Miracle-dz/UniFAR-FTUs huggingface dataset:
Transport of broilers: load microclimate during Brazilian summer

The objective of this study was to characterize the microclimatic profile of broiler transport trucks under commercial conditions, in the summer, by continuous monitoring of environmental variables (temperature and air relative humidity). Three loads were monitored from farm to slaughterhouse, considering factors such as distance and day periods (morning, afternoon, and night). To obtain the profile of the environmental variables during journey, data loggers, that determined the microclimate to which birds were submitted, were installed in the trucks; data loggers also allowed visualization of the Enthalpy Comfort Index (ECI) so that load regions could be classified according to heat comfort limits for 6-week old poultries. Temperature, relative humidity, and ECI in the truck were analyzed, using geostatistics, by the ordinary kriging method. The afternoon was the most critical period, and truck central and rear regions were most problematic for chickens, thus most susceptible to losses.
WiFuzz : detecting and exploiting logical flaws in the Wi-Fi cryptographic handshake

Encrypted Wi-Fi networks are increasingly popular. This is highlighted by new standards such as Hotspot 2.0 and Opportunistic Wireless Encryption. Hotspot 2.0 streamlines network discovery and selection, creating an authenticated roaming experience matching that of cellular phones. On the other hand, Opportunistic Wireless Encryption introduces unauthenticated encryption for Wi-Fi networks. However, these advancements are meaningless if there are implementation flaws in the 4-way Wi-Fi handshake that negotiates fresh session keys. In this report, we show how to detect and abuse logical flaws in implementations of this handshake.
Temporal variability in dissolved organic carbon and radiocarbon in the eastern North Pacific Ocean

The factors regulating the steady state inventories and residence times of dissolved organic carbon (DOC) in the deep ocean are not well established. Previous studies of DOC have been limited to single time-point profiles that provide general information on the potential role of vertical advective-diffusive processes in controlling DOC distributions and mean apparent ages. We present results from a 2-year time series station in the eastern North Pacific (station M) where short-term (months) changes in inventories and Δ14C signatures of DOC as measured in deep profiles were examined in conjunction with changes in particulate organic carbon (POC) pools. Significant long-term (i.e., months to years) changes in both DOC concentrations and Δ14C values were observed. These changes were especially evident at mesopelagic (∼450 and 700 m) depths, close to the oxygen minimum. Both within the mixed layer and at mesopelagic depths, positive relationships were found between DOC Δ14C values and concentrations of station M, primarily reflecting diminishing vertical inputs of “recent” DOC throughout the main thermocline. At abyssal depths (≥1600 m), however, Δ14C was inversely correlated with DOC concentration. The Δ14C signature of the less abundant suspended and sinking POC pools has been observed to fluctuate over seasonal timescales at station M, presumably due in part to sorption of DOC to POC [Druffel et al., 1996]. However, the Δ14C values and concentrations of the correspondingly much larger DOC pool do not appear to be related to seasonal changes in either sinking POC fluxes or suspended POC abundances. Significantly elevated concentrations of DOC were observed at station M when compared with a previously occupied site in the north central Pacific (NCP) in all regions of the water column except mesopelagic depths, where concentrations were lower. The corresponding Δ14C values of DOC at all depths at station M were lower than in the NCP. We speculate that dissimilarities in the size and Δ14C signature of the DOC pools at seasonally productive station M and the oligotrophic NCP result from differences in DOC sources and sinks between the two regions, as well as from the magnitude of interaction between DOC and POC at these sites.
Bidirectional Long Short-Term Memory (BILSTM) with Conditional Random Fields (CRF) for Knowledge Named Entity Recognition in Online Judges (OJS)

