Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy Optimization of Racetrack Memory-Based SIMON Block Cipher

Spin-based memory devices are gaining importance due to multiple advantages like, zero standby power, high-write endurance and fast read, write operations. Besides storage, Spin Torque Transfer (STT)-based Magnetic Tunnel Junctions (MTJs) and Racetrack Memories (RMs) are also being investigated for logic applications, especially in the context of in-memory computing and neuromorphic architectures. Despite multiple innovations at technology-, device-and circuit-level, spin-based circuits suffer from poor energy efficiency, due to the high energy consumption of write operations. In this paper, we propose design optimizations to reduce the number of write operations in RM-based logic circuits, and therefore, achieve overall gain in energy performance. We performed in-depth study of the cutting-edge cryptographic primitive, block cipher SIMON, using experimentally validated Verilog-A models of MTJ and RM. For this benchmark, simulations demonstrate 4.65× reduction in computation energy, 2.66× improvement in computation delay and 1.71× reduction in transistor count compared to its base implementation using RM.
CLUSTERING BASED FEATURE SELECTION AND IDENTIFICATION OF SUBSET FOR HIGH DIMENSIONAL DATA

Feature selection is widely used in preparing high dimensional data for effective data mining. Increasingly popular social media data presents new challenges to feature selection. Social media data consists of traditional highdimensional, attribute value data such as posts, tweets, comments, and images, and linked data that describes the relationships between social media users as well as who post the posts. The nature of social media also determines that its data is massive, noisy, and incomplete, which exacerbates the already challenging problem of feature selection. In this paper, using SVM Algorithm we illustrate the differences between attribute value data and social media data, investigate if linked data can be exploited in a new feature selection framework by taking advantage of social science theories, extensively evaluate the effects of user-user and user-post relationships manifested in linked data on feature selection.
Pricing of Occupation Time Derivatives: Continuous and Discrete Monitoring

In the present work we use different numerical methods (multidimensional inverse Laplace transform, numerical solution of a PDE by finite difference scheme, Monte Carlo simulation) for pricing occupation time derivatives in order to examine the effect of continuous and discrete time monitoring of the underlying asset. In particular we treat the problem of the numerical inversion of a multidimensional Laplace transform and we show that it can be performed very fast and with great accuracy. We conduct also an analysis of the numerical method for the solution of the PDE with discrete monitoring and we show that the proposed method avoids unwanted oscillations in the solution arising near the monitoring dates due to the updating of the occupation time.
Analyzing intra-team strategies for agent-based negotiation teams

An agent-based negotiation team is a group of two or more agents with their own and possibly conflicting preferences who join together as a single negotiating party because they share a common goal which is related to the negotiation. Scenarios involving negotiation teams require coordination among party members in order to reach a good agreement for all of the party members. An intra-team strategy defines what decisions are taken by the negotiation team and when and how these decisions are taken. Thus, they are tightly linked with the results obtained by the team in a negotiation process. Environmental conditions affect the performance of the different intra-team strategies in different ways. Thus, team members need to analyze their environment in order to select the most appropriate strategy according to the current conditions. In this paper, we analyze how environmental conditions affect different intra-team strategies in order to provide teams with the knowledge necessary to select the proper intra-team strategy.
Factors Affecting Nurse Students' Perception Regarding Role Transition at Faculty of Nursing –Benha University

Background: Transition from nursing student to staff nurse can be exciting, stressful and challenging. Aim: aim of this study was to assess nurse students' perception regarding role transition at faculty of nursing - Benha University. Design: a descriptive research design was utilized to meet the aim of this study. The sample of this study included 193 out from 370 nurse students who selected randomly. Tools: tool for data collection: factors affecting role transition questionnaire. Results and Conclusion: More than two thirds of nurse students had moderate agreement level regarding organizational factors, also more than half of nurse students had moderate agreement level regarding personal and educational factors. Recommendation: Creating healthy environment is highly needed for new nurses during role transition. Conduct an orientation program for nursing students before internship year is very beneficial and prior each clinical training area to explain objectives, responsibilities and discuss nurse students' expectations.
Thermodynamic evolution of cosmological baryonic gas I. Influence of non-equipartition processes

