Miracle-dz/UniFAR-FTUs huggingface dataset:
Tweet Analysis Based on Distinct Opinion of Social Media Users'

The state of mind gets expressed via Emojis' and Text Messages for the huge population. Microblogging and social networking sites emerged as a popular communication channels among the internet users. Supervised text classifiers are used for sentimental analysis in both general and specific emotions detection with more accuracy. The main objective is to include intensity for predicting the different texts formats from twitter, by considering a text context associated with the emoticons and punctuations. The novel Future Prediction Architecture Based On Efficient Classification (FPAEC) is designed with various classification algorithms such as, Fisher's Linear Discriminant Classifier (FLDC), Support Vector Machine (SVM), Naïve Bayes Classifier (NBC) and Artificial Neural Network (ANN) Algorithm along with the BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) clustering algorithm. The priliminary stage is to analyze the distinct classification algorithm's efficiency, during the prediction process. Later, the classified data will be clustered to extract the required information from the trained data set using BIRCH method, for predicting the future. Finally, the perfomance of text analysis can get improved by using efficient classification algorithm.
Women Who Emerge as Leaders in Temporarily Assigned Work Groups: Attractive and Socially Competent but Not Babyfaced or Naïve?

The underrepresentation of women in top positions has been in the spotlight of research for decades. Prejudice toward female leaders, which decreases women’s chances of emerging as leaders, has been discussed as a potential reason. Aiming to investigate the underlying mechanisms of this prejudice, we focused on the question of how facial characteristics might influence women’s leadership emergence. Because other research has related ascribed social competence and ascribed naïveté to attractiveness and babyfacedness, respectively, we hypothesized that ascribed social competence would mediate the impact of ascribed attractiveness on leadership emergence and that ascribed naïveté would mediate the impact of ascribed babyfacedness on leadership emergence. In a pilot study, we analyzed data from 101 participants of a women’s leadership contest held in 2015 in Germany. We then confirmed these results in a methodologically improved main study on other women who participated in the contest in one of two other years: 2016 and 2017 (N = 195). Women applied to participate in the contest by recording their answers to several questions in a video interview. In the contest, they were assigned to teams of about ten women each and worked on several assessment-center-like tasks. After each task, each member of each team nominated the three women they believed showed the best leadership potential in their group. We operationalized women’s leadership emergence as the number of nominations received. We measured participants’ facial attractiveness, babyfacedness, social competence, and naïveté by having raters follow a specifically developed rating manual to rate the answers the women gave in the video interviews. In both studies, the results indicated that women with higher ascribed facial attractiveness had higher ascribed social competence, which significantly predicted leadership emergence in the contest. Likewise, women with higher ascribed babyfacedness had higher ascribed naïveté, which significantly, albeit only slightly, negatively predicted leadership emergence. We discuss the implications of the results for personnel selection.
Low-complexity Power Allocation with Positional Information for 4-by-4 MIMO Channel using One Composite Leaky Coaxial Cable

Leaky coaxial (LCX) cable can be used as antenna for wireless communication system and wireless power transfer which can be widely applied for many application scenarios including wireless service for IoT, automated factory using a large number of wireless devices. We have designed that one LCX cable can be used as two antennas which can releases the space requirement of MIMO deployment for wireless application. In this paper, we will further evaluate the 4-by-4 LCX-MIMO channel using one composite cable with two LCX cables. In addition, we also propose a low-complexity power allocation method for the LCX-MIMO system with the user's positional information (PI). The results show that the capacity is promising when the spacing between LCX cables is small as to 2cm. In addition, our proposed low-complexity power allocation has better capacity than that of using equal power allocation when SNR value is over the low range.
Bike Usage Forecasting for Optimal Rebalancing Operations in Bike-Sharing Systems

This article presents the first step of a project focusing on enhancing the management of bike-sharing systems. The objective of the project is to optimize the daily rebalancing operations that need to be performed by operators of bike-sharing systems using machine-learning algorithms and constraint programming. This study presents an evaluation of machine learning algorithms developed for forecasting the availability of bikes on three Swiss bike-sharing networks. The results demonstrate the superiority of the Multi-Layer Perceptron algorithm for forecasting available bikes at station-level for different prediction horizons and its applicability for real-time prediction generation.
Multi-Algorithmic Techniques and a Hybrid Model for Increasing the Efficiency of Recommender Systems

