Miracle-dz/UniFAR-FTUs huggingface dataset:
Image Captioning with Affective Guiding and Selective Attention

Image captioning is an increasingly important problem associated with artificial intelligence, computer vision, and natural language processing. Recent works revealed that it is possible for a machine to generate meaningful and accurate sentences for images. However, most existing methods ignore latent emotional information in an image. In this article, we propose a novel image captioning model with Affective Guiding and Selective Attention Mechanism named AG-SAM. In our method, we aim to bridge the affective gap between image captioning and the emotional response elicited by the image. First, we introduce affective components that capture higher-level concepts encoded in images into AG-SAM. Hence, our language model can be adapted to generate sentences that are more passionate and emotive. In addition, a selective gate acting on the attention mechanism controls the degree of how much visual information AG-SAM needs. Experimental results have shown that our model outperforms most existing methods, clearly reflecting an association between images and emotional components that is usually ignored in existing works.
Enhanced Light Extraction of GaN-Based Light-Emitting Diodes With a Hybrid Structure Incorporating Microhole Arrays and Textured Sidewalls

GaN-based light-emitting diodes (LEDs) with a hybrid structure, including a microhole array and textured sidewalls (45° angle and convex patterns), are fabricated and studied. The use of this hybrid structure, formed using an inductively coupled plasma etching approach, causes reduced total internal reflection and increased scattering probability of photons at the GaN/air interface. Therefore, the opportunity for photons to find an escape cone and the related light extraction efficiency can be effectively increased. This designed hybrid structure provides substantial improvements in optical performance without any degradation in electrical properties. For instance, as compared with a conventional LED, the studied device with this hybrid structure (45° angle sidewalls) shows remarkable improvements of 20.9%, 24.3%, 20.5%, and 21.3% in light output power, luminous fluxes, external quantum efficiency, and wall-plug efficiency, respectively. In addition, higher intensities in the light emission mapping images and far-field patterns of the studied device are found. Therefore, the studied hybrid structure is promising for high-performance GaN-based LED applications.
Hamartia: A Fast and Accurate Error Injection Framework

Single bit-flip has been the most popular error model for resilience studies with fault injection. We use RTL gate-level fault injection to show that this model fails to cover many realistic hardware faults. Specifically, single-event transients from combinational logic and single-event upsets in pipeline latches can lead to complex multi-bit errors at the architecture level. However, although accurate, RTL simulation is too slow to evaluate application-level resilience. To strike a balance between model accuracy and injection speed, we refine the concept of hierarchical injection to prune faults with known outcomes, saving 62% of program runs at 2% margin of error on average across 9 benchmark programs. Our implementation of the hierarchical error injector is not only accurate but also fast because it is able to source realistic error patterns using on demand RTL gate-level fault injection. Our tool outperforms state-of-the-art assembly-level and compiler-based error injectors by up to 6X, while providing higher fidelity.
Porting Bacis Decision Support System for Antenatal Care onto OpenMRS

This study is about documentation of the architectural considerations that need to be made when porting the Bacis (Basic Antenatal Care Information System) program onto the OpenMRS medical record system. This research was an empirical study in information systems which was carried out in the year 2016 by the two authors in South Africa. The OpenMRS medical record system was chosen for the porting because it is a system that is in routine use in many developing country contexts. The main result of the study is a framework that is proposed for carrying out the activities of porting the Bacis program onto OpenMRS. The framework has the following headings: mapping the Bacis program data model onto the OpenMRS concept dictionary; porting Bacis program classes; porting Bacis program interface; and porting the Bacis program rule base.
Towards a Novel Architecture for Enabling Interoperability amongst Multiple Blockchains

Next-generation blockchain ecosystem will be fuelled by a large variety of blockchain systems. These systems increasingly demand proper cross-chain cooperation to provide richer functionalities and enhanced capabilities in the future landscape. How to enable such `interoperability' – effective communication and efficient data transfer across multiple blockchain systems is thus critical and facing many unprecedented theoretical and practical challenges. In this paper, we first clarify the definition of interoperability based on the cross disciplinary nature. Second, a roadmap of challenges needed to be addressed for interoperability has been laid out. Third, we articulate novel architectural approaches to fill in the gap by enforcing the interoperability from different blockchain layers.
Route Recommendations for Idle Taxi Drivers: Find Me the Shortest Route to a Customer!

