Miracle-dz/UniFAR-FTUs huggingface dataset:
Indoor Air Monitoring System Based on Internet of Things and Its Prediction Model

Urban residents work indoors for a long time, and indoor air quality is becoming an important indicator that residents care about in their work and life. Based on the Internet of Things, this paper designs an indoor air monitoring system, which can monitor and collect various parameters of indoor air in real time, upload them to the data center for analysis, and push them to the user end for display. Meanwhile, a prediction model is established to predict the air quality indicator in the next few hours by taking CO2 as an example, and construct a complete air quality evaluation to provide to users, so that other intelligent terminals or users can adjust the doors and windows, to make ensure the excellent indoor air quality.
Problems of detecting fuzzy duplicates

This article discusses the problem of detecting fuzzy duplicates. Recently, much attention has been paid to the development of methods for reducing the computational complexity of the algorithms being created by choosing various heuristics. With the use of approximate approaches is observed a decrease in the detection rate of duplicates. An important factor affecting the accuracy and completeness of the duplicates definition in comparison problems is the selection of the substantive part. Another key requirement for the quality of detection algorithms for fuzzy duplicates is their resistance to "small" data changes and the ability to process them. One of the first studies in the field of finding fuzzy duplicates is the work of U. Manber and N. Heintze. In these works, sequences of adjacent letters are used to construct the sample. Dactogram includes all text substrings of a fixed length. Completeness, accuracy and F-measure were chosen as the main indicators of the quality of the algorithms. It was supposed to compare the algorithms by these parameters, and also to determine their mutual correlation and joint coverage by different algorithms combinations of the initial set of pairs for the fuzzy duplicates.
Identification of EEG-Based Music Emotion Using Hybrid COA Features and t-SNE

Emotion recognition based on EEG signals is one of essential research topics in human-computer interface. Music is regarded as an efficient tool to arouse emotions of human being. This work proposed a scheme that automatically identification the emotions elicited by music. Firstly a music EEG measuring experiment of eight subjects was carried out. After filtering and segmentation, we mainly extract hybrid complexity, oscillation and asymmetry features (COA) from EEG signals, which respectively realized by Hjorth feature, higher order crossing (HOC) features and differential asymmetry features. To reduce and visualize high-dimensional feature data of samples, the t-distributed stochastic neighbor embedding (t-SNE) is applied to all samples of every subject. Classification results of SVM, KNN, Bayes, DT and RF show that the hybrid COA features are more efficient than statistic time-domain features, power spectral density (PSD), wavelet features and their combination in music emotion recognition. Moreover, with optimal parameters, music emotion features can be clearly visualized through the reduced COA features obtained by the t_SNE. Sensitivity of subject to the music can be investigated by the separation degree of happy feature data and negative feature data.
Acquisition of Spectral Signals for Determination of Chronic Disease to Syndrome Using Independent Component Analysis

Medical devices diagnose or monitor internal conditions to aid in early detection, which is a huge help in saving the lives of patients. There are underlying factors that affect a person's body if a disease is present. These diseases may have an impact on the different parameters of the human body. The combination of high and low values of these body parameters may lead to a certain chronic disease. Some examples of chronic diseases include hypertension and metabolic syndrome. In this paper, the researchers were able to obtain the blood pressure reading by getting pulse rate and pulse transit which will be obtained from ATMega328 clock of the Gizduino Mini. The percentage of the body fat can be derived using the principle of bioelectrical impedance analysis wherein an alternating current will flow between two electrodes attached to the surface of the skin to determine its corresponding impedance. The acquired data from the sensors passes through the computational technique of independent component analysis, wherein it separates the data from several sensors. There were 16 test subjects, whose ages are within the range of 55-65 years old. Confusion matrix was used in order to obtain the overall accuracy of the device. The best accuracy value is 1. The researchers obtained an accuracy value of 0.8125. By getting the absolute value of the difference of 1, 0 and the obtained value, it depicts that the obtained value is closer to 1 due to its difference of 0.2 compared to 0 which is 0.8. Hence, the spectral signal acquisition device provided accurate results in comparison with the clinical values.
Fundus Image Classification for Diabetic Retinopathy Using Disease Severity Grading

