Miracle-dz/UniFAR-FTUs huggingface dataset:
Semi-Supervised Gated Spectral Convolution on a Directed Signed Network

A complex network is a powerful tool that enables a complex system in the real world to be represented as a network structure. Due to the heterogeneous edges and nodes implying rich semantic information, network representation has received considerable attention in both research and industrial domain. Over the recent years, the graph convolutional network (GCN) has provided a novel approach for learning network embeddings. However, this primarily supports undirected unsigned networks; that is, it cannot be directly applied to directed signed networks because it is challenging to effectively depict the direction and signs of edges in such models. In this paper, we therefore propose a method for semi-supervised gated spectral convolution in directed signed networks. We first extend the concept of the GCN to directed signed networks, which not only preserves the advantages of the traditional GCN but also properly describes the significance of the directions and signs of the edges. We then innovatively define sign (label) propagation rules in directed signed networks, rendering the networks semi-supervised. Furthermore, we enhance the balance theory to constrain the process of sign propagation to obtain network embedding with better interpretability. To satisfy the needs of large-scale complex networks, we propose a gating mechanism to adaptively forget sign information, which significantly reduces the time–space complexity of the sign propagation process. Finally, we compare the proposed method with state-of-the-art baselines using four real-world data sets for the classical link sign prediction task. Experimental results demonstrate that the proposed method is competitive.
Quasi-Global Optimization of Antenna Structures Using Principal Components and Affine Subspace-Spanned Surrogates

Parametric optimization is a mandatory step in the design of contemporary antenna structures. Conceptual development can only provide rough initial designs that have to be further tuned, often extensively. Given the topological complexity of modern antennas, the design closure necessarily involves full-wave electromagnetic (EM) simulations and—in many cases—global search procedures. Both factors make antenna optimization a computationally expensive endeavor: population-based metaheuristics, routinely used in this context, entail significant computational overhead. This letter proposes a novel approach that interleaves trust-region gradient search with iterative parameter space exploration by means of local kriging surrogate models. Dictated by efficiency, the latter are rendered in low-dimensional subspaces spanned by the principal components of the antenna response Jacobian matrix, extracted to identify the directions of the maximum (frequency-averaged) response variability. The aforementioned combination of techniques enables quasi-global search at the cost comparable to local optimization. These features are demonstrated using two antenna examples as well as benchmarking against multiple-start local tuning.
Compact Dual-band Filter Based on Quarter-Wavelength SIRs with Grounded via coupling

A compact dual-band bandpass filter (DBF) based on quarter-wavelength (λ/4) stepped impedance resonators (SIRs) with miniaturized size is presented in this Letter. The compact size of this filter is realized by using the common grounded via hole and the folded λ/4 SIRs. Common grounded via is used to adjust the coupling coefficient of the dual-band filter. The coupling path to the input/output (I/O) ports of this dual-band filter are two different space thus external quality factor of single passband can be determined independently. Two transmission zeros (TZs) is introduced between passband to realize a sharp skirt characteristics. Finally, a prototype dual-band filter centering at 4.4GHz and 7.4GHz is designed and fabricated. Simulation and measurement results are in good agreement.
A Facial Expression Recognition Method Using Deep Convolutional Neural Networks Based on Edge Computing

The imbalanced number and the high similarity of samples in expression database can lead to overfitting in facial recognition neural networks. To address this problem, based on edge computing, a facial expression recognition method using deep convolutional neural networks is proposed. In order to overcome the shortcoming that circular consensus adversarial network model can only be mapped one-to-one, we construct a constrained circular consensus generative adversarial network by adding class constraint information. Discriminators and classifiers in this network can share network parameters. In addition, for the problems of unstable training and easy to encounter model collapse in original GAN networks, this paper introduces gradient penalty rule into discriminator’s loss function to achieve the normative constraint on gradient changes. Using this network not only generates sample data for a few classes in the training set of expression database, but also performs effective expression classification. Compared with other methods, the improved discriminative classifier network structure can enhance the diversity of samples and get a higher expression recognition rate. Even if other expression feature extraction methods are used, the higher recognition rate can still be obtained after using proposed data augmentation framework.
28 GHz Antenna-Array-Integrated Module with Built-In Filters in LTCC Substrate

