Miracle-dz/UniFAR-FTUs huggingface dataset:
Data Visualization with Probabilistic Clustering and Neighbor Embedding

In the era of information explosion, processing and analyzing large-scale and high-dimensional data sets has become a big challenge for data mining and machine learning. In order to obtain and intuitively understand the information underlying the big data, an effective visualization technique is on demand. Many successful visualization techniques project high-dimensional data sets into low-dimensional spaces so that we can present data points in scatter plots, histograms or parallel coordinate plots. In this paper, we propose a new algorithm called PCNE, the algorithm first performs a probabilistic clustering algorithm for coarse classification on the data sets, and then reconstruct the joint probability with the heuristic information of classification results and neighborhood relationship. Our experimental results on the public data sets demonstrate that the PCNE algorithm outperforms the classical embedding algorithms in revealing both local and global structures of data.
A novel organic coating assisted laser drilling method for TSV fabrication

Through-silicon vias (TSVs) is a promising three-dimensional packaging solution in post-Moore’s law era in the semiconductor industry. The fabrication of through silicon via plays an important role in three-dimensional packaging. Laser drilling is widely used in TSVs fabrication. However, the geometry quality of laser drilling is unsatisfied and heat affected zone (HAZ) is intrinsic. In this work, a novel organic coating assisted picosecond UV laser drilling method is proposed to obtain high-quality TSVs, the HAZ was noticeably eliminated and the TSVs quality was significantly improved. The effects of the organic thickness and laser power on the TSVs’ quality were also studied in detail. It is found that the diameter of vias decreases with the increase of the organic thickness and decrease of the laser power. The minimum diameter of TSV obtained by this method is about 15 μm while the aspect ratio is beyond 30. Most importantly, by coating with the organic layer, the minimized via diameter can be decreased to about only 70% of the laser spot size which breaks the limit that the minimized via should be larger than or at least equal to the laser spot size. These findings will be helpful for TSV technology development in modern three-dimensional packaging.
A New Clustering Algorithm by Using Boundary Information

In view of the shortcomings that many clustering algorithms such as K-means clustering algorithm are not suitable for the non-convex dataset and the Affinity Propagation (AP) algorithm may cluster two adjacent different class points into one class, we proposed a new clustering algorithm by using boundary information. The idea of the proposed algorithm in this paper is as follows: First, use the number of points contained in each point's neighborhood as its density, and consider the points whose density are below the average density as boundary points. Then, count the number of boundary points. If the number of boundary points is larger than a given threshold then clustering is carried out by transfer ideas directly, otherwise boundary points will be regarded as the cluster boundary wall. When the boundary points are encountered in the transitive clustering process, the transfer stopped and selected an unprocessed non-boundary point to start clustering process as above again until all non-boundary points are processed, so as to effectively prevent clustering two adjacent different class points into one class. Because of the clustering of transfer idea, the proposed algorithm is applicable to nonconvex datasets, and different clustering schemes are adopted according to the number of boundary points which increases the applicability of the algorithm. Experimental results on synthetic datasets and standard datasets show that the algorithm proposed in this paper is efficient.
Spousal Dictator Game: Household Decisions and Other-Regarding Preferences

Using a laboratory experiment, we collected data on dictator giving among student strangers and married couples in a suburban area in the United States. Confirming common belief and prior empirical evidence, we find that giving among spouses is greater than giving among anonymous students. We further investigated factors associated with spousal giving which may provide insight for the development of future theories, or into explaining other-regarding preferences. Our data shows that giving is positively associated with who manages household money and controls household income. This result is robust after controlling for each spouse’s personal income and using various econometric specifications. The results suggest that spousal giving may be due to household economic roles in addition to other-regarding preferences.
SGX-Based Cloud Security Module with User's Sole Control