We study the problem of route recommendation to idle taxi drivers such that the distance between the taxi and an anticipated customer request is minimized. Minimizing the distance to the next anticipated customer leads to more productivity for the taxi driver and less waiting time for the customer. To anticipate when and where future customer requests are likely to come from and accordingly recom- mend routes, we develop a route recommendation engine called MDM: Minimizing Distance through Monte Carlo Tree Search. In contrast to existing techniques, MDM employs a continuous learning platform where the underlying model to predict future customer requests is dynamically updated. Extensive experiments on real taxi data from New York and San Francisco reveal that MDM is up to 70% better than the state of the art and robust to anomalous events such as concerts, sporting events, etc.
β-Glucan-Producing Pediococcus parvulus 2.6: Test of Probiotic and Immunomodulatory Properties in Zebrafish Models

Lactic acid bacteria synthesize exopolysaccharides (EPS), which could benefit the host’s health as immunomodulators. Furthermore, EPS could protect bacteria against gastrointestinal stress, favoring gut colonization, thus protecting the host against pathogenic infections. Pediococcus parvulus 2.6, produces a 2-substituted (1,3)-β-D-glucan and, in this work, its probiotic properties as well as the immunomodulatory capability of its EPS have been investigated using Danio rerio (zebrafish). To this end and for a comparative analysis, P. parvulus 2.6 and its isogenic β-glucan-non-producing 2.6NR strain were fluorescently labeled by transfer of the pRCR12 plasmid, which encodes the mCherry protein. For the in vivo studies, there were used: (i) a gnotobiotic larvae zebrafish model for bacterial colonization, pathogen competition, and evaluation of the β-glucan immunomodulation capability and (ii) a transgenic (mpx:GFP) zebrafish model to determine the EPS influence in the recruitment of neutrophils under an induced inflammation. The results revealed a positive effect of the β-glucan on colonization of the zebrafish gut by P. parvulus, as well as in competition of the bacterium with the pathogen Vibrio anguillarum in this environment. The larvae treatment with the purified β-glucan resulted in a decrease of expression of genes encoding pro-inflammatory cytokines. Moreover, the β-glucan had an anti-inflammatory effect, when it was evaluated in an induced inflammation model of Tg(mpx:GFP) zebrafish. Therefore, P. parvulus 2.6 and its EPS showed positive health properties in in vivo fish models, supporting their potential usage in aquaculture.
Immigration Restrictions as Active Labor Market Policy: Evidence from the Mexican Bracero Exclusion

An important class of active labor market policy has received little impact evaluation: immigration barriers intended to raise wages and employment by shrinking labor supply. Theories of endogenous technical advance raise the possibility of limited or even perverse impact. We study a natural policy experiment: the exclusion of almost half a million Mexican 'bracero' farm workers from the United States to improve farm labor market conditions. With novel archival data we measure state-level exposure to exclusion, and model the labor-market effect in the absence of technical change. We reject such an effect and fail to reject a null effect.
SAAGs: Biased Stochastic Variance Reduction Methods

