Miracle-dz/UniFAR-FTUs huggingface dataset:
Study of an integrated-optical slow-light ring-resonator for sensing applications

Integrated-optical (IO) micro-ring, -disk, and -sphere resonators have been long considered as a good candidate to enhance optical sensor performance. To the best of our knowledge, none of previous reported works has explicitly attributed such (expected) enhancement to slow-light phenomenon, i.e. a phenomenon where the group velocity of light is much lower than the light velocity in vacuum (c); a phenomenon which obviously can be expected also for a properly-designed ring-resonator circuiT. Consequently, none of those works has explicitly made use of this phenomenon for optimizing the sensor performance. Hence, so far, the reported detection limit of ring-resonator based sensor (best reported value around 1E-7 RIU) is in general poorer than IO Mach-Zehnder interferometer (MZI) sensor (best reported value around 1E-8 RIU). In this work, we present a theoretically study on slow-light in ring-resonator circuits and discuss quantitatively its role in enhancing the sensor performance. The model is based on the transfer matrix method and the complex transmission coefficient approach, by assuming a homogeneous refractometric IO sensor with MZI read-out scheme. The modeling results show that using realistic structure parameters and a typical read-out capability, a refractive index detection limit of one order better than the present state of the art IO MZI sensing structure can be expected by the inclusion of such a slow-light structure. The realization of such device is under progress at the moment of the preparation of this abstract. The device will be based on $Si_3N_4$ IO technologies with a serrodyne phase readout scheme employing a ZnO electro-optical modulator. The latest results of the realization and experimental progress will be reported in the conference as well.
Outdoor virtual reality

This paper presents our novel concept Outdoor Virtual Reality. By using outdoor augmented reality techniques we propose to build very wide area virtual reality systems, outdoor virtual reality. The concept of outdoor virtual reality is compared and contrasted to traditional definitions of augmented reality and virtual reality. We present our flexible Tinmith-evo5 software architecture as a platform to build outdoor virtual reality applications. We have constructed two outdoor virtual reality applications, a 3D visualisation tool and an outdoor game.
Fuzzy Assisted Event Driven Data Collection from Sensor Nodes in Sensor-Cloud Infrastructure

Wireless Sensor Network(WSN) consists of sensor nodes which are deployed densely in an area of interest. The area is intended to be sensed or monitored. Each sensor node is a tiny and power constrained device which is assigned the task of monitoring. One of the most important requirements for wireless sensor networks is systematic collection of data, where sensed data are collected at sensor nodes and forwarded to a base station through number of hops for further processing. This paper presents an event-driven data gathering scheme in a Sensor-Cloud infrastructure. The scheme uses Fuzzy logic to ensure efficient data collection and report a select set of data which is required. Aim of this work is to minimal use of communication resource and reduce overhead while collecting sensor data. Also, an attempt is made to implement the scheme on Tiny OS using TelosB motes for real-time testing.
Evaluation of the NeuroSky MindFlex EEG headset brain waves data

In human brain, either in the state of wakefulness or sleeping, different frequency changes are observable in the spectrum of measured electric signals of the brain. Regarding the frequency components of these signals that occur as a consequence of this electric activity, different brain waves can be distinguished. The electric impulse alternations that generated during the operation of neurons can be measured by the EEG (electroencephalograph) device. In the past, devices that could measure these brain signal alterations were mainly used in medicine. However, in the past few years cheaper and user friendly brainwave signal processing units have become available and the use of this technology has expanded. In this paper, the so-called Brain Computer Interface unit will be presented that was developed for further brain wave analysis and ensures the detection of brain waves. Moreover, an application for visualization and further procession of the measured and processed signals will also be described. The application can be used for EEG data acquisition, processing and visualization which could be the base of several further researches.
Comparison of a Restricted and Unrestricted Vegan Diet Plan with a Restricted Omnivorous Diet Plan on Health-Specific Measures