Diabetic Retinopathy (DR) is ranked at the top of blindness causes. It progresses without subjective symptoms and leads to blindness in the worst case. However early detections and proper treatments can prevent visual disturbance. Because it takes time and cost for diagnoses by clinicians, research and development of diagnostic support systems has actively been conducted. This research aims to establish a fundus image classification method based on disease severity assessment for a diagnostic support by a fundus image analysis. In this paper, we propose a Graph Neural Network (GNN)-based method to improve accuracy for severity classification. Our method has two features. The first is to extract Region-Of-Interest (ROI) sub-images focusing on regions locally capturing lesions in order to minimize background noise in image preprocessing for the classification. The second is to utilize the GNN which is not yet applied for fundus image classification. In order to evaluate our proposed method, we use Indian Diabetic Retinopathy Image Dataset (IDRiD) utilized in "Diabetic Retinopathy: Segmentation and Grading Challenge" on Biomedical Imaging held at the IEEE International Symposium in 2018. We verified that the accuracy of our method improved 2.9% over the conventional method in this contest.
Interactive Clustering for Exploring Multiple Data Streams at Different Time Scales and Granularity

We approach the problem of identifying and interpreting clusters over different time scales and granularity in multivariate time series data. We extract statistical features over a sliding window of each time series, and then use a Gaussian mixture model to identify clusters which are then projected back on the data streams. The human analyst can then further analyze this projection and adjust the size of the sliding window and the number of clusters in order to capture the different types of clusters over different time scales. We demonstrate the effectiveness of our approach in two different application scenarios: (1) fleet management and (2) district heating, wherein each scenario, several different types of meaningful clusters can be identified when varying over these dimensions.
Melody extraction from polyphonic music based on the amplitude relation

Most of the melody extraction methods only focus on the temporal continuity of the frequency, and seldom consider the temporal correlation of the frequency amplitude, which would result in the poor effect of melody extraction for many algorithms. Consequently, a new main melody extraction method based on the correlation of the frequency amplitude is proposed in the paper. Firstly, an equal loudness filter is used to enhance the ear-sensitive frequency band in music. The STFT is used to convert the spectrum and the phase vocoder is used to correct the frequency and amplitude. Then, lots of most salience frequency points near spectral peaks are selected as pitch candidates by a new salience function based on the correlation of the amplitudes of adjacent frames, and the perceived pitch is reverse-reasoned by a pair of high frequency points. Some pseudo fundamental frequency points are filtered out by detecting the number and distribution of their harmonics. Next, the pitch with the greatest salience, the pitch with the greatest amplitude and the pitch with the most harmonics are selected for creating contours in each frame. After the creation of contour, we analyze the distribution of the amplitude in each contour and clip the fragments with smaller amplitude for determining the start point and the end point of the melody. Finally, the contours with the smaller mean salience and amplitude are removed and the main melody is identified when there is more than one contour simultaneously. The experimental results show that the proposed method can effectively extract the main melody from polyphonic music.
An Effective Adaptive Filter to Reduce Motion Artifacts from ECG Signals Using Accelerometer

Electrocardiogram (ECG) is most commonly used for the diagnosis of heart disease. Physicians utilize high quality ECG to interpret and identify physiological and pathological phenomena. However, motion artifacts are caused by the electrode-skin impedance with electrode motions that are the results of a subject's movement, so it is a classical problem to reduce motion artifacts from ECG signals during real-time heart rate measurements. To the best of our knowledge, Block Least Mean Square (BLMS) algorithm has not been considered in the context of the use of accelerometer measurement data in ECG signals as a reference signal on reducing motion artifacts. Therefore, in this paper, we propose a 3-axis accelerometer to measure the acceleration signal of the movement of the trunk as the reference input of the adaptive filter and the optimal weight of the adaptive filter is adjusted by the BLMS algorithm. Finally, we have applied this algorithm on ECG signals from the subject and compared its performance with the conventional Least Mean Square (LMS) algorithm. The results show that the performance of the BLMS algorithm is superior than the LMS algorithm. And, we have found the R wave of the filtered ECG was clearly appeared.
ReSYCLator: Transforming CUDA C++ source code into SYCL

