Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Distributed Variant of Stochastic Gradient Descent and Its Optimization

In the age of big data, large-scale learning problems become increasingly significant. Distributed machine learning algorithms thus draw a lot of interest, particularly those based on Stochastic Gradient Descent (SGD) with variance reduction technique. In this paper, we propose and implement a distributed programming strategy for a newly developed variance-reducing SGDbased algorithm, and analyze its performance with various parameter. Moreover, a new SGD-based algorithm named BATCHVR is introduced, which computes the full-gradients required by SGD in each stage using batches in an incremental manner. Experiments on the HPC cluster, i.e. TH-1A demonstrate the effectiveness of the distributed strategy and the excellent performance of the proposed algorithm.
Shading correction for volumetric CT using deep convolutional neural network and adaptive filter.

Background Shading artifact may lead to CT number inaccuracy, image contrast loss and spatial non-uniformity (SNU), which is considered as one of the fundamental limitations for volumetric CT (VCT) application. To correct the shading artifact, a novel approach is proposed using deep learning and an adaptive filter (AF). Methods Firstly, we apply the deep convolutional neural network (DCNN) to train a human tissue segmentation model. The trained model is implemented to segment the tissue. According to the general knowledge that CT number of the same human tissue is approximately the same, a template image without shading artifact can be generated using segmentation and then each tissue is filled with the corresponding CT number of a specific tissue. By subtracting the template image from the uncorrected image, the residual image with image detail and shading artifact are generated. The shading artifact is mainly low-frequency signals while the image details are mainly high-frequency signals. Therefore, we proposed an adaptive filter to separate the shading artifact and image details accurately. Finally, the estimated shading artifacts are deleted from the raw image to generate the corrected image. Results On the Catphan©504 study, the error of CT number in the corrected image's region of interest (ROI) is reduced from 109 to 11 HU, and the image contrast is increased by a factor of 1.46 on average. On the patient pelvis study, the error of CT number in selected ROI is reduced from 198 to 10 HU. The SNU calculated from the ROIs decreases from 24% to 9% after correction. Conclusions The proposed shading correction method using DCNN and AF may find a useful application in future clinical practice.
Synthesis, Chronology, and “Late Roman” Cemeteries in Britain

A stark division is usually drawn between Late Roman and Early Medieval burials in Britain. This has allowed works of synthesis to create opposing data sets of osteological information. A close understanding of the period 300–600 C.E. suggests that some graves currently assigned to the Late Roman period may actually date to the fifth or sixth century C.E. Two recent case studies demonstrate this point, and radiocarbon dating is advocated as a partial solution. Until radiocarbon dating is more widely deployed, many “Late Roman” cemetery data sets may contain chronological ambiguities that diminish their significance in wider works of osteological synthesis. This article is available as open access on AJA Online.
Real World Hand Gesture Interaction in Virtual Reality

With the development of virtual reality, the appropriate interaction technology has become the focus of practitioners. Today’s virtual reality interaction is mostly through the interaction of traditional electronic devices, such as handles and other products, temporarily solve the problem of interaction, but also make the user from the virtual world is taken out, immersion greatly reduced. The paper uses hand gestures to interact with virtual reality. By using skin detection in YCbCr color space and indicates the location of hand to determine the approximate range of hand. Then, the histograms of oriented gradient (HOG) which has been widely used in target detection research in recent years is used to extract gesture features, and applies support vector machine (SVM) method to achieve the real-time hand gesture recognition. Finally, experiments prove that the proposed method is accurate and stable in virtual reality interaction.
A Case Study: Energy Efficient High Throughput Chip Multi-Processor Using Reduced-complexity Cores for Transaction Processing Workload

The pursuit of instruction-level parallelism using more transistors produces diminishing returns and also increases power dissipation of general purpose processors. This paper studies a chip multi-processor (CMP) with smaller processor cores as a means to achieve high aggregate throughput and improved energy efficiency. The benefit of this design approach increases as the number of cores on a chip increases, as enabled by semiconductor process scaling. The feasibility of a processor core 40% of the size of a baseline high performance processor that delivers about 70% of its performance is shown. The CMP populated by smaller cores to fill the same silicon area delivers 2.3 times higher performance in transaction processing represented by TPC-C benchmarks than the baseline processor scaled into the same technology. The CMP also achieves 38% higher energy efficiency.
A symmetric cipher not requiring prior sender/receiver key agreement