Stochastic optimization is one of the effective approach to deal with the large-scale machine learning problems and the recent research has focused on reduction of variance, caused by the noisy approximations of the gradients, and momentum acceleration. In this paper, we have proposed simple variants of SAAG-I and II (Stochastic Average Adjusted Gradient) \cite{Chauhan2017Saag}, called SAAG-III and IV, respectively. Unlike SAAG-I, starting point is set to average of previous epoch in SAAG-III, and unlike SAAG-II, the snap point and starting point are set to average and last iterate of previous epoch, respectively. To determine the step size, we introduce Stochastic Backtracking-Armijo line Search (SBAS) which performs line search only on selected mini-batch of data points. Since backtracking line search is not suitable for large-scale problems and the constants used to find the step size, like Lipschitz constant, are not always available so SBAS could be very effective in such cases. We also extend SAAGs (I, II, III, IV), to solve non-smooth problems and design two update rules for smooth and non-smooth problems. Moreover, our theoretical results prove linear convergence of SAAG-IV for all the four combinations of smoothness and strong-convexity, in expectation. Finally, our experimental studies prove the efficacy of proposed methods against the state-of-art techniques, like, SVRG and VR-SGD.
Enhancing Privacy in Social Networks Systems using Homomorphic Cryptographic Techniques in Ubiquitous Computing Environment

This paper presents an innovative approach for addressing privacy violation in social networks systems (SNS) such as Facebook, Twitter and the like. The approach employs homomorphic cryptographic techniques to enhance privacy in SNS. A soft systems methodology is employed in this study to guide the research process. The study assesses and examines the effectiveness and efficiency of the existing privacy controls in SNS. The research findings reveal that the existing privacy controls in the SNS are not effective and efficient in addressing privacy violation in SNS. Thus, the problem of privacy violation in SNS can be addressed by using homomorphic cryptographic techniques. Homomorphic cryptography systems are capable of performing operations on encrypted data in SNS without knowing the private key. The users of SNS merely exchange encrypted value throughout in SNS by using public key infrastructure (PKI). This paper contributes security requirements for improving privacy in SNS using homomorphic cryptographic techniques.
Need for Laboratory Ecosystems To Unravel the Structures and Functions of Soil Microbial Communities Mediated by Chemistry

The chemistry underpinning microbial interactions provides an integrative framework for linking the activities of individual microbes, microbial communities, plants, and their environments. Currently, we know very little about the functions of genes and metabolites within these communities because genome annotations and functions are derived from the minority of microbes that have been propagated in the laboratory. ABSTRACT The chemistry underpinning microbial interactions provides an integrative framework for linking the activities of individual microbes, microbial communities, plants, and their environments. Currently, we know very little about the functions of genes and metabolites within these communities because genome annotations and functions are derived from the minority of microbes that have been propagated in the laboratory. Yet the diversity, complexity, inaccessibility, and irreproducibility of native microbial consortia limit our ability to interpret chemical signaling and map metabolic networks. In this perspective, we contend that standardized laboratory ecosystems are needed to dissect the chemistry of soil microbiomes. We argue that dissemination and application of standardized laboratory ecosystems will be transformative for the field, much like how model organisms have played critical roles in advancing biochemistry and molecular and cellular biology. Community consensus on fabricated ecosystems (“EcoFABs”) along with protocols and data standards will integrate efforts and enable rapid improvements in our understanding of the biochemical ecology of microbial communities.
Factors Impacting the Design of a Mobile Application for Soil Management: A Case Study of the Eastern Cape Crops

Amongst the biggest challenges the global community has to face daily is the loss of non-renewable fertile soil. This does not exclude the South African crop agriculture. New developments within m-agriculture allows for precision agriculture. However, very few mobile applications are focused on South African crop agriculture and even less on soil management for South Africa. Applying user centred design principles ensures that an application will be tailored to satisfy the needs of the targeted user group. This study focused on identifying the factors, which had the most impact on the design of a mobile soil management application. To achieve this, focus was placed on aspects of a mobile application such as the user interface, the user experience and user centred design factors.
Modeling Time in Java Programs for Automatic Error Detection