CUDA™ while very popular, is not as flexible with respect to target devices as OpenCL™. While parallel algorithm research might address problems first with a CUDA C++ solution, those results are not easily portable to a target not directly supported by CUDA. In contrast, a SYCL™ C++ solution can operate on the larger variety of platforms supported by OpenCL. ReSYCLator is a plug-in for the C++ IDE Cevelop[2], that is itself an extension of Eclipse-CDT. ReSYCLator bridges the gap between algorithm availability and portability, by providing automatic transformation of CUDA C++ code to SYCL C++. A first attempt basing the transformation on NVIDIA®'s Nsight™ Eclipse CDT plug-in showed that Nsight™'s weak integration into CDT's static analysis and refactoring infrastructure is insufficient. Therefore, an own CUDA-C++ parser and CUDA language support for Eclipse CDT was developed (CRITTER) that is a sound platform for transformations from CUDA C++ programs to SYCL based on AST transformations.
WMSN Video Image Iterative Reconstruction via Combination of Structural Sparsity and Non-local Self-similarity ?

As a new information acquisition technology, Wireless Multimedia Sensor Networks (WMSN) usually focused on the acquisition and processing of audio, video and so on. However, video image as one kind of WMSN main sensory information, it includes rich structure, texture and other details, the traditional video image reconstruction algorithm did not make full use of structural information between the pixel neighborhood and self-similarity of image sub-block. Aim at solving this problem, this paper puts forward a novel WMSN video image reconstruction model, which mainly includes three aspects. First, in the framework of Compressed Sensing (CS), an adaptive Block Compressed Sensing (BCS) based on the energy is proposed, image blocks with varying structures is assigned sampling rate adaptively; second, based on structural sparsity and non-local self-similarity of image blocks, establishing structural sparsity regularization and non-local regularization objective function; flnally, Iterative Shrinkage Thresholding (IST) algorithm is introduced to solve the model. Numerical experiments show that our method is e‐cient for WMSN video image recovery, especially preserving the global details of the original video image.
Owlie: A Chatbot that Provides Emotional support for Coping with Psychological Difficulties

We’ll first outline the problems faced by patients coping with psychological problems (amount of time between sessions, difficulties in the everyday life, difficulty accessing care or certain types of therapy, isolation). Then, we’ll talk about how we wanted to respond to these challenges in a creative way. We’ll explain how Owlie was created empirically, through a continuous dialogue with users. We’ll discuss our methodology as well as our processes for creating content and creating the personality of our conversational agent. We’ll also talk about the feedback we’ve received from people who have used Owlie and how Owlie is used. We’ll also address how Owlie can be incorporated into treatment programs (at the beginning of a treatment program, during, and at the end of program), give recommendations, and discuss Owlie’s limitations. And finally, we’ll touch on the vital ethical questions surrounding the use of new tools like, such as Owlie, such as: what
Data Integration Tasks on Heterogeneous Systems Using OpenCL

