Miracle-dz/UniFAR-FTUs huggingface dataset:
An integrated approach based on EEG signals processing combined with supervised methods to classify Alzheimer’s disease patients

Alzheimer’s Disease (AD) is the most widespread and incurable neurodegenerative disorder, and together with its preliminary stage - Mild Cognitive Impairment (MCI) - its detection still remains a challenging issue. Electroencephalography (EEG) is a non-invasive and repeatable technique to diagnose brain abnormalities. However, the analysis of EEG spectra is still carried out manually by experts and effective computer science methods to extract relevant information from these signals become a necessity. Through a data mining approach, which guides the automated knowledge discovery process, we aim to achieve an automatic patients classification from the EEG biomedical signals of AD and MCI, in order to support medical doctors in the diagnosis formulation. Specifically, we design an integrated procedure that encompasses the following steps: (1) data collection; (2) data preprocessing of EEG-signals data; (3) features extraction by applying time-frequency transforms on EEG-signals (Fourier and Wavelet analysis); and (3) a supervised learning approach to classify samples in patients suffering from AD, patients affected by MCI, and healthy control (HC) subjects. By applying our procedure, we are able to extract human-interpretable classification models that allow to automatically assign the patients into their belonging class. In particular, by exploiting a Wavelet feature extraction we achieve 83%, 92%, and 79% of accuracy when dealing with HC vs AD, HC vs MCI, and MCI vs AD classification problems, respectively. By comparing the classification performances with both feature extraction methods, we find out that Wavelets analysis outperforms Fourier. Thus, we suggest it in combination with supervised methods for automatic patients classification based on their EEG signals for aiding the medical diagnosis of dementia. We provided processed data from our study at ftp://bioinformatics.iasi. cnr.it/public/EEG/.
3D Neuron Branch Points Detection in Microscopy Images

Neuron tracing (reconstruction) is an important step toward understanding the functionality of neuronal networks. Neuron termination points and branch points, collectively called critical points, play an important role in neuron tracing applications. There are some existing methods for 3D neuron termination points detection. However, 3D branch points detection method has barely been explored. In this paper, we propose a 3D branch points detection method in microscopy images by reverse-mapping the 2D branch points back into the 3D space, according to the pixel intensity distribution along the projection direction. The 2D branch points are detected by an adaptive ray-shooting model in 2D maximum intensity projections (MIPs), where the center is the 3D branch point candidates, of a specified number of adjacent slices along the Z direction. The adaptive ray-shooting model analyzes the intensity distribution of the neighborhood around the branch point candidates and is robust to neurite diameter variations. The experimental results on multiple neuron image datasets show that our proposed method can achieve an average false negative rate and false positive rate of 15.67% and 10.67% for neuron branch point, respectively.
A meta-analysis portal for human breast cancer transcriptomics data: BreastCancerVis

Breast cancer is a major disease posing many therapeutic and societal challenges. The major problem remains the diversity of the disease. Breast cancers are divided in several distinct subtypes, but the specific mechanisms involved in each subtype are not understood. We apply advanced network medicine techniques to the analysis of transcriptomics data of several breast cancer cell lines. We show that different subtypes are characterized by highly heterogeneous pathway activity. Using the overlap between differentially expressed genes and drug-induced expression changes, we present candidate drugs for repurposing. Furthermore, we find different active subnetworks across the subtypes, regulated by distinct transcription factors. All these results are made available via a user-friendly portal at http://phenome.manchester. ac.uk/breastcancer/
Deep Neural Networks for Social Media Word Segmentation of Asian Languages

Information extraction today faces new challenges with noisy, short, unstructured data. This is especially the case for social media messages, such as tweets, in which language can be erroneous or cryptic, and contains references to a great number of new entities. Traditional NLP systems are challenged and need to develop new strategies to handle with these data. With the emergence of the neural network-based approach, the research about the word segmentation has benefited from large-scale raw texts by leveraging them for pretrained character and word embeddings. To this end, we experimented the use of both character and word embeddings to provide extra features to input layer of our neural network-based system architecture. This system has been tested on both Chinese and Japanese social media datasets. With the help of rich pretrained embeddings, our model achieved the promising results both on Chinese and Japanese social media word segmentation task by comparing with the state-of-the-art NLP tools.
A Low Power All-Digital PLL With −40dBc In-Band Fractional Spur Suppression for NB-IoT Applications

