Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-Paced Joint Sparse Representation for the Classification of Hyperspectral Images

In this paper, a self-paced joint sparse representation (SPJSR) model is proposed for the classification of hyperspectral images (HSIs). It replaces the least-squares (LS) loss in the standard joint sparse representation (JSR) model with a weighted LS loss and adopts a self-paced learning (SPL) strategy to learn the weights for neighboring pixels. Rather than predefining a weight vector in the existing weighted JSR methods, both the weight and sparse representation (SR) coefficient associated with neighboring pixels are optimized by an alternating iterative strategy. According to the nature of SPL, in each iteration, neighboring pixels with nonzero weights (i.e., easy pixels) are included for the joint SR of a testing pixel. With the increase of iterations, the model size (i.e., the number of selected neighboring pixels) is enlarged and more neighboring pixels from easy to complex are gradually added into the JSR learning process. After several iterations, the algorithm can be terminated to produce a desirable model that includes easy homogeneous pixels and excludes complex inhomogeneous pixels. Experimental results on two benchmark hyperspectral data sets demonstrate that our proposed SPJSR is more accurate and robust than existing JSR methods, especially in the case of heavy noise.
Efficiency Optimization Control of IPMSM Considering Varying Machine Parameters

In order to further improve the efficiency of permanent magnet synchronous machine (PMSM) drive, this paper has proposed a simple and effective efficiency optimization control (EOC) strategy for interior permanent magnet synchronous machines (IPMSMs), which is based on the loss model and has considered the compensation of varying machine parameters, such as inductance and equivalent circuit resistance. Simulation results show that this method can achieve a higher efficiency compared with the maximum torque per ampere (MTPA) control which has also considered the variation of machine parameters.
Addressing the Cold Start Problem in Active Learning Approach Used For Semi-automated Sleep Stages Classification

Classification of a PSG record to individual sleep stages is an expensive and time-consuming process because a trained human annotator (typically a physician) has to go through all segments of the record and classify them to their classes. In consequence, many semi-automated methods have been proposed in order to reduce the expert’s effort. The active learning approach is also well-suited for this type of task because it allows to select only the most informative instances for labeling without the quality of classification to be reduced. On the other hand the unsatisfactory initialization of active learning can cause a slower learning process. In this paper we introduce the method for creating of the initial set of labeled instances to eliminate this threat. Because k-means algorithm is commonly used as the initialization method, the comparison between these two methods is provided.
Content-based Classification of Political Inclinations of Twitter Users

Social networks are huge continuous sources of information that can be used to analyze people’s behavior and thoughts. Our goal is to extract such information and predict political inclinations of users. In particular, this paper investigates the importance of syntactic features of texts written by users in the process.Our hypothesis is that people belonging to the same political party write in similar ways, thus they can be classified properly on the basis of the words that they use. We analyze tweets because Twitter is commonly used in Italy for discussing about politics; moreover, it provides an official API that can be easily exploited for data extraction.Many classifiers were applied to different kinds of features and NLP vectorization methods in order to obtain the best method capable of confirming our hypothesis. To evaluate their accuracy, a set of current Italian deputies with consistent activity in Twitter has been selected as ground truth, and we have then predicted their political party. Using the results of our analysis, we also got interesting insights into current Italian politics.
Classification of Turkish Documents Using Paragraph Vector

Text processing and mining gained a lot of traction recently due to rising interest in integration of Natural Language Processing with data analytics algorithms, in particular Deep Learning Models. In this study, newspaper columnists are classified according to vector models created by their posts. Hence, we may not only be able to determine an unclassified post’s author, but also author profiles can be formed by grouping similar styles together. DeepLearning4J Java library and Doc2Vec class are mainly the preferred deep learning solutions for text mining. The vector models of 5, 10, 15, and 20 authors were created from 20k corner posts. Two particular implementations, Distributed Memory (PV-DM) and Distributed Bag of Words (PV-DBOW) models were adapted and their performances are compared. According to the results, it is seen that some authors are clearly distinguished from other authors. Such a model can be used for author profile extraction, plagiarism detection and identifying which author styles are similar.
Uncorrelated Conversion Based Filtering for Angle-Only Tracking in Modified Spherical Coordinates