This paper presents an Antenna-Array-Integrated Module (AiM) with a low pass filter (LPF) in LTCC multilayered substrate with low loss, high attenuation and small size in 28 GHz band. A differential feeding method for each patch antenna is employed to improve symmetricity of directivity for beam forming. The insertion loss of the LPF is less than 0.5 dB and the spurious emission in 2nd harmonic band of Band-n257 was suppressed by over 20 dB thanks to putting the LPF in front of the antenna. The symmetricity of the directivity for beam forming was improved owing to the differential feeding.
A Decentralized Latency-Aware Task Allocation and Group Formation Approach With Fault Tolerance for IoT Applications

Development of internet of things (IoT) and smart devices eased life by offering numerous applications targeting to provide real-time low latency services, but they also brought challenges in handling huge data generated from the powerful computations, to get a job done. Decentralized edge computing could help to achieve latency requirements of the applications by executing them closer to the user at edge of network, but most of the current studies actually deployed centralized approaches for cluster computing at edge, which put extra overhead of cluster formation and management. In this article, we propose to group heterogeneous edge nodes on task arrival with a more decentralized method and execute tasks in parallel to meet their deadline. On the other hand, to guarantee successful execution of critical IoT application running in an edge network, fault tolerance has to be significantly considered. For resource limited edge devices, there is a great need for efficient fault tolerance techniques, which can provide reliability based on device’s local information, without worrying about overall network topology. In this article, our novel method is to increase task reliability being executed in distributed edge computing environment through finding reliability of an edge node locally, and by providing fault tolerance to increase overall application availability. Our proposed fault tolerance technique works in decentralized mode by executing new algorithms to handle above mentioned problems. Our experiment results show that our approach is effective as well as providing desired goals of achieving deadline for latency-aware IoT applications, with staggering decrease in overall network traffic along with ensuring reliability and availability.
A Study on the Robustness of Pitch Range Estimation from Brief Speech Segments

Pitch range estimation from brief speech segments is important for many tasks like automatic speech recognition. To address this issue, previous studies have proposed to utilize deep-learning-based models to estimate pitch range with spectrum information as input [1–2]. They demonstrated it could still achieve reliable estimation results when speech segment is as brief as 300ms. In this work, we further investigate the robustness of this method. We take the following situation into account: 1) increasing the number of speakers for model training hugely; 2) second-language(L2) speech data; 3) the influence of monosyllabic utterances with different tones. We conducted experiments accordingly. Experimental results showed that: 1) We further improved the accuracy of pitch range estimation after increasing the speakers for model training. 2) The estimation accuracy on the L2 learners is similar to that on the native speakers. 3) Different tonal information has an influence on the LSTM-based model, but this influence is limited compared to the baseline method. These results may contribute to speech systems that demanding pitch features.
EEG-Based Neurohaptics Research: A Literature Review

Neurohaptics is the field of study that strives to understand the complex neural representation provoked in response to tactile and/or kinesthetic stimuli. This field has garnered a noticeable attention over the past decade not only in neuro-scientific research but also in medical, marketing and engineering fields. In this paper, we review existing literature on Electroencephalography (EEG)-based neurohaptic studies charting out the main themes and significant findings. Furthermore, we provide a brief review of the EEG analytical methods commonly utilized in the neurohaptic domain. Also, we present a case study with the complete flow of conducting neurohaptic research studies. Lastly, we discuss limitations and provide directions for future neurohaptic research, such as: modeling quality of haptic experience, improving neurohaptic systems and neurohatpics in virtual reality.
A 20–60 GHz Ultra-Wideband SPDT Switch for Multi-Band Transceivers

A 20–60 GHz ultra-wideband single-pole double-throw (SPDT) switch is demonstrated using reverse-saturated HBTs in a 0.13-μm SiGe BiCMOS technology. A large inductor is connected in the control signal path at the HBT base, which resonates with the internal capacitances of the HBT at an out of band frequency. This way a flat insertion loss response is achieved from 20–60 GHz, broadening the bandwidth of the switch. The realized switch has a minimum insertion loss of 1.7 dB at 40 GHz and less than 3 dB from 20–60 GHz. The input 1-dB compression point is 17 dBm at 32 GHz, and isolation is better than 22.5 dB for the entire band of interest, while the occupied chip area is 0.19 mm2. The performance of this switch is comparable to any state-of-the-art switches with the advantage of covering most of the frequency band considered for 5G.
A Review on Test Case Generation Methods Using UML Statechart