This paper proposes a low-power fractional-N all-digital PLL (ADPLL) for the narrow-band Internet-of-Things applications. Multi-step lock controlling and oscillator tuning word coarse prediction algorithms help to accelerate the locking process to less than <inline-formula> <tex-math notation="LaTeX">$20~\mu \text{s}$ </tex-math></inline-formula>. A digital-to-time converter (DTC) is used with a phase prediction algorithm to minimize the detection range of the time-to-digital converter for low power consumption. The <inline-formula> <tex-math notation="LaTeX">$\Sigma \Delta $ </tex-math></inline-formula> dither block is designed to improve the nonlinearity of DTC resulting in a −40-dBc in-band spur suppression. A low supply of 0.6-V digitally controlled oscillator has the frequency coverage of 1.5–2.05 GHz. Fabricated in a 55-nm CMOS, the ADPLL occupies an active area of 0.88 mm<sup>2</sup> and consumes 4 mW at 1.8 GHz. With a 24-MHz reference clock, the measurement results show that this work achieves better than −94-dBc/Hz in-band phase noise and −120.5 dBc/Hz at 1-MHz offset of 1.83-GHz carrier frequency. The FoM value is −233.4 dB, and the reference spur is −76.3 dBc.
MPMPA: A Mitigation and Prevention Model for Social Engineering Based Phishing attacks on Facebook

The exponential increase in usage of the Internet can be seen in the present era. The use of social media and cyber-physical systems is rapidly increasing. Social Media platforms like Facebook have become an indispensable part of our lives. A large number of reported Facebook attacks have been observed in past few years. This study presents a novel model to detect and prevent the SEBPA (Social Engineering Based Phishing Attacks) on Facebook. Current research is first attempt to address SEBPA. For the Validation of proposed model four realistic scenarios have been deviced. The results suggest that the proposed model provides comprehensive coverage of nearly all occurrences of SEBPA. Moreover, the proposed model also prefigure the threatening situation to users with different colors during the validation process.
Protein Regulating Network towards Tonifying Primal Qi Delivered by Ginseng

Ginseng is a highly valued traditional Chinese medicine (TCM) with novel function of tonifying primal qi which can restore patients from critical healthy conditions. However, underlying mechanism based on protein regulation network is still obscure. In this study, evidences of proteins activated by ginsenoside rb1/re/rg1 (quality controlling ingredients of ginseng) were collected from literature. Starting with these activated proteins, together with data of protein interaction and biological process, a protein regulating network underlying tonifying primal qi was constructed. These regulating proteins participated in fundamental processes e.g., erythrocyte differentiation, positive regulation of hemopoiesis and leukocyte differentiation, glucose transport and positive regulation of carbohydrate metabolic process. Taken together, these biological processes may carry out the function of tonifying primal qi identified in TCM. The proposed protein regulation network provides innovative targets for further studies for ginseng.
Comparing Path Planning Algorithms for Multiple Mobile Robots

The use of mobile robots in industrial applications is increasing day by day. As a result of this increase, efficiency in the use of mobile robots has also become important. In particular, path planning is a significant area of research to improve efficiency in mobile robot navigation. In path planning, it is aimed to find optimum and obstacle free paths to the starting and ending points to fulfill multiple tasks. In this paper, A *, D * and PSO algorithms, frequently used to optimize the path multiple robots reach targets in an environment with obstacles, have been compared. In addition, a mobile robot simulation software has been developed to measure the performance of algorithms. At the end of the study, the performance of the algorithms was showed by measuring the path lengths and the process time of algorithms.
A Fast Eigenvector-Based Autofocus Method for Sparse Aperture ISAR Sensors Imaging of Moving Target

Motion compensation is of significant importance in generating high-focused inverse synthetic aperture radar (ISAR) images. The range misalignment and the phase error of each received echo are two important parameters in ISAR imaging and they need to be precisely estimated and compensated. Faced with sparse aperture (SA) data, we use the minimum entropy-based method for range alignment and propose a fast eigenvector-based autofocus method for phase adjustment in this paper. The novel autofocus method uses the Lanczos or Arnoldi iteration to compute only the desired eigenvector instead of directly doing time-consuming eigen-decomposition to the sample covariance matrix. Therefore, the computational complexity can be greatly reduced. Simulated scatterer model and raw data are utilized to prove that the proposal is an efficient autofocus algorithm with a lower computational burden.
Real-Time Algorithm for Simultaneous Vehicle Detection and Tracking in Aerial View Videos

Automatic detection and tracking of multiple vehicles in airborne videos is still a challenging problem due to camera movement, vehicle occlusion and the need for computational resources. This paper presents a robust and efficient real-time method for automatic detection and tracking of vehicles in airborne videos. The detection process is based on a combination of Top-hat and Bot-hat transformation aided by the morphological operation. Background objects are removed through analyzing feature points motion of the obtained object regions using K-means clustering and KLT tracker. The obtained vehicles features are grouped and clustered into separate vehicles based on their motion properties. Finally, a connecting scheme is presented to determine the connectivity of vehicle cluster with the corresponding cluster in the vehicles trajectories. Experiments conducted on videos representing airborne cameras verify the excellent performance compared to other existing approaches.
Security and Privacy of Smart Cities: Issues and Challenge

