Miracle-dz/UniFAR-FTUs huggingface dataset:
Microarray Feature Selection and Dynamic Selection of Classifiers for Early Detection of Insect Bite Hypersensitivity in Horses

Microarrays can be employed to better characterise allergies, as interactions between antibodies and allergens in mammals can be monitored. Once the joint dynamics of these elements in both healthy and diseased animals are understood, a model to predict the likelihood of an individual having allergic reactions can be defined. We investigate the potential use of Dynamic Selection (DS) methods to classify protein microarray data, with a case study of equine insect bite hypersensitivity (IBH) disease. To the best of our knowledge DS has not yet been applied to these data types. Since most microarrays datasets have a low number of samples, we hypothesise that DS models will produce satisfactory results due to their ability to perform better when compared to traditional ensemble techniques for similar data. We focus on three research questions: 1) What is the potential of DS for microarray data classification and how does it compare with existing classical classification methods results? 2) how do DS methods perform for the IBH dataset? and 3) does feature selection improve DS performance for this data? A wrapper using backward elimination and embedded with a regularized extreme learning machine are adopted to identify the more relevant features influencing the onset of the disease. Results from traditional classifiers are compared to 21 different DS methods before and after performing feature selection. Our results indicate that DS methods do not outperform single and static classifiers on this high-dimensional dataset and their performance also does not improved after feature selection.
High-Voltage Driver for Fully-Integrated Piezoelectric Inkjet Printhead Module

With the help of the MEMS technology, printhead arrays can be integrated in a piezoelectric inkjet printhead (PIP) module. In this work, a fully-integrated PIP module is proposed. The 3D-packaged module connects the MEMS PIP arrays and the high-voltage driver IC by Through Silicon Via (TSV) technology. The high-voltage driver with the distributed model of TSV is designed and the joint simulations of high-voltage driver, TSV and PIP are carried out to ensure the performance of the 3D-packaged module. The prototype of the driver is designed with a high-voltage 0.18 μm CMOS technology. The maximum driving voltage is 50 V and the driving capacity is 8 channels (0.8 nF) with the slew rate of 37 V/μs.
A Framework for Constrained Graph Partitioning

Social networks offer services such as recommendations of social events, or delivery of targeted advertising material to certain users. In my thesis, I focus on a specific type of services modeled as constrained graph partitioning (CGP). CGP assigns nodes of a graph to a set of classes with bounded capacities so that the similarity and the social costs are minimized. The similarity cost is proportional to the dis-similarity between a node and its class, whereas the social cost is measured in terms of neighbors that are assigned to different classes. I investigate two solutions for CGP: the first utilizes a game-theoretic framework, while the second employs local search. I show that the two approaches can be unified under a common framework, and develop a number of optimization techniques to improve result quality and facilitate efficiency. Experiments with real datasets demonstrate that the proposed methods outperform the state-of-the art in terms of solution quality, while they are significantly faster.
Solution Processed ZnSnO Thin-film Transistors with Peroxide- Aluminum Oxide Dielectric

In this work, we investigate an fabricating route of solution-processed thin-film transistors (TFTs). The high-k dielectric layers of aluminum oxide (AlO) were optimized through hydrogen peroxide (H<inf>2</inf>O<inf>2</inf>). With Zn incorporation in tin oxide (SnO) semiconductor layer, the ZnSnO TFT with a field effect mobility of 4.3 cm<sup>2</sup>·V<sup>−1</sup>·s<sup>−1</sup>, a threshold voltage of 2 V, a subthreshold swing of 0.16 V/dec and an on/off ratio of 10<sup>6</sup>.
Trajectory Prediction from a Mass of Sparse and Missing External Sensor Data