The explosive growth in the amount of available digital information has increased the demand for recommender systems. Recommender systems are information filtering systems that deal with the problem of information overload by filtering vital information fragment out of large amount of dynamically generated information according to user's preferences or interests. Recommender systems have the ability to predict whether a particular user would prefer an item or not based on his/her personal profile. To this direction, this paper presents multi-algorithmic techniques, such as content-based filtering and collaborative filtering, which increase the efficiency of recommender systems. Moreover, a hybrid model for recommendation, employing content-based and collaborative filtering, is introduced. The presented recommender system takes as input information about users from their profile in Facebook, one of the most well-known social networking services. Examples of operation are given and they hold promising results for the described techniques. Finally, the paper attests that the aforementioned techniques can be used for different kind of software, such as e-learning, e-commerce, etc.
Optimal Binning for Genomics

Genome sequencing is expected to be the most prolific source of big data in the next decade; millions of whole genome datasets will open new opportunities for biological research and personalized medicine. Genome sequences are abstracted in the form of interesting regions, describing abnormalities of the genome. The parallel execution on the cloud of complex operations for joining and mapping billions of genomic regions is increasingly important. Genome binning, i.e., partitioning of the genome into small-size segments, adapts classic data partitioning methods to genomics; region distributions to bins must reflect operation-specific correctness rules. As a consequence, determining the optimal bin size for such operations is a complex mathematical problem, whose solution requires careful modeling. The main result of this paper is the mathematical formulation and solution of the optimal binning problem for join and map operations in the context of GMQL, a query language over genomic regions; the model is validated by experiments showing its accuracy and sensitivity to the variation of operations’ parameters. We also optimize sequences of operations by inheriting the binning between two consecutive operations and we show the deployment of GMQL and the tuning of the proposed model on different cloud computing systems.
A Tunable Low-Power Oscillator Based on High- $Q$  Lithium Niobate MEMS Resonators and 65-nm CMOS

This paper presents a comprehensive guide to co-design piezoelectric RF-micro-electromechanical system (MEMS) resonators and CMOS for enabling voltage-controlled MEMS oscillators (VCMOs) that harness the best benefits out of both platforms. The analysis, focusing on understanding different tradeoffs among the tuning range, power consumption, gain, and phase noise, is generic to any kind of piezoelectric resonators and specific for Colpitts VCMOs. As a result of this paper, the first VCMO based on the heterogeneous integration of a high-Q lithium niobate (LiNbO<sub>3</sub>) micromechanical resonator and CMOS has been demonstrated. A LiNbO<sub>3</sub> resonator array with a series resonance at 171.1 MHz, a Q of 410, and an electromechanically coupling factor of 12.7% is adopted, while the TSMC 65-nm RF LP CMOS technology is used to implement the feedback and tuning circuitry with an active area of <inline-formula> <tex-math notation="LaTeX">$220\times 70\,\,\mu \text{m}^{2}$ </tex-math></inline-formula>. The frequency tuning of the VCMO is achieved by programming a binary weighted digital capacitor bank and a varactor that are both connected in series to the resonator. The best measured phase noise performance of the VCMO is −72 and −153 dBc/Hz at 1 kHz and 10-MHz offsets from 178.23- and 175.83-MHz carriers, respectively. The VCMO consumes a dc current of <inline-formula> <tex-math notation="LaTeX">$60~\mu \text{A}$ </tex-math></inline-formula> from a 1.2-V supply while realizing a tuning range of 2.4 MHz (~1.4% fractional tuning range). Such VCMOs can be applied to enable ultralow power, low phase noise, and wideband RF signal synthesis for emerging applications in Internet of Things.
The Hypothetical Impact of Accelerate Pheno on Time to Effective Therapy and Time to Definitive Therapy for Bloodstream Infections Due to Drug-Resistant Gram-Negative Bacilli