Smart city has been emerged as a new paradigm to dynamically optimize the resources in cities and provide better facilities and quality of life for the citizens. Despite the potential vision of smart cities, security and privacy issues remain to be carefully addressed. In this paper, we present a comprehensive survey of security and privacy issues of smart cities, and categorize the present and future developments within this area. We also describe a thematic taxonomy of security and privacy issues of smart cities to highlight the requirements for designing a secure smart city, identify the existing security and privacy solutions, and present open research issues and challenges of security and privacy in smart cities.
Data Quality Aware Task Allocation Under a Feasible Budget in Mobile Crowdsensing

Satisfying spatial-temporal coverage requirement in the interested regions while considering the quality of the sensing data with budget limitation is a major research challenge in mobile crowdsensing. Most existing research in this field focus on the number of sensor readings collected in each covered subarea and do not consider individual differences of participants for contributing to data quality improvement. In this paper, we propose a novel coverage metric, quality coverage, which considers both the spatial coverage and the quality of sensing data and then use task allocation approaches to achieve highly diverse and spatial quality coverage level within a limited budget for different application scenarios.
Oxidative stability of mineral naphthenic insulating oils: Optimization of commercial antioxidants and metal passivators

Mineral naphthenic oil is normally used in transformers, circuit breakers and other electrical equipment for insulating and cooling functions. Due to their usage conditions, it may be subjected to oxidation reactions caused by the presence of oxygen, water and metals, affecting the oil performance. Using the ASTM D2440 method, oxidation studies were carried out on insulating oil, using commercial antioxidants and metal passivators, to assess and compare their thermal oxidative stability, monitored by FTIR analysis, total acid number and sludge content on the oxidized samples. An experimental sequence for combinations of antioxidants and metal passivators was devised to seek the best concentrations that could be used to improve the stability of the insulating naphthenic oil. The combination of antioxidant Irganox L115 and metal passivator Irgamet 039 achieved the best performance in inhibiting the oil oxidation among the studied samples.
Adaptive Query Re-ranking Based on ImageGraph for Image Retrieval

With the exponential growth of images, the accuracy of image retrieval for improving the performance of rank layer and feature layer in content-based image retrieval (CBIR) becomes more and more attractive. Better single feature search results will further enhance the effect. Better sorting results will robust the performance. Therefore, this paper focuses on rank reordering to improve the performance of image retrieval. We propose a rank-level framework for feature reordering based on hierarchical undirected graphs. First, we calculate the K Nearest Neighbors for each image in the database. Then, we propose a method based on the combination of reciprocal nearest neighbor and K nearest neighbor to construct an undirected graph, and a method for calculating the image distance based on the shared nearest neighbor. An undirected graph is used to represent the similarity relationship of an image, where the vertices are composed of pictures and the edges are weighted according to the distance of the pictures. Finally, based on the constructed undirected graph in this paper, we perform sorting optimization. Experiments on four public data sets demonstrate the effectiveness of the method and are challenging.
Hierarchical Sentence Sentiment Analysis Of Hotel Reviews Using The Naïve Bayes Classifier

Traveloka provides a space for its users to write reviews about their hotel reservation services. These reviews are very useful in informing hotel managers of the level of customer satisfaction. Sentiment analysis is a tool that can be used to analyse such reviews to determine whether they express opinions or not, so that the level of customer satisfaction can be measured based on the number of sentiments (positive or negative) contained in the opinions. In this research, the Naïve Bayes classifier was used to perform a hierarchical sentence sentiment analysis on hotel reviews obtained from Traveloka. In addition, two types of term weighting schemes were used for the feature extraction, namely, raw term frequency and TF-IDF. The results of this research indicated that it is better to use a hierarchical classification in sentiment analysis than a flat classification. The average F-measure value for the flat classification model was 75.18%, while for the hierarchical classification model it was 77.48%. These results showed that the use of a hierarchical classification in sentiment analysis improved the average performance of the classification model by 2.3%. The use of the raw term frequency feature extraction in a flat classification provided a higher F-measure value than the use of the TF-IDF feature extraction, with a margin of 3.9%. The average F-measure value for the flat classification using the raw term frequency feature extraction was 75.18%, while for the TF-IDF feature extraction it was 71.23%.
A New Method of CT Imaging: TTBP