In this paper, we predict the movement of objects under the circumstance where external sensors placed on the road-sides (e.g., traffic surveillance cameras) capture their trajectories. This type of trajectories may have very different mobility patterns since they are not restricted to a fleet or a community of users. However, their reported positions are sparse due to the sparsity of the sensor distribution, and incomplete, since the sensors may fail to register the passage of objects. In this paper, we first analyze such external sensor trajectories based on a real dataset, which evidenced the problems of their sparsity and their incompleteness, and hinders the location prediction. In this context, we proposed an approach for coping with the missing data problem. We discussed how to apply this approach in conjunction with the predictors based on Recurrent Neural Networks. In particular, we adjusted the accuracy metrics to account for missing values in the test set, by introducing the distance between the predicted location and the registered next location. We evaluate our approach compared to the baselines, showing an improvement of about 23% in the prediction accuracy while reducing the overall distances. In spite of the contribution of many works in location prediction, at the best of our knowledge, none of those works have studied location prediction for trajectories based on external (road-side) sensors data.
Node Localization of Wireless Sensor Network Based on Improved DV-Hop Algorithm

The effective localization of sensor nodes can ensure stable operation of sensor networks. The sensor node localization in traditional method does not considers the produced sparse of sensor channel due to the uneven distribution of nodes, resulting in much meaningless zero tap estimation in the positioning process, thereby reducing the efficiency of localization. In this paper, an improved node localization algorithm based on DV-Hop is proposed. In this method, the traditional DV-Hop localization algorithm is improved. Using particle swarm algorithm to optimize the positioning process, the positioning results of sensor nodes are finally obtained. The experimental results show that the improved algorithm can improve the positioning accuracy of sensor nodes, which are satisfactory.
Energy-Conscious Scheduling Problem in a Flexible Job Shop Using a Discrete Water Wave Optimization Algorithm

As more and more attention is paid to green manufacturing, production scheduling has been proved to be an efficient method for the reduction of environmental pollution. It is well-known that the flexible job shop scheduling problem (FJSP) is a very complex combinatorial optimization problem with strong theoretical and background for application. However, the problem has been extensively investigated and historically concerned with some indicators related to time, e.g., flow time, makespan, and workload. In this study, an energy-conscious FJSP is investigated with the consideration of the energy consumption. First, a mathematical model of the energy-conscious FJSP is built with the objective of optimizing the sum of the energy consumption cost and the completion-time cost. Due to the fact that the basic water wave optimization (WWO) was developed for various continuous problems, a discrete water wave optimization (DWWO) algorithm is proposed to solve the model. In our DWWO algorithm, a three-string encoding approach is first adopted to represent each individual wave. To make the algorithm adapt for the considered scheduling problem, three discrete evolutionary operations are redesigned according to the characteristics of the problem, i.e., propagation, refraction, and breaking. Finally, extensive experimental simulations are conducted to test the proposed DWWO algorithm. The comparison results demonstrate that the proposed DWWO algorithm is efficient for the energy-conscious FJSP.
Large Scale Analysis of Semantic and Temporal Aspects in Cultural Heritage Collection's Search

Cultural Heritage (CH) collections store and represent numerous historical objects such as images and documents, which are often of unique type and of high cultural and historical value. While the maintained objects are typically well-understood and analyzed, relatively less is known about what type of content is actually interesting to the searching users and how they find such content. These kinds of analyses could help us to understand for what purposes users use cultural heritage collections and could lead to user intent classification and understanding. In this paper we report the results of a large-scale exploratory analysis based on a 15-month long snapshot of query logs generated at the online portal of the National Library of France. Besides understanding the nature of content search in cultural heritage collections and digital libraries, the results of our study can be used for designing content recommendation systems and could help to improve time-aware search applications.
Circular Conformal Array Antenna With Omnidirectional and Beamsteering Capabilities for 5G Communications in the 3.5-GHz Range [Wireless Corner]