For angle-only tracking (AOT), an unscented filter in modified spherical coordinates (MUF) provides a flexible and effective framework. It has the property to decouple the potentially unobservable range from other elements of the state vector and provides a more accurate approximation than the extended Kalman filter (EKF). However, for angle-only tracking, the performance of the MUF is limited by the accuracy of the first two moments and the linear framework. Because it is an approximation to a linear minimum mean squared error (LMMSE) estimator which is only the best when the target state and the measurement are jointly Gaussian. To improve the estimation performance, this paper proposes an uncorrelated conversion based filter in MSC (MUF-UCF), where we combine the recently proposed uncorrelated conversion based filter (UCF) with the MUF. The UCF augments measurement with its uncorrelated conversion (UC) and replaces the original measurement with the augmented measurement in the LMMSE framework. Because the UCF extracts more information from the measurement, it outperforms the original LMMSE estimator. Hence, MUF-UCF can take advantage of the UCF and the MUF at the same time and gain better performance than the MUK. Simulation results show the effectiveness of the proposed estimator.
Evaluation of Security Information and Event Management Systems for Custom Security Visualization Generation

Security Information and Event Management Systems (SIEM) are generally very complex systems encapsulating a large number of functions with different behaviors. Visualization is a common way of data presentation in these systems along with other data presentation ways such as reporting, alerting, text messaging. However, generation of the visualization has different steps. If the data is in a custom format, rather than a predefined format which either obeys a standard or a known file structure, the generation of custom visualizations may not be straightforward. Evaluation information for these tools related to custom visualization generation capabilities may be useful for better decision making. This information can be used while designing visualizations through SIEM systems or purchasing the most useful SIEM system for an organization. In this study, six well-known SIEM systems are evaluated through a common scenario created by the authors to check custom visualization generation capabilities. The contributions include this unique scenario and the advantages and disadvantages regarding various steps of the provided scenario along with the difficulties experienced by the authors during the installation and configuration of these SIEM systems.
Temperature-Dependent Characteristics of Surface Acoustic Wave Resonators Deposited on (0°, 138.5°,  $\psi$ ) Langasite Cuts

Surface acoustic wave (SAW) resonators were deposited on (0°, 138.5°, <inline-formula> <tex-math notation="LaTeX">$\psi $ </tex-math></inline-formula>) LGS (langasite, La<sub>3</sub>Ga<sub>5</sub>SiO<sub>14</sub>) cuts with different Euler angles <inline-formula> <tex-math notation="LaTeX">$\psi $ </tex-math></inline-formula> in this paper. The temperature-dependent characteristics of these SAW resonators were investigated systematically. At room temperature, the (0°, 138.5°, 26.6°) LGS cut has the maximum SAW velocity of about 2716 m/s. The (0°, 138.5°, 3.4°) LGS cut has the minimum SAW velocity of about 2377 m/s. The first-order and second-order temperature coefficients of frequency in the (0°, 138.5°, 26.6°) and (0°, 138.5°, 33.4°) LGS cuts are nearest to zero. The turnover temperature decreases first when the angle <inline-formula> <tex-math notation="LaTeX">$\psi $ </tex-math></inline-formula> varies from 3.4° to 26.6° and increases with the further increase of <inline-formula> <tex-math notation="LaTeX">$\psi $ </tex-math></inline-formula>. The results in this paper may help us design LGS SAW temperature sensors and other types of sensors.
Measuring the three-dimensional structural properties of topologically associating domains

Genome topologically associating domains (TADs) are mega base pair (Mbp) sized genomic regions that may be the structural and functional units of the genome. To better understand their structural properties and the relationship between structural properties and genetic and epigenetic features, we designed and benchmarked two measures to capture the three-dimensional structural signature of TADs. We modified the original definition of radius of gyration so that it could measure the spatial compactness of TADs weighted by one or more genetic or epigenetic feature(s). Another structural measure we have benchmarked is folding degree. We found correlations between structural measures and genetic and epigenetic features. We obtained similar correlations if considering the spatial compactness of the DNA segments containing transcription start sites, RNA polymerase II, and H3K4me3 peaks. Moreover, we designed a new objective function for reconstructing chromosome 3D structures using multidimensional scaling and compared it with two existent objective functions. The three objective functions have been implemented as web-based and standalone tools. The web server and source code are available at http://dna.cs. miami.edu/3DChrom/.
Neural Networks for Bacterial Named Entity Recognition

Bacterial named entity recognition is a challenging task in biomedical field. The task is typically modeled as a sequence labeling problem, and existing work mainly adopts discrete models such as CRF (Conditional Random Fields), requiring a large amount of hand-designed features with domain experience. To address this issue, this paper explores a neural network model for the task. We empirically study the effect of word embeddings and character embeddings on the task by extending a CRF baseline using neural networks. Results show the proposed neural network model achieves competitive performance, outperforming the current best discrete model. Meanwhile, the performance can be further improved by integrating neural and discrete features.
Cooperative and Adaptive Optimal Output Regulation of Discrete-Time Multi-Agent Systems Using Reinforcement Learning