Using N-body/hydrodynamic simulations, the influence of non-equipartition processes on the thermal and dynamical properties of cosmological baryonic gas is investigated. We focus on a possible departure from equilibrium between electrons, ions and neutral atoms in low temperature (10 4 -10 6 K) and weakly ionized regions of the intergalactic medium. The simula- tions compute the energy exchanges between ions, neutrals and electrons, without assuming thermal equilibrium. They include gravitation, shock heating and cooling processes, and follow self-consistently the chemical evolution of a primordial composi- tion hydrogen-helium plasma without assuming collisional ionization equilibrium. At high redshift, a significant fraction of the intergalactic medium is found to be warmer and weakly ionized in simulations with non-equipartition processes than in simu- lations in which the cosmological plasma is considered to be in thermodynamic equilibrium. With a semi-analytical study of the out of equilibrium regions we show that, during the formation of cosmic structures, departure from equilibrium in accreted plasma results from the competition between the atomic cooling processes and the elastic processes between heavy particles and electrons. Our numerical results are in agreement with this semi-analytical model. Therefore, since baryonic matter with temperatures around 10 4 K is a reservoir for galaxy formation, non-equipartition processes are expected to modify the properties of the objects formed.
Compact Cascaded-Spiral-Patch EBG Structure for Broadband SSN Mitigation in WLAN Applications

A compact cascaded-spiral-patch electromagnetic bandgap (CSP-EBG) structure for wideband simultaneously switching noise suppression is proposed. Instead of using double open stubs to provide dual-band noise suppression for a wireless local area network (WLAN), the proposed CSP-EBG structure offers an even wider bandgap, covering multiple adjacent WLAN bands from 2.4 to 6 GHz. A 1-D equivalent circuit model is proposed and validated by full-wave simulation and measurement. The circuit model could also be adopted to investigate design concepts, optimizing the EBG design by the theory of stepped-impedance resonator. The stopband of the proposed EBG structure covers a frequency range from 2.4 to 6.4 GHz with 91% fractional bandwidth (FBW). It not only provides three times wider FBW than single and double open stubs EBG but also occupies 95% smaller unit-cell size than double open stubs EBG and four times smaller than the mushroom EBG.
The Literacy Skills of English Language Learners in Canada

The purpose of this article is to review published studies of the English literacy of children in Canada who are English language learners (ELLs) with the goal of understanding the reading development of ELLs and characteristics of reading disabilities (RD) in this population. Phonological processing, syntactic awareness, and working memory of ELLs with and without RD were compared to that of native English–speaking (L1) students with and without RD. Our review found that ELLs with RD experienced reading difficulties similar to those of L1 students with RD. On the basis of the evidence, ELLs are not at greater risk for RD than their native English–speaking peers. We propose that the diagnosis of a reading disability can be made in a similar manner in both ELL and L1 students.
Unobtrusive real-time heart rate variability analysis for the detection of orthostatic dysregulation

The possibilities for wearable health care technology to improve the quality of life for chronic disease patients has been increasing within recent years. For instance, unobtrusive cardiac monitoring can be applied to people suffering from a disorder of the autonomic nervous system (ANS) which show a significantly lower heart rate variability (HRV) than healthy people. Although recent work presented solutions to analyze this relationship, they did not perform it during daily life situations. For that reason, this work presents a system for a real-time analysis of the user's HRV on an Android-based mobile device throughout the day. The system was used for the detection of an orthostatic dysregulation which can be an indicator for a disorder of the ANS. Measures for HRV analysis were computed from acquired ECG data and compared before and after a posture change. For triggering the HRV analysis, an IMU-based algorithm which detects stand up events was developed. As a proof of concept for an automatic assessment of an orthostatic dysregulation, a classification based on the derived HRV measures was performed. The performance of the stand up detection was evaluated in the first part of this study. The second part was conducted for the evaluation of the derived HRV measures and involved healthy subjects as well as patients with idiopathic Parkinson's Disease. The results of the evaluation showed a recognition rate of 90.0 % for the stand up detection algorithm. Furthermore, a clear difference in the change of HRV measures between the two groups before and after standing up was observed. The classification provided an accuracy of 96.0%, and a sensitivity of 93.3%. The results demonstrated the possibility of unobtrusive HRV monitoring during daily life situations.
Adaptive Anomaly Detection in Cloud Using Robust and Scalable Principal Component Analysis