Despite continuous effort in test case generation in UML diagrams, especially UML statecharts, there are inconsistencies in the processes used in this domain that have raised needs for a framework that will have a complete set of process to generate test cases. As a prerequisite for creating this framework and to better understand the existing processes used in test case generation, a review on existing work in this domain was conducted to discover the current methods, types of coverage criteria and types of evaluation conducted to evaluate test cases quality. The review was conducted on 24 existing work in test case generation using UML statecharts using content analysis method. The results revealed that most of the work in this domain, translated the UML statechart diagram into an intermediate model. Among the common algorithm for establishing test cases is Depth First Search. Furthermore, this review emphasizes the importance of achieving all-state coverage and all-transaction coverage in conducting coverage criteria.
A Computer Vision System for Guava Disease Detection and Recommend Curative Solution Using Deep Learning Approach

Guava disease has become a tremendous problem for the production of guava which has undeviating effect on the socioeconomic development of the farmers. This phenomenon leads to initiate an automated computer vision based guava disease detection system that may detect malicious guava and guide to early cure approaches, resulting reduction of relative economic loss. Considering the fact, in this paper we have proposed a convolutional neural network (CNN) based guava disease detection and curative suggestion providing system. We have collected the images of guava affected by anthracnose, fruit rot and fruit canker along with disease free guava from different districts of Bangladesh. In this paper, we have applied three CNN models and experimentally found that the third model has outperformed the other two with an accuracy of 95.61%. For meticulous experimentation, performance metrics like precision, recall and F1 score is evaluated and found to yield great results.
The UTOPIA project. Using Telecare for Older People In Adult social care: The findings of a 2016-17 national survey of local authority telecare provision for older people in England

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Comparison of prothrombin time and international normalized ratio values using point-of-care system with a standardized laboratory method in patients on long-term oral anticoagulation – A prospective study

Background: In our country, methods of prothrombin time (PT) and international normalized ratio (INR) testing have not been standardized across various centers. Often, we find disparity in INR values from different laboratories, posing a challenge to make an appropriate clinical decision. Objective: The objective of the study was to compare and correlate the PT/INR values using a point-of-care (POC) system with standardized laboratory (SL) testing and to evaluate the efficiency of the POC system in monitoring patients. Methods: We have prospectively compared PT/INR values between a commercially available POC, CoaguChek XS System, Roche, and SL testing in 205 patients and 353 samples from July 2017 to April 2018. Results: The overall strong correlation between POC PT/INR values and simultaneous standard laboratory values was noted. The overall coefficient of correlation among the two groups was 0.919 (P = 0.001) for INR values. In INR range of 2–3.5, the values correlated well with a coefficient of correlation, 0.756 (P = 0.001). In INR range <2, the values correlated with a coefficient of 0.98. The correlation was poor when the INR values were >4. Conclusions: In this comparison study, statistical analysis yielded a good correlation between POC PT/INR values and SL values in therapeutic and subtherapeutic range. POC testing is a good alternative to SL testing. If widely available, POC testing may enable patient self-testing and self-monitoring. In our experience, POC testing had added benefits in emergency settings. However, clinicians and laboratory professionals should be aware of the occasional disagreement between POC INR and standard laboratory INR values.
Are the urban slum population physically inactive? A descriptive study from urban Puducherry

Context: Physical inactivity is an important modifiable behavioral risk factor for noncommunicable diseases (NCDs). However, adequate attention has not been paid to it, especially in low- and middle-income countries like India as like other risk factors due to challenges in the administration of tools to measure physical activity. Aims: This study was conducted to assess the level of physical inactivity and contributing factors in an urban slum of Puducherry. Settings and Design: Urban community, cross-sectional study design. Materials and Methods: After obtaining consent, relevant information on sociodemographic details and physical inactivity was collected, during the house-to-house survey, using a pretested semistructured questionnaire from each household of the study areas. A total of 3300 adults (aged 30 years or above) were included in the study. Physical inactivity was defined as <150 min of leisure time moderate exercise in a week. Statistics: The results were summarized as means and proportions. Results: Nearly three-fourth of the participants were physically inactive. About 79% of the females against 70% of the males were found to be physically inactive. Physical inactivity was found to be decreasing with increase in age: 30–44 years age (77.2%), 45–59 years age (75.3%), and 60 years and above (72.2%). Individuals with chronic diseases and obesity were found to be less physically inactive. Gender, age, education, socioeconomic status, occupation, alcohol use, and obesity were found to be associated with physical inactivity. Conclusion: Physical inactivity was very high in the study population, especially among young adults and females. Health education intervention targeting these populations can decrease the level of physical inactivity and in the long-run burden of NCDs in the study population.
In vitro anticancer activity of methanolic extract of Granulocystopsis sp., a microalgae from an oligotrophic oasis in the Chihuahuan desert