A circular conformal array antenna (CAA) with beamsteering capabilities as well as omnidirectional performance at 3.5 GHz (S-band) is presented. The single radiating elements are formed by double-stacked microstrip patches placed on the planar faces of an octagonal holding structure fabricated with a 3D printer. An eight-way tunable feeding network (TFN) is designed, manufactured, and integrated into the conformal structure to excite the array elements. Novel tunable T-junctions (TTs) based on p-i-n diodes are proposed to form the TFN in microstrip technology. This novel T-junction allows a splitting configuration with uniform distribution to the two output ports or a switching mode to deliver the signal to either output port. In all T-junction operation modes, good input matching performance is achieved. The TFN network is integrated into the conformal antenna and digitally controlled by a microcontroller Arduino MEGA. A total of 29 states grouped in six modes with a global matching bandwidth of 17.2% can be selected. The omnidirectional radiation and beamsteered directional patterns (covering 360°) can be electronically tuned. The proposed antenna is suitable for future 5G terrestrial communications in the 3.5-GHz International Mobile Telecommunication (IMT) range, the primary candidate band for the first 5G networks.
Information Seeking in Pokemon Go: A Preliminary Study

This paper presents a preliminary study of issues pertaining to the information needs and sources of Pokemon Go players. A semi-structured interview revealed three categories of information needs. Interestingly, participants were found to be passive in the fulfillment of their information needs, relying on friends, and online chat groups as major sources of information. There was also a surprisingly high trust in these information sources.
Random Key Self–Organizing Migrating Algorithm for Permutation Problems

Self–organizing migrating algorithm (SOMA) is a modern stochastic optimization algorithm. It is built upon the principles of evolutionary and swarm computation and has been successfully applied to a variety of theoretical and practical optimization problems. The candidate solutions in SOMA are real– valued and the use of the algorithm for continuous optimization is straightforward. Its application to combinatorial optimization, on the other hand, requires a translation of candidate solutions from continuous search space to discrete problem solution space. In this work, a version of SOMA suitable for permutation problems is proposed and evaluated on two well–known hard permutation problems.
Two-Layered Weight Vector Specification in Decomposition-Based Multi-Objective Algorithms for Many-Objective Optimization Problems

Recently high performance of decomposition-based algorithms such as MOEA/D, NSGA-III and MOEA/DD for many-objective optimization has been repeatedly reported. When they are applied to many-objective problems, weight vectors are usually generated by a two-layered approach with boundary and inside layers. However, the specification of the two layers has not been discussed in detail in the literature. They are usually intuitively specified: More weight vectors are included in the boundary layer than the inside layer, and the size of the simplex in the inside layer is a half of that in the boundary layer. In this paper, we discuss the following two issues about the specification of the two layers: (i) how to specify the number of weight vectors in each layer, and (ii) how to specify the size of the simplex in the inside layer. We address these two issues through computational experiments on many-objective problems with six types of Pareto fronts: linear triangular, convex triangular, concave triangular, linear inverted triangular, convex inverted triangular, and concave inverted triangular. Our experimental results clearly demonstrate that the appropriate specification of the two layers strongly depends on the problem (i.e., the shape of the Pareto front) and the performance indicator. We also address the two issues from a viewpoint of the relation between the weight vector distribution for each shape of the Pareto front and the optimal distribution of solutions for each performance indicator.
Feature Assessment and Classification of Diabetes Employing Concept Lattice

Diabetes is the 5th leading cause of death in the world, which threatens people's physical and mental health. However, in the course of studying diabetes, the missing medical data has caused great trouble for researchers. There are few researches focusing on sparse medical data. Common method of filling missing values is not desirable. In this study, we select and analyze a large physical check dataset containing more than 900 records with missing data. Using these data, a model for predicting diabetes is designed and implemented. Before predicting, logistic regression is utilized to remain the most significant attributes as the input variables of prediction model. Concept lattice is considered an effective mathematical tool for conceptual data analysis and knowledge processing in mathematics. Experimental results demonstrate that concept lattice illustrates great capacity of predicting diabetes while remaining missing data.
On Dynamic Recovery of Cloud Storage System Under Advanced Persistent Threats