Background: We have previously noted beneficial health outcomes when individuals follow a dietary restriction plan in accordance with the Daniel Fast (DF). This is true whether individuals eliminate all animal products or include small amounts of meat and dairy in their plan. The present study sought to compare anthropometric and biochemical measures of health in individuals following a traditional DF (i.e., restricted vegan) or modified DF (i.e., restricted omnivorous; inclusive of ad libitum meat and skim milk consumption), with those following an unrestricted vegan diet plan. Methods: 35 subjects (six men; 29 women; 33 ± 2 years; range: 18–67 years) completed a 21-day diet plan. Subjects reported to the lab for pre- (day 1) and post-intervention testing (day 22) in a 10 h fasted state. Blood samples were collected and assayed for complete blood count, metabolic panel, lipid panel, insulin, HOMA-IR, C-reactive protein, and oxidative stress biomarkers (malondialdehyde, advanced oxidation protein products, and nitrate/nitrite). Heart rate and blood pressure were measured and body composition was determined via dual energy X-ray absorptiometry. Subjects’ self-reported compliance, mental and physical health, and satiety in relation to the dietary modification were recorded. Results: No interaction effects were noted for our outcome measures (p > 0.05). However, subjects in the traditional DF group reported an approximate 10% increase in perceived mental and physical health, with a 25% reduction in malondialdehyde and a 33% reduction in blood insulin. Systolic BP was reduced approximately 7 mmHg in subjects assigned to the traditional DF, with an approximate 5 mmHg reduction in subjects assigned to the modified DF and the unrestricted vegan plan. A small (2 mmHg) reduction in diastolic BP was noted for subjects in both DF groups; a slight increase in diastolic BP was noted for subjects assigned to the unrestricted vegan group. An approximate 20% reduction was noted in total and LDL cholesterol for subjects in the traditional DF group, with an approximate 10% decrease for subjects in the modified DF group. No decrease in total or LDL cholesterol was noted for subjects in the unrestricted vegan group. Conclusion: These data indicate that both a traditional or modified DF may improve blood pressure and blood lipids in a clinically meaningful manner if these results are sustained over the long term. A traditional DF also results in a significant reduction in blood insulin and oxidative stress. An unrestricted vegan diet may improve systolic blood pressure, but in the absence of measures to strictly monitor adherence, it does not favorably impact other markers of health measured in the present study.
A Secure Clustering in MANET through Direct Trust Evaluation Technique

Ad hoc network is a self organizing wireless network, made up of mobile nodes that act in both way as node and router. Wired network with fixed infrastructure defense against attacks using firewalls and gateways, nevertheless for wireless network with dynamic structure attacks can come from anywhere and at any time, because mobile nodes are unguarded to security attacks. To ensure secure data transmission and for proper functioning of network operations trustworthiness of the node has to be proved before initiating any group activity. When MANET nodes are used for large scale operations, dynamic nature of the MANET induces many problems in terms of routing delay, bandwidth and resource consumption. Consequently many clustering algorithms invented by researchers for betterment of MANET resources. With this objective trust based clustering is used to divide the whole network into sub groups based on trust value. The trustworthiness of the node is evaluated by direct trust evaluation technique and the trust value at each node is calculated as fuzzy value and it lies in between zero and one. The sub group(cluster) security is ensured by electing trustworthy node as Cluster Head(CH). Finally the proposed Trust based Clustering Algorithm(TBCA) is proved its superiority with existing Enhanced Distributed Weighted Clustering Algorithm(EDWCA) based on some metrics like delay, PDR, packet drop and overhead etc.
Direct out-of-memory distributed parallel frequent pattern mining

Frequent itemset mining is a well studied and important problem in the datamining community. An abundance of different mining algorithms exists, all with different flavor and characteristics, but almost all suffer from two major shortcomings. First, in general frequent itemset mining algorithms perform exhaustive search over a huge pattern space. Second, most algorithms assume that the input data fits into main memory. The first problem was recently tackled in the work of [2], by direct sampling the required number of patterns over the pattern space. This paper extends the direct sampling approach by casting the algorithm into the MapReduce framework, effectively ceasing the memory requirements that the data should fit into main memory. The results show that the algorithm scales well for large data sets, while the memory requirements are solely dependent on the required number of patterns in the output.
A novel relay selection method for decode-and-forward relaying

In this paper, we consider a multi-relay network operating in decode-and-forward mode. We propose a novel relay selection method with a low implementation complexity. Unlike the competing schemes, it requires neither error detection methods at relay nodes nor feedback information at the source. Our proposed selection method is able to extract the full diversity. We further formulate a power allocation strategy to minimize the symbol error rate which brings further improvements over the equal power allocation among the source and relay nodes. Extensive Monte Carlo simulations are also presented to compare the performance of the proposed scheme with its competitors. Our proposed relay selection method outperforms competing schemes and is able to perform within 0.3 dB of the genie performance bound.
Tele-operation of a robotic exoskeleton for rehabilitation and passive arm movement assistance

