Miracle-dz/UniFAR-FTUs huggingface dataset:
Privacy Protection of Electrical Data With Scene Differentiation Based on Generative Adversarial Networks

The growing openness and frequent flow of electricity data in today's environment pose complex challenges, including pervasive risks of data leakage throughout its lifecycle. This paper proposes a privacy protection method for electricity data using generative adversarial networks (GANs) tailored to scenario-specific variations. The approach involves parsing data interaction interfaces and encoding variables with different feature encoders based on their types and characteristics. A GAN, enhanced with information, downstream, generator loss, and Was+GP improvements, is employed to generate anonymized data, introducing random noise for differentiated privacy protection across scenarios. The Jensen-Shannon Divergence of our model is lower by 0.04 and 0.053 than that of CT-GAN and TableGAN, respectively, demonstrating a significant reduction in statistical similarity differences between the synthesized and original data. This approach effectively replaces the original data for analysis and sharing, ensuring privacy protection.
Enterprise Microblog as a New Marketing Strattegy for Companies: Enterprise Microblog Commitment and Brand Loyalty

With the emergence and bloom of microblogging, companies are showing their increasing interests in this platform and try to establish their own enterprise accounts (called as enterprise microblogs in this case) in it, which enables them to broadcast product information, interact with consumers, and promote their brands. Despite much work focusing on how virtual brand communities take effect on consumers’ loyalty in the area of IS and marketing research, this paper aims to explore how companies can take advantage of their enterprise microblogs to enhance consumers’ brand loyalty. Our study aims to develop and empirically validate a brand loyalty model within the context of enterprise microblogs. In our framework, users’ enterprise microblog trust and sense of community positively affect their enterprise microblog commitment, and then enterprise microblog commitment leads to brand loyalty as a result.
Goal-directed, habitual and Pavlovian prosocial behavior

Although prosocial behaviors have been widely studied across disciplines, the mechanisms underlying them are not fully understood. Evidence from psychology, biology and economics suggests that prosocial behaviors can be driven by a variety of seemingly opposing factors: altruism or egoism, intuition or deliberation, inborn instincts or learned dispositions, and utility derived from actions or their outcomes. Here we propose a framework inspired by research on reinforcement learning and decision making that links these processes and explains characteristics of prosocial behaviors in different contexts. More specifically, we suggest that prosocial behaviors inherit features of up to three decision-making systems employed to choose between self- and other- regarding acts: a goal-directed system that selects actions based on their predicted consequences, a habitual system that selects actions based on their reinforcement history, and a Pavlovian system that emits reflexive responses based on evolutionarily prescribed priors. This framework, initially described in the field of cognitive neuroscience and machine learning, provides insight into the potential neural circuits and computations shaping prosocial behaviors. Furthermore, it identifies specific conditions in which each of these three systems should dominate and promote other- or self- regarding behavior.
Visual Explanation of Evidence with Additive Classifiers

Machine-learned classifiers are important components of many data mining and knowledge discovery systems. In several application domains, an explanation of the classifier's reasoning is critical for the classifier's acceptance by the end-user. We describe a framework, ExplainD, for explaining decisions made by classifiers that use additive evidence. ExplainD applies to many widely used classifiers, including linear discriminants and many additive models. We demonstrate our ExplainD framework using implementations of naive Bayes, linear support vector machine, and logistic regression classifiers on example applications. ExplainD uses a simple graphical explanation of the classification process to provide visualizations of the classifier decisions, visualization of the evidence for those decisions, the capability to speculate on the effect of changes to the data, and the capability, wherever possible, to drill down and audit the source of the evidence. We demonstrate the effectiveness of ExplainD in the context of a deployed web-based system (Proteome Analyst) and using a downloadable Python-based implementation.
Survival Analysis of Taiwan Renal Registry Data System (TWRDS) 2000-2009