This paper proposes an original data-driven intelligent control solution to the cooperative output regulation problem of discrete-time multi-agent systems. Based on the combination of internal model principle and reinforcement learning, a distributed suboptimal controller is learned realtime via online input-state data collected from system trajectories. Rigorous theoretical analysis guarantees the convergence of the proposed algorithm and the asymptotic stability of the closed-loop system. Numerical results validate the effectiveness of the proposed control methodology.
Initial Safety and Efficacy Results of a Second-Generation Humanized Anti-CD20 Antibody, IMMU-106 (hA20), in Non-Hodgkin’s Lymphoma.

Background. Infusion reactions, prolonged infusion times and immunogenicity of the chimeric anti-CD20 antibody, rituximab, may in part be related to its murine component. The humanized anti-CD20 antibody, IMMU-106 (hA20), may offer potential advantages because only the CDRs are of murine origin and the remaining >90% human framework regions are identical to humanized anti-CD22 IgG1 antibody epratuzumab, whose safety and short infusion times have been reported. Methods. An open-label, multicenter, phase I/II, dose escalation study conducted in patients with recurrent B cell lymphoma to establish the safety, tolerance, pharmacokinetics, and immunogenicity of hA20 administered once weekly for 4 doses. Results. Twenty patients (11 M/9 F, 40–81 y.o) with grade 1–2 follicular (n=16) or other CD20+ B-cell lymphomas (mantle cell, small lymphocytic, marginal zone x 2) were enrolled in the dose escalation portion of this study, receiving 120 mg/m 2 (n=7), 200 mg/m 2 (n=6), 375 mg/m 2 (n=4) or 750 mg/m 2 (n=3) weekly x 4. These patients had previously received 1-7 prior therapies (median, 2.5), including treatments with one (n=12) or 2–4 (n=6) rituximab-containing regimens (without progression within 6 months). Five patients (25%) had grade 1–2 infusion reactions predominantly with first hA20 infusion. Median infusion times for 375 mg/m 2 of hA20 were 3.3 hours for first infusion, 2.0 hours for subsequent infusions, and were generally shorter at lower doses. Standard laboratory values obtained at each infusion were generally observed to be within normal limits, and human anti-human antibody (HAHA) serum evaluations are negative to date. Peripheral blood B-cell depletion occurred after first infusion and persisted for samples obtained up to 12 weeks following fourth infusion with analysis ongoing. Pharmacokinetic results after first and fourth infusions demonstrate a mean antibody serum half-life of 5.3 ± 5.3 and 12.0 ± 8.7 days, respectively. To date, of 15 pts with at least one post-treatment assessments now completed, there are at least 8 objective responses (53%) by Cheson criteria, including 6 patients (40%) with complete responses [CR, CRu] at a median follow-up of 12 weeks. Complete responses have been observed at all dose levels, including 3/7 patients receiving 120 mg/m 2 /wk. Conclusion. This study demonstrates the safety, tolerability, and preliminary efficacy of this humanized anti-CD20 antibody administered weekly for 4 doses, mostly to patients with follicular lymphomas who relapsed after rituximab-containing regimens. The demonstration of CRs, tolerability, and relatively short infusion times is encouraging. This study is continuing and expanding to assess the durability of responses and the optimal dose for subsequent studies.
Passivity-Based Compliant Walking on Torque-Controlled Hydraulic Biped Robot

This paper presents an experimental evaluation of passivity-based whole-body motion control framework for compliant walking. The controller computes joint torques without requiring much computation cost and contact force measuring. Instead of limiting the walking speed slow (static walking), in this work we specifically address the difficulties of walking on unstable and uneven ground. No terrain information is used in the experiments, that is, the ground is assumed to be flat, and the desired motion trajectories are given offline. With this setup we evaluate the terrain adaptability by force control alone. The controller is applied to our torque-controllable hydraulic humanoid robot, TaeMu. The robot could walk on a rocker board stably, and even climbed the small step with a little modification of the controller (quasi-dynamic walking).
Software-Defined Label Switching: Scalable Per-Flow Control in SDN

