Miracle-dz/UniFAR-FTUs huggingface dataset:
Prototype of Blockchain in Dental care service application based on Hyperledger Composer in Hyperledger Fabric framework

Most enterprise blockchain applications rely on real world trust relationship such as the business in financial institutions, entertainment industry, supply chain industry, transportation, education, healthcare services, etc. Hyperledger aims to advance blockchain technology that confederate in the cross-industry to open standard platform for distributed ledgers, which can transform the way business transactions are controlled globally. As we know currently the model of blockchain have permissionless blockchain and permissioned blockchain, which the permissionless blockchain such as Bitcoin cryptocurrency that anyone can operate a node and participate through spending CPU cycle and demonstrating a proof of work. On the other hand, permissioned blockchain model control who participates in validation and in the protocol. More over these nodes typically have established identities and form a consortium. Therefore, to make the innovation application that need the authority and permission for submitting the transaction we will select the hyperledger fabric framework to develop our project. Hence this paper will focus to study to develop a prototype of healthcare service application in dental clinic service. In order to keep records transactions that are accessible and controlled the authority by participants which it has a transaction id number that generate by hash number mechanism running by hyperledger composer. This application will create a trust relationship wherein the goal is to set up a set of participants in an ecosystem that it needed in health care service in the field of sharing the medical equipment and transaction that can save a variety of costs and make an interoperability process in the clinics.
Investigation of Chaotic and Periodic Attractors in Nonlinear Autonomous Circuit

Chaotic attractors for various electronic circuits have been proposed over the past few decades. Chaotic behavior can be explained by both autonomous and non autonomous circuits. The operation of non-autonomous circuits has usually exchanged physical simplification of the circuit for mathematically complex analysis, or vice versa. So, autonomous circuits are preferred from practical standpoint. In this paper, a simple autonomous circuit is presented with robust chaotic behavior. This circuit minimizes both physical and model component counts offering simple analysis. Nonlinear dynamical behavior of the proposed circuit will be explored towards fixed point attractor. Circuit simulation will be done on Multisim software as well as experimental analysis will also be performed for exploring the nonlinear behavior of the circuit.
Scaling up Network Centrality Computations *

Network science methodology is increasingly applied to a large variety of real-world phenomena. Thus, network data sets with millions or billions of edges are more and more common. To process and analyze such graphs, we need appropriate graph processing systems and fast algorithms. Many analysis algorithms have been pioneered, however, on small networks when speed was not the highest concern. Developing an analysis toolkit for large-scale networks thus often requires faster variants, both from an algorithmic and an implementation perspective.In this paper we focus on computational aspects of vertex centrality measures. Such measures indicate the importance of a vertex based on the position of the vertex in the network. We describe several common measures as well as algorithms for computing them. The description has two foci: (i) our recent contributions to the field and (ii) possible future work, particularly regarding lower-level implementation.
An Efficient Coding Method for Spike Camera Using Inter-Spike Intervals

Recently, a novel bio-inspired spike camera has been proposed, which continuously accumulates luminance intensity and fires spikes once the dispatch threshold is reached. It has shown great advantages in capturing fast-moving scene in a frame-free manner with full texture reconstruction capabilities. However, it is difficult to transmit or store the large amount of spike data. By investigating the spatiotemporal distribution of the spikes, we propose an intensity-based measurement for spike train distance and design an efficient coding method to meet the challenge. First, the spike train is transformed into inter-spike intervals (ISIs), and ISIs are adaptively partitioned into multiple segments in temporal. Then, intra-and inter-pixel prediction are performed to find the best reference candidate. The prediction residuals are quantized to achieve lossy compression. Finally, the quantized residuals are fed into an adaptive context-based entropy coder. Overall, to achieve the best performance, each prediction mode will be tried and the one with minimum rate-distortion cost is chosen.
A Fast Parallel Genetic Algorithm Based Approach for Community Detection in Large Networks

For large scale networks, under various formulations, the community detection problem is known to be NP-hard. Several genetic algorithm based community detection methods have been reported in the literature so far. But most of them exhibit a very slow convergence and hence not scalable. To achieve faster convergence, in this paper, we propose a parallel genetic algorithm for community detection based on diameter of the communities. To the best of our knowledge, so far, ours is the first work where the diameter of the community has been considered as a key parameter for community detection. This new concept expedites the convergence of the proposed genetic algorithm significantly without sacrificing the quality of the solution. Implementation of the algorithm on GP-GPU platform finds good quality community structures with near optimal modularity value with significantly less number of generations. Experiments show that it achieves 3 to 5 times speedup on synthetically created LFR (Lancichinetti–Fortunato–Radicchi) benchmark networks compared to the existing sequential genetic algorithm. Simulation on well-known real-world benchmark networks with up to 58 K nodes and 428 K edges results speedup of 4.27 at maximum.
ION-DTN based on UAV System for Emergency Communication During Natural Disaster