A robotic exoskeleton, the MARSE-4 has been developed to provide passive rehabilitation therapy to the physically disabled individuals with impaired upper-limb function. In this paper we focused on the tele-operation of the MARSE-4 using an upper-limb prototype 7DoFs (lower scaled) master exoskeleton arm (mExoArm.) The MARSE-4 has 4DoFs and is able to assist elbow (flexion/extension), forearm (pronation/supination) and wrist joint movements (radial/ulnar deviation, and flexion/extension). Modified Denavit-Hartenberg conventions were used in the kinematic modeling of the exoskeleton. In control, a nonlinear computed torque control and a linear PID control techniques were employed to maneuver the MARSE-4 to follow the desired trajectory. Experimental results show that the MARSE-4 can efficiently track the desired trajectories as generated by mExoArm and therefore guarantee to tele-operate the MARSE to deliver passive therapy for wrist, elbow, and forearm movements.
Application of ANN in agarwood oil grade classification

This paper presents the application of Artificial Neural Network (ANN) in agarwood oil grade classification. The work involved of the extraction of chemical compounds by GC-MS, identification the significant chemical compounds using Z-score, generating the synthetic data using a dedicated formulae and application of ANN classification. The ANN classification is performed and its performance is measured using accuracy, sensitivity and specificity. The result showed that the performance of ANN classification for original GC-MS data is increasing when the data is added with synthetic data. This study showed that the ANN application in this study required a large number of sample size for it to have high accuracy in classification.
Multi-Organ Cancer Classification and Survival Analysis

Accurate and robust cell nuclei classification is the cornerstone for a wider range of tasks in digital and Computational Pathology. However, most machine learning systems require extensive labeling from expert pathologists for each individual problem at hand, with no or limited abilities for knowledge transfer between datasets and organ sites. In this paper we implement and evaluate a variety of deep neural network models and model ensembles for nuclei classification in renal cell cancer (RCC) and prostate cancer (PCa). We propose a convolutional neural network system based on residual learning which significantly improves over the state-of-the-art in cell nuclei classification. Finally, we show that the combination of tissue types during training increases not only classification accuracy but also overall survival analysis.
Pathways from parental stimulation of children’s curiosity to high school science course accomplishments and science career interest and skill

ABSTRACT Curiosity is fundamental to scientific inquiry and pursuance. Parents are important in encouraging children’s involvement in science. This longitudinal study examined pathways from parental stimulation of children’s curiosity per se to their science acquisition (SA). A latent variable of SA was indicated by the inter-related variables of high school science course accomplishments, career interest, and skill. A conceptual model investigated parental stimulation of children’s curiosity as related to SA via science intrinsic motivation and science achievement. The Fullerton Longitudinal Study provided data spanning school entry through high school (N = 118). Parental stimulation of curiosity at age 8 years comprised exposing children to new experiences, promoting curiosity, encouraging asking questions, and taking children to a museum. Intrinsic motivation was measured at ages 9, 10, and 13 years, and achievement at ages 9, 10, and 11 years. Structural equation modelling was used for analyses. Controlling for socio-economic status, parental stimulation of curiosity bore positive and significant relations to science intrinsic motivation and achievement, which in turn related to SA. Gender neither related to stimulation of curiosity nor contributed to the model. Findings highlight the importance of parental stimulation of children’s curiosity in facilitating trajectories into science, and relevance to science education is discussed.
New Threshold Setting Method for the Extraction of Facial Areas and the Recognition of Facial Expressions

A new method for setting a threshold value on a quasi-chroma histogram to extract facial areas based on the methods used in forecasting stock market features, such as dead cross and golden cross, is proposed. In general, when a threshold value is set to isolate objects from a background, the histogram of an image is taken and examined. However, the shape of the histogram constructed from an original image is generally very jagged. Before setting the threshold, a smoothing operation is applied using a mask with the interval n. Here, the problem is in deciding the interval n and the iteration time. In general, the required interval n differs from picture to picture. Therefore, choosing the interval n is quite a delicate process. However, in this paper two feature points, dead cross and golden cross , which are generally used to decide the timing of selling or buying stocks, are adopted to set the threshold value for a quasi-chroma element image. Based on the hue element, the skin-color area is first isolated. A facial area is then extracted from the skin-color area based on a quasi-chroma histogram constructed from the skin-color area. In this step, the threshold value is set at the point where the frequency is at a minimum between the two feature points. The evaluation of the repetition time of smoothing is very important. An adequate number of iteration and interval n are also examined through experiments. In the final part of this paper, the recognition of expressions is presented
Social Learning with Private and Common Values 1