Most modern encryption / decryption systems are asymmetric, based on prime number theory, and rely on the known intransigence of factoring numbers which are the product of two very large prime numbers. The scheme described here is very different and embodies a symmetric cipher which does not require the sender and receiver to agree on a key in advance. It relies on results from digital filter theory and asymmetric inversion properties of the discrete Wiener-Hopf equations. It is highly efficient and its sensitivity and essential non-uniqueness may make it computationally infeasible to crack. 0.0. 0.1 : This paper demonstrates the theory, a practical implementation, timing results and discusses possible ways in which it might
EDUCATION IN THE 5G AND THE AI CONTEXT

Recent research has advanced the knowledge on the impact of motivation and engagement on student performance and retention. In this spectrum, the most relevant factors are the personal and demographic data, general perception about motivation, perception about the university, student behavior, perception about program and perception about classes as well as about teachers. Joining 5G and AI will open new possibilities for eLearning platforms, and will offer unprecedented access and depth to all of the variables in the spectrum. While 5G networks are known for improved device capacity, faster network speed and lower latency the AI networks are capable of offering real time analysis of the dynamic data streams. Data mining algorithms can uncover unseen patterns, unexpected associations, and inadvertencies in the collected data. The speed of the first and the precision of the second, can critically enhance the decision-making processes for more accurate predictions and recommendations in the area of student performance. The unique combination will offer students the option of personalized curriculums, will favor higher engagement rates and further enhance academic success as an economic model
Technologies and Best Practices for Building Bio‐Ontologies

Genomics technologies generate vast amounts of data of a wide variety of types and complexities, and at a growing pace. The analysis of such data and the mining of the resulting information is insufficient without a contextual interpretation, that is, biological knowledge deduced from the data. This knowledge states the data’s biological meaning in terms of, for instance, molecular function, cellular location, or network interactions. Biological knowledge is diverse, vast, complex, and volatile. These factors, together with the nature of evolved systems, make the knowledge generated by the life sciences difficult to capture. As molecular biology has relatively recently included a systems approach, it has become increasingly important to have precise and rich representations of the catalogs that in turn form the basis of the networks and pathways that describe biological systems. Therefore, biological knowledge management is becoming essential for current research in life sciences (Antezana et al., 2009a). Biological knowledge has traditionally been represented in human interpretable formats like natural language in scientific literature, or somewhat more
Measuring chest expansion; A study comparing two different instructions

Abstract The aim of this study was to examine and compare the effect of two alternative instructions when measuring chest expansion. In 100 healthy subjects, chest expansion was measured using a circumference tape. In 30 healthy subjects, chest expansion was measured by a Respiratory Movement Measuring Instrument (RMMI). Both measurements were made at the level of the fourth rib and the xiphoid process. The two instructions evaluated were the traditional one: “breathe in maximally” and “breathe out maximally”, which were compared with a new one “breathe in maximally and make yourself as big as possible” and “breathe out maximally and make yourself as small as possible”. The addition of “make yourself as big/small as possible” in the new instruction resulted in a significantly increased thoracic excusion, 1.4 cm in upper and 0.9 cm in lower level of thorax, measured by tape, compared with the traditional instruction (p < 0.001). Measurements obtained using the RMMI also showed a significant difference, 2.3 mm in upper and 4.1 mm in lower level of thorax, between the two instructions in favour of the new instruction (p < 0.05). The verbal instruction during measurement of chest expansion is of importance when measured by tape and RMMI. To assess the maximal range of motion in the chest, the patient should be instructed not only to “breathe in/out maximally”, but also instructed to “make yourself as big/small as possible”.
Improving the performance of software-defined networks using dynamic flow installation and management techniques

As computer networks evolve, they become more complex, introducing several challenges in the areas of performance and management. Such problems can lead to stagnation in network innovation. Software Defined Networks (SDN) framework could be one of the best candidates for improving and revolutionising networking by giving the full control to the network administrators to implement new management and performance optimisation techniques. This thesis examines performance issues faced in SDN due to the introduction of the SDN Controller. These issues include the extra delay due to the round-trip time between the switch and the controller as well as the fact that some packets arrive at the destination out-of-order. We propose a novel dynamic flow installation and management algorithm (OFPE) using the SDN protocol OpenFlow, which preserves the controller to a non-overloaded CPU state and allow it to dynamically add and adjust flow table rules to reduce packet delay and out-of-order packets. In addition, we propose OFPEX, an extension to OFPE algorithm that includes techniques for managing multi-switch environments as well as methods that make use of the packets interarrival time in categorising and serving packet flows. Such techniques allow topology awareness, helping the controller to install flow table rules in such a way to form optimal routes for high priority flows thus increasing network performance. For the performance evaluation of the proposed algorithms, both hardware testbed as well as emulation experiments have been conducted. The performance results indicate that OFPE algorithm achieves a significant enhancement in performance in the form of reduced delay by up to 92.56% (depending on the scenario), reduced packet loss by up to 55.32% and reduced out-of-order packets by up to 69.44%. Furthermore, we propose a novel placement algorithm for distributed Mininet implementations which uses weights in order to distribute the experiment components to the appropriately distributed machines. The proposed algorithm uses static code analysis in order to examine the experimental code as well as it measures the capabilities of physical components in order to create a weights table which is then used to distribute the experiment components properly. The performance results of the proposed algorithm evaluation indicated reductions in delay and packet loss of up to 65.51% and 86.35% respectively, as well as a decrease in the standard deviation of CPU usage by up to 88.63%. These results indicate that the proposed algorithm distributes the experiment components evenly across the available resources. Finally, we propose a series of Benchmarking tests that can be used to rate all the available SDN experimental platforms. These tests allow the selection of the appropriate experimental platform according to the scenario needs as well as they indicate the resources needed by each platform.
Motion lecture annotation system to learn Naginata performances