Massive natural disaster not only causing big casualty and loss property, but it may interference or damage the infrastructure of communication network. Since communications system is needed on victim search and rescue process, thus we proposed an Interplanetary Overlay Network on Delay Tolerant Network (ION - DTN) as transmission network based on Feedback Unmanned Aerial Vehicle (UAV) system for emergency communication during natural disaster. Delay Tolerant Network (DTN) can be one of suitable solution for intermittent connectivity. DTN is a network that designed for the challenging conditions, where the Internet Protocol Suite could not handle it properly. QoS test results on the implementation of DTN network in this project, shows that the best Delivery Probability is 1, the smallest Delay is 37.7 second, the smallest jitter is 0.534 second, and the biggest throughput is 69.5 kbps.
Case‐parents design for gene‐environment interaction

The scientific and public health implications of gene‐environment interaction warrant that the most powerful study designs and methods of analysis be used. Because traditional case‐control designs, which use nonrelated subjects, have demonstrated the need for large samples to detect interactions, alternative study designs may be worthwhile, such as sampling diseased cases and their parents. If the transmission of particular alleles from parents to their diseased child appears to be distorted from Mendelian expectation, then this suggests an etiologic association of the alleles with disease; if the frequency of transmission differs between exposed and nonexposed cases, then gene‐environment interaction is suggested. We present likelihood‐based methods to assess interaction, as well as an extension of the transmission/disequilibrium test (TDT). For these statistical tests, we also derive methods to compute sample size and power. Comparisons of sample size requirements between the case‐parents design and the case‐control design indicate that the case‐parents design can be more powerful to detect gene‐environment interactions, particularly when the disease susceptible allele is rare. Also, one of the derived likelihood methods, based on additive effects of alleles, tended to be the most robust in terms of power for a broad range of genetic mechanisms, and so may be useful for broad applications to assess gene‐environment interactions. Genet. Epidemiol. 16:261–273, 1999. © 1999 Wiley‐Liss, Inc.
Functionally graded porous femoral stem: Computational Analysis

Hip implants fail for many reasons. One of these reasons is due to aseptic loosening which causes bone resorption. The paper is oriented to investigate functionally graded porous stems by incorporating unique porous structures. The porosity levels investigated varied between 0% (fully densed), to 90%. The functional graded function used was sigmoid power law with three grading exponents. The gradation scheme was decreasing the porosity distally. From this study, it was found that functionally graded porous stems in comparison to fully dense stems, transfers up to 170% of the stresses to the bone (reduce stress shielding) and have micromotion that supports bone in growth.
Combining Reused Frequency, Most Recently Used and Program Counter Predictor as Last Level Cache Replacement Policy

This paper proposed a last-level cache (LLC) replacement policy which evicts line based on reused frequency and most recently reused information; and inserts the line based on Program Counter (PC) Predictor prediction. The most recently used information is used to keep the most recently used (MRU) line from being evicted. The line with the lowest reused frequency value and non-MRU is the candidate of replacement. To avoid potential line from being evicted before it is being reused; a PC-based predictor is used to modify the reused frequency information of the incoming line during insertion. If a line is predicted to be reused, the proposed replacement policy will keep the line longer in the LLC memory. Simulation experiments are run using ChampSim at single-core and quad-core configurations with SPEC CPU 2006 and Cloudsuite benchmarks. The proposed replacement policy overall outperformed LRU by 0.94% while SRRIP outperformed LRU by 0.04%. The proposed replacement policy required additional 0.90% and 0.97% storage hardware compared to 2 MB and 8 MB LLC memory respectively.
Cloud, Fog, and Dew Robotics: Architectures for Next Generation Applications

Robots are rapidly moving from isolated to connected systems, with more and more operations offloaded to external systems. Cloud Robotics has indeed become a new important trend in this research field. In this paper we advocate the necessity to move a step forward in this direction considering the real needs of new generation robotics systems. Such systems have indeed very peculiar requirements with respect to the traditional applications that have progressively exploited Cloud infrastructures. Cloud is surely an important addition, but robots still require important computations to be performed closer to or onto them for, e.g., critical decisions involving human interaction. This motivates our work in which we delve into the possible alternatives to pure Cloud Robotics, considering Fog- and Dew-computing as better suited to next generation applications. We describe these complementary but alternative architectures and how they match the requirements of robotics applications. We identify Dew Robotics as the most promising architecture and motivate our choice. We present three use cases we have been working on, to better illustrate our proposal. We finally draw some conclusions for this emerging yet important research field.
A Case of Tardive Tourette‐Like Syndrome

