Miracle-dz/UniFAR-FTUs huggingface dataset:
Secure RSA implementation against horizontal correlation power analysis attack

Since passive leakage information analysis and active fault injection attacks on naive implementation of the Rivest, Shamir, and Adelman (RSA) cryptosystem can be used to retrieve a secret key, several countermeasures against these attacks have been developed. In this paper, we point out that the horizontal correlation power analysis (HCPA) attack can be applied to the square-multiply ladder exponentiation algorithm and its variants, which are used for secure RSA implementation. Furthermore, we propose a novel exponentiation algorithm to defeat previous implementation attacks, as well as the HCPA attack, in particular. This algorithm is designed to overcome weakness against the HCPA attack by adopting an intermediate message update technique based on an extended modulus. We can employ the proposed exponentiation algorithm to implement a secure Chinese remainder theorem-based RSA (CRT-RSA) cryptosystem by thwarting the Bellcore attack.
Visual technologies in shared control mode of robot teleoperation system

This paper introduces a dual arm space robot teleoperation system (DSRTS), and key technologies of the system are presented in detail, which include graphical simulation, task planning, shared control, EKF (extended Kalman filter) based modeling, parameter calibration, etc. Several experiments implemented in our system are introduced at last. This system has been a nice test-bed for research on internet control, sensor fusion, graphical modeling, etc.
Incidence of the Os incae ( Interparietal anatolian adult skulls

Introduction: The occipital bone is the one of the singul much of the base and posterior aspect of the skull. The occipital bone is saucer divided into four parts: a squamous part (squama), a basilar part (basioccipital part), and two latera l parts (condylar parts). Sometimes the pars inte supraoccipitale by a transverse uture study is to determine inca bones presence, incidenc e and sex characteristic morphologies in the middle and south Anatolian population. 25 female) without any sign of trauma or primer cr anial surgery were studied which are belong to the laboratories of the Department of Anatomy, Faculty of Medi Faculty of Letter of the Cumhuriyet University. The presence and types of inca bones were determined macroscopically by observation. Kadanoff & Mutafov's method was used as a method of typing. Results: The incidence of in male, respectively. Conclusion about population’s cranial variations which they wo rking with
Implementation and performance evaluation of SMI adaptive array

We present a description of, and results for, a hardware implementation of an adaptive array. A sample matrix inversion (SMI) algorithm was used, and its bit error rate (BER) performance on frequency-selective fading channels is tested. There are four antenna elements. The transmission rate is 4.096 Mb/s. We applied digital signal processing to the control of the array weights, using a multi-CPU/DSP system with a real-time operating system. The laboratory experiments show that our four-element SMI adaptive array successfully suppresses the signal on delayed paths and provides a spatial diversity for channels suffering from frequency-selective fading, when individual paths for each antenna element are all independently Rayleigh faded.
M-Walk: Learning to Walk in Graph with Monte Carlo Tree Search

Learning to walk over a graph towards a target node for a given input query and a source node is an important problem in applications such as knowledge base completion (KBC). It can be formulated as a reinforcement learning (RL) problem that has a known state transition model, but with sparse reward. To overcome the challenge, we develop a graph walking agent called M-Walk, which consists of a deep recurrent neural network (RNN) and a Monte Carlo Tree Search (MCTS). The RNN encodes the state (i.e., history of the walking path) and map it into the Q-value, the policy and the state value. In order to effectively train the agent from sparse reward, we combine MCTS with the neural policy to generate trajectories with more positive rewards. From these trajectories, we improve the network in an off-policy manner using Q-learning, which indirectly improves the RNN policy via parameter sharing. Our proposed RL algorithm repeatedly applies this policy improvement step to learn the entire model. At testing stage, the MCTS is also applied with the neural policy to predict the target node. Experiment results on several graph-walking benchmarks show that we are able to learn better policies compared to other RL-based baseline methods, which are mainly based on policy gradient method. It also outperforms traditional KBC baselines.
Single-machine production and maintenance : a unified approach

We are presenting a general unifying framework for production-maintenance systems on a single machine. Several performance criteria are included as well as most of the maintenance systems that are proposed in the literature. No particular analytical functions are required and no new algorithms have to be developed. One may simply utilize standard software for min-cost network flow problems.
Neural Mechanisms of Intuitive Tactical Decision-making Predominance of High-level Fencing Athletes

