Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Lexical Features of Programming Languages from Imagery Using Convolutional Neural Networks

We demonstrate the ability of deep architectures, specifically convolutional neural networks, to learn and differentiate the lexical features of different programming languages presented in coding video tutorials found on the Internet. We analyze over 17,000 video frames containing examples of Java, Python, and other textual and non-textual objects. Our results indicate that not only can computer vision models based on deep architectures be taught to differentiate among programming languages with over 98% accuracy, but can learn language-specific lexical features in the process. This provides a powerful mechanism for carrying out program comprehension research on repositories where source code is represented with imagery rather than text, while simultaneously avoiding the computational overhead of optical character recognition.
Context-aware Academic Collaborator Recommendation

Collaborator Recommendation is a useful application in exploiting big academic data. However, existing works leave out the contextual restriction (i.e., research topics) of people's academic collaboration, thus cannot recommend suitable collaborators for the required research topics. In this work, we propose Context-aware Collaborator Recommendation (CACR), which aims to recommend high-potential new collaborators for people's context-restricted requests. To this end, we design a novel recommendation framework, which consists of two fundamental components: the Collaborative Entity Embedding network (CEE) and the Hierarchical Factorization Model (HFM). In particular, CEE jointly represents researchers and research topics as compact vectors based on their co-occurrence relationships, whereby capturing researchers' context-aware collaboration tendencies and topics' underlying semantics. Meanwhile, HFM extracts researchers' activenesses and conservativenesses, which reflect their intensities of making academic collaborations and tendencies of working with non-collaborated fellows. The extracted activenesses and conservativenesses work collaboratively with the context-aware collaboration tendencies, such that high-quality recommendation can be produced. Extensive experimental studies are conducted with large-scale academic data, whose results verify the effectiveness of our proposed approaches.
Resolving Abstract Anaphora Implicitly in Conversational Assistants using a Hierarchically stacked RNN

Recent proliferation of conversational systems has resulted in an increased demand for more natural dialogue systems, capable of more sophisticated interactions than merely providing factual answers. This is evident from usage pattern of a conversational system deployed within our organization. Users not only expect it to perform co-reference resolution of anaphora, but also of the antecedent or posterior facts presented by users with respect to their query. Presence of such facts in a conversation sometimes modifies the answer of main query, e.g., answer to 'how many sick leave do I get? ' would be different when a fact 'I am on contract' is also present. Sometimes there is a need to collectively resolve three or four such facts. In this paper, we propose a novel solution which uses hierarchical neural network, comprising of BiLSTM layer and a maxpool layer that is hierarchically stacked to first obtain a representation of each user utterance and then to obtain a representation for sequence of utterances. This representation is used to identify users' intention. We also improvise this model by using skip connections in the second network to allow better gradient flow. Our model, not only a)~resolves the antecedent and posterior facts, but also b)~performs better even on self-contained queries. It is also c)~faster to train, making it the most promising approach for use in our environment where frequent training and tuning is needed. It slightly outperforms the benchmark on a publicly available dataset, and e)~performs better than obvious baselines approaches on our datasets.
On the Universality of Hot Carrier Degradation: Multiple Probes, Various Operating Regimes, and Different MOSFET Architectures

Comprehensive review and reanalysis are done on previously reported experimental hot carrier degradation data in various MOSFET architectures. The universality of time kinetics and voltage acceleration of degradation, obtained using multiple measurement techniques, is analyzed for OFF- and different ON-state modes in long and short channel planar and drain extended MOSFETs and FinFETs. The similarities and differences of such diverse measurement methods are analyzed. Existing theories are qualitatively benchmarked against this analysis, and capabilities as well as inconsistencies of those theories are discussed.
Dynamic Handwriting Signal Features Predict Domain Expertise

As commercial pen-centric systems proliferate, they create a parallel need for analytic techniques based on dynamic writing. Within educational applications, recent empirical research has shown that signal-level features of students’ writing, such as stroke distance, pressure and duration, are adapted to conserve total energy expenditure as they consolidate expertise in a domain. The present research examined how accurately three different machine-learning algorithms could automatically classify users’ domain expertise based on signal features of their writing, without any content analysis. Compared with an unguided machine-learning classification accuracy of 71%, hybrid methods using empirical-statistical guidance correctly classified 79–92% of students by their domain expertise level. In addition to improved accuracy, the hybrid approach contributed a causal understanding of prediction success and generalization to new data. These novel findings open up opportunities to design new automated learning analytic systems and student-adaptive educational technologies for the rapidly expanding sector of commercial pen systems.
Immune Mechanisms Involved in Schistosoma mansoni-Cathepsin B Vaccine Induced Protection in Mice