The increasing dialysis population is a global phenomenon. Taiwan still sat on the top of list regarding to dialysis prevalence, although incidence rate continuously fell in 2009 analysis. It is very interesting to ask the reason of dissociation between prevalence and incidence, and the survival trends of modern era. The purpose of this short review is to present trends of prevalence and survival after nationwide implementation of continuous quality improvement measurements since 2005. The 2009 data from TWRDS suggested 2 interesting finding and 1 prediction. First, the patient survival was reduced in the modern cohort in comparison to 1990-2001 cohorts. Second, the demographic characteristics of patients entering dialysis are changing. There are more diabetic and elderly patients in 2000- 2009 cohorts than older cohort. This ultimate phenomenon may contribute in part to the decrease survival of modern cohort. The current nationwide epidemiological data analysis suggested tentative prediction of future dialysis trend. Approximately 100 patients per million population increase in ESRD prevalence would be expected if the practice pattern and patient demography kept the same pace. Further effort should be given to slow down the increase of ESRD prevalence.
CirdoX: an on/off-line multisource speech and sound analysis software

Vocal User Interfaces in domestic environments recently gained interest in the speech processing community. This interest is due to the opportunity of using it in the framework of Ambient Assisted Living both for home automation (vocal command) and for call for help in case of distress situations, i.e. after a fall. C IRDO X, which is a modular software, is able to analyse online the audio environment in a home, to extract the uttered sentences and then to process them thanks to an ASR module. Moreover, this system perfoms non-speech audio event classification; in this case, specific models must be trained. The software is designed to be modular and to process on-line the audio multichannel stream. Some exemples of studies in which C IRDO X was involved are described. They were operated in real environment, namely a Living lab environment.
Understanding the impact of immersive study abroad experiences on college of agricultural and environmental sciences student soft skill development

EXPERIENCES ON COLLEGE OF AGRICULTURAL AND ENVIRONMENTAL SCIENCES STUDENT SOFT SKILL DEVELOPMENT By: SARAH GAINES DELOACH (Under the Direction of Nicholas Fuhrman) ABSTRACT The purpose of this study was to determine the significance of immersive study abroad experiences in relation to building soft skills in College of Agriculture undergraduate students. This study specifically examined (1) motivation factors for completing an immersive study abroad experience, (2) the self-perceived competency of students at using soft skills prior to, during their experience abroad, and once they returned home, and (3) what about this experience makes it so unique. Qualitative research methods were used to conduct two focus groups, one focus group with students who participated in individual travel and the other with students who engaged in group travel. Significant Life Experience Research was used to examine the emotional and revealing side of human experiences while abroad and the development of soft skills through this experience. Results conduced that participants developed different soft skill depending on whether they traveled individually or with others.
Short-Term Caloric Restriction Normalizes Hypothalamic Neuronal Responsiveness to Glucose Ingestion in Patients With Type 2 Diabetes

The hypothalamus is critically involved in the regulation of feeding. Previous studies have shown that glucose ingestion inhibits hypothalamic neuronal activity. However, this was not observed in patients with type 2 diabetes. Restoring energy balance by reducing caloric intake and losing weight are important therapeutic strategies in patients with type 2 diabetes. We hypothesized that caloric restriction would have beneficial effects on the hypothalamic neuronal response to glucose ingestion. Functional magnetic resonance imaging was performed in 10 male type 2 diabetic patients before and after a 4-day very-low-calorie diet (VLCD) at a 3.0 Tesla scanner using a blood oxygen level–dependent technique for measuring neuronal activity in the hypothalamus in response to an oral glucose load. Hypothalamic signals were normalized to baseline value, and differences between the pre- and postdiet condition were tested using paired t tests. Pre-VLCD scans showed no response of the hypothalamus to glucose intake (i.e., no signal decrease after glucose intake was observed). Post-VLCD scans showed a prolonged signal decrease after glucose ingestion. The results of the current study demonstrate that short-term caloric restriction readily normalizes hypothalamic responsiveness to glucose ingestion in patients with type 2 diabetes.
Prefixes and the Delimitation of Events

