Reviewerly/exHarmony huggingface dataset:
Dual-Streams Global Guided Learning for High Dynamic Range Image Reconstruction

High dynamic range (HDR) images capture the luminance information of the real world and have more detailed information than low dynamic range (LDR) images. In this paper, we propose a dual-streams global guided end-to-end learning method to reconstruct HDR image from a single LDR input that combines both global information and local image features. In our framework, global features and local features are separately learned in dual-streams branches. In the reconstructed phase, we use a fusion layer to fuse them so that the global features can guide the local features to better reconstruct the HDR image. Furthermore, we design mixed loss function including multi-scale pixel-wise loss, color similarity loss and gradient loss to jointly train our network. Comparative experiments are carried out with other state-of-the-art methods and our method achieves superior performance.
Student Learning Achievement through Augmented Reality in Science Subjects

Augmented Reality or AR is a new technology that combines the real world with the virtual world through the smart technology devices. Using Augmented Reality technology to manage learning in classrooms is a new dimension in educational media and students give attention in learning. Teaching and learning of science subjects is challenging to make students understand when there are limited of equipment and student do not practice by his/herself. Now Thailand is facing with an education crisis and needs to reform education by focus on critical thinking skills. Therefore, this research proposes learning model by using AR technique and game based learning to enhance students' ability and data was collected both questionnaire and log data from e-Learning and e-Learning class activities consisted of the ability to perform problem analysis and design, development and testing, and evolution and testing. The participants were 200 teachers and students in the secondary school in Bangkok and surrounding areas who had applied the Augmented Reality (AR) in science subjects. The results showed that the mean score of posttest was significantly higher than the pretest and the average mean score of exercises was at a high level. To evaluate the result of students' attitudes toward learning via the proposed model, the questionnaire and interview form were applied to test students and the finding revealed that this proposed model are effective tools to learn and enhance self-practice and syntactic coding and problem-solving ability and student have positive attitudes to-wards learning model.
Directed probabilistic graphical models

This chapter formally introduces the directed probabilistic graphical models, i.e., Bayesian Networks (BNs). It first provides a definition of BNs, their important properties, and the types of BNs. It then discusses various BN inference methods, including both exact inference and approximate inference methods, for both posterior probability and MAP inferences. This chapter continues to discuss various learning methods to learn both the parameters and structures of BNs under both complete and incomplete training data. Finally, the chapter introduces dynamic Bayesian Networks, Hidden Markov models, and their associated learning and inference methods. This chapter ends with a discussion of the hierarchical Bayesian Networks, including both hierarchical Bayes models and the hierarchical deep models.
Are we represented as who we are? An assessment of library faculty online profiles within the City University of New York

Academic librarians have been wrestling with faculty status and rank for many decades and their dual identities as professionals and faculty made their identity representations in the online profile environment designed by colleges and universities even more complicated. Misrepresentation or insufficient representation of academic librarians' identities could lead to jeopardy of their public images within colleges and universities, or even trigger suspicion that academic librarians bring an impediment to academic standards by achieving less or none. Therefore, this study surveyed library faculty's online profiles within the libraries of the City University of New York and tried to assess whether library faculty are represented as who they are. The results revealed three categories of profiles: Business-Card Profiles, Quasi-Faculty Profiles, and Full-Level Faculty Profiles, which brought out the discussion about business identification, the creative Me, and the collective We, as well as their relations to institutional culture.
Metadata and Rights Management for Media Assets Using Blockchain Technology

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">This paper first explains the blockchain technology from a technical perspective. The concept of smart contracts is described, which allows hosting executable code inside blockchains. The second part presents a schema of how metadata and rights contracts can be stored in a blockchain, and how the relations are established. The last part sketches a system for automated execution of usage of rights contracts.</i>
Block-Chain - Security Advancement in Medical Sector for sharing Medical Records

Block-Chain security is the hot arising topic now-a-days. Block-Chain helps to get more secure, accurate and transparent data security in all sectors. In Health Sector, maintaining medical records is crucial, sharing medical records between record room to researcher needs security system for sharing. Here, Block-Chain provides best security in maintaining records and medical reports transmission. In this paper, we discuss the impact of Block-Chain in health sector for increasing security during data transmission from medical Record room to medical Institutions for their research study. We proposed a Block-Chain architecture for managing data of patients. In addition, we discussed the characteristics of the block-chain in medical perspective and then propose Medical Record based architecture for secure transmission of data between connected nodes.
Palm Reading: Using Palm Deformation for Fingers and Thumb Pose Estimation

