Miracle-dz/UniFAR-FTUs huggingface dataset:
Community Member Retrieval on Social Media Using Textual Information

This paper addresses the problem of community membership detection using only text features in a scenario where a small number of positive labeled examples defines the community. The solution introduces an unsupervised proxy task for learning user embeddings: user re-identification. Experiments with 16 different communities show that the resulting embeddings are more effective for community membership identification than common unsupervised representations.
The semi-supervised classification of petrol and diesel passenger cars based on OBD and support vector machine algorithm

This paper proposes a novel semi-supervised classification method of petrol and diesel passenger cars using OBD data and support vector machine (SVM) algorithm. The proposed method first develops a classification rule of petrol and diesel passenger cars based on vehicle speed as well as engine RPM obtained from the on-board diagnostic (OBD) data with specific passenger car identification number (ID). Then the proposed method could primarily label petrol or diesel to the passenger car with specific ID using the classification rule. Next this paper apply support vector machine to create a classification model of petrol and diesel passenger cars based on the primary classification results, and to perform refined classification tasks. Experimental results show the correctness of the proposed semi-supervised petrol and diesel passenger car classification method can achieve 1.5% calibration rate from more than 35,000 real OBD data. The proposed method has the potential of applying to internet of vehicle (IoV) and to improve on-road CO2 emission estimation.
Combining expert knowledge and unsupervised learning techniques for anomaly detection in aircraft flight data

Abstract Fault detection and isolation schemes are designed to detect the onset of adverse events during operations of complex systems, such as aircraft, power plants, and industrial processes. In this paper, we combine unsupervised learning techniques with expert knowledge to develop an anomaly detection method to find previously undetected faults from a large database of flight operations data. The unsupervised learning technique combined with a feature extraction scheme applied to the clusters labeled as anomalous facilitates expert analysis in characterizing relevant anomalies and faults in flight operations. We present a case study using a large flight operations data set, and discuss results to demonstrate the effectiveness of our approach. Our method is general, and equally applicable to manufacturing processes and other industrial applications.
CORP: Uma Abordagem Baseada em Regras e Conhecimento Semântico para a Resolução de Correferências

In this paper we propose the use of lexical, syntactic and semantic knowledge for coreference resolution. We conducted several experiments involving different heuristics. As a result of this study, we generated a practical system that solves coreference in Portuguese texts. In addition, it was possible to increase our recall through semantic knowledge provided by Onto.PT.
An Unsupervised Alignment Algorithm for Text Simplification Corpus Construction

We present a method for the sentence-level alignment of short simplified text to the original text from which they were adapted. Our goal is to align a medium-sized corpus of parallel text, consisting of short news texts in Spanish with their simplified counterpart. No training data is available for this task, so we have to rely on unsupervised learning. In contrast to bilingual sentence alignment, in this task we can exploit the fact that the probability of sentence correspondence can be estimated from lexical similarity between sentences. We show that the algoithm employed performs better than a baseline which approaches the problem with a TF*IDF sentence similarity metric. The alignment algorithm is being used for the creation of a corpus for the study of text simplification in the Spanish language.
Discovering Political Slang in Readers' Comments

Slang is a continuously evolving phenomenon of language.The rise of social media has resulted in numerous slang terms circulating across the globe. In this paper, we aim to find novel and creative slang in the comments sections of online political news articles covering the 2016 US Presidential Election. First, we define creative political slang and partition it into sub-classes. Next, we extract a dataset of partisan news articles and comments ranging from the left wing to the right wing. Then, we develop PoliSlang, an unsupervised algorithm for detecting creative slang, evaluating its performance using expert human judgments. Finally, we use this algorithm to compare and contrast political slang usage by commenters across different news media.
Basic construction of a natural finger outline extraction system with a color glove

The authors have created a new color glove which has markers between the fingers and the palm or dorsal area, and have developed methods to eliminate border outlines caused between colored regions to make a natural finger outline extraction system. The authors select 6 colors used for markers and the positions of them. In the elimination process, outlines of marker regions' border are extracted. Elimination regions are created by delating these outlines from 1 pixel to 15 pixels. An image including only border outlines are created by AND operation of an image including only elimination regions and an image including only finger outlines. The authors investigated the relationship between wrist rotation angles and success rate of elimination of border outlines. Success rate of elimination was about 80% or more except when wrist rotation angles were -30, -20, or -10 degrees. The average was 84.6%. Through these experiments, the proposed method has indicated one way to solve the border outline problems with the color glove. Besides, this has created a problem that parts of finger outlines are often eliminated. In the discussion section, the authors show one way to solve this problem. Concerned with this section, the authors conclude that the proposed color glove and method are effective for basic construction of the natural finger outline extraction system with the color glove.
Antioxidant and antiproliferative activities of water spinach (Ipomoea aquatica Forsk) constituents