Advanced persistent threat (APT) for data theft poses a severe threat to cloud storage systems (CSSs). An APT actor may steal valuable data from the target CSS even in a strategic fashion. To protect a CSS from APT, the cloud defender has to dynamically allocate the limited security resources to recover the compromised storage servers, aiming at mitigating his total loss. This paper addresses this dynamic cloud storage recovery (DCSR) problem by employing differential game theory. First, by introducing an expected state evolution model capturing the CSS’s expected state evolution process under a combination of attack strategy and recovery strategy, we measure the APT attacker’s net benefit and the cloud defender’s total loss. On this basis and in the worst-case situation where the cloud defender assumes that the APT attacker has full knowledge of his expected loss, we reduce the DCSR problem to a differential game-theoretic problem (the DCSR* problem) to characterize the strategic interactions between the two parties. Second, we derive a necessary condition for Nash equilibrium of the DCSR* problem and thereby introduce the concept of competitive strategy profile. Next, we study the structural properties of the competitive strategy profile, followed by some numerical examples. Then, we conduct extensive comparative experiments to exhibit that the competitive strategy profile is superior to a large number of randomly generated strategy profiles in the sense of Nash equilibrium solution concept. Finally, we briefly analyze the practicability (scalability and feasibility) of this paper. Our findings will be helpful to enhance the APT defense capabilities of the cloud defender.
Micro-texturing into DLC Coating on the SKD11 Die via the High-density Plasma Oxidation

Diamond-like carbon (DLC) has been widely utilized in industries; e.g., the protective coating of punches and dies. The high-density plasma oxidation was used to make selective removal of the DLC films and to construct the three-dimensional micro-texture on the SKD11. Two-dimensional micro-texture was drawn onto these films via the maskless lithography patterning. Micro-dots with various diameters were utilized as a mask to make selective removal of unmasked DLC surfaces. The optical emission spectroscopy (OES) was used to make oxygen plasma diagnosis and to identify the active species for this plasma etching. The Raman spectroscopy was employed to analyze the masked and unmasked area after the etching process. The etching took place homogeneously within 7.2 ks process. The three-dimensional micro-texture of DLC was formed with the height of $18\ \mu \mathrm{m}$. The formed micro-textures have a standard deviation less than $1\ \mu \mathrm{m}$.
Towards an Autonomic and Distributed Device Management for the Internet of Things

Device Management (DM) is currently industrially deployed for LAN devices, phones and workstation management. Internet of Things (IoT) devices are massive, dynamic, heterogeneous, and inter-operable. Existing solutions are not suitable for IoT management. This doctoral research in an industrial environment addresses these limitations with a novel autonomic and distributed approach for the DM.
State Estimation under Undetectable Single and Double Line Failures: Anonymised for review

Control of power networks requires timely and accurate state estimation. Whilst the security of state estimation against maliciously induced faults, primarily in the form of bad data has been studied extensively in recent years, substantially less effort has been devoted to correct topology processing that is a prerequisite for state estimation. Only when combining topology processing and removing any faulty or malicious measurements can a correct and reliable state estimate be obtained. In this paper we therefore study classes of attacks including multiple topology modifications. We particularly examine induced double line faults and analyse the effects on state estimators e.g., possible state forcing or also state estimator divergence. We propose a mechanism to determine optimal conditions for attackers with limited system knowledge, compare these to conventional contingency cases, and evaluate results for common IEEE test cases.
Blockchain Technology: Practical P2P Computing (Tutorial)

Blockchain technology comes with the promise to revolutionize the way current IT systems are organized as well as to revise how trust is perceived in the wider society. In spite of the wide attention that cyrpto-currencies (such as Bitcoin) have attracted, Blockchain technology is more likely to make an impact beyond ongoing speculations on cyrpto-currencies. Decentralized identity management, transparent supply-chain systems, and IoT governance and security are only few examples of research challenges for which this technology may hold substantial potential. Blockchain technology has emerged at the intersection of two well established research areas: peer-to-peer (P2P) computing and cryptography. In this tutorial, we provide a general overview of the main components behind this technology, we present the difference between the types of Blockchain available today, and we make a high level discussion on its potentials and limitations as well as possible research challenges.
Optimization of Hole-Transport Layer in Solution-Processed Silver Bismuth Iodide Solar Cells