We consider an environment where individuals sequentially choose among several actions. The payoff to an individual depends on her action choice, the state of the world, and an idiosyncratic, privately observed preference shock. Under weak conditions, as the number of individuals increases, the sequence of choices always reveals the state of the world. This contrasts with the familiar result for pure common-value environments where the state is never learned, resulting in herds or informational cascades. The medium run dynamics to convergence can be very complex and non-monotone: posterior beliefs may be concentrated on a wrong state for a long time, shifting suddenly to the correct state. JEL classification numbers: D82, D83
Using Model Predictive Control in Data Centers for Dynamic Server Provisioning

Abstract The ever-increasing energy consumption of data centers is becoming a crucial problem nowadays. This paper discusses the benefits and challenges of coordinating dynamic server provisioning and thermal dynamics for energy control of data centers. A model predictive control approach, considering the computational and thermal characteristics of a data center and their interactions, together with dynamic server provisioning is used to optimize the energy consumption of the total data center. Quality of service and thermal constraints are enforced in the optimization process. Simulation results demonstrate that the proposed control approach can lead to a significant energy saving for a data center.
LETTER TO THE EDITOR: The exact distribution of the oscillation period in the underdamped one-dimensional Sinai model

We consider the Newtonian dynamics of a massive particle in a one-dimensional random potential which is a Brownian motion in space. This is the zero-temperature nondamped Sinai model. As there is no dissipation the particle oscillates between two turning points where its kinetic energy becomes zero. The period of oscillation is a random variable fluctuating from sample to sample of the random potential. We compute the probability distribution of this period exactly and show that it has a power law tail for large period, P(T ) ~ T -5/3, and an essential singularity P(T ) ~ exp (-1/T ) as T →0. Our exact results are confirmed by numerical simulations and also via a simple scaling argument.
Download Malware? No, Thanks. How Formal Methods Can Block Update Attacks

In mobile malware landscape there are many techniques to inject malicious payload in a trusted application: one of the most common is represented by the so-called update attack. After an apparently innocuous application is installed on the victim’s device, the user is asked to update the application, and a malicious behavior is added to the application. In this paper we propose a static method based on model checking able to identify this kind of attack. In addiction, our method is able to localize the malicious payload at methodlevel. We obtain an accuracy very close to 1 in identifying families implementing update attack using a real Android dataset composed by 2,581 samples.
A trust-enhanced collaborative filtering recommender system

Collaborative filtering(CF) strategy is widely used in recommender systems, but it also exists many weaknessses, for example:, chanages of preference and no user control of the system. In this paper, we propose a novel personalized strategy, which is used to deal with the weaknesses. On one part, a mechanism is introduced for user to manage his own trust relationship, which could increase user confidence for the system A Trust table is adopt for a single user to keep his own trust neighbors, trust degree can be changed or viewed. On another part trust value is used as a complementary factor to user similarity, which makes the recommendation more accurate, Experiment shows that the recommendation method has a better performance than traditional CF method, and it is believed to strengthen consumer confidence.
Distressed or Deliberately Defiant?: Managing challenging student behaviour due to trauma and disorganised attachment

Complex childhood trauma includes the early exposure to ongoing and repeated, interpersonal, traumatic experience (e.g. physical, sexual, and/or emotional abuse, serious neglect, family violence) and also the wide-ranging, long-term impact of this exposure. This trauma can have profound impact on school behaviours, attendance and engagement and educational/life outcomes of learners. Worrying behaviours can also have a significant impact on the well-being, efficacy and retention of teachers and other educators. This book is written for busy teachers. In user-friendly, non-academic language, the author explains the neurological and physiological basis for the challenging behaviours of complex trauma-survivng students and provides practical advice to educators on ways to better support, manage and educate them. " It is not unusual for educators today, whether in the early childhood, primary or secondary sectors, to be confronted with severely challenging student behaviour - students who fly into unexplained violent and oppositional outbursts with little warning; who respond poorly to tried-and-true behaviour management processes. Such behaviour has considerable impact on the delivery of teaching and learning programs and the emotional wellbeing of the teachers themselves as well as raising safety risks for the entire school community. This book explains the basis for such behaviour as the neurological, physiological and behavioural outcomes of "disorganised attachment" due to prolonged exposure to a traumatic home life and provides practical advice to educators on ways that schools can effectively manage these students. By examining the science behind attachment theory, the neurobiology of behaviour, and the manifestation of disorganised attachment in the school context, this book will help educators: - minimise such challenging behaviour, - manage crises and disciplinary responses such as suspension and expulsion, - improve student compliance, - enhance education and overall wellbeing, and - deal with parents. "-- Publisher website