With the evolution of information technologies there is also a significant growth of threats in the digital realm, therefore more and more applications are using cryptographic primitives to ensure the authenticity, integrity and confidentiality of their data. It is absolutely necessary that these cryptographic operations are performed in dedicated security modules which can provide a strong level of security assurance, depending on certification. This paper describes a software security module architecture which uses Intel Software Guard Extensions (SGX) enclaves to protect the cryptographic material. The proposed security module can be used as a service in cloud environment, while still assuring a reasonable level of user's sole control.
Stabilized Mixed Finite-Element Time-Domain Method for Fast Transient Analysis of Multiscale Electromagnetic Problems

This paper describes the mixed E-B finite-element time-domain (FETD) method that is stabilized for an arbitrary time step size. Since the existing mixed FETD (M-FETD) method is based on a fully explicit leapfrog time marching procedure, it has a numerical stability condition: if not satisfied, the instability arises. The condition gives the upper limit of the time step size, and it becomes much strict if there exist small meshes in a computational domain. As a result, the smaller the time step size is, the higher the computational cost becomes. In this paper, we propose the stabilized M-FETD method through developing the stabilization process after specifying the root cause of the instability of the existing method. By virtue of the stabilization process, we can derive the fully explicit updating process which is efficient and numerically stable for the arbitrary time step size without degrading the accuracy. Numerical results of example problems demonstrate that our approach is superior to the existing one, especially in a multiscale electromagnetic problem in two and three dimensions.
Human Activity Recognition Using BPNN Classifier on HOG Features

With an increase in number of surveillance cameras being used everywhere, there is a need of automatic systems for analysis of human activities. Human Activity Recognition is an active research area in computer vision. Research in computer vision has always focused on developing the systems capable of operating automatically. Human activity recognition has many applications such as gesture recognition, robot learning, security systems etc. Human activity recognition is achieved by human detection, feature extraction and classification of activities. In this paper human activity recognition is performed by human detection using background subtraction, feature extraction using HOG and activity classification using BPNN.
Sparse Reconstruction of Hyperspectral Image using Bregman Iterations

Hyperspectral image processing plays an important role in satellite communication. Hyperspectral Image (HSI) processing generally requires very high ‘computational resources’ in terms of computational time and storage due to extremely large volumes of data collected by imaging spectrometers on-board the satellite. The bandwidth available to transmit the image data from satellite to the ground station is limited. As a result, Hyperspectral image compression is an active research area in the research community in past few years. The research work in the paper proposes a new scheme, Sparsification of HSI and reconstruction (SHSIR) for the reconstruction of hyperspectral image data acquired in Compressive sensing (CS) fashion. Compressed measurements similar to compressive sensing acquisition are generated using measurement matrices containing gaussian i.i. d entries. Now the reconstruction is solving the constrained optimization problem with non smooth terms. Adaptive Bregman iterations method of multipliers is used to convert the difficult optimization problem into a simple cyclic sequence problem. Experimental results from research work indicates that the proposed method performs better than the other existing techniques.
The Autoencoder Based on Generalized Neo-Fuzzy Neuron and its Fast Learning for Deep Neural Networks

In this paper the autoencoder based on the generalized neo-fuzzy neurons is proposed. Also its fast learning algorithm based on quadratic criterion was proposed. Such system can be used as part of deep learning systems. The proposed autoencoder is characterized by high learning speed and less number of tuned parameters in comparison with well-known autoencoders of “bottle neck” type. The efficiency of proposed approach has been justified based on different benchmarks and real data sets.
Application of the Case-Based Reasoning Approach for Identification of Acoustic-Emission Control Signals of Complex Technical Objects

The work is devoted to the problem of developing a case-based reasoning module, capable of performing identification and filtration of data, obtained during acoustic emission control of complex technical objects. The application of case-based reasoning approach for identifying and filtering noise (non-standard, distorted) signals is considered. The parametric case representation is proposed to solve this problem.
Adaptive Control of a Marine Vessel Based on Reinforcement Learning