In Slavic languages, verbal prefixes can be applied to perfective verbs deriving new perfective verbs, and multiple prefixes can occur in a single verb. This well-known type of data has not yet been adequately analyzed within current approaches to the semantics of Slavic verbal prefixes and aspect. The notion “aspect” covers “grammatical aspect”, or “viewpoint aspect” (see Smith 1991/1997), best characterized by the formal perfective vs. imperfective distinction, which is often expressed by inflectional morphology (as in Romance languages), and corresponds to propositional operators at the semantic level of representation. It also covers “lexical aspect”, “situation aspect” (see Smith ibid.), “eventuality types” (Bach 1981, 1986), or “Aktionsart” (as in Hinrichs 1985; Van Valin 1990; Dowty 1999; Paslawska and von Stechow 2002, for example), which regards the telic vs. atelic distinction and its Vendlerian subcategories (activities, accomplishments, achievements and states). It is lexicalized by verbs, encoded by derivational morphology, or by a variety of elements at the level of syntax, among which the direct object argument has a prominent role, however, the subject (external) argument is arguably a contributing factor, as well (see Dowty 1991, for example). These two “aspect” categories are orthogonal to each other and interact in systematic ways (see also Filip 1992, 1997, 1993/99; de Swart 1998; Paslawska and von Stechow 2002; Rothstein 2003, for example). Multiple prefixation and application of verbal prefixes to perfective bases is excluded by the common view of Slavic prefixes, according to which all perfective verbs are telic and prefixes constitute a uniform class of “perfective” markers that that are applied to imperfective verbs that are atelic and derive perfective verbs that are telic. Moreover, this view of perfective verbs and prefixes predicts rampant violations of the intuitive “one delimitation per event” constraint, whenever a prefix is applied to a perfective verb. This intuitive constraint is motivated by the observation that an event expressed within a single predication can be delimited only once: cp. *run a mile for ten minutes, *wash the clothes clean white.
Analysis and comparison of multimodal cancer treatments.

We analyse the sequence in which the three most commonly prescribed cancer treatments--surgery (S), chemotherapy (C) and radiotherapy (R)--should be administered. A system of ordinary differential equations is formulated that captures the various local and systemic effects of the three modes of treatment, as well as the first-order effects of the inter-relationship between the primary tumour and the distant metastatic tumours, including primary tumour shedding and the primary tumour's effect on the rate of angiogenesis in the metastatic tumours. Under a set of stated assumptions on the parameter values, we find the exact cancer cure probability (subject to toxicity constraints) for the six permutation schedules (i.e. SCR, CSR, CRS, SRC, RSC, RCS) and for two novel schedules, SRCR and RSCR, that apply radiotherapy in disjoint, optimally timed portions. We show analytically that SRCR and RSCR are the two best-performing (i.e. highest cure probability) schedules among the eight considered. Further, SRCR is shown to be optimal among all possible schedules, provided a modest condition is satisfied on the delay of initial angiogenesis experienced by the patient's dormant tumours.
A Refined Multi-Grained Attention Graph for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) is a finegrained task to determine the sentiment polarities towards given aspect terms, which plays an important role in understanding the opinions of the public in the era of the Internet. Recent studies tackled natural language as sequences and tend to apply larger total computation to improve model performance. However, with the instruction of prior domain knowledge, much more refined and efficient neural networks can be designed. In this paper, we embed grammar knowledge through dependency parsing to generate a graph from text, and propose a global-dependency multi-grained attention graph (G-D graph) that imitates the attention pattern of the human intensive reading process. Then we construct a specially designed refined intelligent graph neural network (GNN) with the guidance of refined intelligence theory to exploit the potential of the attention graph structure to the maximum extent. Extensive experiments are conducted to compare the performances of our refined intelligent model with the dynamic graph construction and representation learning model, which does not have any instruction from humans in the training progress. Experimental results show that our model is more effective and more efficient than baseline models in ABSA tasks.
IoT-Based Efficient Storage System for Sustainable Agriculture