This study investigates the effectiveness of Knowledge Named Entity Recognition in Online Judges (OJs). OJs are lacking in the classification of topics and limited to the IDs only. Therefore a lot of time is consumed in finding programming problems more specifically in knowledge entities.A Bidirectional Long Short-Term Memory (BiLSTM) with Conditional Random Fields (CRF) model is applied for the recognition of knowledge named entities existing in the solution reports.For the test run, more than 2000 solution reports are crawled from the Online Judges and processed for the model output. The stability of the model is also assessed with the higher F1 value. The results obtained through the proposed BiLSTM-CRF model are more effectual (F1: 98.96%) and efficient in lead-time.
Les déterminants de l’adoption des réseaux sociaux numériques en situation professionnelle : étude empirique au sein des bibliothèques des universités ivoiriennes Determinants of adoption of social digital networks by Ivory Coast university librarians

Cet article vise a documenter et a apporter une nouvelle validation de la Theorie Unifiee de l’Acceptation et de l’Utilisation de la Technologie (UTAUT), principalement en situation professionnelle. Notre recherche, s’est effectuee aupres d’une population composee de 82 bibliothecaires des universites ivoiriennes, et qui a utilisee comme methode d’analyse, la regression lineaire, nous a permis d’identifier quatre facteurs qui influencent significativement l’intention d’adoption de la technologie. Ce sont « l’utilite percue », « l’influence du superieur hierarchique », « l’adequation tâche-technologie » et « le sentiment d’efficacite percue ». Egalement notre etude a montre avec surprise l’absence de l’effet significatif de la variable « facilite d’utilisation percue » (l’une des plus importantes de l’UTAUT) sur l’intention d’adoption.
Visibility of eigen-distortions of hierarchical models

We compare several models of visual representation in terms of their ability to predict human judgements of visual distortion. Each model is defined by a differentiable mapping from image inputs to a response vector, which is then corrupted by additive Gaussian noise. We use the Fisher Information matrix to predict discrimination thresholds for the visibility of arbitrary distortions, up to an unknown scale factor. We test this by generating, for each model, a pair of specific distortions corresponding to the largest and smallest eigenvectors of the Fisher Information matrix, which represent the model-predicted most and least noticeable changes to the image, respectively. We distort the image by adding multiples of each vector, and measure detection thresholds for human subjects in a two-alternative forced-choice task. Results are quantified using the difference, D, of the log vector length at threshold for the two extremal eigenvectors, T (~vn) and T (~ wn). Two random perturbation vectors would yield of value of approximately D = 0, and larger values of D indicate that the sensitivity of the model is better aligned with that of humans. We used this methodology to test three models: a simple model of LGN neurons that includes local luminance and contrast normalization[1, 4], and two different 4-stage convolutional neural networks. Each model was trained on a database of human perceptual judgments[5]. We found that, despite performing slightly better in terms of cross-validated correlation with the database, both artificial neural networks performed much worse than the LGN model on this test. We conclude that in this situation, where data are somewhat scarce, cross validation is not powerful enough to expose failures of a particular model class. Our method provides a more general form of cross-validation, based on the synthesis of model-optimized stimuli and comparison with human judgments on these targeted stimuli, and ensures that the model generalizes beyond curated data.
Characterization of Alkaliphilic, Surfactant Stable and Raw StarchDigesting A-Amylase from Bacillus subtilis Strain JS-16

An alkali-thermophilic and surfactant stable α-amylase was obtained from Bacillus subtilis strain JS-16, isolated from sludge samples of a soda ash industry, located on west coast of Gujarat. It was active over a wide temperature range (30°C-80°C) exhibiting optimum activity at 50°C and pH 9.0. Unique feature of this amylase was two-fold enhancement of activity in SDS. Three-step purification yielded 15.16-fold purified enzyme with specific activity of 13.5 U/mg proteins. Km and Vmax were 10 mg/ml and 0.2 μmol/min/ml (11.56 μmol/ min/mg protein) respectively. Enzyme activity enhanced with Fe3+ but was strongly inhibited by Hg2+ ions. The amylase hydrolyzed 12% raw wheat starch and 5% corn starch granules after 12 h incubation. Surfactant stability, alkaliphilic nature, activity under wide temperature range and hydrolyses of raw starch makes this amylase a promising candidate for liquid detergent and starch industry.
Multilocus phylogeography of the sea snake Hydrophis curtus reveals historical vicariance and cryptic lineage diversity