In this paper, our main goal is to solve optimal control problem by using reinforcement learning (RL) algorithm for marine surface vessel system with known dynamic. And this algorithm is an optimal control algorithm based on policy iteration (PI), and it can obtain the suitable approximations of cost function and the optimized control policy. There are two neural networks (NNs), where critic NN aims to estimate the cost-to-go and actor NN is utilized to design suitable input controller and minimize the tracking error. A novel tuning method is given for critic NN and actor NN. The stability and convergence are proven by Lyapunov's direct method. Finally, the numerical simulations are conducted to demonstrate the feasibility and superiority of presented algorithm.
SMC Difference of Normal and Cancerous Human Urothelial Cells Quantified with an Opto-Electrokinetic Device

This paper reports the use of an opto-electrokinetic (OEK) device to measure the specific membrane capacitance (SMC) of malignant human bladder cancer cells (RT4) and benign human urothelial cells (SV-HUV-l). Experimental results, for the first time, show that RT4 cells (n=54) have a significantly larger SMC than SV-HUV-l cells (n=48), 15.8 ± 3.5 mF/m2vs. 12.5 ± 2.3 mF/m2, indicating that SMC could be a potentially useful biomarker for distinguishing malignant and benign human urothelial cells to aid bladder cancer detection.
Purpose-of-Visit-Driven Semantic Similarity Analysis on Semantic Trajectories for Enhancing The Future Location Prediction

The number of people that are using or are even dependent on Location Based Services (LBS) is growing rapidly every year. In order to offer timely and user-tailored services, providers rely increasingly on forward-looking algorithms. For this reason, location prediction plays a key role in LBS. Recent approaches in location prediction leverage semantics in order to overcome drawbacks that characterise conventional non-semantic systems. However, when it comes to modelling locations, the majority of them constrain themselves to static semantical constructs and hierarchies, without taking the current situation, and most importantly, the users’ varying personal perception into account. In this work, we introduce a novel dynamic approach that aims at taking the variation of the users’ perception explicitly into consideration when describing locations, in order to elevate the overall prediction performance. For this purpose, we consider explicitly time and purpose of visit by building so called Purpose-of-Visit-Dependent Frames (PoVDF). Our framework is hybrid and combines both a data-driven, as well as a knowledge-driven model. To fuse these two models, we define a Purpose-of-Visit-Driven Semantic Similarity (PoVDSS) metric and use it as a fusing component between the two models. We conducted a user study to evaluate our approach on a real data set and compared it with two state of the art semantic and non-semantic algorithms. Our evaluation shows that our approach yields a location prediction accuracy of up to 80%.
Surface characteristics of a modified acidulated phosphate fluoride gel with nano‐hydroxyapatite coating applied on bovine enamel subjected to an erosive environment

This study evaluated the antierosive effect applying a modified acidulated phosphate fluoride (APF) gel containing nano‐hydroxyapatite (nHAp) on the enamel surface before erosion. After polishing, the exposed flat enamel surfaces (n = 7/group) from bovine incisors were treated with artificial saliva (S ‐ negative control), orange juice (ERO), APF gel (positive control) and APF_nHAp gel. All samples were subjected to six cycles of demineralization (orange juice, pH ~ 3.5, 10 min) followed by remineralization (saliva, 37°C, 1 hr). The enamel surface morphology, topography, and inorganic composition were analyzed using scanning electron microscopy, roughness testing, and micro energy‐dispersive X‐ray fluorescence spectrometry, respectively. The mean (standard deviation) roughness values (Ra, μm) were S, 0.13 (0.05); ERO, 0.25 (0.07); APF, 0.22 (0.08); and APF_nHAp, 0.17 (0.04). Ra values were significantly higher after ERO (p < .01) and APF (p < .05) treatments than after S. The enamel surface morphology was altered by the treatments, except for the S specimens. The mineral content of the enamel showed a clear trend with Ca and P reduction in the order of APF < S < APF_nHAp < ERO and APF < S < ERO < APF_nHAp, respectively. We can conclude that APF gel increased mineral concentration on the enamel. Moreover, the APF_nHAp material modified the composition and morphology of the enamel surface.
A hybrid ensemble learning method for tourist route recommendations based on geo-tagged social networks