Strategies are needed to improve time to optimal therapy in patients with bloodstream infections (BSI) due to resistant Gram-negative (GN) pathogens. Accelerate Pheno (ACC) can provide antimicrobial susceptibility results within 7 h of a positive culture and may more rapidly optimize therapy. ABSTRACT Strategies are needed to improve time to optimal therapy in patients with bloodstream infections (BSI) due to resistant Gram-negative (GN) pathogens. Accelerate Pheno (ACC) can provide antimicrobial susceptibility results within 7 h of a positive culture and may more rapidly optimize therapy. The primary objective of this study was to evaluate the hypothetical impact of ACC on time to effective therapy (TTET) and time to definitive therapy (TTDT) among patients with BSI due to resistant GN pathogens. ACC was performed on resistant GN BSI isolates, and results were not available to clinicians in real time. A potential benefit of having ACC on TTET or TTDT was determined if modifications to antimicrobial regimens could have been made sooner with ACC. Comparisons on the impact of ACC in the presence or absence of testing by the Verigene Gram-negative blood culture test (Verigene GN-BC) were performed. Sixty-one patients with resistant GN BSI were evaluated. The median actual TTET and TTDT in the cohort were 25.9 h (interquartile range [IQR], 18.5, 42.1) and 47.6 h (IQR, 24.9, 79.6), respectively. Almost half of the patients had potential improvement in TTET and/or TTDT with ACC. In patients who would have had a benefit the median potential decreases in TTET and TTDT were 16.6 h (IQR, 5.5 to 30.6) and 29.8 h (IQR, 13.6 to 43), respectively. The largest potential improvements were seen in patients for whom Verigene results were not available. In conclusion, among patients with resistant GN BSI in a setting where other rapid diagnostic technologies are utilized, ACC results could have further improved TTET and TTDT.
Shallow and Deep Learning Architecture for Pests Identification on Pomelo Leaf

In this research, we proposed using two methods for the problem of pest identification from leaf patterns. Firstly, we use a traditional recognition shallow architecture with extracted three features: Color moments, Color correlograms, Zernike moments, then these features used to classifying by SVM algorithm. Secondly, we apply a deep convolutional neural network (CNN) for recognition purpose. We consider four different kind of pests in pomelo leaf: black bugs, snails, mealybugs, scales insects, each with 400 images and 700 images leaves are not pestilent. The introduction of a CNN avoids the use of handcrafted feature extractors as it is standard in state of the art pipeline and this approach improves the accuracy of the referred pipeline. These results show that both proposed methods achieve promising results and can be applied to identify the pests in reality.
An 87% Peak Efficiency, 37W, Class H Audio Amplifier with GaN Output Stage

This paper presents a novel high output power class H audio amplifier design with GaN transistors output stage. The design is implemented in 0.18-$\mu$m BCD process. Several design techniques to drive the GaN output stage and to improve the efficiency of the class H amplifier are introduced in this paper. The design consumes 706.3mW quiescent power and can deliver up to 37W peak output power to the load of 32$\Omega$ with the peak power efficiency of 87%. This design achieves the lowest THD+N ratio of -70.17dB.
The Radiation Characteristics of 3.43:1 Bandwidth Dipole Antenna with Radar Absorbing Material

In this paper it is shown that the use of radar absorbing material (RAM) allows to expand the working frequency band of the antenna by 330 MHz (6.3%). The antenna with RAM had a working frequency band by the level of $\mathbf{VSWR}\leq 3$ in the frequency range 2.29-7.85 GHz (3.43:1 bandwidth). The antenna without RAM had a working frequency band by level of $\mathbf{VSWR}\leq 3$ in the frequency range 2.30-7.53 GHz. The use of RAM allowed to increase the realized gain at the frequency of 7.85 GHz by 1.1 dB (from −11.2 to −10.2 dB).
Detection of Phalange Region Based on U-Net

