Miracle-dz/UniFAR-FTUs huggingface dataset:
Highly sensitive temperature sensor based on a Mach-Zehnder interferometer created in graded index fiber

A Mach-Zehnder interferometer (MZI) based on a graded index fiber (GIF) is proposed and demonstrated for temperature measurement. The device is constructed by splicing a half-pitch GIF between two single mode fibers (SMFs). A microcavity was drilled in GIF near the core and cladding interface. The microcavity and the remaining core of the GIF perform as two interference arms. Such a device exhibits a high refractive index (RI) sensitivity of 24611.54 nm/RIU, and high temperature sensitivity of -12.73 nm/°C with the assistance of high thermo-optic coefficient (TOC) liquids filling the microcavity.
Novel Sybil Defense Scheme for Peer–to–peer Applications

The importance of peer–to–peer (P2P) network overlays produced enormous interest in the research community due to their robustness, scalability, and increase of data availability. P2P networks are overlays of logically connected hosts and other nodes including servers. P2P networks allow users to share their files without the need for any centralized servers. Since P2P networks are largely constructed of end–hosts, they are susceptible to abuse and malicious activity, such as sybil attacks. Impostors perform sybil attacks by assigning nodes multiple addresses, as opposed to a single address, with the goal of degrading network quality. Sybil nodes will spread malicious data and provide bogus responses to requests. To prevent sybil attacks from occurring, a novel defense mechanism is proposed. In the proposed scheme, the DHT key–space is divided and treated in a similar manner to radio frequency allocation incensing. An overlay of trusted nodes is used to detect and handle sybil nodes with the aid of source–destination pairs reporting on each other. The simulation results show that the proposed scheme detects sybil nodes in large sized networks with thousands of interactions.
Vocal and Non-vocal Segmentation based on the Analysis of Formant Structure

The process of classifying vocal and non-vocal regions in an audio clip is the base for many Music Information Retrieval (MIR) tasks. In this work, we have computed novel features based on formant structure for segmenting the vocal and non-vocal regions of a given music clip. The features such as obtuse angles at formant peak, valley locations, convexity, and concavity have been proposed for this task after thorough analysis. The obtuse angles have been computed for second, third and fourth formants as much discrimination is not found for the first formant. The computed formant related features have been added to the base-line Mel frequency cepstral coefficients (MFCCs) in order to improve the performance. Moreover, singer formant (F5) has also been computed forming a 19-dimensional feature vector. As artificial neural networks (ANNs) are more suitable for handling nonlinear data, they have been considered as a classifier. Further, the 11-point moving window has been applied to avoid intermittent misclassifications. An accuracy of 88% is obtained using the proposed approach with a 19-dimensional feature vector.
Rise of Big Data – Issues and Challenges

The recent rapid rise in the availability of big data due to Internet-based technologies such as social media platforms and mobile devices has left many market leaders unprepared for handling very large, random and high velocity data. Conventionally, technologies are initially developed and tested in labs and appear to the public through media such as press releases and advertisements. These technologies are then adopted by the general public. In the case of big data technology, fast development and ready acceptance of big data by the user community has left little time to be scrutinized by the academic community. Although many books and electronic media articles are published by professionals and authors for their work on big data, there is still a lack of fundamental work in academic literature. Through survey methods, this paper discusses challenges in different aspects of big data, such as data sources, content format, data staging, data processing, and prevalent data stores. Issues and challenges related to big data, specifically privacy attacks and counter-techniques such as k-anonymity, t-closeness, l-diversity and differential privacy are discussed. Tools and techniques adopted by various organizations to store different types of big data are also highlighted. This study identifies different research areas to address such as a lack of anonymization techniques for unstructured big data, data traffic pattern determination for developing scalable data storage solutions and controlling mechanisms for high velocity data.
Research on Key Technologies of the 10kV Wide Range and Arcless on-Load Automatic Voltage Regulation Distribution Transformer

At present, a small number of distribution transformers with on-load tapping mode are used, which still adopts the combination of traditional mechanical devices and electric operating mechanisms. The moving speed is slow, the number of voltage regulation is limited, and the mechanical and electric parts have high failure rate. The arc generated by the switch causes the insulation of the transformer oil to age, which greatly affects the reliability of the power supply of the grid. Aiming at the above problems, an arcless on-load voltage regulation scheme using power electronic devices as OLTC auxiliary contacts is proposed, and the control hardware and software control flow design of the arcless on-load automatic voltage regulation module is completed, and the arcless switching of the power electronic switch is utilized. The use of the power electronic switcher has the advantages of arcless turn on or off and controllability of turn on or off time, which reliably triggers the turn on or off of the switcher. Thereby achieving the purpose of fast and frequent phase separation of the transformer and precise wide amplitude regulation.
Study on Applicability of Curriculum Framework to Feature Selection Algorithms