The aim of this study was to examine possible antioxidant and antiproliferative activities of 95% ethanol or water extract from water spinach (Ipomoea aquatica Forsk) organs. DPPH staining, total phenolic compounds, total flavonoid content, DPPH radical, reducing power method, FTC method, and inhibition of cancer cell prolifera- tion were employed. Ethanol extract of stem demonstrated a positive effect in DPPH staining when it was diluted to 6.25 mg dry matter/mL while all other fractions showed no effect at the same dilution. This fraction also had the highest content of the total phenolic compounds, as well as the highest reducing power and FTC activity. Ethanol extract of leaf had the highest amount of flavonoids. Using DPPH colorimetric method, it was found that ethanol extract of stem had the highest radical-scavenging activity, followed by ethanol extract of leaf. The antiproliferative activities of water spinach extracts were studied in vitro using human lymphoma NB4 cells, and the following results were found: water extract of stem had the highest antiproliferative activity with an EC 50 of 661.40 ± 3.36 µg dry matter/mL, followed by ethanol extract of stem and ethanol extract of leaf. The water extract of leaf had the lowest antiproliferative activity (EC 50 >1000 µg dry matter/mL) under the experimental conditions.
Dracena: A Real-Time IoT Service Platform Based on Flexible Composition of Data Streams

How to process the increasing amount of IoT data in real-time collected from a huge number of devices in the real world into a cloud data center with high frequency is a challenging problem. Furthermore, to share the data among multiple data processing pipelines each of which is to provide an IoT service and to allow service developers agilely develop IoT services through a trial-and-error process, a new IoT platform which enables flexible composition of data streams is required. We developed the data processing platform “Dracena” which enables the flexible composition of real-time IoT services. Every real-world device can synchronize its state to the corresponding virtual object on Dracena through sending the state data to the platform. The behavior of each virtual object, that is data processing on the object, can be agilely defined through Dracena’s plug-in mechanism. In this paper, we introduce the architecture of Dracena, show a trial application built on it for mobility services designed to handle a large number of connected vehicles, and experimentally evaluate its performance. The results of the evaluation demonstrate that Dracena has the ability to process frequently generated data in real time sent from hundreds of thousands of devices, while allowing the data produced by real-world people or things to be used across multiple services.
Network Anomaly Intrusion Detection Using a Nonparametric Bayesian Approach and Feature Selection

Anomaly-based intrusion detection systems (IDSs) have been deployed to monitor network activity and to protect systems and the Internet of Things (IoT) devices from attacks (or intrusions). The problem with these systems is that they generate a huge amount of inappropriate false alarms whenever abnormal activities are detected and they are not too flexible for a complex environment. The high-level rate of the generated false alarms reduces the performance of IDS against cyber-attacks and makes the tasks of the security analyst particularly difficult and the management of intrusion detection process computationally expensive. We study here one of the challenging aspects of computer and network security and we propose to build a detection model for both known and unknown intrusions (or anomaly detection) via a novel nonparametric Bayesian model. The design of our framework can be extended easily to be adequate for IoT technology and notably for intelligent smart city web-based applications. In our method, we learn the patterns of the activities (both normal and anomalous) through a Bayesian-based MCMC inference for infinite bounded generalized Gaussian mixture models. Contrary to classic clustering methods, our approach does not need to specify the number of clusters, takes into consideration the uncertainty via the introduction of prior knowledge for the parameters of the model, and permits to solve problems related to over- and under-fitting. In order to get better clustering performance, feature weights, model’s parameters, and the number of clusters are estimated simultaneously and automatically. The developed approach was evaluated using popular data sets. The obtained results demonstrate the efficiency of our approach in detecting various attacks.
ZCopy-Vhost: Replacing Data Copy With Page Remapping in Virtual Packet I/O

Virtualization technology is the core technology of cloud computing. While virtualization technology offers flexibility in many ways, it also introduces additional performance overhead. For network systems, it needs to provide additional virtual switching, the virtual packet I/O, and other functions. Providing these features requires a lot of CPU resources. As cloud services grow, more and more CPU resources are being used to provide virtualization support, which means that fewer CPUs can be sold to tenants. Therefore, cloud service providers have been constantly seeking more efficient virtualization solutions for network systems. In this paper, we identify one of the main reasons for the large consumption of CPU resources in the virtual networking system – copy and propose to use memory page remapping technology to eliminate the copy. Moreover, to adapt to the zero-copy technology, we have integrated the technology with a mature virtual switch software in the host and a userspace protocol stack in the virtual machine. These components together build a more efficient network system in virtual environments. The evaluations with microbenchmarks and macrobenchmark show that our system performs much better than the state-of-the-art solutions.
IoT Based Predictive Model for Cloud Seeding