Deploying Software-Defined Networks (SDNs) faces various challenges, and one of them is to implement per-flow control while preserving data plane scalability. Due to the limited rule storage space of commodity SDN switches, achieving flexible control and having a low-latency data plane with a low storage cost are often at odds. Unfortunately, existing SDN architectures fail to implement per-flow control efficiently: they either incur extra delays to packets or pose high storage burden to switches. In this paper, we propose Software-Defined Label Switching (SDLS) to achieve both data plane scalability and per-flow control. SDLS combines central control with label switching to reduce storage burden while maintaining per-flow control. SDLS introduces software switches into the data plane and manages the network in regions for scalability. SDLS is OpenFlow-compatible and employs a hybrid data plane to provide efficient flow setups. We evaluate SDLS by comparing with the state-of-the-art SDN architectures and show that SDLS can rival the best on the latency performance while reducing the number of flow entries and overflows by more than 47%.
Performance Comparison of an AI-Based Adaptive Learning System in China

Adaptive learning systems stand apart from traditional learning systems by offering a personalized learning experience to students according to their different knowledge states. Adaptive systems collect and analyse students' behavior data, update learner profiles, then accordingly provide timely individualized feedback to each student. Such interactions between the learning system and students can improve the engagement of students and the efficiency of learning. This paper evaluates the effectiveness of an adaptive learning system, “Yixue Squirrel AI” (or Yixue), on English and math learning in middle school. The effectiveness of the Yixue's math and English learning systems is respectively compared against (1) traditional classroom math instruction conducted by expert human teachers and (2) BOXFiSH, another adaptive learning platform for English language learning. Results suggest that students achieved better performance using Yixue adaptive learning system than both traditional classroom instruction by expert teachers and another adaptive learning platform.
Blockchain Challenges and Security Schemes: A Survey

With the increasing number of connected devices and the number of online transactions today, managing all these transactions and devices and maintaining network security is a research issue. Current solutions are mainly based on cloud computing infrastructures, which require servers high-end and broadband networks to provide data storage and computing services. These solutions have a number of significant disadvantages, such as high maintenance costs of centralized servers, critical weakness of Internet Of Things applications, security and trust issues, etc. The blockchain is seen as a promising technique for addressing the mentioned security issues and design new decentralization frameworks. However, this new technology has a great potential in the most diverse technological fields. In this paper, we focus on presenting an overview of blockchain technology, highlighting its advantages, limitations and areas of application.The originality of this work resides in the comparison between the different blockchain systems and their security schemes and the perspective of integrating this technology into secured systems models for our comfort and our private life.
Probabilistic Relational Supervised Topic Modelling using Word Embeddings

The increasing pace of change in languages affects many applications and algorithms for text processing. Researchers in Natural Language Processing (NLP) have been striving for more generalized solutions that can cope with continuous change. This is even more challenging when applied on short text emanating from social media. Furthermore, increasingly social media have been casting a major influence on both the development and the use of language. Our work is motivated by the need to develop NLP techniques that can cope with short informal text as used in social media alongside the massive proliferation of textual data uploaded daily on social media. In this paper, we describe a novel approach for Short Text Topic Modelling using word embeddings and taking into account any informality of words in the social media text with the aim of addressing the challenge of reducing noise in messy text. We present a new algorithm derived from the Term Frequency -Inverse Document Frequency (TF-IDF), named Term Frequency - Inverse Context Term Frequency (TF-ICTF). TF-ICTF relies on a probabilistic relation between words and context with respect to time. Our experimental work shows promising results against other state-of-the-art methods.
Drug-Protein-Disease Association Prediction and Drug Repositioning Based on Tensor Decomposition

The old paradigm “one gene, one drug, one disease” of drug discovery is challenged in many cases, where many drugs act on multiple targets and diseases rather than only one. Drug repositioning, which aims to discover new indications of known drugs, is a useful and economical strategy for drug discovery. It is also important to identify the functional clustering of target proteins, drugs and diseases, and to understand the pathological reasons for their interactions among these clusters and individuals. In this study, we propose a novel computational method to predict potential associations among drugs, proteins and diseases based on tensor decomposition. First, we collect pairwise associations between drugs, proteins and diseases, and integrate them into a three-dimensional tensor, representing the drug-protein-disease triplet associations. Then, we carry out tensor decomposition on the association tensor together with some additional information, and get three factor matrices of drugs, proteins and diseases respectively. Finally, we reconstruct the association tensor by the factor matrices to derive new predictions of triplet associations. We compare our method with some baseline methods and find our method outperforming the others. We validate our top ranked predictions by literature search and computational docking. In addition, we cluster the drugs, proteins and diseases using the factor matrices, which reflect the functional patterns of the drugs, proteins and diseases. Comparing our clustering to existing classifications/clusters, we find some agreement between them and that the factor matrices indeed reflect the functional patterns.