With the purpose of discovering new anticancer molecules that might have fewer side effects or reduce resistance to current antitumor drugs, a bioprospecting study of the microalgae of the Cuatro Cienegas Basin (CCB), an oasis in the Chihuahuan desert in Mexico was conducted. A microalgae was identified as Granulocystopsis sp. through sequencing the rbcL gene and reconstruction of a phylogenetic tree, and its anticancer activities were assessed using various in vitro assays and different cell lines of human cancers, including lung, skin melanoma, colorectal, breast and prostatic cancers, as well as a normal cell line. The values of IC50 of the microalgae methanolic extract using the MTT assay were lower than 20 μg/ml, except that in the lung cancer line and the normal cell line. In vitro, the microalgae extract caused the loss of membrane integrity, monitored by the trypan blue exclusion test and exhibited marked inhibition of adhesion and cell proliferation in cancer cell lines, through the evaluation of the clonogenic assay. Also, typical nuclear changes of apoptotic processes were observed under the microscope, using the dual acridine orange/ethidium bromide fluorescent staining. Finally, the microalgae extract increased the activity of caspases 3 and 7 in skin melanoma, colon, breast and prostate cancer cells, in the same way as the apoptotic inductor and powerful antitumoral drug, doxorubicin. This study shows the anticancer activity from Granulocystopsis sp. , a microalgae isolated from the CCB.
Biomarkers for Duchenne muscular dystrophy: myonecrosis, inflammation and oxidative stress

ABSTRACT Duchenne muscular dystrophy (DMD) is a lethal, X-linked disease that causes severe loss of muscle mass and function in young children. Promising therapies for DMD are being developed, but the long lead times required when using clinical outcome measures are hindering progress. This progress would be facilitated by robust molecular biomarkers in biofluids, such as blood and urine, which could be used to monitor disease progression and severity, as well as to determine optimal drug dosing before a full clinical trial. Many candidate DMD biomarkers have been identified, but there have been few follow-up studies to validate them. This Review describes the promising biomarkers for dystrophic muscle that have been identified in muscle, mainly using animal models. We strongly focus on myonecrosis and the associated inflammation and oxidative stress in DMD muscle, as the lack of dystrophin causes repeated bouts of myonecrosis, which are the key events that initiate the resultant severe dystropathology. We discuss the early events of intrinsic myonecrosis, along with early regeneration in the context of histological and other measures that are used to quantify its incidence. Molecular biomarkers linked to the closely associated events of inflammation and oxidative damage are discussed, with a focus on research related to protein thiol oxidation and to neutrophils. We summarise data linked to myonecrosis in muscle, blood and urine of dystrophic animal species, and discuss the challenge of translating such biomarkers to the clinic for DMD patients, especially to enhance the success of clinical trials. Summary: This Review discusses biomarkers in blood and urine linked to myonecrosis, inflammation and oxidative stress, to enhance development of therapies for DMD, and the challenges to be overcome for clinical translation.
Ghana case study: Give them the power. A repositioning family planning case study.

Family planning saves lives and has long been considered a key aspect to socioeconomic development. Although this is widely acknowledged and well-documented in recent years the attention and resources directed toward improving family planning programs in developing countries have been decreasing even though need remains high. By one estimate satisfying the unmet need for contraceptive services in developing countries would avert 52 million unintended pregnancies a year thereby saving 1.5 million lives and preventing 505000 children from losing their mothers (Singh et al. 2003). To address this need the U.S. Agency for International Development (USAID) has identified Repositioning Family Planning as a priority for its work in Africa. In the face of scarce resources weak infrastructure and a growing focus on HIV/AIDS it is extremely difficult for African country programs to make significant gains in strengthening their family planning programs and thereby raising contraceptive prevalence. USAID has committed itself to providing incremental assistance to a selected number of focus countries at the very early stages of program development. To help guide these investments lessons learned by countries in the region which have made significant progress will be very useful. (excerpt)
Methodological Issues on the Importance of Instrument Validation in Cross-Sectional Health Research

Dear editor, Recently, we read with great interest the article authored by Jadidi et al. [1] that was entitled“Is emergency medical services (EMS) in Islamic Republic of Iran practical and efficient in facingEbola? ” and published in Bull Emerg Trauma in 2019, in 7th volume and 3rd issue. First of all, wewould like to extend our gratitude to the authors of this article. Although the mentioned study wasappropriate and valuable, there was a fundamental flaw in the method, which has led to an ambiguous interpretation of the findings. Therefore, the purpose of this letter is to raise concerns about the data collection instrument and emphasize the importance of reporting its validity and reliability in crosssectional studies.