Weather prediction is an attempt to predict the conditions of weather in the near future and what should be expected from the weather. The parameters which determine the conditions are temperature, humidity, wind.We will consider temperature and humidity to predict the future weather conditions. The data will be collected from DHT11 sensor and FC-37, they are low-cost sensors which are used to collect temperature, humidity and rain values. The sensors installed on the raspberry pi collects the data and stores it in the pi. The data is stored in the form of CSV file which is used for analyzing the data further. R is used to do analysis on the data collected. R is a very effective language and software for analyzing data and comes with many inbuilt functionalities for statistical data analysis. The motive is to find situations in which drought could occur and generate artificial clouds over the place for temporary relief.
Coordinated Control of Energy Storage in Networked Microgrids under Unpredicted Load Demands

In this paper a nonlinear control design for power balancing in networked microgrids using energy storage devices is presented. Each microgrid is considered to be interfaced to the distribution feeder though a solid-state transformer (SST). The internal duty cycle based controllers of each SST ensures stable regulation of power commands during normal operation. But problem arises when a sudden change in load or generation occurs in any microgrid in a completely unpredicted way in between the time instants at which the SSTs receive their power setpoints. In such a case, the energy storage unit in that microgrid must produce or absorb the deficit power. The challenge lies in designing a suitable regulator for this purpose owing to the nonlinearity of the battery model and its coupling with the nonlinear SST dynamics. We design an input-output linearization based controller, and show that it guarantees closed-loop stability for either the autonomous operation of a SST or co-operative operation between SSTs, in which each SST assist a given SST whose storage capacity is insufficient to serve the unpredicted load. The design is verified using the IEEE 34-bus distribution system with nine SST-driven microgrids.
Nutrition Module Intervention to Improve Nutrition Knowledge, Attitude and Practice of Trainers for Persons with Disabilities in Rehabilitation Centers in Malaysia.

Introduction: Other than parents, primary care providers such as community workers and rehabilitation service personnel are also important nutritional gatekeepers for persons with disabilities (PWD). Good nutrition management for PWD by joint efforts is most desirable to prevent further disablement in later years. This study aimed to evaluate the effectiveness of a nutrition module intervention on improving trainers' knowledge, attitude and practice in nutrition management for PWD in rehabilitation centres. Methods: This quasi-experimental study which employed mixed methods design recruited 45 trainers as intervention group and another 42 trainers as comparison group. Intervention group participated in a three-consecutive-day nutrition inter- vention while comparison group was trained on general health care services for PWD. Nutrition knowledge, attitude and practice scores for both groups were assessed using a validated questionnaire at baseline (T0), 1-month (T1) and 6-month (T2) intervals after intervention. Intervention trainer's on-the-job nutrition practice was further assessed by qualitative interviews. Results: Repeated measures ANCOVA demonstrated that intervention group attained significantly higher nutrition knowl - edge (p<0.001) and practice (p=0.001) scores than comparison group at T1 (mean difference for knowledge= 6.95; mean difference for practice= 2.93) and T2 (mean difference for knowledge= 10.30; mean difference for practice= 4.78). Signifi - cant higher nutrition attitude (p=0.004) score for intervention group was observed at T2 (mean difference= 3.08). Conclusion: This program produced favourable outcome on improving nutrition knowledge, attitude and practice of rehabilitation service personnel. The nutrition module is certainly relevant to serve as an educational or reference material for care providers of PWD in Malaysia.
DKN: Deep Knowledge-Aware Network for News Recommendation

Online news recommender systems aim to address the information explosion of news and make personalized recommendation for users. In general, news language is highly condensed, full of knowledge entities and common sense. However, existing methods are unaware of such external knowledge and cannot fully discover latent knowledge-level connections among news. The recommended results for a user are consequently limited to simple patterns and cannot be extended reasonably. To solve the above problem, in this paper, we propose a deep knowledge-aware network (DKN) that incorporates knowledge graph representation into news recommendation. DKN is a content-based deep recommendation framework for click-through rate prediction. The key component of DKN is a multi-channel and word-entity-aligned knowledge-aware convolutional neural network (KCNN) that fuses semantic-level and knowledge-level representations of news. KCNN treats words and entities as multiple channels, and explicitly keeps their alignment relationship during convolution. In addition, to address users» diverse interests, we also design an attention module in DKN to dynamically aggregate a user»s history with respect to current candidate news. Through extensive experiments on a real online news platform, we demonstrate that DKN achieves substantial gains over state-of-the-art deep recommendation models. We also validate the efficacy of the usage of knowledge in DKN.
Fast Exact CoSimRank Search on Evolving and Static Graphs