The Indo‐Australian archipelago (IAA) supports the world's highest diversity of marine fish, invertebrates and reptiles. Many of the marine fish and invertebrates show congruent phylogeographic patterns, supporting a view that the region's complex geo‐climatic history has played an important role in generating its exceptional biodiversity. Here, we examine population genetic structure of the viviparous sea snake, Hydrophis curtus, to assess how past and present barriers to gene flow in the IAA have contributed to genetic and species diversity in a fully marine reptile. Mitochondrial and anonymous nuclear sequences and ten microsatellite loci were used to identify patterns of historical genetic structure and population expansion, reconstruct dated genealogies and assess levels of recent gene flow. These markers revealed strong concordant geographic structure within H. curtus with a prominent genetic break between populations broadly distributed in the Indian Ocean and the West Pacific. These populations were estimated to have diverged in the late Pliocene or early Pleistocene, and microsatellite admixture analyses suggested limited recent gene flow between them despite the current lack of barriers to dispersal, indicating possible cryptic species. Subsequent divergence in the mid–late Pleistocene was detected within the West Pacific clade among the populations in the Phuket‐Thailand region, South‐East Asia and Australia, and two of these populations also showed genetic signals of recent range expansions. Our results show that climatic fluctuations during the Plio‐Pleistocene generated high levels of cryptic genetic diversity in H. curtus, and add to similar findings for diverse other marine groups in the IAA.
Video Rain Streak Removal by Multiscale Convolutional Sparse Coding

Videos captured by outdoor surveillance equipments sometimes contain unexpected rain streaks, which brings difficulty in subsequent video processing tasks. Rain streak removal from a video is thus an important topic in recent computer vision research. In this paper, we raise two intrinsic characteristics specifically possessed by rain streaks. Firstly, the rain streaks in a video contain repetitive local patterns sparsely scattered over different positions of the video. Secondly, the rain streaks are with multiscale configurations due to their occurrence on positions with different distances to the cameras. Based on such understanding, we specifically formulate both characteristics into a multiscale convolutional sparse coding (MS-CSC) model for the video rain streak removal task. Specifically, we use multiple convolutional filters convolved on the sparse feature maps to deliver the former characteristic, and further use multiscale filters to represent different scales of rain streaks. Such a new encoding manner makes the proposed method capable of properly extracting rain streaks from videos, thus getting fine video deraining effects. Experiments implemented on synthetic and real videos verify the superiority of the proposed method, as compared with the state-of-the-art ones along this research line, both visually and quantitatively.
Context Prediction in the Social Web Using Applied Machine Learning: A Study of Canadian Tweeters

In this ongoing work, we present the Grebe social data aggregation framework for extracting geo-fenced Twitter data for analysis of user engagement in health and wellness topics. Grebe also provides various visualization tools for analyzing temporal and geographical health trends. Grebe currently has over 18 million indexed public tweets, and is the first of its kind for Canadian researchers. The large dataset is used for analyzing three types of contexts: geographical context via prediction of user location using supervised learning, topical context via determining health-related tweets using various learning approaches, and affective context via sentiment analysis of tweets using rule-based methods. For the first, we define user location as the position from which users are posting a tweet and use standard precision metrics for evaluation with promising results for predicting provinces and cities from tweet text. For the second, we use a broader definition of health using the six dimensions of wellness model and evaluate using manually annotated documents with good results using supervised and semi-supervised machine learning. For the third, we use the indexed tweets to show current trends in emotions and opinions and demonstrate trends in polarity and emotions across various Canadian provinces. The combination of these contexts provides useful insights for digital epidemiology. Ultimately, the vision of Grebe is to provide researchers with Canada-specific social web datasets through an open source platform with an accessible RESTful API, and this paper showcases Grebe's potential and presents our progress towards achieving these goals.
Towards a framework to analyse theories use for the study of change