In the era of big data, many new algorithms are developed to try and find the most efficient way to perform computations with massive amounts of data. However, what is often overlooked is the preprocessing step for many of these applications. The Data Integration Benchmark Suite (DIBS) [1] was designed to understand the characteristics of dataset transformations in a hardware agnostic way. While on the surface these applications have a high amount of data parallelism, there are caveats in their specification that can potentially affect this characteristic. Even still, OpenCL can be an effective deployment environment for these applications. In this work we take a subset of the data transformations from each category presented in DIBS and implement them in OpenCL to evaluate their performance for heterogeneous systems. For targeting heterogeneous systems, we take a common application and attempt to deploy it to three platforms targetable by OpenCL (CPU, GPU, and FPGA). The applications are evaluated by their average transformation data rate (see Figure 1). We illustrate the advantages of each compute device in the data integration space along with different communications schemes allowed for host/device communication in the OpenCL platform.
Practice of non-financial reporting disclosure by Russian companies: bridging the gap between company disclosures on sustainability and stakeholders' needs

The article reveals the significance of disclosing non-financial statements by Russian companies in modern business conditions. Despite the active development of corporate reporting, there is a gap between the requirements of stakeholders and actual disclosures of companies in the area of sustainability. The paper considers the practice of disclosing non-financial reporting by companies in context of the stakeholders' demands.
Simulating Visual Acuity for Autonomous Agent: A Data-Driven Approach

The system that links intelligent agents to their world is their synthetic senses, allowing them to perceive and interact with the world around them. How such a system is modelled is important since an agent uses the data generated by its synthetic senses to make decisions and change behaviours. This paper will discuss a data-driven synthetic sight model for an autonomous agent that incorporates the concepts of human peripheral vision and visual acuity. We have developed and implemented a synthetic sight model that facilitates a good simulation of these physiological aspects of human sight.
Design and optimization of a 0.5 V CMOS LNA for 2.4-GHz WSN application

This paper presents a low noise amplifier (LNA), which could work at an ultra-low voltage of 0.5 V and was optimized for WSN application using 0.13 μm RF-CMOS technology. The circuit was analyzed and a new optimization method for a folded cascode LNA was introduced. Measured results of the proposed circuit demonstrated a power gain of 14.13 dB, consuming 3 mW DC power, showing 1.96 dB NF and an input 1-dB compression point of −19.9 dBm. Both input power matching (S11) and output power matching (S22) were below −10 dB. The results indicate that this LNA is fully applicable to low voltage and low power applications.
Odd-weight-column SEC–DED–TAED codes

Single-error correction, double-error detection (SEC–DED) codes are a type of error-correction codes widely used in electronics to protect memory devices from data corruption. Odd-weight-column SEC–DED codes are a type of these codes where the parity-check matrix is built with every column including an odd number of ones. With this approach, double errors have an even-weight syndrome and can be differentiated from single errors and, consequently, easily detected. There are applications, such as avionics or space, where a multiple error usually affects adjacent cells. Adapting SEC–DED codes to protect against triple-adjacent errors is interesting in these applications. A modification to existing odd-weight-column SEC–DED codes to add triple-adjacent error detection (TAED), creating SEC–DED–TAED codes, is presented. The implementation of the additional triple-adjacent detection logic for these codes can be performed with limited performance and area overhead.
Association of pvl gene with incomplete hemolytic phenotype in clinical Staphylococcus aureus