Hand pose estimation is recognised as being one of the most challenging topics in the field of human pose estimation. Accurate estimation and tracking of multi degree of freedom hand joints can be beneficial to many research areas such as robotic tele-manipulation, motion patterns, robotic hand design and, more generally, human computer/robot interaction. Current solutions to hand tracking are unsatisfactory due to numerous simplifications used in modeling of the hand kinematics and noise-prone hand and finger position sensing methods. In this paper, we propose alternative hand pose sensing approach that includes detecting palm shape in order to more accurately estimate joint angles of middle and index fingers and thumb. We use Inertia Measurement Unit (IMU) sensors on the palm to detect forming of palm arches in different fingers and thumbs' poses. Principal component analysis as well as Dynamic Neural Networks are utilized to create three different models for fingers and thumb poses, while Polaris optical motion capture system is used as a ground truth. Validating through the unused data shows that using the palm shape as an additional parameter in hand tracking can estimate the hand digit joint angles with the average error of under 4.1%.
Lightweight optimization design of arm frame for aerial work platform based on NSGA-II algorithm

Abstract In the previous lightweight design of the arm of aerial work platform, it is difficult to balance the relationship between lightweight and stiffness reduction. This paper proposes a lightweight design that minimizes the variation stiffness while reducing weight. Taking the arm mass and stiffness variation as the objective, which constrained by yield strength and stiffness of the material, the Pareto front of each arm section size is obtained by the second generation of elite strategy non-dominated sequencing genetic algorithm (NSGA-II). Finally, the finite element model is established by ANSYS to compare the static and dynamic performance of the arm before and after optimization. The results show that the weight of the optimized arm is reduced by the original 13.3%, lightweight effect is remarkable.
Deep Forest Approach for Facial Expression Recognition

Facial Expression Recognition is a prospective area in Computer Vision (CV) and Human-Computer Interaction (HCI), with vast areas of application. The major concept in facial expression recognition is the categorization of facial expression images into six basic emotion states, and this is accompanied with many challenges. Several methods have been explored in search of an optimal solution, in the development of a facial expression recognition system. Presently, Deep Neural Network is the state-of-the-art method in the field with promising results, but it is incapacitated with the volume of data available for Facial Expression Recognition task. Therefore, there is a need for a method with Deep Learning feature and the dynamic ability for both large and small volume of data available in the field. This work is proposing a Deep Forest tree method that implements layer by layer feature of Deep Learning and minimizes overfitting regardless of data size. The experiments conducted on both Cohn Kanade (CK+) and Binghamton University 3D Facial Expression (BU-3DFE) datasets, prove that Deep Forest provides promising results with an impressive reduction in computational time.
Flexible, Personal Service Robot for ALS Patients

Diseases that cause motor impairment leave people dependent on the help of caregivers or new technologies for their daily tasks. Care robots could support these patients and help them gaining autonomy in some of their daily activities. In this paper, a robotic assistant is introduced based on Amyotrophic Lateral Sclerosis patients requests for care robotics. The presented use cases are derived from their feedback and integrated using a compliant lightweight robotic arm. The system has been developed to be used independently from the input device owned by the patient, which grants an easy access to the robot without any special training. The system is flexible enough to not harm the patient for tasks that involve physical humanrobot interaction, and yet precise enough to manipulate different objects. Despite the increasing acceptance of care robots in the community nowadays, the robotic assistant is one of the few robot solutions that combines autonomous behaviors and teleoperated control.
Deep Learning vs. Traditional Probabilistic Models: Case Study on Short Inputs for Password Guessing

The paper focuses on the comparative analysis of deep learning algorithms and traditional probabilistic models on strings of short lengths (typically, passwords). The password is one of the dominant methods used in user authentication. Compared to the traditional brute-force attack and dictionary attack, password guessing models use the leaked password datasets to generate password guesses, expecting to cover as many accounts as possible while minimizing the number of guesses. In this paper, we analyze the password pattern of leaked datasets and further present a comparative study on two dominant probabilistic models (i.e., Markov-based model and Probabilistic Context-Free Grammars (PCFG) based model) and the PassGAN model (which is a representative deep-learning-based method). We use Laplace smoothing for the Markov model and introduce particular semantic patterns to the PCFG model. Our output shows that the Markov-based models can cover the vast majority of the passwords in the test set and PassGAN demonstrates surprisingly the worst effect. Nevertheless, considering the threat that an attacker may adjust the training set, the PCFG model is better than the Markov model. Using Passcode with high-frequency passwords can increase the coverage while reducing the number of guesses. Brute-force attack can also work better when used in conjunction with probabilistic models. For the same billion guesses, brute-force attack can be used to crack pure digital passwords of 4 to 8 lengths, and original-PCFG and modified-PCFG could increase by 11.16% and 8.69%, respectively.
Scale-Informed Density Estimation for Dense Crowd Counting