The present work demonstrates the applicability of consistency based and entropy based feature selection metrics to curriculum framework. Feature selection aims at reducing the dimensionality of datasets and is a preprocessing step for classification and clustering. In general, feature selection algorithms make use of the entire training set for identifying important features. Curriculum based feature selection uses instances in the order of their complexity in an incremental paradigm. Curriculum framework deals with easy and then difficult instances in a guided environment. Curriculum methods are advantageous over no-curriculum methods in two ways. Firstly, the time consumed by curriculum methods is very less; secondly, curriculum methods allow the feature selection methods to be applied in incremental paradigm. Incremental nature of curriculum methods allows feature selection to work upon newly added samples in less time. In this paper, curriculum methods are compared with no-curriculum methods using performance indices - classification accuracy, time and selection ratio. Experimental results on various datasets show (varying degrees of) superiority of using curriculum framework in feature selection.
A New Efficacious Nutrition Education Tool for Seniors (50 + years)

Abstract The game Pyramid Power(tm) was assessed as a nutrition education tool for seniors engaged in learning three basic nutritional recommendations based on the Food Guide Pyramid. Average knowledge scores of game players increased by 18.6 percent between pre- and post-assessment while the scores of participants who listened to a standard lecture increased by 9.7 percent. Both the game and lecture proved to be efficacious at teaching three recommendations of the Food Guide Pyramid. The game offered a fun way for older adults to learn about nutrition.
The Dark Side of Digital Technologies

Digital technology is at the forefront of the newest revolution in the worlds of science and communication. The foundational concepts for this area of knowledge were based upon a system of binary computing. Digital technology has accomplished much in its relatively short lifespan, including the inspiration of the invention of ASCII (American Standard Code for Information Interchange) codes used to encode basic symbols using numerical digits. This can be seen as the most important use of digital technology - to vastly increase the rate of data transmission by encoding information in the combinations of the binary digits 0 and 1. It has become so influential that the modern world as we know it is virtually incapable of proceeding without its assistance. However, despite of its benefits, there exists, undeniably, a dark side to this technology. The chief aim of this paper is to present a detailed analysis of the negative aspects related to the use of digital technology, which too often go unmentioned. The discussion will be based upon an organized review of such literature as uses a sound research methodology. This will include undertaking a review of some crucial studies conducted around the globe related to this. Other reviews currently in existence will also be taken into consideration. This method review will help assemble innovative and fresh pragmatic solutions regarding the titular topic. The paper will be concluded with recommendations to help eliminate future threats and to mitigate possible risk factors. A few sectors which could be aided by further scientific exploration will also be highlighted in the final concluding statements.
Smart Weighing Scale with Feet-Sampled ECG

In a smart home, health and well-being monitoring systems could be embedded in everyday devices providing a pervasive care. A home bathroom scale is an example of such a device, typically used to measure body weight and very often its composition (e.g. body water/fat percentage). In this paper, we analyzed a potential use of the bathroom scale to measure electrocardiogram (ECG) from electrodes located on the scale's tile. In particular we used both, simulations and real measurements to analyze the possibility of a such approach. We originally created a simple finite element model to analyze a role of a vector cardiogram projected on the foot-foot lead. We also analyzed results obtained during experiments in which took part (in total) 13 healthy volunteers. They were examined both, in sitting and standing positions. As a result we showed that the quality of the recorded ECG signal from feet is highly related to the electrical vector orientation of the individual's heart. We additionally compared the detected QRS complexes from hands-and feet-sampled ECG for sitting and standing positions. Results showed that for the sitting position the difference was only for 3.6% of all QRS complexes, while for standing one about 24.9%.
Critical Success Factors for Learning Management System Implementation: Non-Scientific Colleges Case A Conceptual Model

The evolution of information and communications technology (ICT) has impacted various sectors, including the educational sector. Accordingly, several methods have emerged, such as electronic learning (e-learning), to support the education process. Learning management systems (LMSs) include various features that could be used by higher education institutions to support e-learning activities. However, the implementation of LMSs in non-scientific colleges is not as simple as it is for scientific colleges, such as computer and information sciences colleges, because of the information technology (IT) background differences between the system’s stakeholders (students and academic staff). Thus, the improper implementation of LMSs in non-scientific colleges may play a major role in not realizing the expected benefits from such initiatives. Consequently, the objective of this paper is to develop a conceptual model for non-scientific colleges to successfully implement LMSs. This model gives a look at the critical success factors (CSFs) for LMS implementation. The CSFs are related to the student, instructor, system, university support, and change management.
Similarity-Based Active Learning for Image Classification Under Class Imbalance