This paper describes a learning assistant system using motion capture data and annotation to teach “Naginata-jutsu” (a skill to practice Japanese halberd) performance. There are some video annotation tools such as YouTube. However these video based tools have only single angle of view. Our approach that uses motion-captured data allows us to view any angle. A lecturer can write annotations related to parts of body. We have made a comparison of effectiveness between the annotation tool of YouTube and the proposed system. The experimental result showed that our system triggered more annotations than the annotation tool of YouTube.
User-controlled identity management: the key to the future of privacy?

The processing of personal data is a prerequisite of today's participation in the information society, which increasingly implies threats to individuals' privacy. Classical Identity Management Systems (IMS) may even intensify those privacy threats. However, comprehensive user-controlled IMS take a different approach, as they implement privacy principles and aim to help users manage their privacy. This text distinguishes the various notions of privacy and identifies linkage control as a key concept that underlies all privacy issues. The described building blocks for user-controlled identity management support privacy in different ways, e.g., by separating contexts, workflows and the related partial identities from each other, by stating and enforcing the privacy policies that also govern linkability or by enhancing transparency to inform the user about the potential linkages. User-controlled IMS are not a panacea for all privacy problems, but they can act as a gateway and guardian for individuals' privacy. Thereby, they have the potential to become a key tool for future privacy concepts.
An Integrated & Secure System for Wearable Devices

Health services are under increasing pressure to reduce large waiting times for appointments. The delayed diagnosis of a human illnesses could have profound consequences for the patient. The large waiting times may be attributed to lack of data, the accuracy of the data or the timeliness of the availability of the data. In addition to this people often priorities their busy lives over their health. Other factors that affect delayed diagnosis of patients could be attributed to ill-health and the difficulty in the patient visiting the health-care practitioner, this is more evident in elderly patients that may have suffered a deterioration of their health. To overcome these obstacles eHealth systems could be put in place to facilitate the transmission of real-time and accurate data regarding a patient’s health directly to secure database that can be accessed by authorized practitioners. This paper outlines the use of eHealth (electronic health) to improve health care services which makes use of a wearable set of secure sensors and a secure database that is accessible using a web application. The proposed wearable system records and stores encrypted data which is related to the body’s physical parameters. Then, the encrypted data are transmitted using wireless technologies from the wearable device to a secure relational multi-user database using a protected protocol. The data can be accessed by the patient and professionals, such as general practitioners, using a web interface once authentication has taken place. This secure wearable architecture alleviates the problems of the lack of data, the inaccuracy of data and the timeliness of data by recording vital body parameters throughout the day and by sending real-time live data to a system that can be immediately accessed by the practitioner. The system also allows for the automatic analysis of data and presentation of the data using graphs which could aid the practitioner in determining patterns in the patients’ health statistics.
Contribution of Respiratory Muscle Oxygen Consumption to Breathing Limitation and Cyspnea