Intuitive decision-making is a typical form of thinking in sports situation. The purpose of the study was to explore the neural mechanisms of tactical intuition predominance of elite fencing athletes. This study selected 39 subjects, including elite fencing athletes (N=8), level-1 fencing athletes (N=14), level-2 fencing athletes (N=11) and non-level fencing athletes (N=6). Event-related potential (ERP) recording was collected to compare between different level of fencing athletes. According to the operational standards of sports intuition, the responses of elite, level-1 and level-2 fencing athletes belonged to intuitive decision-making and that of non-level fencing athletes belonged to random guessing. ERP data showed that the mean amplitude of P1 and peak amplitude of P3, positives slow wave (PSW) of elite fencing athletes were higher than those of general (level-1 and level-2) fencing athletes. The peak latency of PSW was shorter than that of general fencing athletes at relevant cerebral cortex regions during the process of intuitive tactical decision-making. The results indicated that the tactical decision-making strategy of elite fencing athletes mainly aimed at the accuracy. Their anticipating speed was faster than the other general sport activities (1500 ms). The pointing focus degree of exogenous attention and the updating degree of mental token of judging tactics intention were higher. The neural mechanisms of performance predominance maybe the nerve activity levels of P1, P3 and PSW evoked at the special cerebral cortex being higher and the evoked time of P3 and PSW being earlier.
Implementing the Use of Personal Activity Data in an Introductory Statistics Course

Implementing the Use of Personal Activity Data in an Introductory Statistics Course by Lacy Christensen, Master of Science Utah State University, 2018 Major Professor: Kady Schneiter Department: Mathematics and Statistics Integrating real data into a classroom is one of the recommendations in the Guidelines for Assessment and Instruction in Statistics Education (GAISE) college report (Committee, GAISE College Report ASA Revision, 2016). In order to assess the effect of using real data in a classroom, the students received physical activity trackers to wear during an undergraduate introductory statistics course taught in the summer. This tracker, a Fitbit, enabled students to monitor and record their steps, calories, and active time throughout the class. Collecting personal activity data (PAD) creates a large database which students can then analyze and use to build statistical thinking. Since the students are intimately familiar with the data they gathered, they could focus on the patterns they saw in the data based on their own personal experiences. With this data, the students completed tasks that asked them to analyze their physical activity using methods including summary statistics and bivariate analysis. These projects encouraged students to think about problems that arise from data collection and analysis in real life situations. We saw that using PAD helped the tasks become more personal, increased interest and engagement, and reinforced the material taught in class.
Assigning Weights to Training Instances Increases Classification Accuracy

The decision tree (DT) approach is most useful in classification problem. In conventional decision tree learning the weights of every training instances are set to one or equal value, which contradicts general intuition. In this paper, we proposed a new decision tree learning algorithm by assigning appropriate weights to each training instance in the training data that increases classification accuracy of the decision tree model. The main advantage of this proposed approach is to set appropriate weights to training instances using naïve Bayesian classifier before trying to construct the decision tree. In our approach the training instances are assigned to weight values based on the posterior probability. The training instances having less weight values are either noisy or posses unique characteristics compared to other training instances. The experimental results manifest that the proposed approach for decision tree construction can achieve high classification accuracy with compare to traditional decision tree algorithms on different types of benchmark datasets from UCI machine learning repository.
A Deep Reinforced Sequence-to-Set Model for Multi-Label Classification

Multi-label classification (MLC) aims to predict a set of labels for a given instance. Based on a pre-defined label order, the sequence-to-sequence (Seq2Seq) model trained via maximum likelihood estimation method has been successfully applied to the MLC task and shows powerful ability to capture high-order correlations between labels. However, the output labels are essentially an unordered set rather than an ordered sequence. This inconsistency tends to result in some intractable problems, e.g., sensitivity to the label order. To remedy this, we propose a simple but effective sequence-to-set model. The proposed model is trained via reinforcement learning, where reward feedback is designed to be independent of the label order. In this way, we can reduce the dependence of the model on the label order, as well as capture high-order correlations between labels. Extensive experiments show that our approach can substantially outperform competitive baselines, as well as effectively reduce the sensitivity to the label order.
User Taste Prediction Service Using Big Data

In this paper, we propose a personalized contents recommendation service using big data. We presented a user similarity-based contents recommendation service using NFC. This service used a small sample because it used only information of the users who had watched a museum. So it had some drawback that it is difficult to accurately predict the user's preference. To solve this problem, the proposed service predicts the user's taste using big data such as Twitter and blogs. It recommends a personalized content to the corresponding user based on the predicted taste. It is possible to predict the exact user's preference and might recommend more suitable contents to the user's taste because it predicts the user using big data with a variety of user's information.
Traitement du signal audio-visuel et visiophone personne libre

Both visual and acoustical informations provide effective means of telecommunication between persons. In this context, the face provides the most important visual and acoustical informations. Regions of interest obtained by movement and skin color detection delimit parts of an image where faces can be found. A neural network detects the face position and scale. The detected face is then tracked, in real time, by a motorized camera and by the steerable beam of a microphone array. Thus video and audio acquisition are always focussed on the user which is free to move and free from specific equipments. These processing techniques of the audio-visual signal are the kernel of LISTEN, a “free person” videophone.
A High-Speed Parallel Implementation of CCSDS LDPC codec

