Miracle-dz/UniFAR-FTUs huggingface dataset:
SCHEME OF IOT MIDDLEWARE BASED ON MESSAGE SERVICE

Subscription-based message-pushing can effectively reduce the processing pressure of data servers in the Internet of Things (IOT) system. This paper describes the architecture of an IOT middleware based on message service. The middleware integrates processing chips for data acquisition and Wi-Fi modules for networking. It provides hardware and software interfaces for locale applications and web services. Both debugging mode and working mode is supplied. Using this middleware to build an IOT system, the development cost will be greatly reduced. The stress testing of a module of middleware-based lighting landscape system is described. With 90 concurrent threads, the response speed and throughput are normal in a stable network environment. At the same time, using the web service framework nginx+ tomcat + microservice + zookeeper based on EMQTTD, a distributed system can be built easily by adding servers in a flat way, and cluster services be developed.
Factors controlling halogenated and nonhalogenated alkene growth substrate range of vinyl chloride -utilizing bacteria

Vinyl fluoride (VF) is a stable molecule in aqueous solution and its aerobic transformation potentially yields F. This work evaluated if VF is a suitable surrogate for monitoring aerobic vinyl chloride (VC) utilization or cometabolic transformation. Experiments were carried out with three isolates, Mycobacterium strain EE13a, Mycobacterium strain JS60 and Nocardioides strain JS614 to evaluate if their affinities for VF and VC and their rates of transformation were comparable and whether the transformation of VF and F accumulation could be correlated with VC utilization. JS614 grew on VF in addition to VC, making it the first organism reported to use VF as a sole carbon and energy source. EE13a cometabolized VC and VF, and JS60 catabolized VC and cometabolized VF. There was little difference among the three strains in the Ks or kmax values for VC or VF. Competitive inhibition modeled the temporal responses of VF and VC transformations and Cl and F release when both substrates were present. Both the rate of VF transformation and rate of F accumulation could be correlated with the rate of aerobic transformation of VC, and showed promise for estimating VC rates in situ using VF as a reactive surrogate.
Navigating Existential, Transformative Game Design

Games have the power to move players profoundly and ignite personal transformation and growth, as illustrated by our own gameplay histories as well as numerous anecdotes from our gameplaying network of friends, colleagues and students. The only way, though, to legitimately claim a game to be a game for change, it seems, is if said change can be quantified and measured. Measuring change, however, is most possible, if it is agenda driven and has clearly defined pre / post conditions. Such an approach is proposed by Culyba’s ( 2018 ) Transformational framework, which has become the state of the art in the games for change community. This is problematic for two main reasons: 1) it limits the kinds of change we aim to design for to those that are measurable and 2) it precludes us from being able to acknowledge and meaningfully discuss powerful, but less clearly measurable, transformations. A large part of games’ transformative potential thus remains either unrealized or invisible. In fact, as Paulo Pedercini (2014) noted at a keynoted at Games 4 Change, “the kinds of change we can clearly measure are not all that interesting”. This work explores how we can intentionally design for personal transformation without pre-determining the kind of change the game should ignite or seeks to impose upon its players. It discusses how we can facilitate profound, organic inner shifts through our designs that allow players to “respond to them of themselves, with recognition, uncoerced. ” (Campbell 1991 , p.3).
Rinodina brauniana (Physciaceae, Teloschistales), a new species with pseudoisidia from the southern Appalachian Mountains of eastern North America

Abstract. Rinodina brauniana is described as new to science from collections made throughout the southern Appalachian Mountains in the eastern United States (Alabama, North Carolina and Tennessee). It appears to be widespread on corticolous substrates in deciduous forests throughout the region, occurring at sites spanning an elevation gradient of 285–1585 meters. The species differs from R. colobinoides in its pseudoisidiate, rather than blastidiate thallus. Ultrastructural studies of the pseudoisidia in R. brauniana demonstrated that they are apically ecorticate and thus unlike vegetative reproductive structures in other Rinodina species. Although rare, apothecia were located and ascospores were found to have an unusual type-B development that is most similar to Pachysporaria-type II. The new species honors E. Lucy Braun, pioneering American field biologist and champion of temperate eastern North American forests.
Social Norms and Entrepreneurial Intention in University Researchers in Colombia