Dense crowd counting (DCC) remains challenging due to the scale variation and occlusion. Several deep learning based DCC methods have achieved the state-of-arts on public datasets. However, experimental results show that the scale variation is still the main factor to hinder the DCC performance. In this work, we propose a scale-informed dense crowd counting method focusing on handling the negative effect caused by scale variation. More specifically, we propose a method to obtain the scale information of the patch from its GT density maps via estimating the mean value of the Gaussian kernel width and then a scale-classifier is deigned and trained accordingly. Moreover, with the estimated scale information, two sub-nets are dedicatedly deigned to learn the density maps for large-scale head patch and small-scale patch separately. Experimental results validate the performance of our proposed method which achieves the best performance on three dense crowd datasets.
Cultural Frames of Ethics, a Challenge for Information and Knowledge Organization

Purpose/Thesis: Culture is the common frame that can embrace the other facets like social and political aspects of classification that are at play when we talk about ethical dimensions. This article explores the cultural frames of ethics by identifying fundamental characteristic of biases of KOSs, namely classifications and indexing languages as variants of KOSs. These biases are examined through the lenses of ethics. The main focus is on structural biases generated by mainstream classification and indexing schemes. Conceptual issues, some examples of cultural and racial biases and some directions for future work are discussed.Approach/Methods: Based on a literature review of methods used in detecting biases, two inde­xing languages are chosen: subject authority file RAMEAU and LCSH. To show how knowledge organization systems contribute to marginalization, exclusion and stigmatization of a category of the population, and/or of a culture with reference to the French context a comparison between RAMEAU and LCSH was conducted.Results and conclusions: In order to deal effectively with the ethical issues they face, library professio­nals and institutions providing knowledge must have a good working knowledge of information ethics.Originality/Value: The author defines the cultural frame of ethics and demonstrates that a bias is hard to shift. The research conducted by the author and her students to verify the range of biases in RAMEAU showed that the efforts made to fight biases in KOSs brought positive results when it comes to LCSH, but it showed that the other systems based on this indexing language have main­tained their initial biases. The author calls for solid and sustainable actions to fight against KOSs inherited persistent biases.
BIT Problem: Is There a Trade-off in the Performances of Blockchain Systems?

Blockchain technology, as a revolutionary concept, born with Bitcoin, is triggering the start of a new era on information. It is generally recognized that the blockchain-based systems achieve high security in the process of information sharing in a distributed network, at the cost of low efficiency in terms of data throughput or high costs in consuming computational resources. A natural question we are interested in is whether there exists a trade-off principle between different performances of a blockchain system, which is also regarded as blockchain impossibility triangle (BIT) problem. In this paper, we propose an analysis method which can be used to verify the existence of the BIT. Our analysis method is composed of two layers. In the first layer the basic and core attributes are abstracted by building a consensus model, and in the second layer the analysis approach is designed to verify the existence of a BIT. Specifically, in the first layer, we firstly define a leaderless consensus model to quantitatively abstract the basic parameters in the process of system consensus, then three core attributes are justified to serve as the three vertices of the discussed triangle problem, namely security, cost, and efficiency. Based on the core attributes, we propose the credibility metric which measures the difficulty of achieving credible consensus for a given blockchain system. In the second layer, we analyze the existence of BIT, where we have demonstrated the conditions that a BIT exists and the conditions that the triangle does not exist. Finally, the proposed analysis method is applied in analyzing PoW based blockchain systems, where we have shown that there is no BIT problem in PoW based systems. Furthermore, the proposed analysis method can also help the blockchain developers in finding the promising directions of a new blockchain consensus mechanism.
Fast and energy-efficient FPGA realization of RNS reverse converter for the ternary 3-moduli set {3n–2, 3n–1, 3n}

Ternary number representation has been known as one of the efficient methods for the implementation of digital systems. Employing residue number system (RNS) in ternary digital signal processing (DSP) applications could offer the advantages of ternary number representation along with further privileges such as improving parallelism and fault tolerance. Efficacious design of reverse converter as a crucial block of residue number system has always been of great interest to researchers. In this paper, an efficient reverse converter for ternary RNS with moduli set {3n–2, 3n–1, 3n}, based on the mixed-radix conversion (MRC) algorithm is presented. The proposed algorithm reduces the number of adders with large bit-width and the dependencies between consecutively process blocks. The employed technique in the proposed algorithm and the reverse converter architecture improves the delay and area on the FPGA Virtex Ultrascale + family platform in 14 nm/16 nm FinFET technology as compared to the related works.
L-Heron: An open-source load-aware online scheduler for Apache Heron