Abstract QC-LDPC codes have been chosen by CCSDS for near-earth and deep-space applications. In this paper, an efficient high-speed parallel implementation of CCSDS LDPC codec is presented. SRAA circuits are used to complete the encoding computing, and min-sum algorithm is implemented with a parallel architecture on FPGA. The codec has a throughput of 2Gbps and good BER performance, taking only about 4M memory bits.
Web-Based Interactive Tool to Support Shared Decision-Making in the Choice of Treatment for Advanced Pancreatic Cancer

Pancreatic cancer currently has no cure in its advanced stage and often, systemic chemotherapy is used to improve survival and quality of life of patients. Chemotherapy treatments are available with varying degrees of benefits, harms, and uncertainties, and patients need to be aware of these characteristics and make informed choices or decide whether to avoid chemotherapy altogether. Web-based tools may help patients in making this informed choice with the help of their clinicians and relatives. Therefore, the aim of the study is to investigate the potential of a web-based interactive tool to support shared decision-making in the choice of treatment for people with advanced pancreatic cancer. A four-phase mixed methods paradigm has been adopted for the study. Participants will be clinicians (oncologists, clinical nurse specialists), people diagnosed with advanced (inoperable) pancreatic cancer, and relatives who support these patients. The first phase of the study identified 60 relevant citations on advanced pancreatic cancer treatment from four electronic databases from 1997 to 2018.
AWEswit: A switched capacitor circuit simulator

A switched capacitor circuit simulator, AWEswit, is presented. AWEswit exploits the cyclical reconfiguration of switched capacitor circuits through a sequence of continuous-time circuits. It employs asymptotic waveform evaluation (AWE) to estimate the response of the state variables (capacitor voltages) in each switching phase. For accuracy, a horizontal Pade/spl acute/ sequence is employed to extract dominant pole models for the response of the state variables. In addition, a general technique for handling complications in the AWE analysis resulting from loops of voltage sources and capacitors is presented. This technique makes AWEswit compatible with the class of switched capacitor simulators that operate in the charge-voltage regime. Simulation results are presented.
Teacher Views of Headmasters Regarding to Fulfilling Management Functions of Human Resources

The aim of this research is to determine and evaluate teacher views of headmasters regarding to fulfilling management functions of human resources. A conceptional frame is provided regarding to importance of human resources at educational institutions and for the definition of human resources primarily. In the research, sufficiency of headmasters regarding teacher views on fulfilling functions of human resources were discussed as: a) Management problems b) socio-economic problems c) what kind of management is indigenised by headmasters. In the research, a qualitative research was conducted which views of teachers are taken, semi-structured data collection model was used which is based on open-ended questions and views of experts were included. The environment of the research is the teachers who are on duty at a private school in the T.R. N.C. In the frame of the research, three questions which were prepared before was used and a way of research which is conducted as interview method was used. Presentment method was used at analyse of the data.
Fault Identification using Combined Adaptive Neuro-Fuzzy Inference System and Gustafson–Kessel Algorithm

Issues on detecting the occurrence of a fault, justifying the type, and estimating the exact location of the fault should be resolved to eliminate faults promptly and restore power supply with minimum interruption. Conventional approaches have contributed in assisting power utility in overcoming these issues. However, these approaches rely on line parameters and involve a few complex mathematical equations. In this paper, a new method for fault identification pertinent to classification and location is proposed by utilizing the combined adaptive neuro-fuzzy inference system (ANFIS) and Gustafson–Kessel (GK) clustering algorithm. The effectiveness and practicability of this method is demonstrated by simulation result. This method uses the GK fuzzy clustering algorithm to decide on the premise configuration and its parameter, and identifies its succeding parameter using orthogonal least square. The proposed method is independent of line parameter information and obtains high accuracy on estimation of fault locations.
The quasi-equilibrium framework revisited: analyzing long-term CO2 enrichment responses in plant–soil models