Abstract: We have had experience in treating tardive Tourette‐like syndrome on a chronic schizophrenic patient. The patient was a 38‐year‐old woman. A diagnosis of schizophrenia was made in 1971 and she received repeated medications for 17 years. In 1989, she began to show vocal tic with coprolalia and motor tic. The medications were haloperidol 18 mg, zotepine 200 mg, levomepromseine 100 mg, biperiden 3 mg and nitrazepam 10 mg at the beginning of Tourette‐like syndrome. We have tried to change the medications but this tardive Tourette‐like syndrome continued to hang on. However, the symptoms gradually improved after a change in drugs; cessation of biperiden 3 mg and the administration of clonazepam 3 mg. The present case suggested that tardive Tourette‐like syndrome might be a subtype of neuroleptic‐associated tardive syndromes which might be treated with clonazepam.
Does Race/Ethnicity or Socioeconomic Status Influence Patient Satisfaction in Pediatric Surgical Care?

Objective To evaluate patient satisfaction in outpatient pediatric surgical care and assess differences in scores by race/ethnicity and socioeconomic status (SES). Study Design Observational, cross-sectional analysis. Setting Outpatient pediatric surgical specialty clinics at a tertiary academic center. Subject and Methods Families of patients received a patient satisfaction survey following their initial care visit in 2012. Mean scores were calculated and compared by child race/ethnicity and insurance type, where insurance with medical assistance (MA) served as a proxy for low SES. Kruskal-Wallis tests were used to compare scores between groups. Surveys were dichotomized to low and high scorers, and multivariate logistic regression was used to calculate the likelihood of high satisfaction. Results Of 527 surveys completed, 132 (25%) were for children with MA and 143 (27%) were for racial/ethnic minority children. The overall satisfaction score for all specialties was 84.8, which did not significantly differ by SES (P = .98) or minority status (P = .52). The survey item with the highest score in both SES groups was “degree to which provider talked with you using words you could understand” (overall mean 91.94, P = .23). Multivariate analysis showed that patient age, sex, race/ethnicity, insurance type, neighborhood SES, neighborhood diversity, or surgical department did not significantly influence satisfaction. Conclusion This is the first study to evaluate the relationship between SES and race/ethnicity with patient satisfaction in outpatient pediatric surgical specialty care. In this analysis, no disparities were identified in the patient experience by individual- or community-level factors. Although the survey methodologies may be limited, these findings suggest that provision of care in pediatric surgical specialties can be simultaneously equitable, culturally competent, and family centered.
Review of Methodologies for Pre- and Post-Silicon Analog Verification in Mixed-Signal SOCs

The integration of increasingly more complex and heterogeneous SOCs results in ever more complicated demands for the verification of the system and its underlying subsystems. Pre-silicon design validation as well as post-silicon test generation of the analog and mixed-signal (AMS) subsystems within SOCs proves extremely challenging as these subsystems do not share the formal description potential of their digital counterparts. Several methods have been developed to cope with this lack of formalization during AMS pre-silicon validation, including model checkers, affine arithmetic formalisms and equivalence checkers. However, contrary to the industrial practice for digital circuits of using formal verification and ATPG tools, common industry practice for analog circuits still largely defaults to simulation-based validation and test generation. A new formal digital-inspired technique, called AMS-QED, can potentially solve these issues in analog and mixed-signal verification.
Idiothetic Verticality Estimation Through Head Stabilization Strategy

The knowledge of the gravitational vertical is fundamental for the autonomous control of humanoids and other free-moving robotic systems such as rovers and drones. This letter deals with the hypothesis that the so-called “head stabilization strategy” observed in humans and animals facilitates the estimation of the true vertical from inertial sensing only. This problem is difficult because inertial measurements respond to a combination of gravity and fictitious forces that are hard to disentangle. From simulations and experiments, we found that the angular stabilization of a platform bearing inertial sensors enables the application of the separation principle. This principle, which permits one to design estimators and controllers independently from each other, typically applies to linear systems, but rarely to nonlinear systems. We found empirically that, given inertial measurements, the angular regulation of a platform results in a system that is stable and robust and which provides true vertical estimates as a byproduct of the feedback. We conclude that angularly stabilized inertial measurement platforms could liberate robots from ground-based measurements for postural control, locomotion, and other functions, leading to a true idiothetic sensing modality, that is, not based on any external reference but the gravity field.
Industry 4.0: Development of Smart Sunroof Ambient Light Manufacturing System for Automotive Industry