CT imaging technology plays an important role in the field of medical diagnosis. Filtered Back-Projection (FBP) algorithm is usually used for image reconstruction. However, there are some noise defects in the reconstructed image, and CT imaging requires higher accuracy of the image. In order to reduce these noise defects, use TT transform instead of Fourier filtering in FBP, and a TT back projection algorithm (TTBP) is proposed. Firstly, The image projection signal is transformed by TT. Then, using the TT transform spectral features of the diagonal elements, the noise signal of the projected signal is processed. Finally, back projection is used to reconstruct the original image. Using Shepp-Logan skull in medical CT imaging as a model and algorithm is implemented in MATLAB. Compared with the simulation results, the obtained image has better control of noise flaws, and the resulting image is clearer and smoother, which has a high application value.
A Self-Adaptive Robot Control Framework for Improved Tracking and Interaction Performances in Low-Stiffness Teleoperation

The improved adaptability of a robotic teleoperation system to unexpected disturbances in remote environments can be achieved by compliance control. Nevertheless, complying with all types of interaction forces while performing realistic manipulation tasks may deteriorate the teleoperation performance. For instance, the loading effect of the objects and tools that are held and manipulated by the robot can introduce undesired deviations from the reference trajectories in case of low-stiffness (or high payload) teleoperation. Although this can be addressed by updating the robot dynamics with the external loading effect, a sudden loss of the object may also generate undesired and potentially dangerous robot behaviours. To address this problem, we propose a novel and self-adaptive teleoperation framework. The method uses the feedback from robot's force sensors to recognize the interaction aspects that must be compensated by robot dynamics. Thanks to this online compensation, the slave robot reduces the tracking error with respect to the commanded motion by the human operator, while performing complex interactive tasks without the haptic feedback. The robot local controller also includes an energy tank based passivity paradigm to be able to manage unexpected collisions or a contact loss without resulting in an unsafe behaviour. We validate the proposed approach by experiments on a torque-controlled robotic arm performing manipulation tasks that require both object manipulation and environment Interaction.
Energy Management of Microgrid Integrated with Hybrid Renewable Power

This paper investigates the energy management problem of a building microgrid integrated with solar and wind power. The purpose of this work is to improve the building energy efficiency through the efficient use of renewable power. The following major contributions are made in this paper. First, the power management problem is formulated into a two-stage stochastic programming with the objective minimizing the electricity cost. Then, scenario method is proposed to handle the uncertainty of solar and wind power and the problem is further reformulated into a stochastic mixed-integer linear programming. At last, numerical examples are used to demonstrate the performance of the proposed method. Test results show that the energy efficiency of the building microgrid is improved through the efficient scheduling of solar, wind and battery power.
Explainable Text Classification in Legal Document Review A Case Study of Explainable Predictive Coding

In today’s legal environment, lawsuits and regulatory investigations require companies to embark upon increasingly intensive data-focused engagements to identify, collect and analyze large quantities of data. When documents are staged for review – where they are typically assessed for relevancy or privilege – the process can require companies to dedicate an extraordinary level of resources, both with respect to human resources, but also with respect to the use of technology-based techniques to intelligently sift through data. Companies regularly spend millions of dollars producing ‘responsive’ electronically-stored documents for these types of matters. For several years, attorneys have been using a variety of tools to conduct this exercise, and most recently, they are accepting the use of machine learning techniques like text classification (referred to as predictive coding in the legal industry) to efficiently cull massive volumes of data to identify responsive documents for use in these matters. In recent years, a group of AI and Machine Learning researchers have been actively researching Explainable AI. In an explainable AI system, actions or decisions are human understandable. In typical legal ‘document review’ scenarios, a document can be identified as responsive, as long as one or more of the text snippets (small passages of text) in a document are deemed responsive. In these scenarios, if predictive coding can be used to locate these responsive snippets, then attorneys could easily evaluate the model’s document classification decision. When deployed with defined and explainable results, predictive coding can drastically enhance the overall quality and speed of the document review process by reducing the time it takes to review documents. Moreover, explainable predictive coding provides lawyers with greater confidence in the results of that supervised learning task. The authors of this paper propose the concept of explainable predictive coding and simple explainable predictive coding methods to locate responsive snippets within responsive documents. We also report our preliminary experimental results using the data from an actual legal matter that entailed this type of document review. The purpose of this paper is to demonstrate the feasibility of explainable predictive coding in the context of professional services in the legal space.
Balance Mechanism Design for the Fusion of Pure Pursuit and PI Tracking Controller

Making use of the PID controller to make up the defeat of the geometric controller like Pure Pursuit is a very effective way to improve the effects of the Pure Pursuit (PP). However, existed methods didn't explain the internal principle of the fusion. This paper will try to give a detailed analysis about the fusion mechanism. Firstly, we will discuss the error caused by PP, and then we will separately analyze how the PI controller correct the error. Based on the above discussion, we conclude that the weight parameters are very important to the performance of the fusion controller. Thereby, we proposed a Genetic Algorithm (GA) based parameter tuning method to improve the accuracy of the fusion method. Finally, the experiments on CARLA simulator prove the effects of GA based PP_PI (PP fusion with PI) method in urban driving scenario.