In real Web applications, CoSimRank has been proposed as a powerful measure of node-pair similarity based on graph topologies. However, existing work on CoSimRank is restricted to static graphs. When the graph is updated with new edges arriving over time, it is cost-inhibitive to recompute all CoSimRank scores from scratch, which is impractical. In this study, we propose a fast dynamic scheme, \DCoSim for accurate CoSimRank search over evolving graphs. Based on \DCoSim, we also propose a fast scheme, \FCoSim, that greatly accelerates CoSimRank search over static graphs. Our theoretical analysis shows that \DCoSim and \FCoSim guarantee the exactness of CoSimRank scores. On the static graph G, to efficiently retrieve CoSimRank scores $\mathbfS $, \FCoSim is based on three ideas: (i) It first finds a "spanning polytree»» T over G. (ii) On T, a fast algorithm is designed to compute the CoSimRank scores $\mathbfS (T)$ over the "spanning polytree»» T. (iii) On G, \DCoSim is employed to compute the changes of $\mathbfS (T)$ in response to the delta graph $(G øminus T)$. Experimental evaluations verify the superiority of \DCoSim over evolving graphs, and the fast speedup of \FCoSim on large-scale static graphs against its competitors, without any loss of accuracy.
A Novel Approach to Classify Motor-Imagery EEG with Convolutional Neural Network Using Network Measures

Electroencephalogram (EEG) signal recorded throughout motor imaging (MI) tasks has been wide applied in brain-computer interface (BCI) applications as a communication approach. To improve the classification success rate of MI EEG classification tasks, this paper proposes a completely unique input form based on brain network connectivity measures for the datasets from BCI Competition IV. First, using connectivity patterns between brain regions during MI task, six more frequent network features were selected and their maps were generated in 2D format; then a simple yet powerful convolutional neural network (CNN) with one convolutional layer was deployed for binary classification of MI tasks (left-hand, right-hand, both feet and tongue movements). The discrimination ability of these features was compared with each other. Our results demonstrate that CNN fed with path length feature map can further improve classification performance in most binary problems. While all classification results are better than 86%, the best accuracy using brain network features is 96.69% in right-tongue separation. The present study shows that the proposed method is efficient to classify MI tasks, and provides a practical method for classification of non-invasive EEG signals in BCI applications.
Imaging Enzymes at Work: Metabolic Mapping by Enzyme Histochemistry

For the understanding of functions of proteins in biological and pathological processes, reporter molecules such as fluorescent proteins have become indispensable tools for visualizing the location of these proteins in intact animals, tissues, and cells. For enzymes, imaging their activity also provides information on their function or functions, which does not necessarily correlate with their location. Metabolic mapping enables imaging of activity of enzymes. The enzyme under study forms a reaction product that is fluorescent or colored by conversion of either a fluorogenic or chromogenic substrate or a fluorescent substrate with different spectral characteristics. Most chromogenic staining methods were developed in the latter half of the twentieth century but still find new applications in modern cell biology and pathology. Fluorescence methods have rapidly evolved during the last decade. This review critically evaluates the methods that are available at present for metabolic mapping in living animals, unfixed cryostat sections of tissues, and living cells, and refers to protocols of the methods of choice.
Through-Wall Movement Detection based on S-Band FMCW radar: An Experimental Assessment

This paper describes a set of experimental tests for through-wall detection based on FMCW radar. For this purpose, a FMCW radar was implemented at 2.4 GHz and the acquired data is processed with an in-house real-time software to enhance the results. The experimental measurements were evaluated indoors to study and analyze the performance of the system. There is a good agreement between the position indicated by the processed data and the actual target location for a range less than 5 meters with a resolution of 30 cm.
An Improved Spread Factor Assignment Method for Large-Scale LoRaWAN Deployments in IoT

Long-range communication is a major requirement for large-scale IoT deployments and LoRaWAN is one of the widely adopted low-power wide area network solutions used for IoT connectivity. Although LoRaWAN advantages from simplicity and flexibility in deployment and operation, it suffers from collisions that limit the scalability of the network because of its random access nature and large time-bandwidth product. In this paper, we model a network in which nodes select the spread factor parameter and start transmitting in an ALOHA based manner. We have shown that when the number of nodes transmitting periodically increases, collision becomes a serious challenge in the network which limits the scalability of the technology. We then propose a metaheuristic method for the selection of spread factors in the network to address this problem. The results show that using the proposed method, packet drop rate can be improved by 42% in low-density networks and up to 8% in the worst case for a scenario with thousands of nodes.