The importance of studying change has grown as a result of the rapid social, environmental, and other changes. Yet little or no scholarly research has been conducted on the theories deployed to the study of change This paper narrows this gap in the literature. It develops a framework that researchers can use to ensure their choice of theory supports the topic and research questions of their study. This allows for greater validity, reliability and generalizability of the findings. The framework consists of two dimensions: Radical / Incremental change and Planned / Emergent change. The two dimensions form four change quadrants in which studies can be located.
ShieldFS : The Last Word In Ransomware Resilient

Preventive and reactive security measures can only partially mitigate the damage caused by modern ransomware attacks. The remarkable amount of illicit profit and the cybercriminals’ increasing interest in ransomware schemes demonstrate that current defense solutions are failing, and a large number of users are actually paying the ransoms. In fact, pure-detection approaches (e.g., based on analysis sandboxes or pipelines) are not sufficient, because, when luck allows a sample to be isolated and analyzed, it is already too late for several users! Moreover, modern ransomware implements several techniques to prevent detection by common AV. Similarly, for performance reasons, backups leave a small-but-important window of recent files unprotected. We believe that a forward-looking solution is to equip modern operating systems with generic, practical self-healing capabilities against this serious threat. We present ShieldFS, a drop-in driver that makes the Windows native filesystem immune to ransomware attacks, even when detection fails ShieldFS dynamically toggles a protection layer that acts as a copy-onwrite mechanism whenever its detection component reveals suspicious activity. For this, ShieldFS monitors the filesystem’s internals to update a set of adaptive models that profile the system activity over time. This detection is based on a study of the filesystem activity of over 2,245 applications, and takes into account the entropy of write operations, frequency of read, write, and folder-listing operations, fraction of files renamed, and the file-type usage statistics. Additionally, ShieldFS monitors the memory pages of each “potentially malicious” process, searching for traces of the typical block cipher key schedules. We show how ShieldFS can shadow the write operations. Whenever one or more processes violate our detection component, their operations are deemed malicious and the side effects on the filesystem are transparently rolled back. We demonstrate how effective ShieldFS is against samples from state of the art ransomware families, showing that it is able to detect the malicious activity at runtime and transparently recover all the original files.
Automated Error Detection for Developing Grammar Proficiency of ESL Learners.

Thanks to natural language processing technologies, computer programs are actively being used not only for holistic scoring, but also for formative evaluation of writing. CyWrite is one such program that is under development. The program is built upon Second Language Acquisition theories and aims to assist ESL learners in higher education by providing them with effective formative feedback to facilitate autonomous learning and improvement of their writing skills. In this study, we focus on CyWrite’s capacity to detect grammatical errors in student writing. We specifically report on (1) computational and pedagogical approaches to the development of the tool in terms of students’ grammatical accuracy, and (2) the performance of our grammatical analyzer. We evaluated the performance of CyWrite on a corpus of essays written by ESL undergraduate students with regards to four types of grammatical errors: quantifiers, subject-verb agreement, articles, and run-on sentences. We compared CyWrite’s performance at detecting these errors to the performance of a well-known commercially available AWE tool, Criterion. Our findings demonstrated better performance metrics of our tool as compared to Criterion, and a deeper analysis of false positives and false negatives shed light on how CyWrite’s performance can be improved.
Manifold Learning in Quotient Spaces

When learning 3D shapes we are usually interested in their intrinsic geometry rather than in their orientation. To deal with the orientation variations the usual trick consists in augmenting the data to exhibit all possible variability, and thus let the model learn both the geometry as well as the rotations. In this paper we introduce a new autoencoder model for encoding and synthesis of 3D shapes. To get rid of undesirable input variability our model learns a manifold in a quotient space of the input space. Typically, we propose to quotient the space of 3D models by the action of rotations. Thus, our quotient autoencoder allows to directly learn in the space of interest, ignoring side information. This is reflected in better performances on reconstruction and interpolation tasks, as our experiments show that our model outperforms a vanilla autoencoder on the well-known Shapenet dataset. Moreover, our model learns a rotation-invariant representation, leading to interesting results in shapes co-alignment. Finally, we extend our quotient autoencoder to quotient by non-rigid transformations.
Phytochemical Evaluation, Anti-obesity and Antihyperlipidemic Effects of Combined Administration of Green Coffee, Cinnamon and Ginger