A vaccine against schistosomiasis would contribute to a long-lasting decrease in disease spectrum and transmission. Our previous protection studies in mice using Schistosoma mansoni Cathepsin B (Sm-Cathepsin B) resulted in 59 and 60% worm burden reduction with CpG oligodeoxynucleotides and Montanide ISA720 VG as adjuvants, respectively. While both formulations resulted in significant protection in a mouse model of schistosomiasis, the elicited immune responses differed. Therefore, in this study, we aimed to decipher the mechanisms involved in Sm-Cathepsin B vaccine-mediated protection. We performed in vitro killing assays using schistosomula stage parasites as targets for lung-derived leukocytes and serum obtained from mice immunized with Sm-Cathepsin B adjuvanted with either Montanide ISA 720 VG or CpG and from non-vaccinated controls. Lung cells and immune sera from the Sm-Cathepsin B + Montanide group induced the highest killing (63%) suggesting the importance of antibodies in cell-mediated parasite killing. By contrast, incubation with lung cells from Sm-Cathepsin B + CpG immunized animals induced significant parasite killing (53%) independent of the addition of immune serum. Significant parasite killing was also observed in the animals immunized with Sm-Cathepsin B alone (41%). For the Sm-Cathepsin B + Montanide group, the high level killing effect was lost after the depletion of CD4+ T cells or natural killer (NK) cells from the lung cell preparation. For the Sm-Cathepsin B + CpG group, high parasite killing was lost after CD8+ T cell depletion, and a reduction to 39% was observed upon depletion of NK cells. Finally, the parasite killing in the Sm-Cathepsin B alone group was lost after the depletion of CD4+ T cells. Our results demonstrate how the different Sm-Cathepsin B formulations influence the immune mechanisms involved in parasite killing and protection against schistosomiasis.
Condiciones de contorno no reflectantes para dinámica de defectos en nanocristales

Resumo. La simulación efectiva de la dinámica de defectos en nanocristales requiere acoplar modelos atómicos no lineales en las zonas de concentración de defectos con descripciones linealizadas del resto del cristal. Expresando la dinámica exterior en términos de funciones de Green, obtenemos condiciones que evitan reflexiones espúreas en las fronteras artificiales. Pueden usarse para generar condiciones de transmisión con el fin de acoplar dos regiones con dinámicas distintas, o bien como condiciones de contorno artificiales que permiten truncar el recinto computacional.
Architectural Technical Debt Identification: The Research Landscape

Architectural Technical Debt (ATD) regards sub-optimal design decisions that bring short-term bene ts to the cost of long-term gradual deterioration of the quality of the architecture of a software system. The identi cation of ATD strongly in uences the technical and economic sustainability of software systems and is attracting growing interest in the scienti c community. During the years several approaches for ATD identi cation have been conceived, each of them addressing ATD from di erent perspectives and with heterogeneous characteristics. In this paper we apply the systematic mapping study methodology for identifying, classifying, and evaluating the state of the art on ATD identi cation from the following three perspectives: publication trends, characteristics, and potential for industrial adoption. Speci cally, starting from a set of 509 potentially relevant studies, we systematically selected 47 primary studies and analyzed them according to a rigorously-de ned classi cation framework. The analysis of the obtained results supports both researchers and practitioners by providing (i) an assessment of current research trends and gaps in ATD identi cation, (ii) a solid foundation for understanding existing (and future) research on ATD identi cation, and (iii) a rigorous evaluation of its potential for industrial adoption.
Recognizing Software Bug-Specific Named Entity in Software Bug Repository

Software bug issues are unavoidable in software development and maintenance. In order to manage bugs effectively, bug tracking systems are developed to help to record, manage and track the bugs of each project. The rich information in the bug repository provides the possibility of establishment of entity-centric knowledge bases to help understand and fix the bugs. However, existing named entity recognition (NER) systems deal with text that is structured, formal, well written, with a good grammatical structure and few spelling errors, which cannot be directly used for bug-specific named entity recognition. For bug data, they are free-form texts, which include a mixed language studded with code, abbreviations and software-specific vocabularies. In this paper, we summarize the characteristics of bug entities, propose a classification method for bug entities, and build a baseline corpus on two open source projects (Mozilla and Eclipse). On this basis, we propose an approach for bug-specific entity recognition called BNER with the Conditional Random Fields (CRF) model and word embedding technique. An empirical study is conducted to evaluate the accuracy of our BNER technique, and the results show that the two designed baseline corpus are suitable for bug-specific named entity recognition, and our BNER approach is e? ective on cross-projects NER.
How Slim Will My System Be? Estimating Refactored Code Size by Merging Clones