Many image classification tasks (e.g., medical image classification) have a severe class imbalance problem. Convolutional neural network (CNN) is currently a state-of-the-art method for image classification. CNN relies on a large training dataset to achieve high classification performance. However, manual labeling is costly and may not even be feasible for medical domain. In this paper, we propose a novel similarity-based active deep learning framework (SAL) that deals with class imbalance. SAL actively learns a similarity model to recommend unlabeled rare class samples for experts' manual labeling. Based on similarity ranking, SAL recommends high confidence unlabeled common class samples for automatic pseudo-labeling without experts' labeling effort. To the best of our knowledge, SAL is the first active deep learning framework that deals with a significant class imbalance. Our experiments show that SAL consistently outperforms two other recent active deep learning methods on two challenging datasets. What's more, SAL obtains nearly the upper bound classification performance (using all the images in the training dataset) while the domain experts labeled only 5.6% and 7.5% of all images in the Endoscopy dataset and the Caltech-256 dataset, respectively. SAL significantly reduces the experts' manual labeling efforts while achieving near optimal classification performance.
A Deep Learning Approach for Wind Power Prediction Based on Stacked Denoising Auto Encoders Optimized by Bat Algorithm

With rapid growth of wind power integration into the modern power system, Wind Power Prediction (WPP) plays an increasingly important role in the planning and operation of electric power system. However, the wind power time series always exhibits nonlinear and non-stationary characteristics, which is still with great challenge to be predicted accurately. To overcome the challenge, a Stacked Denoising Auto Encoders (SDAE) based deep learning approach for WPP is proposed in this paper. Firstly, SDAE with three hidden layers is designed to capture the nonlinear and complex characteristics from the reference data sequence, and the optimal initial connection weights of the deep neural network is obtained by the layer-wise pre-training process. Secondly, the back propagation algorithm is applied to fine-tune the weights of the whole network. To achieve the optimal network architecture, the Bat Algorithm (BA) is adopted to identify the number of neurons of the hidden layers for each denoising auto encoder (DAE). Finally, the proposed method is evaluated by the data from a real wind farm and compared with the Back-propagation Neural Network (BPNN) and the Support Vector Machine (SVM). The results show that SDAE is with the ability to learn the nonlinear and non-stationary characteristics of wind power data and the WPP Root Mean Square Error (RMSE) are reduced by 3.49% and 1.59% in comparison with BPNN and SVM, respectively, which is applicable for practical applications in electric power system.
Thermoregulated Microvalve for Self-Adaptive Microfluidic Cooling

This work aims at developing a passive and thermoregulated microvalve in order to manage a local and adaptive cooling for microelectronics. Such microvalve can serve as flow regulators in an array of liquid cooling cells below a chip. Two Ag valves of 2000 and $1733 {\mu } \mathrm {m}$ long each are designed, microfabricated and experimentally tested to validate their proper use in this cooling context. The $1733 {\mu } \mathrm {m}$ valve opening has shifted from 2 to $24.7 {\mu } \mathrm {m}$, when exposed to a hot plate at room temperature and at $80 ^{\circ}\mathrm {C}$ respectively, demonstrating a significant fluidic cross-section shift. The valve also stayed in its elastic deformation range up to $160 ^{\circ}\mathrm {C}$. Finally, the $1733 {\mu } \mathrm {m}$ microvalve demonstrated that it was possible to regulate water flow rate from $ \lt 0.5$ to 3.0 ml/min at a constant pressure drop of 5 kPa. This makes the microvalve a suitable organ for thermoregulating the cooling cells matrices.
Performance Evaluation of VANETs Routing Protocols Using SUMO and NS3

Vehicular Ad-hoc network (VANET) is a self-organized ad hoc network, in which each vehicle, equipped with On Board Unit (OBU), participates in routing by forwarding data for other nodes. VANETs become a really hot topic and a challenging research field as it has several issues related to security, quality of service, routing protocols, etc. Although the big challenge in VANET is to design a routing protocols more suitable to route packets efficiently to their final destination, in spite of the high speed of vehicles, frequent disconnection and the highly changeable topology. This paper presents and analyses, the impact of vehicle density on the performance of the most well-known routing protocols. Quantitative metrics like overhead, packet delivery ratio, average throughput and average end to end delay are evaluated using the Network Simulator NS-3 and SUMO.
Fast Rectangle Counting on Massive Networks