Apache Heron has emerged as a promising Data Stream Processing System (DSPS). However, it lacks intelligent scheduling strategy, which results in significant performance degradation for streaming applications in certain scenarios. In this paper, we first illustrate the inefficiencies and challenges of Heron default scheduling in current practice through experimental observations and analysis. Motivated by our observations, we propose L-Heron, an online scheduler based on Heron, which has the following features: (i) based on runtime information, it can improve the data processing efficiency by using the load-aware online scheduling, which heuristically minimizes the overall communication overhead by identifying the traffic load; (ii) it is load aware, which can effectively balance the workload of a topology to avoid heavy performance loss caused by overloading of worker nodes; (iii) it provides an online scheduling interface that is transparent to users, which allows users to focus on their scheduling logic and easily deploy them to the system. Additionally, we have evaluated L-Heron on well-known example topologies and a realistic application. Extensive experimental results show that the effectiveness of L-Heron is consistent among multiple metrics including the system completion latency, inter-node traffic, CPU utilization and throughput, with respect to Heron and recent related work.
Aggregate Estimates for Probability of Social Engineering Attack Success: Sustainability of the Structure of Access Policies

The article is devoted to the development of a method for analyzing the sustainability of the structure of access policies to the social engineering attack actions of the malefactor. The analysis of sustainability in this case is the calculation of aggregated probabilistic estimates of success of a social engineering attack actions of the malefactor to certain confidential data that is stored in the information system. The approach is based on data on the user’s vulnerabilities profile, the calculation of probability estimates of this profile, as well as the formation of a structure of access policies that is more stable to social engineering attacks, and is built on the basis of genetic algorithms. This approach makes it possible to determine the configuration of users of the information system that is the most stable to the malefactor’s social engineering attacks. The report describes the general principles of the proposed approach and presents the results of computational experiments.
Comparative Study on Face Detection by GPU, CPU and OpenCV

Optimization of processes and functions on Software side or on Hardware side are constantly remaining under the research consideration. Optimization can decrease the average time taken by a functional element to complete a particular task. Space-Time Complexity of various algorithms has been determined. These algorithms are widely used in the real-time systems. One of the algorithms is Face detection Algorithm. This project focuses on finding the easiest way of implementing the algorithm so that it can work in real time. In this comparative study, the Viola Jones Algorithm for Face Detection is implemented in 4 forms – CPU, Multi-threaded CPU, OpenCV and GPU using CUDA may be on cloud. The Algorithm is tested over Face Detection Dataset by FDDB and the results are framed on a graph to get the comparison among the methods used. This research project also discusses the limitations, future scope and implementation of the algorithm in real-time video streaming in the most efficient way.
The Maximum Nearby Flow Problem

We present a new Linear Programming model that formulates the problem of computing the Kantorovich-Wasserstein distance associated with a truncated ground distance. The key idea of our model is to consider only the quantity of mass that is transported to nearby points and to ignore the quantity of mass that should be transported between faraway pairs of locations. The proposed model has a number of variables that depends on the threshold value used in the definition of the set of nearby points. Using a small threshold value, we can obtain a significant speedup. We use our model to numerically evaluate the percentage gap between the true Wasserstein distance and the truncated Wasserstein distance, using a set of standard grey scale images.
OGBPS: Orientation And Gradient Based Path Smoothing Algorithm For Various Robot Path Planners

It is significant to plan a smooth trajectory for high-speed wheeled mobile robots working in a cluttered environment. The trajectory generated by most of the previously proposed path planners is not smooth enough for robot motion, especially under kino-dynamic constraints. An improved smoothing algorithm is proposed in this work as a solution for most of the previously proposed path planners to deal with the rugged paths, which may cause abrupt and angular turns of robots. The improved solution we proposed could be applied to many mainstream path planners (like Theta*, A*, RRT, RRT*, RRT#, SORRT*, PRM) as a post-smoothing algorithm, which is called orientation and gradient-based path smoothing (OGBPS). The OGBPS algorithm is derived from both orientation-angle-based and gradient-based path deformations to obtain a high-quality path. The objective of path deformations in this work is to improve path smoothness, lower maximum curvature and path length. Sufficient simulation experiments are well conducted to demonstrate the effectiveness of our approach. It is verified that the proposed algorithm can improve the quality of the previous path while respecting the kino-dynamic constraints through experiments. The simulation results indicate the advantages (smaller maximum curvature and smaller path length) of the proposed algorithm compared with several state-of-the-art smoothing algorithms.