facial Expression Recognition

Facial expression recognition (FER) is a challenging task due to the lack of sufficient training data. Most conventional approaches usually rotate or flip the images for data augmentation. More recently, numerous methods synthesize images automatically by using Generative Adversarial Network (GAN). However, paired images are always required in these methods. Different from existing methods, in this paper, we propose an end-to-end deep learning model for simultaneous facial expression synthesis and facial expression recognition. In our method, paired images are not required, which makes the proposed model much more flexible and general. Furthermore, different expressions are encoded in a disentangled manner in a latent space, which enables us to generate facial images with arbitrary expressions by exchanging certain parts of their latent identity features. Finally, the facial expression synthesis and facial expression recognition tasks can further boost their performance for each other via our model. Quantitative and qualitative evaluations on both controlled and in-the-wild datasets demonstrate that the proposed method performs favorably against state-of-the-art methods.
Countermeasures for the Development of College Physical Education at the Age of Internet+ Education

The application of the information technology makes physical education more dynamic. On the base of the researches on the influence of the Internet on the goal and the content, the educators, the educatees and the theoretical structure of physical education, this paper puts forward some countermeasures to deal with the phenomenon, such as renewing education idea, enriching sports information resources, increasing equipment investment in intelligent classrooms, enhancing teachers' information level, promoting students' information literacy, etc. to enhance students' self-learning sports ability, improve the quality of sports teaching and realize the educational goal of physical education.
Tekstil Tasarımında İşbirlikçi Yaklaşım ve Dijital Baskı Tasarımı Üzerine Örnek Bir E-Ticaret Web Sitesinin İncelenmesi

The "co-design" approach, which transforms the consumer into a manufacturer, is becoming increasingly important, especially in the textile printing industry, by enabling the integration of computer-aided manufacturing and design (CAD / CAM) systems with the Internet technologies and consumers to participate in the design process through e-commerce applications.In this review, in which the concept of collaborative design is discussed in the context of textile design, following the literature review, domestic and foreign e-commerce companies offering co-design opportunities for digital print designs are investigated one of these companies is examined and the flowchart diagrams of their processes are created.The aim of this study is to offer a roadmap to attract attention of local designers and entrepreneurs to the potential of e-commerce in terms of co-design and to contribute to academic literature.
Code Clone Detection with Convolutional Neural Networks

Similar or identical code portions which are generated by copying and reusing code portions within the source code are named as code clones. While so many works have been conducted to detect these clones, they generally use string comparison techniques and very few of them take advantage of popular learning based approaches, such as deep learning. This paper proposes a new approach based on a popular and successful image classification technique named as convolutional neural network. It simply tokenizes each candidate clone pair in order to generate image files. Then, convolutional neural network is used to classify these image data with labels “clone” and “not clone”. In order to train and test the network, clone and not clone pairs are chosen from a public database including six million methods. As a result, the approach gives 99% accuracy, effectively detects clones and not clones with 2-5% false alarms rates at method granularity.
RocketRML - A NodeJS implementation of a use-case specific RML mapper

The creation of Linked Data from raw data sources is, in theory, no rocket science (pun intended). Depending on the nature of the input and the mapping technology in use, it can become a quite tedious task. For our work on mapping real-life touristic data to the this http URL vocabulary we used RML but soon encountered, that the existing Java mapper implementations reached their limits and were not sufficient for our use cases. In this paper we describe a new implementation of an RML mapper. Written with the JavaScript based NodeJS framework it performs quite well for our uses cases where we work with large XML and JSON files. The performance testing and the execution of the RML test cases have shown, that the implementation has great potential to perform heavy mapping tasks in reasonable time, but comes with some limitations regarding JOINs, Named Graphs and inputs other than XML and JSON - which is fine at the moment, due to the nature of the given use cases.
Genomic Research in International, European, and Russian Jurisprudence

Modern science achievements contribute to the development of international and national ethical and legal approaches in the field of genome research while respecting human rights, ensuring safety, and maintaining the potential for scientific and technological progress. At the same time, there are no legal documents devoted exclusively to biomedicine and genomic research. This situation has led to the development of international standards either through the general principles of protecting human rights or through “soft law” norms. In addition, a significant regulatory gap exists caused by the lack of security measures in international jurisprudence. A similar trend occurs at the regional level. At the same time, in the framework of the Council of Europe, a single international treaty has been adopted that is directly devoted to bioethics and genomic research. The Convention for the Protection of Human Rights and Dignity of the Human Being with regard to the Application of Biology and Medicine: Convention on Human Rights and Biomedicine was adopted by the Council of Europe in April 1997 in Oviedo. The active development of biomedicine and genomic research caused conflicts between ethics and law. These issues became cases in highest judicial instances, including supranational structures, in particular, the Council of Europe and the European Court of Human Rights (ECHR). The ECHR influences Russia through the so-called “non-prohibitive practice.” Nevertheless, despite the law’s imperfections, Russia develops its own approach to the existing system of international standards that admit national uniqueness in this sphere. Despite attempts to restrict the influence of supranational judicial institutions on the Russian legal order in the middle of this decade, the practice of the ECHR as a source of Russian law influences the practice of Russian courts even in this sphere (Russian judges refer to the decisions of the ECHR).
Sequence Analysis and Related Approaches: Innovative Methods and Applications

"Sequence Analysis and Related Approaches: Innovative Methods and Applications." Technometrics, 62(1), pp. 139–140
A Double-Weighted Parametric Model for Academic Software Project Effort Estimation

The effort, cost, and time play a vital role in the success or failure of the software. The ratio of software project failure nowadays is growing like a storm in the world. One of the reasons behind this failure proportion is an imprecise and inappropriate estimate of required effort, cost, and budget for particular software project development. The motivation behind our research is to estimate the effort for software development accurately. Accurately estimate effort for software development is one of the most challenging tasks because from a very early stage it requires in-depth study as well as detailed statistics. Participation of members of the team is important during the development of the project along with various activities like domain area, sector, nature, software process methodology, and other resources. All these proofs do not appear in the initial phase of development, but on the other side, they discovered as software growth progresses. Many models proposed for estimating effort related to software development, but only a few are adaptable for effort estimation task in the academic software project. From the study of nearly 600 academic software projects and inputs from more than 30 Software Engineering experts, this research paper proposes an innovative model based on 8 parameters and 24 sub-parameters. Each of the sub-parameters is weighted twice, and the final effort estimation obtained by summation of the individual product of the weight of the parameter and weight of sub-parameter. We have coined the term “ASPEE units” (Academic Software Project Effort Estimation) for the estimated effort.