ABSTRACT Geo-tagged travel photos on social networks often contain location data such as points of interest (POIs), and also users’ travel preferences. In this paper, we propose a hybrid ensemble learning method, BAyes-Knn, that predicts personalized tourist routes for travelers by mining their geographical preferences from these location-tagged data. Our method trains two types of base classifiers to jointly predict the next travel destination: (1) The K-nearest neighbor (KNN) classifier quantifies users’ location history, weather condition, temperature and seasonality and uses a feature-weighted distance model to predict a user’s personalized interests in an unvisited location. (2) A Bayes classifier introduces a smooth kernel function to estimate a-priori probabilities of features and then combines these probabilities to predict a user’s latent interests in a location. All the outcomes from these subclassifiers are merged into one final prediction result by using the Borda count voting method. We evaluated our method on geo-tagged Flickr photos and Beijing weather data collected from 1 January 2005 to 1 July 2016. The results demonstrated that our ensemble approach outperformed 12 other baseline models. In addition, the results showed that our framework has better prediction accuracy than do context-aware significant travel-sequence-patterns recommendations and frequent travel-sequence patterns.
Random Forest Based Bus Operation States Classification Using Vehicle Sensor Data

In bus companies, it is important for an operation manager to grasp operation states of vehicles from a viewpoint of safety management and improving an operation efficiency. Currently, for allowing operation managers to grasp operation states of vehicles, drivers should record operation states by man- ually operating a recorder called ”Digital-tachograph. ” However, operating the digital tachograph is a heavy burden to the driver. In addition, the records may have driver’s human error. In order to solve these problems and to realize efficient operation, we propose a method for automatic classification of operation states using sensor data obtained from buses. We implemented a classifier using Random Forest with the sensor data. As a results of experiments, the correct answer rate was 0.92 or more in each condition unless it was irregular operation.
Propagation in Waveguides With Transversal Twist-Symmetric Holey Metallic Plates

Here, we study the properties of a new type of twist-symmetric configuration inside a waveguide. The structure consists of a periodic repetition of transversal metallic sheets with perforated holes. This twist-symmetric configuration permits the propagation of TM modes inside the waveguide and prevents the propagation of the first TE mode at the conventional band employed on those waveguides. The TM modes exhibit a broader band of operation as the periodicity is reduced, i.e., the structure is more compact. In addition, we demonstrate that the twist-symmetric structure has a broader band than that of the aligned holes in which the twist symmetry is absent. Furthermore, the number of folds of the twist symmetry determines the propagation constant of the propagating mode. Therefore, a reconfigurable structure based on a different number of folds creates future opportunities to produce fully metallic reconfigurable waveguides with different phase delays.
Silicon Nanowires Passivated by TiO2 Layer for Supercapacitors in Aqueous Electrolyte

Silicon nanowires (SiNWs) passivated by TiO2 have been produced and used as electrode material for supercapacitor in aqueous electrolyte. SiNWs were produced by metal-assisted chemical etching method, subsequently passivated with a TiO2 layer by a simple dripping method. The TiO2-coated SiNWs were thermal treated at 400 °C for 1 h in air. The capacitive properties of the electrode materials were investigated. The TiO2-coated SiNWs presented lower capacitance while higher rate capability compared to bare SiNWs in the aqueous environment. The cyclic stability of the electrode was improved significantly by the passivation of TiO2 onto the SiNWs.
Distributed Adaptive Integral Sliding Mode Attitude Consensus Control for Multiple Autonomous Underwater Vehicles