Abstract. Elevated carbon dioxide (CO2) can increase plant growth, but the magnitude of this CO2 fertilization effect is modified by soil nutrient availability. Predicting how nutrient availability affects plant responses to elevated CO2 is a key consideration for ecosystem models, and many modeling groups have moved to, or are moving towards, incorporating nutrient limitation in their models. The choice of assumptions to represent nutrient cycling processes has a major impact on model predictions, but it can be difficult to attribute outcomes to specific assumptions in complex ecosystem simulation models. Here we revisit the quasi-equilibrium analytical framework introduced by Comins and McMurtrie (1993) and explore the consequences of specific model assumptions for ecosystem net primary productivity (NPP). We review the literature applying this framework to plant–soil models and then analyze the effect of several new assumptions on predicted plant responses to elevated CO2. Examination of alternative assumptions for plant nitrogen uptake showed that a linear function of the mineral nitrogen pool or a linear function of the mineral nitrogen pool with an additional saturating function of root biomass yield similar CO2 responses at longer timescales (>5 years), suggesting that the added complexity may not be needed when these are the timescales of interest. In contrast, a saturating function of the mineral nitrogen pool with linear dependency on root biomass yields no soil nutrient feedback on the very-long-term (>500 years), near-equilibrium timescale, meaning that one should expect the model to predict a full CO2 fertilization effect on production. Secondly, we show that incorporating a priming effect on slow soil organic matter decomposition attenuates the nutrient feedback effect on production, leading to a strong medium-term (5–50 years) CO2 response. Models incorporating this priming effect should thus predict a strong and persistent CO2 fertilization effect over time. Thirdly, we demonstrate that using a “potential NPP” approach to represent nutrient limitation of growth yields a relatively small CO2 fertilization effect across all timescales. Overall, our results highlight the fact that the quasi-equilibrium analytical framework is effective for evaluating both the consequences and mechanisms through which different model assumptions affect predictions. To help constrain predictions of the future terrestrial carbon sink, we recommend the use of this framework to analyze likely outcomes of new model assumptions before introducing them to complex model structures.
Plan-based proactive computing

As the field of ubiquitous computing (ubicomp) has matured, the issue of how ubicomp applications should make use of all the devices available to them has not received much attention. We address this issue by presenting a plan-based execution model for creating proactive ubiquitous computing applications. Three applications, each from different domains, were built using this paradigm. These applications demonstrate how knowledge of a person’s plan can be used to proactively assist that person. This thesis also discusses the benefits this paradigm provides application developers. Thesis Supervisor: Howard E. Shrobe Title: Principal Research Scientist
Exploiting Loops in the Camera Array for Automatic Focusing Depth Estimation

Abstract Autofocus is a fundamental and key problem for modern imaging sensor design. Although this problem has been well studied in single camera literature, unfortunately, little research has been done on large-scale camera arrays. Most of the existing synthetic aperture imaging systems still need to manually select the optimal focus plane when an object moves. Unlike the conventional autofocus method, which sweeps the focus plane to find the maximal contrast, we present a novel optimization framework to handle the above challenges. In particular, we formulate the camera array autofocus problem as a constrained optimization problem by minimizing the temporal and spatial correspondences error subject to global loop constraint. Then this problem is relaxed as a quadratic program and solved using sequential quadratic programming. The experimental results show that the proposed method achieves a better performance compared with the results of traditional methods. To the best of our knowledge, our proposed method is the first optimization framework for solving camera array autofocus problem and it is of great importance to improve the performance of the existing synthetic aperture imaging system.
A COMPARATIVE STUDY OF DIFFERENT TEACHING STRATEGIES BASED ON TEXT TYPES ON STUDENTS' SPEAKING COMPETENCY

Abstract This study aimed at comparing the effect of different teaching strategies based on text types on students’ speaking competency. Post-test Only Comparison Group Design was applied as the research design. After the treatment sessions, post-test was administered to discover the impact of the treatments. The data obtained from the post-test were analyzed by using descriptive and inferential statistical analyses. Through descriptive analysis, it was found that the mean scores of the two samples for descriptive text were 71.63 and 73.96 and for procedure text were 74.93 and 76.80. The difference between students’ speaking competency who were taught using different teaching strategies was analyzed through two-way Anova. Based on the result of the hypothesis testing, it was found that; there was a significant difference between two teaching strategies on students’ speaking competency and there was no interactional effect between two teaching strategies and the text types on students’ speaking competency. These findings provide empirical evidence of the importance to determine teaching strategies that suit the text types taught in speaking class. Key terms : speaking competency, speaking random club, panauricon and text types
Design and Implementation of Low Power Multiplier Using Proposed Two Phase Clocked Adiabatic Static CMOS Logic Circuit

This paper presents a design and implementation of 2*2 array and 4*4 array multiplier using proposed Two Phase Clocked Adiabatic Static CMOS logic (2PASCL) circuit. The proposed 2PASCL circuit is based on adiabatic energy recovery principle which consumes less power. The proposed 2PASCL uses two sinusoidal power clocks which are 1800 phase shifted with each other. The measurement result of 2*2 array proposed 2PASCL multiplier gives 80.16 % and 97.67 %power reduction relative to reported 2PASCL and conventional CMOS logic and the measurement result of 4*4 array proposed 2PASCL multiplier demonstrate 32.88 % and 82.02 %power reduction compared to reported 2PASCL and conventional CMOS logic . Another advantage of the proposed circuit is that it gives less power though the number of transistor in proposed and reported 2PASCL circuit is same. From the result we conclude that proposed 2PASCL technology is advantageous to application in low power digital systems, pacemakers and sensors. The circuits are simulated at 180nm technology mode.