espanolLas Normas Sociales o Subjetivas (NS), pueden definirse como las pautas que buscan moldear la conducta de las personas dentro de la sociedad a partir de las diferentes creencias y costumbres de los distintos grupos que la conforman. Por lo general, las NS no estan escritas, pero son conocidas por las personas. La Intencion Emprendedora (IE), por su parte, es un paso previo a la creacion de una empresa que no siempre se concreta en el montaje de nuevos negocios. Los objetivos de este trabajo son: hallar asociaciones entre las NS y las demas variables analizadas, e identificar las NS que determinan la IE entre los investigadores universitarios en Colombia. La investigacion es de tipo descriptivo y correlacional, y tiene un diseno no experimental y transversal. Se toma una muestra aleatoria estratificada de los evaluadores pares del Departamento Administrativo de Ciencia, Tecnologia e Innovacion de Colombia (Colciencias), en las areas de ciencias, tecnologia, ingenieria y matematicas. Los resultados muestran que las NS si determinan la IE de los investigadores universitarios y que estas se relacionan con pautas que moldean su comportamiento en tres vertientes: a) la imagen positiva del empresario a partir de valores como su dedicacion al trabajo y su autonomia, b) la motivacion social para ser empresario en vez de ser empleado, y c) la admiracion por el empresario y el dinero como elementos secundarios que motivan la IE. EnglishSocial or subjective norms (SN) can be defined as the guidelines that seek to shape the behavior of people within society based on the different beliefs and customs of the distinct groups that comprise it. Usually, SN are not written, but are known by people. Entrepreneurial intention (EI), on the other hand, is a previous step to the creation of a company that does not always take shape in the assembly of new businesses. The objectives of this work were to find associations between the SN and the other variables analyzed, and to identify the SN that determine EI among university researchers in Colombia. The research is descriptive and correlational, and has a non-experimental and cross-sectional design. A stratified random sample is taken from peer evaluators from the Colombian Administrative Department of Science, Technology, and Innovation (COLCIENCIAS) in the areas of science, technology, engineering, and mathematics. The results show that SN do determine the EI of university researchers and that these are related to guidelines that shape their behavior in three ways: a) the positive image of the entrepreneur from values, such as their dedication to work and their autonomy; b) social motivation to become an entrepreneur instead of being employed; and c) admiration for the entrepreneur and money as secondary elements that motivate EI
Short-term depression and long-term plasticity together tune sensitive range of synaptic plasticity

Synaptic efficacy is subjected to activity-dependent changes on short- and long time scales. While short-term changes decay over minutes, long-term modifications last from hours up to a lifetime and are thought to constitute the basis of learning and memory. Both plasticity mechanisms have been studied extensively but how their interaction shapes synaptic dynamics is little known. To investigate how both short- and long-term plasticity together control the induction of synaptic depression and potentiation, we used numerical simulations and mathematical analysis of a calcium-based model, where pre- and postsynaptic activity induces calcium transients driving synaptic long-term plasticity. We found that the model implementing known synaptic short-term dynamics in the calcium transients can be successfully fitted to long-term plasticity data obtained in visual- and somatosensory cortex. Interestingly, the impact of spike-timing and firing rate changes on plasticity occurs in the prevalent firing rate range, which is different in both cortical areas considered here. Our findings suggest that short- and long-term plasticity are together tuned to adapt plasticity to area-specific activity statistics such as firing rates. Author summary Synaptic long-term plasticity, the long-lasting change in efficacy of connections between neurons, is believed to underlie learning and memory. Synapses furthermore change their efficacy reversibly in an activity-dependent manner on the subsecond time scale, referred to as short-term plasticity. It is not known how both synaptic plasticity mechanisms – long- and short-term – interact during activity epochs. To address this question, we used a biologically-inspired plasticity model in which calcium drives changes in synaptic efficacy. We applied the model to plasticity data from visual- and somatosensory cortex and found that synaptic changes occur in very different firing rate ranges, which correspond to the prevalent firing rates in both structures. Our results suggest that short- and long-term plasticity act in a well concerted fashion.
A Big Bang – Big Crunch Type-2 Fuzzy Logic Based System for Fraud-Detection: Case Study Balad Bank in Sudan