This study aims to evaluate anti-obesity and antihyperlipidemic effects of individual and combined administration of ginger, cinnamon and green coffee on the hyperlipidimic rats. It is the first report dealing with the effects of the combined administration of these three plants as anti-obesity and antihyperlipidemic agents. Sprague-Dawley rats were used, where they divided into 6 groups (7 rats each). Group 1 was kept as a normal control, while the other groups fed on high-fat diet to induce obesity and hyperlipidemia for 4 weeks. Group 2 positive control that was kept obese, while other groups were orally given individual and combined powders of ginger, cinnamon and green coffee in doses 200mg/kg, respectively, for 6 weeks, At the end of the experiment, body weight of rats were recorded. Blood samples were collected for analyses. The results showed that each of individual and combined administration of these plants significantly (P<0.05) decreased the body weight and serum lipids including total lipid, triglyceride and low density lipoprotein when compared with positive control group, while they significantly (P<0.05) increased high density lipoprotein when compared with positive control group. Furthermore, our study showed that the combination of these three plants exhibited better effects than when compared to the individual powder of the plants. In this study we compared the effects of separated and combined administration of cinnamon bark, ginger root, green coffee beans as anti-obesity and antihyperlipidemic agents. The separated and combined administration exhibited significant anti-obesity and antihyperlipidemic effects in obese and hyperlipidemic rats, while the combined administration showed more powerful effects. Previous reports on cinnamon, ginger and green coffee as separately administered were thus confirmed; however, this is, to our knowledge, the first report dealing with the combined form of the three plants. Such combined administration might be useful in developing natural food additives to help address obesity and hyperlipidemia.
Adaptive Scheduling for Systems with Asymmetric Memory Hierarchies

Conventional multicores rely on deep cache hierarchies to reduce data movement. Recent advances in die stacking have enabled near-data processing (NDP) systems that reduce data movement by placing cores close to memory. NDP cores enjoy cheaper memory accesses and are more area-constrained, so they use shallow cache hierarchies instead. Since neither shallow nor deep hierarchies work well for all applications, prior work has proposed systems that incorporate both. These asymmetric memory hierarchies can be highly beneficial, but they require scheduling computation to the right hierarchy. We present AMS, an adaptive scheduler that automatically finds high-quality thread-to-hierarchy mappings. AMS monitors threads, accurately models their performance under different hierarchies and core types, and adapts algorithms first proposed for cache partitioning to produce high-quality schedules. AMS is cheap enough to use online, so it adapts to program phases, and performs within 1% of an exhaustive-search scheduler. As a result, AMS outperforms asymmetry-oblivious schedulers by up to 37% and by 18% on average.
Participatory workshops : everyday objects and sound metaphors

The Legos project aims at studying sound gesture relationship, and specifically, how sound can affect a sensory-motor learning process. During the project, two participatory workshops related to sonic interaction design have been organized. The aim of the workshops was to provide a framework to favor exchanges between members, first, to stimulate ideas related to the control of everyday objects using sound, and then, to create and experiment with new sonic augmented objects. The present article provides an overview of the two workshops in order to reveal how this framework can be use for the creation of new sonic objects. The first workshop focused on the analyze and the possible sonification of everyday objects. New scenarios of use were obtained and tested. The second workshop focused on sound metaphor, questioning the relationship between sound and gesture using the concept of basic actions and the work on the sound synthesis engines. During the two workshops, experiments using sensors and real-time synthesis were carried on a selection of case studies.