This paper proposes a novel and scalable model for automatic anomaly detection on a large system such as a cloud. Anomaly detection issues early warning of unusual behavior in dynamic environments by learning system characteristic from normal operational data. Anomaly detection in large systems is difficult to detect due heterogeneity, dynamicity, scalability, hidden complexity, and time limitation. To detect anomalous activity in the cloud, we need to monitor the datacenter and collect cloud performance data. In this paper, we propose an adaptive anomaly detection mechanism which investigates principal components of performance metrics. It transforms the performance metrics into a low-rank matrix and then calculates the orthogonal distance using the Robust PCA algorithm. The proposed model updates itself recursively learning and adjusting the new threshold value in order to minimize reconstruction errors. This paper also investigates the robust principal component analysis in distributed environments using Apache Spark as the underlying framework, specifically addressing cases in which a normal operation might exhibit multiple hidden modes. The accuracy and sensitivity of the model is tested on Google data center traces and Yahoo! datasets. The model achieves an 87.24% accuracy.
Estimated-Regression Planning for Interactions with Web Services

"Web services" are agents on the web that provide services to other agents. Interacting with a web service is essentially a planning problem, provided the service exposes an interface containing action definitions, which in fact is an elegant representation of how web services actually behave. The question is what sort of planner is best suited for solving the resulting problems, given that dealing with web services involves gathering information and then acting on it. Estimated-regression planners use a backward analysis of the difficulty of a goal to guide a forward search through situation space. They are well suited to the web-services domain because it is easy to relax the assumption of complete knowledge, and to formalize what it is they don't know and could find out by sending the relevant messages. Applying them to this domain requires extending classical notations (e.g., PDDL) in various ways. A preliminary implementation of these ideas has been constructed, and further tests are underway.
MODIFICATION OF THE WEIGHTED CHECKSUM METHOD FOR DERIVING FAULT TOLERANT VERSIONS OF THE MAIN LINEAR ALGEBRA ALGORITHMS

The modified weighted checksum method is proposed, which can be used for deriving fault tolerant versions of most linear algebra algorithms. The purpose is the detection and correction of calculation errors occurred due to transient hardware faults during algorithm execution. Using the proposed method, the faulttolerant versions of Jordan-Gauss and Faddeeva algorithms are designed. The computational complexity of new algorithms is increased approximately on O(N) multiply add operations in comparison with the original algorithms. However, new algorithms enable to detect and to correct a single error in an arbitrary row or column of input data matrices at the each algorithm step. Hence, it is possible to correct up to N and (N/2 + N⋅P) single errors during realization of whole Jordan-Gauss and Faddeeva algorithms respectively. Finally, the results of experimental verification of the proposed algorithms are represented.
The role of the social network and the usage of communication in travel behavior measured with Smartphone data

In this paper we investigate the use of a smartphone database to explore influences on travel behavior. Our aim is to exploit the rich individual-level data available from the smartphone to study the influence of communication and social contacts (collected via phone call and sms logs) on spatial movement (collected via GPS). An advantage of smartphone data is the ability to collect such rich data without user input over a long period of time, and the disadvantage is the difficulty associated with processing the data. We work with three months of data from 111 people collected via a snowball sample. In studying travel behavior, we focus on high level measures of mobility as represented by the size of one's activity space and one's travel intensity (our dependent variables). We use as explanatory variables sociodemographics, spatial relationship between home and work, communication use (number of phone calls and sms), and the travel behavior of those in the sample who are connected to the respondent (where connectivity is measured by phone and sms contact). We describe how these variables were processed from the smartphone data and present estimation results from the regression analysis. We find that people tend to travel in a similar manner as those they are socially connected to (consistent with the social network and travel literature) and that communication use is a compliment to physical travel (consistent with the telecommunication and travel literature). The results, although preliminary, illustrate how smartphone data can be exploited to reveal complex features of travel behavior.
Action Recognition in Meeting Videos Using Head Trajectories and Fuzzy Color Histogram

People attending teleconference meetings usually follow specific trajectories corresponding to their intentions. In most situations the meeting video content can by sufficiently characterized by capturing the head trajectories. The tracking of the head is done using a particle filter built on cues such as color, gradient and shape. The head is represented by an ellipse with fuzzy color histogram in its interior and an intensity gradient along the ellipse boundary. By comparing pixels in entry zones to a model of the background we can detect the entry of the person quickly and reliable. The fuzzy color is constructed then in the interior of an ellipse fitting best the oval shape of the head. When a new person appears in the scene a creation of new trajectory is initialized. The recognition of actions is performed using kernel histograms built on head positions as well as segmented trajectories that are related to the layout of the room.
ORBIT : A Smartphone-Based System Platform for Embedded Sensing Applications