This work presents our study on solution-processed silver bismuth iodide solar cells aiming at the optimization of their performance. The attractiveness of silver bismuth iodides for solar harvesting arises from their significant similarity to three- dimensional lead-halide perovskites, which, in recent years, have emerged as wonder material for photovoltaics [1] . In view of their solution processability, their lead-free, low-toxicity nature, and the positive preliminary indications on their stability [2] – [6] , silver bismuth iodide solar cells are particularly relevant to applications in flexible and printed electronics, where they could potentially be deployed as compact energy harvesters.
Accurate Discrete-Time Modeling for Improved Torque Control Accuracy for Induction Machine Drives at Very Low Sampling-to-Fundamental Frequency Ratios

Operations at low sampling-to-fundamental frequency (S2F)ratios of induction machines are required for high-power or high-speed applications. The traditional indirect field-oriented control is open-loop control on torque, which is sensitive to machine parameters and degrades at low S2F ratios. Deadbeat-direct torque and flux control (DB-DTFC)is closed-loop control on torque but also degrades at low S2F ratios with discrete-time approximations. This paper presents accurate discrete-time modeling for the flux and current observer and the deadbeat controller used in DB-DTFC. Experimental results show that the torque control accuracy is significantly improved even at very low S2F ratios.
Printed Temperature Sensor based on Graphene Oxide/PEDOT:PSS

Temperature is an important physical parameter which need to be monitored for various applications ranging from health monitoring to robotics [1] , [2] . In humans, accurate measurement of the variations in the skin temperature is utilized for investigation of homeostasis, physical activities, cardiovascular health and several other health diagnostics methods [1] – [5] . For robotics, the integrated temperature sensing can help in distinguishing the hot and cold objects. Among a variety of temperature sensors (e.g. thermocouple, mercury thermometer etc.) the resistive method based temperature detection is widely used due to its rapid response, stability and accuracy [4] , [6] . Various materials (e.g. semiconductors, metals, graphite , metal oxides and ceramics etc.) have been used to develop the temperature sensors [7] – [11] . However, owing to the complex processing steps along with the lack of flexibility, many times it is difficult to integrate these sensors on surfaces that can confirm to curvy body parts of a robot or prosthetic limb. In this context, printing technologies with simplified processing steps are aimed to provide low cost route for flexible/bendable sensors [12] – [15] .
Gamma radiation measurements of naturally radioactive samples at INFN Bari laboratory

High-resolution gamma-ray spectrometry is a widely used non-destructive measurement technique for the assessment of gamma-ray emitting radionuclides present in environmental samples. These measurements allow the evaluation of the radiological risk to the human beings and environment. The gamma radiation measurements laboratory, in the Physics Department of the University of Bari in collaboration with the Italian Institute for Nuclear Physics (INFN), is active since 2008. The laboratory is equipped with a an Ortec n-type coaxial high purity germanium (HPGe) detector.In this paper, an overview of the ongoing activities on gamma radiation measurements in our laboratory, will be given. These measurements are based on the precise knowledge of the absolute full energy-peak efficiency (FEPE) of the HPGe detector. The FEPE, evaluated using point and volume calibrated sources, will be presented and will be compared with the efficiency obtained using a dedicated software for gamma spectrometry (GammaVision and ANGLE). Furthermore, we present the efficiency calculated via Monte Carlo (GEANT4) simulation. We also describe a case study of gamma measurements application to the dating technique based on the Thermoluminescence (TL). A preliminary study on the evaluation of the gamma annual dose absorbed by TL dating samples, using GEANT4, will be described.