This paper investigates the attitude consensus problem of multiple Autonomous Underwater Vehicles (AUVs) in the case of communication delay with the directed communication network. A distributed adaptive integral sliding mode controller is designed for each vehicle to track the desired attitude. The inertia uncertainty and external disturbance in the dynamics of AUV are considered in designing the control law. The stability of the multi-AUV system is proved by Lyapunov theory. Finally, the simulation results are given to confirm the effectiveness of the proposed control law.
“I Was Grabbed by My Pussy and Its #NotOkay”: A Twitter Backlash Against Donald Trump’s Degrading Commentary

The present study assessed how individuals used the #NotOkay hashtag on Twitter to respond to Donald Trump’s comments about grabbing women by their genitals. We analyzed 652 tweets which included commentary about the hashtag. Three main themes emerged: (a) users’ acknowledgment and condemnation of rape culture, (b) Donald Trump and the national state of sexual assault, and (c) engaging men and boys to end violence against women. Our findings emphasize that powerful political leaders can be salient symbols of rape culture, and Twitter is used as a public platform to organize and challenge problematic social discourse and call for action/change.
Return on CPS (RoCPS): An Evaluation Model to Assess the Cost Effectiveness of Cyber-Physical Systems for Small and Medium-Sized Enterprises

This paper addresses the challenge of evaluating the cost effectiveness of cyber-physical systems (CPS) for small and medium-sized enterprises (SMEs) in manufacturing industries. CPS are key technologies to implement the digitalization and interlinking of production processes with the advantage of increasing production efficiency and flexibility. However, it is difficult to evaluate properly how much CPS can contribute to profit growth because of various costs of complex CPS components and ambiguous estimation of potential CPS benefits. For SMEs, the investment decision on CPS is especially difficult due to the lack of human resources with CPS competence and limited financial resources. Thus, an evaluation model for assessing the economic effectiveness of CPS aimed at SMEs is developed in this paper. To assess the effectiveness, different cost types of implementing CPS are identified and classified. Furthermore, potential benefits of CPS utilization are analyzed comprehensively and calculation rules for monetization of benefits are suggested. The metric "Return on CPS (RoCPS)" is defined to value the profit based on the invested capital. The RoCPS will facilitate the selection of economically efficient CPS solutions, supporting SMEs with economic growth by reasonable technology management. Finally, the evaluation model is validated by a use case of an electric vehicle start-up.
Coordination in project supply chain based on uncertainty theory

In this paper, we investigate channel coordination of project supply chain based on uncertainty theory. The project is conducted by one manufacturer, whose materials are provided by different suppliers. To complete the project before its deadline, the manufacturer needs to consider two elements – the on-site task duration and the material delivery lead time. Due to the lack of historical data, these two elements are assumed as uncertain variables. Through modeling and analyzing, the time-based contract is proved to achieve channel coordination if the manufacturer can decide the following two terms: the targeted material delivery date, and the fraction and the timing of the delayed payment. The manufacturer and his suppliers can have a win-win outcome by contract design. The impacts of variables’ uncertainty degrees are discussed as well. Generally speaking, uncertainty degrees’ increases bring more risks to the project’s completion. However, it is proved that the manufacturer can still keep his profit by changing the bonus and penalty even if the on-site duration and material delivery lead time’s uncertainty degrees increase.
Segmentation and Parametrization of the Phonocardiogram for the Heart Conditions Classification in Newborns

Phonocardiographs are analyzed for diagnostics of heart conditions in newborns. The algorithms of allocation of heart tones and selection of stationary periods on phonocardiograms are proposed. Dedicated heartbeats can be parameterized in different ways. The first set of parameters characterizes the shape and the time-amplitude features. The second set of parameters is the coefficients of the frequency-time decomposition of cardiac cycles with spline bases. This approach allows detecting of the Patent ductus arteriosus (PDA) by machine learning methods. Software for phonograms analysis has been developed.