We have been doing code clone analysis with industry collaborators for a long time, and have been always asked a question, "OK, I understand my system contains a lot of code clones, but how slim will it be after merging redundant code clones? " As a software system evolves for long period, it would increasingly contain many code clones due to quick bug fix and new feature addition. Industry collaborators would recognize decay of initial design simplicity, and try to evaluate current system from the view point of maintenance effort and cost. As one of resources for the evaluation, the estimated code size by merging code clone is very important for them. In this paper, we formulate this issue as "slimming" problem, and present three different slimming methods, Basic, Complete, and Heuristic Methods, each of which gives a lower bound, upper bound, and modest reduction rates, respectively. Application of these methods to OSS systems written in C/C++ showed that the reduction rate is at most 5.7% of the total size, and to a commercial COBOL system, it is at most 15.4%. For this approach, we have gotten initial but very positive feedback from industry collaborators.
Multi-Pattern Active Cell Balancing Architecture and Equalization Strategy for Battery Packs

Active cell balancing is the process of improving the usable capacity of a series-connected Lithium-Ion (Li-Ion) battery pack by redistributing the charge levels of individual cells. Depending upon the State-of-Charge (SoC) distribution of the individual cells in the pack, an appropriate charge transfer pattern (cell-to-cell, cell-to-module, module-to-cell or module-to-module) has to be selected for improving the usable energy of the battery pack. However, existing active cell balancing circuits are only capable of performing limited number of charge transfer patterns and, therefore, have a reduced energy efficiency for different types of SoC distribution. In this paper, we propose a modular, multi-pattern active cell balancing architecture that is capable of performing multiple types of charge transfer patterns (cell-to-cell, cell-to-module, module-to-cell and module-to-module) with a reduced number of hardware components and control signals compared to existing solutions. We derive a closed-form, analytical model of our proposed balancing architecture with which we profile the efficiency of the individual charge transfer patterns enabled by our architecture. Using the profiling analysis, we propose a hybrid charge equalization strategy that automatically selects the most energy-efficient charge transfer pattern depending upon the SoC distribution of the battery pack and the characteristics of our proposed balancing architecture. Case studies show that our proposed balancing architecture and hybrid charge equalization strategy provide up to a maximum of 46.83% improvement in energy efficiency compared to existing solutions.
Living in the intertidal: desiccation and shading reduce seagrass growth, but high salinity or population of origin have no additional effect

The limiting effects of stressors like desiccation, light and salinity on seagrass growth and distribution are well-studied. However, little is known about their interactive effects, and whether such effects might differ among populations that are adapted to different local conditions. In two laboratory experiments we tested (a) if growth and development of intertidal, temperate Zostera noltii is affected by emergence time (experiment 1 and 2), and (b) how this is affected by an additional, second stressor, namely shading (experiment 1) or high salinity (25, 30 and 35, experiment 2). In addition, we tested (c) whether the effects of emergence time and salinity varied between three different European seagrass populations (Saint-Jacut/France, Oosterschelde/The Netherlands, and Sylt/Germany), which are likely adapted to different salinity levels (experiment 2). In both experiments, emergence of 8 h per tidal cycle (of 12 h) had a negative effect on seagrass relative growth rate (RGR), and aboveground biomass. Emergence furthermore reduced either rhizome length (experiment 1) or belowground biomass (experiment 2). Shading (experiment 1) resulted in lower RGR and a two-fold higher aboveground/belowground ratio. We found no interactive effects of emergence and shading stress. Salinity (experiment 2) did not affect seagrass growth or morphology of any of the three populations. The three tested populations differed greatly in morphology but showed no differential response to emergence or salinity level (experiment 2). Our results indicate that emergence time and shading show an additive negative effect (no synergistic or antagonistic effect), making the plants still vulnerable to such combination, a combination that may occur as a consequence of self-shading during emergence or resulting from algal cover. Emergence time likely determines the upper limit of Z. noltii and such shading will likely lower the upper limit. Shading resulted in higher aboveground/belowground ratios as is a general response in seagrass. Z. noltii of different populations originating from salinity 30 and 35 seem tolerant to variations in salinity within the tested range. Our results indicate that the three tested populations show morphotypic rather than ecotypic variation, at least regarding the salinity and emergence, as there were no interactive effects with origin. For restoration, this implies that the salinity regime of the donor and receptor site of Z. noltii is of no concern within the salinity range 25–35.
Nuclear Factor of Activated T Cell-regulated Cytokine Gene Expression for Adjustment of Tacrolimus in Kidney Transplant Recipients: A Randomized Controlled Pilot Trial