Purpose: This experiment aimed to evaluate the correlation between the hemolytic phenotype of Staphylococcus aureus and pvl gene in terms of characteristics of antibiotic resistance. Materials and methods: Two-hundred and eleven strains of hospital-acquired S. aureus and their bacterial susceptibility to 20 antibiotics were determined by MicroScan WalkAway96. All strains were cultured on Columbia sheep blood agar plates for 24 hours and then underwent ten passages for investigation of their hemolytic phenotypes. S. aureus produced incomplete β-hemolytic phenotype, termed as S. aureus strains with incomplete hemolytic phenotype (SIHP). The pvl gene was identified by PCR amplification followed by DNA sequencing. Statistical analyses of the data were performed using SPSS version 16.0 software. Results: Fifty-two (24.64%) strains were confirmed to maintain the incomplete hemolytic phenotype of S. aureus (SIHP). Meanwhile, 15 (7.11%) of 211 strains were found to carry the pvl gene, and eight of the 15 strains were SIHP. Compared with S. aureus strains with complete hemolytic phenotype (SCHP), SIHP showed higher susceptibility to seven of the 20 antibiotics (oxacillin, ciprofloxacin, gentamicin, ceftriaxone, cefoxitin, levofloxacin, and moxifloxacin) (P<0.05). The pvl-positive bacteria had a higher rate of resistance to four antibiotics (rifampin, ciprofloxacin, levofloxacin, and moxifloxacin) in comparison with the pvl-negative strains (P<0.05). Conclusion: SIHP had a high frequency of pvl gene. The pvl-positive isolates showed less resistance to rifampin, ciprofloxacin, levofloxacin, and moxifloxacin. Additionally, the majority of SIHP isolates (61.54%) were methicillin-resistant S. aureus. SIHP strains had significantly higher antibiotic resistance to cefoxitin when compared with SCHP, while SCHP strains had a high rate of antibiotic resistance to ciprofloxacin, gentamicin, ceftriaxone, levofloxacin, and moxifloxacin. The results may help to provide medical advice for selection of antibiotics for patients with SIHP-associated infections.
WARDROP EQUILIBRIA —TO BE PUBLISHED IN “ENCYCLOPEDIA OF OPERATIONS RESEARCH AND MANAGEMENT SCIENCE. EDITED BY J. J. COCHRAN. WILEY”—

Wardrop equilibria are commonly used as a solution concept of network games when modeling transportation and telecommunication networks with congestion. This concept assumes that players select a route that minimizes the time or cost incurred in its traversal. This behavioral assumption admits convenient mathematical descriptions, and efficient algorithms for the computation of equilibria are available. For that reason, planners have been making use of this concept for decades for evaluating projects, optimizing tolls, estimating demands, and a myriad of applications arising from extensions of the basic model. In this article, we introduce the basic model, explain strategies for computation of equilibria, and discuss the extent of the inefficiency arising from the self-minded behavior of the players. In addition, we provide some generalizations of the basic model.
GENETIC DIVERSITY OF SCORODOPHLOEUS FISCHERI IN THE COASTAL FORESTS OF TANZANIA Mligo

Tanzanian coastal forests are diverse in endemic plant species, Scorodophloeus fischeri being among them. It was aimed to investigate the genetic diversity of S. fischeri within and among populations. The total genomic DNA was extracted from young leaves of S. fischeri using CTAB procedures and then determined genetic diversity using PCR-RAPD markers. 73.19% of 97 scorable bands were polymorphic. UPGMA showed three clusters, each being a distinct population. Partitioning of genetic variability indicated that 60% occurred within and 40% among S. fischeri populations. This implies that more polymorphic loci existed within S. fischeri populations than among populations. The genetic differentiation (Gst =0.252) but low gene flow among populations (Nm = 2.301). It was concluded that there is higher genetic diversity at molecular level within S. fischeri than among populations of this species. S. fischeri experienced different complexities of micro-environmental conditions within populations. Human activities created a significant barrier of gene flow among populations and each forest warrants conservation.
Home range of Sunda scops owl Home range and activity patterns of Sunda scops owl in Peninsular Malaysia

The Sunda scops owl Otus lempiji is commonly found in many wooded habitat types in Malaysia. However, as with other owl species in Southeast Asia, there is a lack of published ecological studies. We examined the home range and activity patterns of six adult Sunda scops owls based on radio-telemetry in an isolated forest, namely Ayer Hitam Forest Reserve, Selangor, Peninsular Malaysia from December 2012 to June 2013. Mean minimum convex polygon and harmonic mean home range areas of the owls were 2.40 ± 0.28 SD ha and 4.00 ± 0.78 SD ha, respectively. The birds exhibited territorial behaviour with little overlapping of home ranges. Movements (the means of inter-fix speed) were not associated with lunar illumination nor with time. This study provided baseline information on the spatial requirements of the Sunda scops owl in a lowland forest.