Osteoporosis is one of the famous bone diseases. It is a major cause of deteriorating the quality of life, and early detection and early treatment are becoming socially important. Visual screening using Computed Radiography (CR) images is effective for diagnosis of osteoporosis, but there are problems of increasing the burden on doctors, variation in diagnostic results due to differences in experiences of doctors, and undetected lesions. In order to solve this problem, we are working on a computer-aided diagnosis (CAD) system for osteoporosis. In this paper, we propose segmentation methods of the phalange region from the phalangeal CR images as a preprocessing of classification of osteoporosis. In the proposed method, we construct a segmentation model using U-Net, which is a type of deep convolution neural network (DCNN). The proposed method was applied to input images generated from CR images of 101 patients with both hands, and evaluated using the Intersection over Union (IoU) values. The result was 0.914 in IoU.
Cross-Language Aspect Extraction for Opinion Mining

Aspect extraction is a subtask of aspect-based opinion mining, which aims to identify and categorize opinion targets such as product features in opinionated text. This is a challenging problem since users often use different words to express the same aspect or even mention about aspects implicitly. Supervised methods, while accurate, are not widely used for this problem mainly because they require annotated data which is costly to obtain, especially for new product and aspect categories. In this paper, we present a supervised aspect extraction method that utilizes annotated data from another language (English), which is translated to the target language (Vietnamese) by a machine translation tool (Google Translate). To alleviate the negative effect of the difference in expressions and words in different languages, we propose using word embedding features to capture the similarity and the relationships between words in order to increase the effectiveness of the extraction process. We also introduce an annotated corpus of aspect categories extracted from restaurant reviews in Vietnamese, in which we conducted experiments to evaluate our method. Experimental results demonstrate the effectiveness of the proposed approach.
A 1.2 GSps, 8 bit RF DAC for multi-Nyquist applications in GaAs technology

High-speed digital-to-analog converter (DAC) is key component in instrument and automatic test equipment, radar and ultra-wideband (UWB) systems. In this paper, a two-channel 1.2 GSps, 8 bit RF DAC for Multi-Nyquist applications in 1 µm GaAs Technology is presented. Combining mode select circuit with synchronous latch simplifies design of the current source and layout of DAC core circuit. Measurement results demonstrate that the Differential Nonlinearity (DNL) is within ±0.15 LSB, and the Integral Nonlinearity (INL) is within ±0.4 LSB. For the normal mode, the Spurious Free Dynamic range (SFDR) is larger than 40 dB; for the mixing mode, output bandwidth is up to 1.8 GHz, and the SFDR is larger than 30 dB. Under a supply voltage of 5 Volts, the output swing is 1.1 Vpp, and the total power consumption is 1.7 Walts for both channels working.
Visual Life Sciences Workflow Design Using Distributed and Heterogeneous Resources

Programming or querying usually presupposes some degree of technical familiarity with the syntax of a language and the peculiarity of the objects it manipulates to produce useful information. The degree of abstractions supported in a language helps lessen the depth of such familiarity needed, and aids in improving access to and usability of these resources. To help biologists concentrate more on their science questions and not on how to compute it, several successful workflow orchestration languages and systems have been proposed. Despite their popularity, significant limitations reduce their usability and limit applicability in novel applications. In this paper, we present a visual language, called VisFlow, for workflow orchestration using heterogeneous and distributed resources. We advance the idea that once resources are minimally described and abstracted, arbitrary workflows can be designed solely using query primitives supported in VisFlow. Its capabilities can be augmented by including computational artifacts in the form of library functions written in R, Python, and Java, or even in SQL and XQuery, making it a truly extensible system. We discuss its salient features and illustrate its capabilities using a substantial set of examples.
Cell Selection and Resource Allocation in LTE-Advanced Heterogeneous Networks