Owing to the rich processing, multi-modal sensing, and versatile networking capabilities, smartphones are increasingly used to build data-intensive embedded sensing applications. However, various challenges must be systematically addressed before smartphones can be used as a generic embedded sensing platform, including high power consumption, lack of real-time functionality and embedded programming support. This paper presents ORBIT, a smartphone-based system platform for embedded sensing applications. ORBIT features a tiered architecture, in which smartphone is interfaced to an energy efficient peripheral board and a cloud server. Time-critical tasks, such as high-rate sensor sampling and lightweight signal processing, are executed on the peripheral board, while computationintensive tasks are offloaded to the smartphone or cloud. By intelligently dispatching the processing tasks among the peripheral board, smartphone, and cloud, ORBIT minimizes the system energy consumption, subject to upper-bounded processing delays. ORBIT also includes a signal processing library that supports such task partition through new embedded programming primitives. Microbenchmark evaluation and three case studies, which include seismic sensing, visual tracking using an ORBIT robot, and multi-camera 3D reconstruction, validate the design of ORBIT.
Addressing learner issues with StepUp!: an evaluation

This paper reports on our research on the use of learning analytics dashboards to support awareness, self-reflection, sensemaking and impact for learners. So far, little research has been done to evaluate such dashboards with students and to assess their impact on learning. In this paper, we present the results of an evaluation study of our dashboard, called StepUp! , and the extent to which it addresses issues and needs of our students. Through brainstorming sessions with our students, we identified and prioritized learning issues and needs. In a second step, we deployed StepUp! during one month and we evaluated to which extent our dashboard addresses the issues and needs identified earlier in different courses. The results show that our tool has potentially higher impact for students working in groups and sharing a topic than students working individually on different topics.
On the realized volatility of the ECX CO2 emissions 2008 futures contract: distribution, dynamics and forecasting

This article documents the conditional and unconditional distributions of the realized volatility for the 2008 futures contract in the European climate exchange (ECX), which is valid under the EU emissions trading scheme (EU ETS). Realized volatility measures from naive, kernel-based and subsampling estimators are used to obtain inferences about the distributional and dynamic properties of the ECX emissions futures volatility. The distribution of the daily realized volatility in logarithmic form is shown to be close to normal. The mixture-of-normals hypothesis is strongly rejected, as the returns standardized using daily measures of volatility clearly departs from normality. A simplified HAR-RV model (Corsi in J Financ Econ 7:174–196, 2009) with only a weekly component, which reproduces long memory properties of the series, is then used to model the volatility dynamics. Finally, the predictive accuracy of the HAR-RV model is tested against GARCH specifications using one-step-ahead forecasts, which confirms the HAR-RV superior ability.
What keeps online customers repurchasing through the internet?

Factors affecting the intention of purchasing online have been investigated for many years. On the other hand, the question of what keeps online customers repurchasing through the Internet has rarely been examined. By expanding the existing customer satisfaction/continuance model based on the expectation-confirmation model, this study aims to provide a theoretical model that can support technical, business, and consumer issues involved in online repurchasing. The results of an online questionnaire of 1,215 Japanese online customers pointed out that not only basic factors of confirmation, satisfaction, perceived usefulness, and perceived incentives, but a new factor, customer loyalty, are factors significantly influencing the online customers' intention to repeat purchase through the Internet. This study sheds light on the development of online incentive technology and the means of enhancing customer loyalty to promote online repurchasing.
Stability Analysis of a Clock-Driven Rigid-Body SLIP Model for RHex

We apply the stability analysis for hybrid legged locomotion systems, introduced in our companion paper in this issue, to a new simple clock-driven SLIP model inspired by the robot RHex. We adopt in stance phase the three-degrees-of-freedom (3DoF) spring loaded inverted pendulum (SLIP) model introduced in our companion paper to capture RHex’s pitching dynamics in the sagittal plane. The coordinating influence of RHex’s open-loop clock controller is subsumed into a leg placement strategy derived from a bipedal abstraction of RHex. The “symmetric” factorization analysis introduced in our companion paper yields a necessary condition for gait stability expressed in closed form, which can be imposed directly on the clock parameter space. This represents the first reported analytical insight into how a dynamical runner might be stabilized by a completely feed forward rhythmic limb coordination pattern. Correspondence in steady-state gait location and stability characteristics with an appropriately tuned 24DoF model of RHex provides numerical evidence that the 3DoF SLIP model offers a descriptive explanation for the robot’s empirical running behavior.