Modern programming languages, such as Java, represent time as integer variables, called timestamps. Timestamps allow developers to tacitly model incorrect time values resulting in a program failure because any negative value or every positive value is not necessarily a valid time representation. Current approaches to automatically detect errors in programs, such as Randoop and FindBugs, cannot detect such errors because they treat timestamps as normal integer variables and test them with random values verifying if the program throws an exception. In this paper, we present an approach that considers the time semantics of the Java language to systematically detect time related errors in Java programs. With the formal time semantics, our approach determines which integer variables handle time and which statements use or alter their values. Based on this information, it translates these statements into an SMT model that is passed to an SMT solver. The solver formally verifies the correctness of the model and reports the violations of time properties in that program. For the evaluation, we have implemented our approach in a prototype tool and applied it to the source code of 20 Java open source projects. The results show that our approach is scalable and it is capable of detecting time errors precisely enough allowing its usability in real-world applications.
In Situ Diazotroph Population Dynamics Under Different Resource Ratios in the North Pacific Subtropical Gyre

Major advances in understanding the diversity, distribution, and activity of marine N2-fixing microorganisms (diazotrophs) have been made in the past decades, however, large gaps in knowledge remain about the environmental controls on growth and mortality rates. In order to measure diazotroph net growth rates and microzooplankton grazing rates on diazotrophs, nutrient perturbation experiments and dilution grazing experiments were conducted using free-floating in situ incubation arrays in the vicinity of Station ALOHA in March 2016. Net growth rates for targeted diazotroph taxa as well as Prochlorococcus, Synechococcus and photosynthetic picoeukaryotes were determined under high (H) and low (L) nitrate:phosphate (NP) ratio conditions at four depths in the photic zone (25, 45, 75, and 100 m) using quantitative PCR and flow cytometry. Changes in the prokaryote community composition in response to HNP and LNP treatments were characterized using 16S rRNA variable region tag sequencing. Microzooplankton grazing rates on diazotrophs were measured using a modified dilution technique at two depths in the photic zone (15 and 125 m). Net growth rates for most of the targeted diazotrophs after 48 h were not stimulated as expected by LNP conditions, rather enhanced growth rates were often measured in HNP treatments. Interestingly, net growth rates of the uncultivated prymnesiophyte symbiont UCYN-A1 were stimulated in HNP treatments at 75 and 100 m, suggesting that N used for growth was acquired through continuing to fix N2 in the presence of nitrate. Net growth rates for UCYN-A1, UCYN-C, Crocosphaera sp. (UCYN-B) and the diatom symbiont Richelia (associated with Rhizosolenia) were uniformly high at 45 m (up to 1.6 ± 0.5 d-1), implying that all were growing optimally at the onset of the experiment at that depth. Differences in microzooplankton grazing rates on UCYN-A1 and UCYN-C in 15 m waters indicate that the grazer assemblage preyed preferentially on UCYN-A1. Deeper in the water column (125 m), both diazotrophs were grazed at substantial rates, suggesting grazing pressure may increase with depth in the photic zone. Constraining in situ diazotroph growth and mortality rates are important steps for improving parameterization for diazotrophs in global ecosystem models.
Ship Size Extraction for Sentinel-1 Images Based on Dual-Polarization Fusion and Nonlinear Regression: Push Error Under One Pixel

In this paper, we present a method of ship size extraction for Sentinel-1 synthetic aperture radar (SAR) images, which is composed of the image processing stage and the regression stage. In order to achieve extraction with high accuracy, considering the data characteristics of Sentinel-1 images, we propose to use the dual-polarization fusion and the nonlinear regression with the gradient boosting. The experiments and analyses on a relatively large data set show that: 1) compared with the existing and related studies, the proposed method achieves an improved performance. The extraction errors are pushed under one pixel, and they are 4.66% (8.80 m) and 7.01% (2.17 m) for length and width, respectively; 2) the dual-polarization information fusion does improve the size extraction accuracy; and 3) the nonlinear regression does exploit the relationship between the influential factors and the size parameters and provide a better performance than the linear regression. The experimental results verify that the proposed design is suitable for ship size extraction in Sentinel-1 SAR images.
Input-Splitting of Large Neural Networks for Power-Efficient Accelerator with Resistive Crossbar Memory Array