Background The aim of this pilot study was to assess the feasibility of a pharmacodynamics assay that measures Nuclear Factor of Activated T Cell–dependent cytokines expressed as % mean residual expression (MRE) to adjust tacrolimus (tac) dose (intervention [INT] arm) in comparison with the standard of care of tac trough levels (control [CTL] arm). Methods We conducted a single-center randomized controlled trial involving 40 stable kidney transplant recipients over 1 year. In the INT arm, the dose of tac was reduced by 15% if the MRE was less than 20% and was increased by 15% if the MRE was greater than 60%. Controls were adjusted based on tac trough levels. Results There was a median of 2 tac dose changes per arm. Ten subjects had 1 or more infections in the INT arm and 6 subjects had 1 or more infection in the CTL arm. Rates for hospitalizations, rejections, malignancies and death were similar in both arms. In subjects whose tac dose was not adjusted in the first 6 months, those with infections had a lower MRE at enrollment compared with those without infections (P = 0.049). This was not true for tac trough levels (P = 0.80). There was no correlation between MRE and rejection. Conclusions Our study suggests that adjusting tac based on this pharmacodynamics assay is feasible. Quantitative analysis of nuclear factor of activated T-regulated gene expression may serve as a reliable assay to lower tac dosing. Further studies with larger populations are needed.
Understanding Synaptic Mechanisms in SrTiO3 RRAM Devices

In this paper, we investigated the performance of SrTiO3 (STO) resistive random-access memory (RRAM) as a candidate to realize short-term synaptic elements in neuromorphic circuits. Ionic defect movement within oxides is postulated to play a significant role in resistive state reconfiguration of these devices. This relatively small movement in oxides was sensed by developing a novel measurement technique using differential voltammetry. Using this technique, the widely hypothesized belief of drift and diffusion of ionic defects in oxides was systematically studied and linked to the state reconfiguration in STO RRAM. Next, the devices’ state retention was examined and a consistent decay of the resistive state was observed. The decay rate was found to be a function of the conditioning voltage, allowing us to control the state decay via operational voltages. We then developed a test battery that quantified how these devices operate within a spike-rate encoded learning paradigm. By utilizing a carefully tuned test battery, we were able to quantify the magnitude of potentiation and depression due to varying spike amplitudes and spike rates. These tests provide a systematic methodology to capture the constantly changing resistive state of short-term synaptic elements.
A Controlled Experiment on Oil Release Beneath Thin Sea Ice and Its Electromagnetic Detection

This paper presents a multidisciplinary research on the thermodynamic and geophysical effects of crude oil released underneath thin sea ice, and further evaluates the ability of a combined frequency- and time-domain approach toward its detection. To this end, a controlled oil release experiment in an artificially grown sea ice mesocosm was performed during the winter of 2017 at the Sea-Ice Research Environmental Facility located at the University of Manitoba. Ice cores extracted during the evolution of the sea ice prior and post oil injection allowed the investigation of the profile’s properties and the oil distribution. Furthermore, chemical composition and microstructure analysis were performed via a gas chromatography-time-of-flight mass spectrometry and X-ray, respectively. The time-series radar signature of the profile was measured utilizing ground penetration radar at 500 MHz and a C-band scatterometer. For this experiment, it was shown that the retrieval of the oil presence underneath the young sea ice layer was feasible, provided that the measured data were utilized simultaneously in a unified cost function.
Characterizing the algorithmic complexity of reconfigurable data center architectures

Emerging data center architectures are becoming reconfigurable. While prior work has shown the practical benefits of reconfigurable topologies, the underlying algorithmic complexity is not yet well understood. In particular, most reconfigurable topologies are hybrid, where parts of the network are reconfigurable (consisting of optical or wireless devices) while other parts are static (consisting of electrical switches). Current proposals enforce a routing policy that routes flows on either part "exclusively" by labeling flows as mice or elephant. We show that such artificial segregation in routing policy results in non-optimal paths and argue for algorithms that route packets across the network seamlessly. In doing so, we present the first algorithmic study of reconfigurable network architectures and provide optimality and hardness proofs in terms of topology and routing policy. Our results show that classical matching algorithms, as used in prior work, are optimal only when the topology consists of one reconfigurable switch, and the routing policy is enforced to be segregated. In other words, if there is an option of routing flows seamlessly along reconfigurable and non-reconfigurable parts of the network, matching algorithms are not optimal. In fact, when the hybrid network is seen from a joint perspective, optimal routing is an NP-hard problem. We further show that optimally routing even two flows in a network with multiple reconfigurable switches is an NP-hard problem as well.