– Improved fraud detection systems are vital tools for the evolution of the Sudanese banking sector where the traditional fraud detection models are incapable of overcoming the emerging, innovative and new attacks that threaten large financial institutions. Hence, there is a need for accurate and transparent techniques which can automatically detect fraud with high speed and identify its causes and common patterns. Many of the Artificial Intelligence (AI) techniques are effective and provide good predictive models. Nevertheless, they are considered as black-box models. On the other hand, the white box models are easy to understand and analyze, but result in a large number of rules, besides having many parameters in each rule. In this paper, we present a novel system based on the Big Bang–Big Crunch optimization (BB– BC) approach, which is combined with type-2 Fuzzy Logic Systems to result in a small set of short IF-Then rules for the fraud detection within the Sudanese banking sector. The proposed system uses real-world dataset from Balad Bank – Sudan, which contains 803,386 transactions with 107 fraud transactions. Hence, the positive class (frauds) rate is 0.0133% of all transactions. The experimental results demonstrate that the performance of proposed system is effective in tuning the parameters of the rule base and membership functions of the Type-2 FLSs (T2FLSs) to improve the accuracy, where the proposed T2FLSs outperformed the Type-1 FLSs (T1FLSs) counterpart, as well as each rule can be simply explainable. Therefore, this can be very helpful for the Sudanese banks to start tracking the fraud cases.
Demographical, personality, alcohol use, and mental health characteristics associated with different alcoholic beverage preferences among students

Abstract The study investigates correlates (i.e. demographics, personality, alcohol use and alcohol-related harm, alcohol expectancies, and mental health) of different alcoholic beverage preferences (i.e. beer/alcopops/cider, wine, and liquor/spirits). Data were collected by an online survey during fall 2016. Participants were invited to the survey based on participation in a former survey that was sent to students in Bergen, Norway, in fall 2015. The current sample consists of 5,217 participants. A multinomial regression analysis was conducted, where alcoholic beverage preferences comprised the dependent variable. Several correlates were associated with beverage preferences. For instance, being a woman and the personality trait conscientiousness were inversely related to a preference for beer/alcopops/cider while positively associated with a preference for wine. Preferences for wine or liquor/spirits were positively associated with depression and inversely related to anxiety. Conscientiousness as a personality trait might be a common factor in the relationship between wine preference and favourable health outcomes, and this trait should be controlled for in future studies. The current findings seem contrary to the assumed gender equality in Norway, where the strong association between sex and alcoholic beverage preferences suggests that traditional gender divisions prevail even in the current young and urban sample.
Explainable AI meets Healthcare: A Study on Heart Disease Dataset

With the increasing availability of structured and unstructured data and the swift progress of analytical techniques, Artificial Intelligence (AI) is bringing a revolution to the healthcare industry. With the increasingly indispensable role of AI in healthcare, there are growing concerns over the lack of transparency and explainability in addition to potential bias encountered by predictions of the model. This is where Explainable Artificial Intelligence (XAI) comes into the picture. XAI increases the trust placed in an AI system by medical practitioners as well as AI researchers, and thus, eventually, leads to an increasingly widespread deployment of AI in healthcare. In this paper, we present different interpretability techniques. The aim is to enlighten practitioners on the understandability and interpretability of explainable AI systems using a variety of techniques available which can be very advantageous in the health-care domain. Medical diagnosis model is responsible for human life and we need to be confident enough to treat a patient as instructed by a black-box model. Our paper contains examples based on the heart disease dataset and elucidates on how the explainability techniques should be preferred to create trustworthiness while using AI systems in healthcare.
Multilingual Bottleneck Features for Improving ASR Performance of Code-Switched Speech in Under-Resourced Languages

In this work, we explore the benefits of using multilingual bottleneck features (mBNF) in acoustic modelling for the automatic speech recognition of code-switched (CS) speech in African languages. The unavailability of annotated corpora in the languages of interest has always been a primary challenge when developing speech recognition systems for this severely under-resourced type of speech. Hence, it is worthwhile to investigate the potential of using speech corpora available for other better-resourced languages to improve speech recognition performance. To achieve this, we train a mBNF extractor using nine Southern Bantu languages that form part of the freely available multilingual NCHLT corpus. We append these mBNFs to the existing MFCCs, pitch features and i-vectors to train acoustic models for automatic speech recognition (ASR) in the target code-switched languages. Our results show that the inclusion of the mBNF features leads to clear performance improvements over a baseline trained without the mBNFs for code-switched English-isiZulu, English-isiXhosa, English-Sesotho and English-Setswana speech.
Learning Rolling Shutter Correction from Real Data without Camera Motion Assumption

The rolling shutter mechanism in modern cameras generates distortions as the images are formed on the sensor through a row-by-row readout process; this is highly undesirable for photography and vision-based algorithms (e.g., structure-from-motion and visual SLAM). In this paper, we propose a deep neural network to predict depth and camera poses for single-frame rolling shutter correction. Compared to the state-of-the-art, the proposed method has no assumptions on camera motion. It is enabled by training on real images captured by rolling shutter cameras instead of synthetic ones generated with certain motion assumption. Consequently, the proposed method performs better for real rolling shutter images. This makes it possible for numerous vision-based algorithms to use imagery captured using rolling shutter cameras and produce highly accurate results. Our evaluations on the TUM rolling shutter dataset using DSO and COLMAP validate the accuracy and robustness of the proposed method.
Partial decay widths of $\phi(2170)$ to kaonic resonances