Rectangle has been recognized as an essential motif in a large number of real-world networks. Counting rectangles in a network plays an important role in network analysis. This paper comprehensively studies the rectangle counting problem on large networks. We propose a novel counting paradigm called the wedge-centric counting, where a wedge is a simple path consisting of three vertices. Unlike the traditional edge-centric counting, the wedge-centric counting uses wedges instead of edges as building blocks of rectangles. The main advantage of the wedge-centric counting is that it does not need to access two-hop neighbors. Based on this paradigm, we develop a collection of rectangle counting algorithms, including an in-memory algorithm with lower time complexity, an external-memory algorithm with the optimal I/O complexity, and two randomized algorithms with provable error bounds. The experimental results on a variety of real networks verify the effectiveness and the efficiency of the proposed wedge-centric rectangle counting algorithms.
Powering 12-V LED luminiaries with supercapacitor-based energy storage in DC-microgrid systems

In configuring energy-efficient renewable energy systems, rechargeable batteries are typically used with maximum power point tracking (MPPT) based DC-DC converters. With the commercial supercapacitor (SC) devices available in three different forms namely (i) symmetric electrical doubled layer capacitors (EDLC) (ii) hybrid and (iii) capa-batteries, where device cycle-life is exponentially larger than rechargeable batteries, new converter techniques with SC energy storage are possible for DC Microgrid systems. This paper presents the preliminary concept of a new and commercially-useful SC-based LED powering technique, currently under development as the SC assisted LED (SCALED) technique. Challenges of developing this technique which can be extended into the powering of DC powered electrical appliances is presented with the theory and early experimental validations carried out in a sea-port based industrial location.
Deep Learning Based Recommender Systems

Recommender Systems (RSs) are valuable and practical tools that help users to find interesting products in a large space of possible options. Many hybrid recommender systems combine collaborative filtering and content-based approach to build a more robust system. This paper aims to propose a new deep learning based recommender system to enhance recommendation performance and to overcome the limitations of existing approaches, especially when dealing with the cold start problem. So, a hybrid model based on Deep Belief Networks and item-based collaborative filtering is proposed. We conducted experiments on MovieLens 100K dataset. The results showed that our method outperforms existing hybrid recommender systems.
Differential Geometric Approach to Robust Control of an Oscillatory Base Robot Manipulator

Ahstract- This paper studies the control of a robotic manipulator mounted on an oscillatory base. The system is subjected to bounded uncertainties, particularly those bounded by constants and positive non-decreasing functions. Through manifolds defining the appropriate configuration space, a Lagrangian formulation in terms of global coordinates is constructed, leading to well-defined equations of motion. A nonlinear tracking control law is designed in kind and proven to achieve semi-global asymptotic stability in spite of these disturbances. Proof of the controller performance is demonstrated through simulation.
Differentially Private Prescriptive Analytics

Privacy preservation is important. Prescriptive analytics is a method to extract corrective actions to avoid undesirable outcomes. We propose a privacy preserving prescriptive analytics algorithm to protect the data used during the construction of the prescriptive analytics algorithm. We use differential privacy mechanism to achieve strong privacy guarantee. Differential privacy mechanism requires computation of sensitivity: maximum change in the output between two training datasets, which is differed by only one instance. The main challenge we addressed is the computation of sensitivity of the prescription vector. In absence of any analytical form, we construct a nested global optimization problem to compute the sensitivity. We solve the optimization problem using constrained Bayesian optimization, as the nested structure makes the objective function expensive. We demonstrate our algorithm on two real world datasets and observe that the prescription vectors remains useful even after making them private.
Mixed Bagging: A Novel Ensemble Learning Framework for Supervised Classification Based on Instance Hardness

We introduce a novel ensemble learning framework for supervised classification. Our proposed framework, mixed bagging, is a form of bootstrap aggregating (bagging) in which the sampling process takes into account the classification hardness of the training instances. The classification hardness, or simply hardness, of an instance is defined as the probability that the instance will be misclassified by a classification model built from the remaining instances in the training set. We incorporate instance hardness into the bagging process by varying the sampling probability of each instance based on its estimated hardness. Bootstraps of differing hardness can be created in this way by over-representing, under-representing and equally representing harder instances. This results in a diverse committee of classifiers induced from the bootstraps, whose individual outputs can be aggregated to achieve a final class prediction. We propose two versions of mixed bagging – one where the bootstraps are grouped as easy, regular or hard, with all bootstraps in one group having the same hardness; and the other where the hardness of bootstraps change gradually from one iteration to the next. We have tested our system on 47 publicly available binary classification problems using C4.5 Decision Trees of varying depth as base learners. We find that the proposed mixed bagging methods perform better than traditional bagging and weighted bagging (wagging) regardless of the base learner. The proposed method also outperforms AdaBoost when the base learner consists of deeper decision trees. We examine the results of mixed bagging in terms of bias-variance decomposition and find that mixed bagging is better than AdaBoost at reducing variance and better than traditional bagging at reducing inductive bias.