During exercise, the sustainable activity of large muscle groups is limited by oxygen delivery. The purpose of this study was to see whether the oxygen consumption of the respiratory muscles reaches a similar critical value under maximal resistive loading and hyperventilation. A secondary objective was to see whether dyspnea (estimated discomfort experienced with breathing using the Borg 0-10 scale) and the oxygen consumption of the respiratory muscles are closely related across conditions. This would be expected if intramuscular sensory nerve fibres stimulated as a consequence of metabolic events contributed to this sensation. In six normal subjects the respiratory muscles were progressively activated by the addition of incremental inspiratory resistive loads to a maximum of 300 cm H20×s/L (SD=66.4), and incremental dead space to a maximum of 2638 mL (SD=452), associated with an increase in ventilation to 75.1 L/min (SD=29.79). Each increment was maintained for 5 mins to allow the measurement of oxygen uptake in a steady state. During resistive loading total oxygen consumption increased from 239 mL/min (SD=38.2) to 299 mL/min (SD=52.3) and dyspnea increased to "very severe" (Borg scale 7.5, SD=1.55). During dead space loading total oxygen consumption increased from 270 mL/min (SD=20.2) to 426 mL/min (SD=81.9) and dyspnea increased to "very severe" (7.1, SD=0.66). Oxygen cost of inspiratory muscle power was 25 mL/watt (95% confidence limits 16.7 to 34.3) with dead space loading and 91 mL/watt (95% confidence limits 54 to 128) with resistive loading. Oxygen consumption did not reach a critical common value in the two types of loading, 60 mL/min (SD 22.3) during maximal resistive loading and 156 mL/min (SD 82.4) during maximal dead space loading (P<0.05). Physiological factors limiting the respiratory muscles are not uniquely related to oxygen consumption and appear to be expressed through the activation of sensory structures, perceptually manifested as dyspnea.
A Comparison of Supervised Machine Learning Classification Techniques and Theory-Driven Approaches for the Prediction of Subjective Mental Workload

In the modern world of technological progress, systems and interfaces are becoming more and more complex. As a consequence, it is a crucial to design the humancomputer interaction in the most optimal way to improve the user experience. The construct of Mental Workload is a valid metric that can be used for such a goal. Among the different ways of measuring Mental Workload, self-reporting procedures are the most adopted for their ease of use and application. This research is focused on the application of Machine Learning as an alternative to theory-driven approaches for Mental Workload measurement. In particular, the study is aimed at comparing the classification accuracy of a set of induced models, from an existing dataset, to the mental workload indexes generated by well-known subjective mental workload assessment techniques namely the Nasa Task Load Index and the Workload profile instruments.
"To array a man's will against his sickness is the supreme art of medicine". An analysis of multiple spells of sickness

This paper analyzes the long-term sickness absences in Sweden using a longitudinal database that contains all compensated sickness spells for 2,789 persons during 1986-1991. Given the political focus on the improved collaboration between the individual, physician, employer, and social insurance officer, the strategy is to analyze the spells of long-term sickness grouping them by all available factors that concern these actors. The estimates of a mixed proportional hazards model suggest that there was more heterogeneity among spells grouped by the factors related to the health status of the individual and the physician's evaluation than among spells grouped by the factors expected to be related to the social insurance praxis or other sorting processes.
An overview of techniques for predicting the performance of GPU accelerated applications

The ability to predict the performance of applications in large-scale parallel systems is essential. One of the main incentives for this is the high cost of executing non-production tasks on these systems. An entity may also want to predict the performance in a system that does not yet exist. One popular alternative for increasing a systems performance is the use of accelerators. This created the need for new methods for performance prediction, that take into account the use of these components. In this paper, we will present some recent works in the area of performance prediction in systems with accelerators. More specifically, we will focus on systems that use Graphics Processing Units (GPUs) as accelerators.
Implementation and Analysis of Symmetrical and Asymmetrical Configurations of Series Connected Sub-Multilevel Inverter with Nearest Level Control Modulation

Multilevel Inverters are the emerging research area since last decade and in this duration so many configurations of Multilevel Inverters are introduced, Some of those are application oriented and some configurations are generalized and combinational in nature. In this paper a configuration with Symmetrical and Asymmetrical types of Series connected Sub-Multilevel Inverter is analyzed with the Nearest Level Control Modulation. In general view of preferred configuration gives the more levels in output signal with the reference of Basic Multilevel Inverters. The article discuss about the implementation of Nearest Level Control Modulation to the referred configuration of Multilevel Inverter and its operation and performance by the indication of Total Harmonic Distortion of output signal for the both Symmetrical and Asymmetrical topologies. Keywords—Sub-Multilevel Inverter; Nearest Level Control Modulation; Symmetrical and Asymmetrical Topology; Optimal Configurations; Generalized structure.
A Survey of Answer Extraction Techniques in Factoid Question Answering

n question answering. In this paper, we survey several different techniques to answer extractio n for factoid question answering, which aims at accurately pin-pointing the exact answer in re trieved documents. We compare these techniques under a unified view, from the perspective o f the sources of information that each model uses, how they represent and extract these inform ation, and the model they use for combining multiple sources of information. From our com parison and analysis, we draw conclusions of the successes and deficiencies in past approa ches, and point out directions that may be interesting to future research.