This paper introduces a systematic methodology to develop an Industry 4.0-based smart manufacturing system in a sunroof ambient light production system. The developed system starts with transformation process to convert the current manufacturing environment to become smart through installation of real-time data capturing systems specially RFID system to convert the entire systems’ entities in the process-flow to smart-objects and become wireless communicable in order to achieve a smart manufacturing system to smartly monitor and control the entire manufacturing processes. The aim of this project was to benefit from the recent advancements in the areas of Cyber-Physical Systems (CPS) and Internet of Things (IoT) as key enablers of Industry 4.0 to increase the productivity of the production system and eliminate the most wasteful activities to enhance the competitive edges of the manufacturer company to face the challenges in the global markets.
Surface-accessible Residues in the Monomeric and Assembled Forms of a Bacterial Surface Layer Protein*

The S-layer protein SbsB of the thermophilic, Gram-positive organism Bacillus stearothermophilus PV72/p2 forms a crystalline, porous array constituting the outermost component of the cell envelope. SbsB has a molecular mass of 98 kDa, and the corresponding S-layer exhibits an oblique lattice symmetry. To investigate the molecular structure and assembly of SbsB, we replaced 75 residues (mainly serine, threonine, and alanine), located throughout the primary sequence, with cysteine, which is not found in the wild-type protein. As determined by electron microscopy, 72 out of 75 mutants formed regularly-structured self-assembly products identical to wild-type, thereby proving that the replacement of most of the selected amino acids by cysteine does not dramatically alter the structure of the protein. The three defective mutants, which showed a greatly reduced ability to self-assemble, were, however, successfully incorporated into S-layers of wild-type protein. Monomeric SbsB mutants and SbsB mutants assembled into S-layers were subjected to a surface accessibility screen by targeted chemical modification with a 5-kDa hydrophilic cysteine-reactive polyethylene glycol conjugate. In the monomeric form of SbsB, 34 of the examined residues were not surface accessible, while 23 were classified as very accessible, and 18 were of intermediate surface accessibility. By contrast, in the assembled S-layers, 57 of the mutated residues were not accessible, six were very accessible, and 12 of intermediate accessibility. Together with other structural information, the results suggest a model for SbsB in which functional domains are segregated along the length of the polypeptide chain.
History-Based Motion Vector Prediction in Versatile Video Coding

In this paper, History-based Motion Vector Prediction (HMVP) is presented for video coding. With the proposed method, a table of HMVP candidates is maintained and updated on-the-fly. After decoding one inter-coded block, the table is updated by appending the associated motion information to the table as a new HMVP candidate. A First-In-First-Out (FIFO) rule is applied to manage the table. The HMVP candidates could be added to the Advanced Motion Vector Prediction (AMVP) candidate list as additional motion vector predictors. And they could also be added to the merge candidate list as additional merge candidates. With the proposed method, the motion information of previously coded blocks even not adjacent to the current block can be utilized for more efficient motion vector prediction. Simulation results have validated the efficiency of HMVP, wherein up to 4% BD rate saving could be achieved. The proposed method has been adopted by the next generation video coding standard, named Versatile Video Coding (VVC) developed by Joint Video Exploration Team (JVET).
Comparison of Supervised Learning Image Classification Algorithms for Food and Non-Food Objects

Object recognition is a method in the computer vision to identify and recognize objects in the picture or video. When humans see photos or watch videos, they can quickly recognize some object like a car, bus, human, cat, food, and other visual artifacts. However, how do we apply it to the computer? Classification is the technique or method in object recognition that can be used on a computer to distinguish one object from another object contained in the image or video. In this paper, the author proposes about testing some popular image binary classification algorithms used along with the results of the performance matrix of each algorithm, among these are Logistic Regression with Perceptron, Multi-Layer Perceptron (MLP), Deep Multi-Layer Perceptron, and Convolutional Neural Network (ConvNet). The author uses the Food-5K dataset to distinguish two classes of objects, namely food /non-food, and then try to train and test how accurate the computer is in recognizing food and non-food objects, where it will be useful to anyone who needs to identify a food object using auto recognizing tools. This paper is expected to contribute in the field of computer vision related algorithm that is used to solve the problem in image classification, with the state of optimal hyperparameter and validation accuracy level above 90%. From the test results obtained the level of testing accuracy using ConvNet reached above 90% and loss function less than 25% while indicating that ConvNet has a significant advantage on the image classification problem compared to the generic artificial neural network.
An Automated Big Data Accuracy Assessment Tool

Data analysis is the most important aspect of any business as it is critical to decision-making. Data quality assessment is a necessary function to be performed before data analysis, as the quality of data has high impact on the outcome of the analysis. Data quality is a multi-dimensional factor that affects the analysis in numerous ways. Among all the dimensions, accuracy is the most important and hardest dimension to assess. With the advent of big data, this problem becomes more complicated. There are only few studies that focus on data accuracy with minimal domain expert dependency. In this paper, we propose an extensive data accuracy assessment tool that uses machine learning to determine the accuracy of data. In addition, our model also addresses the intrinsic and contextual categories of data accuracy. Our model was developed on Apache Spark which serves as the big data environment for handling large datasets.