In this work we study the strong decays of φ(2170) to final states involving the kaonic resonances K(1460), K1(1270) and K1(1400), on which experimental data have recently been extracted by the BESIII Collaboration. The formalism developed here is based on interpreting φ(2170) and K(1460) as states arising from three-hadron dynamics, which is inspired by our earlier works. For K1(1270) and K1(1400) we investigate different descriptions, such as a mixture of states belonging to the nonet of axial resonances, or the former one as a state originating from the vectorpseudoscalar dynamics. The ratios among the partial widths of K+(1460)K−, K 1 (1400)K − and K 1 (1270)K − obtained are compatible with the experimental results, reinforcing the three-body nature of φ(2170). Within our formalism, we can also explain the suppressed decay of φ(2170) to K∗(892)K̄∗(892), as found by the BESIII Collaboration. Furthermore, our results can be useful in clarifying the properties of K(1460), K1(1270) and K1(1400) when higher statistics data would be available.
DRAST - A Deep Learning and AST Based Approach for Bug Localization

Context: Given a bug report and source code of the project, bug localization can help developers to focus on fixing probable buggy files rather than searching the entire source code repository. While existing research uses information retrieval (IR) and/or combination of machine learning (ML) or deep learning (DL) approaches, they focus primarily on benchmark Java projects, and also motivate the need for multi-language bug localization approach. Objective: To create a novel bug localization approach that leverages the syntactic structure of source code, bug report information and which can support multi-language projects along with a new dataset of C projects. Method: The proposed DRAST approach represents source code as code vectors by using its high-level AST and combines rVSM, an IR technique with ML/DL models such as Random Forest and Deep Neural Network regressor to rank the list of buggy files. We also use features such as textual similarity using IR techniques, lexical mismatch using DNNs, and history of the project using the metadata of BugC dataset. Results: We tested DRAST on seven projects from the BugC dataset, which consists of 2462 bug reports from 21 open-source C projects. The results show that DRAST can locate correct buggy files 90% of the time from top 1, 5, and 10 suggested files with MAP and MRR scores of above 90% for the randomly selected seven projects. We also tested DRAST on Tomcat and AspectJ, projects from benchmark dataset with better results at accuracy@1, MAP and MRR when compared with state-of-the-art. Conclusions: This paper presents a novel bug localization approach that works on C and Java projects and a bug localization C dataset along with a novel source code representation. The results for C projects using DRAST are promising and could motivate researchers/practitioners to focus on developing and creating multi-language bug localization approaches.
Modular primitives for high-performance differentiable rendering

We present a modular differentiable renderer design that yields performance superior to previous methods by leveraging existing, highly optimized hardware graphics pipelines. Our design supports all crucial operations in a modern graphics pipeline: rasterizing large numbers of triangles, attribute interpolation, filtered texture lookups, as well as user-programmable shading and geometry processing, all in high resolutions. Our modular primitives allow custom, high-performance graphics pipelines to be built directly within automatic differentiation frameworks such as PyTorch or TensorFlow. As a motivating application, we formulate facial performance capture as an inverse rendering problem and show that it can be solved efficiently using our tools. Our results indicate that this simple and straightforward approach achieves excellent geometric correspondence between rendered results and reference imagery.
Fast and stable deep-learning predictions of material properties for solid solution alloys

We present a novel deep learning (DL) approach to produce highly accurate predictions of macroscopic physical properties of solid solution binary alloys and magnetic systems. The major idea is to make use of the correlations between different physical properties in alloy systems to improve the prediction accuracy of neural network (NN) models. We use multitasking NN models to simultaneously predict the total energy, charge density and magnetic moment. These physical properties mutually serve as constraints during the training of the multitasking NN, resulting in more reliable DL models because multiple physics properties are correctly learned by a single model. Two binary alloys, copper–gold (CuAu) and iron–platinum (FePt), were studied. Our results show that once the multitasking NN’s are trained, they can estimate the material properties for a specific configuration hundreds of times faster than first-principles density functional theory calculations while retaining comparable accuracy. We used a simple measure based on the root-mean-squared errors to quantify the quality of the NN models, and found that the inclusion of charge density and magnetic moment as physical constraints leads to more stable models that exhibit improved accuracy and reduced uncertainty for the energy predictions.