The integration of real-time sensing via Internet of Things (IoT) technology has paved the way for innovative solutions to address various crucial challenges in agriculture. One such challenge is the effective management of agricultural storage facilities for reducing the spoilage and storage losses prone to happen during the storage of farm produce such as onions, potatoes, fruits like mangoes. The losses can happen due to moisture loss, sprouting, rottening, fungal/ biological infections. Some of these deteriorations are directly related to microclimate conditions inside the storage units. Thereby, preventive measures based on real-time monitoring of microclimate conditions such as temperature, humidity, CO2 build-up inside the storage can help to increase the shelf-life and reduce the losses. Further, microclimate conditions at different storage compartments are different. This paper presents a comprehensive study on the implementation of a smart agricultural storage monitoring system utilizing a Bluetooth Low Energy (BLE)-based wireless sensor network and IoT technology for recording microclimate conditions at every storage compartment (zone) in real-time, and mobile application to give remote access to real-time, zone-wise data on the mobile. This enables timely decision-making to prevent spoilage, optimize storage conditions, and efficient storage monitoring, leading to sustainable agriculture practices for storage.
The Impact of Inter-Modulation Components on Interferometric GNSS-Reflectometry

The interferometric Global Navigation Satellite System Reflectometry (iGNSS-R) exploits the full spectrum of the transmitted GNSS signal to improve the ranging performance for sea surface height applications. The Inter-Modulation (IM) component of the GNSS signals is an additional component that keeps the power envelope of the composite signals constant. This extra component has been neglected in previous studies on iGNSS-R, in both modelling and instrumentation. This letter takes the GPS L1 signal as an example to analyse the impact of the IM component on iGNSS-R ocean altimetry, including signal-to-noise ratio, the altimetric sensitivity and the final altimetric precision. Analytical results show that previous estimates of the final altimetric precision were underestimated by a factor of 1 . 5 ∼ 1 . 7 due to the negligence of the IM component, which should be taken into account in proper design of the future spaceborne iGNSS-R altimetry missions.
Psychosocial components of cardiac recovery and rehabilitation attendance

OBJECTIVE To examine the relations between demographic factors, specific psychosocial factors, and cardiac rehabilitation attendance. DESIGN Cohort, repeated measures design. SETTING A large tertiary care centre in western Canada PATIENTS 304 consecutive consenting patients discharged following acute myocardial infarction and/or coronary artery bypass graft surgery. MAIN OUTCOME MEASURES The Jenkins self-efficacy expectation scales and activity checklists of behaviour performance for maintaining health and role resumption, modified version of the self-motivation inventory, and the shortened social support scale. RESULTS Those who had higher role resumption behaviour performance scores at two weeks after discharge were significantly less likely to attend cardiac rehabilitation programmes. At six months after discharge, those who attended cardiac rehabilitation demonstrated higher health maintenance self-efficacy expectation and behaviour performance scores. Health maintenance self-efficacy expectation and behaviour performance improved over time. Women reported less social support but showed greater improvement in health maintenance self-efficacy expectation. Changes in self-efficacy scores were unrelated to—but changes in health maintenance behaviour performance scores were strongly associated with—cardiac rehabilitation attendance. CONCLUSIONS Cardiac patients and practitioners may have misconceptions about the mandate and potential benefits of rehabilitation programmes. Patients who resumed role related activities early and more completely apparently did not see the need to “rehabilitate” while those who attended cardiac rehabilitation programmes enhanced their secondary prevention behaviours.
Wearable finger ring dual band antenna made of fabric cloth for BAN use

This paper describes the simulated results of wearable finger ring dual band antenna made of fabric cloth for BAN use. VSWR less than 2 was obtained at ISM band (2.5GHz) and UWB high band (7.25 - 10.25 GHz). The mechanism was cleared by the current distributions. The influence of the next finger was made cleared.
f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization

Generative neural samplers are probabilistic models that implement sampling using feedforward neural networks: they take a random input vector and produce a sample from a probability distribution defined by the network weights. These models are expressive and allow efficient computation of samples and derivatives, but cannot be used for computing likelihoods or for marginalization. The generative-adversarial training method allows to train such models through the use of an auxiliary discriminative neural network. We show that the generative-adversarial approach is a special case of an existing more general variational divergence estimation approach. We show that any f-divergence can be used for training generative neural samplers. We discuss the benefits of various choices of divergence functions on training complexity and the quality of the obtained generative models.
Ductal lavage findings in women with known breast cancer undergoing mastectomy.

BACKGROUND Ductal lavage has the potential to detect cancer by sampling breast epithelium in asymptomatic high-risk women. To assess the utility of ductal lavage as a cancer diagnostic test, we investigated the association between ductal lavage cytologic findings and histologic findings in women with known breast cancer undergoing mastectomy. METHODS Ductal lavage was performed in the operating room before mastectomy on 44 breasts from 32 women with known cancer and on eight breasts from seven women undergoing prophylactic mastectomy, two with occult malignancy. If the ductal lavage sample from one or more ducts contained enough epithelial cells for a cytologic diagnosis, lavaged ducts were injected with a mixture of colored dye, gelatin, and a radiographic contrast compound after mastectomy, and breast tissue was radiographed and sectioned. Histologic findings in ducts with and without dye were recorded. Associations between cytologic results and histologic results were examined by univariate and multivariable analyses. RESULTS At least one duct was lavaged in 36 breasts (mean = 1.4 ducts per breast); all histologic and cytologic procedures were completed in 28 breasts and in 39 ducts. Markedly atypical or malignant cytology was found in five cancer-containing breasts. In 39 ducts with complete cytologic and histologic data and when marked atypia or malignant cells defined a positive cytologic test, sensitivity was 43% (95% confidence interval [CI] = 23% to 72%), specificity was 96% (95% CI = 86% to 100%), and accuracy was 77% (95% CI = 63% to 89%). When mild or marked atypia or malignant cells defined a positive cytologic test, sensitivity was 79% (95% CI = 57% to 96%), specificity was 64% (95% CI = 46% to 83%), and accuracy was 69% (95% CI = 55% to 83%). When all 31 cytologically evaluable breasts were analyzed, sensitivity was 17% (95% CI = 7% to 35%), specificity was 100% (95% CI = 5% to 100%), and accuracy was 19% (95% CI = 9% to 38%). CONCLUSION In breasts with cancer, ductal lavage appears to have low sensitivity and high specificity for cancer detection, possibly because cancer-containing ducts fail to yield fluid or have benign or mildly atypical cytology.
Wavelet Windows for leakage reduction in remote sensing images

Wavelets are usually used for data analysis and other applications. The remote-sensing images are usually defocused/ blurred due to the target's motion or improper backscatter received. This defocusing cannot be eliminated unless a proper defocusing algorithm is used. One of the simple techniques to remove defocusing is by applying windowing. Due to the bell-shaped structure of the wavelets, they can also be used as windows. This paper shows the application of window functions derived from different wavelets for leakage reduction in remote sensing images. Finally, it is shown that the window function derived from the complex Morlet wavelet gives better results than already existing windows by using inverse synthetic aperture (ISAR) data.
Experience as a Free Parameter in the Cognitive Modeling of Language

To account for natural variability in cognitive processing, it is standard practice to optimize the parameters of a model to account for behavioral data. However, variability reflecting the information to which one has been exposed is usually ignored. Nevertheless, most language theories assign a large role to an individual’s experience with language. We present a new way to fit language-based behavioral data that combines simple learning and processing mechanisms using optimization of language materials. We demonstrate that benchmark fits on multiple linguistic tasks can be achieved using this method and will argue that one must account not only for the internal parameters of a model but also the external experience that people receive when theorizing about human behavior.