Resistive Crossbar memory Arrays (RCA) have been gaining interest as a promising platform to implement Convolutional Neural Networks (CNN). One of the major challenges in RCA-based design is that the number of rows in an RCA is often smaller than the number of input neurons in a layer. Previous works used high-resolution Analog-to-Digital Converters (ADCs) to compute the partial weighted sum in each array and merged partial sums from multiple arrays outside the RCAs. However, such approach suffers from significant power consumption due to the need for high-resolution ADCs. In this paper, we propose a methodology to more efficiently construct a large CNN with multiple RCAs. By splitting the input feature map and retraining the CNN with proper initialization, we demonstrate that any CNN model can be represented with multiple arrays without using intermediate partial sums. The experimental results show that the ADC power of the proposed design is 32x smaller and the total chip power of the proposed design is 3x smaller than those of the baseline design.
Aryl Hydrocarbon Receptor Regulates Apoptosis and Inflammation in a Murine Model of Experimental Autoimmune Uveitis

Uveitis is characterized as a common cause of blindness worldwide. Aryl hydrocarbon receptor (AhR), a ligand-activated nuclear receptor, has been implicated to play a role in human uveitis, although the exact mechanisms remain poorly understood. The purpose of this study was to enhance our knowledge concerning the role of AhR during intraocular inflammation. We immunized wild-type and AhR-knockout C57BL/6J mice with IRBP651–670 to induce experimental autoimmune uveitis (EAU). Disease severity was evaluated with both clinical and histopathological grading. Blood–retinal barrier (BRB) integrity was tested by Evans blue and tight junction proteins qualifications. Apoptosis was measured using TdT-mediated dUTP nick end labeling staining. Macrophage/microglia activation and polarization were studied by immunofluorescence and Western blot. Following EAU induction, AhR−/− mice had more severe clinical and histopathological manifestations of uveitis than AhR+/+ mice. Increased vascular permeability and apoptotic cells were observed in AhR−/− EAU mice when compared with AhR+/+ EAU mice. In addition, AhR−/− EAU mice showed evidence of a significantly increased macrophage/microglia cells and a stronger polarization from the M2 to the M1 phenotype as compared to AhR+/+ EAU mice. The levels of pro-inflammatory cytokines including tumor necrosis factor-α (TNF-α), interleukin (IL)-6, and IL-1β were increased in AhR−/− EAU mice, which was associated with the activation of NF-κB and signal transducers and activators of transcription (STAT) pathways. 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), an agonist of AhR, caused a significant decrease in the clinical and histopathological manifestations, preserved BRB integrity, reduced apoptotic cells, inhibited macrophage/microglia activation, and shifted their polarization from M1 toward M2. Moreover, decreased expression of pro-inflammatory cytokines including TNF-α, IL-6, and IL-1β and inhibition of NF-κB and STAT pathways were found in EAU mice following TCDD treatment. In conclusion, AhR activation with TCDD exhibits an immunomodulatory effect by reducing BRB breakdown, inhibiting retinal cell apoptosis, and reducing pro-inflammatory cytokine expression during EAU. The underlying mechanism may involve the modulation of macrophages/microglia polarization and the downregulation of NF-κB and STAT pathways.
Discrete Ranking-based Matrix Factorization with Self-Paced Learning

The efficiency of top-k recommendation is vital to large-scale recommender systems. Hashing is not only an efficient alternative but also complementary to distributed computing, and also a practical and effective option in a computing environment with limited resources. Hashing techniques improve the efficiency of online recommendation by representing users and items by binary codes. However, objective functions of existing methods are not consistent with ultimate goals of recommender systems, and are often optimized via discrete coordinate descent, easily getting stuck in a local optimum. To this end, we propose a Discrete Ranking-based Matrix Factorization (DRMF) algorithm based on each user's pairwise preferences, and formulate it into binary quadratic programming problems to learn binary codes. Due to non-convexity and binary constraints, we further propose self-paced learning for improving the optimization, to include pairwise preferences gradually from easy to complex. We finally evaluate the proposed algorithm on three public real-world datasets, and show that the proposed algorithm outperforms the state-of-the-art hashing-based recommendation algorithms, and even achieves comparable performance to matrix factorization methods.