A LTE-advanced heterogeneous network consists of many macrocells, and within each macrocell, there can have many picocells and user equipments (UEs). A UE within a macrocell can request resource blocks (RBs) from either the macrocell or a picocell within the macrocell. In this paper, two optimal problems are defined for the association of UEs, RBs, and heterogeneous cells. We show that both problems are equivalent to a 0/1 knapsack problem and, therefore, can be solved in pseudo-polynomial time by dynamic programming. Based on above two optimal problems, we, then, propose a cell selection and resource allocation (CSRA) scheme to solve the problem of allocating a UE to its macrocell or to a picocell within the macrocell. The design goal of picocells is to unload the traffic for the macrocell. When there are available RBs and UEs within the picocell, the CSRA scheme offloads UEs to the picocell to release the most number of RBs for the macrocell. Only when there are not enough RBs within a picocell and there are available RBs within the macrocell, for load balancing, the CSRA scheme moves some UEs to the macrocell. The CSRA scheme stops whenever the picocell is not overloaded or the macrocell does not have available RBs. Simulation results show that our proposed scheme performs better than the cell range expansion scheme in terms of goodput, loss rate, and delay.
An Incentive Mechanism for Cross-Organization Data Sharing Based on Data Competitiveness

In the era of big data and artificial intelligence, data sharing is desirable for vigorous development of data-driven services, which improves our daily life. Although data sharing is supported to a certain extent by current mechanisms and technologies, organizations especially with potential competitive relationships might refuse to share their data due to the worry that data sharing improves competitors’ competitiveness. To address this problem, this paper focuses on the competitiveness-driven target of win-win, and provides incentives to encourage potentially competing organizations to share their data. By introducing the concept of data competitiveness as a data transaction driving force, an incentive mechanism based on data competitiveness is established, which is formulated as a Stackelberg game. A gradient-based iteration algorithm is proposed to obtain the Stackelberg equilibrium solution to the data sharing incentive problem. Simulation results substantiate that performance of data sharing can be improved significantly by the proposed scheme.
The relationship between physical activity, clinical and cognitive characteristics and BDNF mRNA levels in patients with severe mental disorders

Abstract Background: Here we aimed to clarify the association of physical activity with cognitive function and current mood in severe mental disorders in the most extensive sample to date. Secondly, we aimed to investigate the relationship between physical activity and BDNF mRNA levels. Methods: Three hundred and six patients with a DSM-IV schizophrenia (SZ) or bipolar disorder (BD) spectrum diagnosis were included. Clinical characteristics were assessed using the Structured Clinical Interview for DSM-IV. Depressive symptomatology was measured using the Inventory of Depressive Symptoms (IDS-C) and the Calgary Depression Scale for Schizophrenia (CDSS). All patients underwent neuropsychological assessment. Physical activity was measured as hours spent on any regular physical activity (≥ or ˂90 min) per week. BDNF mRNA was measured in plasma using standardised procedures. Results: Patients with ≥90 min of physical activity per week had fewer depressive symptoms (P ˂0.001, Cohen’s d = 0.48) and performed significantly better on working memory (P ˂ 0.001, d = 0.44) and executive functioning tasks (P ˂ 0.001, d = 0.50) compared to the ˂90-min group. BDNF mRNA was positively associated with physical activity (P = 0.046) and cognitive functioning (P = 0.037). Conclusions: Our study suggests a positive association between self-reported physical activity, cognitive function, mood and BDNF mRNA levels in severe mental disorders.
Accelerated Sensorimotor Learning of Compliant Movement Primitives

Autonomous trajectory generation through generalization requires a database of motion, which can be difficult and time consuming to obtain. In this paper, we propose a method for autonomous expansion of a database for the generation of compliant and accurate motion, achieved through the framework of compliant movement primitives (CMPs). These combine task-specific kinematic and corresponding feed-forward dynamic trajectories. The framework allows for generalization and modulation of dynamic behavior. Inspired by human sensorimotor learning abilities, we propose a novel method that can autonomously learn task-specific torque primitives (TPs) associated to given kinematic trajectories, encoded as dynamic movement primitives. The proposed algorithm is completely autonomous, and can be used to rapidly generate and expand the CMP database. Since CMPs are parameterized, statistical generalization can be used to obtain an initial TP estimate of a new CMP. Thereby, the learning rate of new CMPs can be significantly improved. The evaluation of the proposed approach on a Kuka LWR-4 robot performing a peg-in-hole task shows fast TP acquisition and accurate generalization estimates in real-world scenarios.