Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of intravenous infusion of E.coli lipopolysaccharide in early pregnant cows.

The objective was to characterize effects of Escherichia coli LPS (given iv) on corpus luteum (CL) and embryonic viability in early pregnant cattle. Eight non-lactating German Holstein cows were given 0.5 µg/kg LPS on 35 ± 3 d (mean ± SEM) of pregnancy, whereas seven heifers, 41 ± 6 d pregnant, were given 10 ml saline (Control group). Transrectal B-mode examinations of the CL were done at -1, 3, 6, 12, 24, 48, 72, and 96 h relative to treatment. Blood samples were collected at -1, 0.5, 1, 2, 3, 4, 6, 9, 12, 24, 48, 72, and 96 h. At 12 and 48 h, the CL was biopsied. None of the cows still in the experiment 10 d after LPS (n=7) had embryonic loss. In LPS-treated cows, luteal area decreased (from 4.1 to 3.1 cm2; P≤0.05) within 6 h and until 48 h. Luteal blood flow decreased by 39% (P≤0.05) within the first 6 h after LPS, but returned to pre-treatment values by 48 h. Plasma P4 decreased by 62% (P≤0.05), reached a nadir (2.7±0.6 ng/mL) at 12 h after LPS and was not restored to pre-treatment (P≤0.05). In luteal tissue, mRNAs for StAR and for FGF1 were lower (P≤0.05) in LPS- than in saline-treated cattle at 12 h, with no difference between groups at 48 h. Levels of mRNAs for Casp3 and FGF2 were not different between groups (P>0.05) at 12 or 48 h after treatment. In conclusion, LPS transiently suppressed CL function, but did not induce embryonic mortality.
Maximizing I/O Throughput and Minimizing Performance Variation via Reinforcement Learning Based I/O Merging for SSDs

Merging technique is widely adopted by I/O schedulers to maximize system I/O throughput. However, I/O merging could increase the latency of individual I/O, thus incurring prolonged I/O latencies and enlarged performance variations. Even with better system throughput, higher worst-case latency experienced by some requests could block the SSD storage system, which violates the QoS (Quality of Service) requirement. In order to improve QoS performance while providing higher I/O throughput, this paper proposes a reinforcement learning based I/O merging approach. Through learning the characteristic of various I/O patterns, the proposed approach makes merging decisions adaptively based on different I/O workloads. Evaluation results show that the proposed scheme is capable of reducing the standard deviation of I/O latency by 19.1 percent on average, worst-case latency by 7.3-60.9 percent at the 99.9th percentile compared with the latest I/O merging scheme, while maximizing system throughput.
Seamless GPU Evaluation of Smart Expression Templates

Expression Templates is a technique allowing to write linear algebra code in C++ the same way it would be written on paper. It is also used extensively as a performance optimization technique, especially as the Smart Expression Templates form which allows for even higher performance. It has proved to be very efficient for computation on a Central Processing Unit (CPU). However, due to its design, it is not easily implemented on a Graphics Processing Unit (GPU). In this paper, we devise a set of techniques to allow the seamless evaluation of Smart Expression Templates on the GPU. The execution is transparent for the user of the library which still uses the matrices and vector as if it was on the CPU and profits from the performance and higher multi-processing capabilities of the GPU. We also show that the GPU version is significantly faster than the CPU version, without any change to the code of the user.
A Scalable Distributed Louvain Algorithm for Large-Scale Graph Community Detection

We present a new distributed community detection algorithm for large graphs based on the Louvain method. We exploit a distributed delegate partitioning to ensure the workload and communication balancing among processors. In addition, we design a new heuristic strategy to carefully coordinate the community constitution in a distributed environment, and ensure the convergence of the distributed clustering algorithm. Our intensive experimental study has demonstrated the scalability and the correctness of our algorithm with various large-scale real-world and synthetic graph datasets using up to 32,768 processors.
Energy-efficient link-layer jamming attacks against wireless sensor network MAC protocols

A typical wireless sensor node has little protection against radio jamming. The situation becomes worse if energy-efficient jamming can be achieved by exploiting knowledge of the data link layer. Encrypting the packets may help prevent the jammer from taking actions based on the content of the packets, but the temporal arrangement of the packets induced by the nature of the protocol might unravel patterns that the jammer can take advantage of even when the packets are encrypted. By looking at the packet interarrival times in three representative MAC protocols, S-MAC, LMAC and B-MAC, we derive several jamming attacks that allow the jammer to jam S-MAC, LMAC and B-MAC energy-efficiently. The jamming attacks are based on realistic assumptions. The algorithms are described in detail and simulated. The effectiveness and efficiency of the attacks are examined. Careful analysis of other protocols belonging to the respective categories of S-MAC, LMAC and B-MAC reveal that those protocols are, to some extent, also susceptible to our attacks. The result of this investigation provides new insights into the security considerations of MAC protocols.
Acquisition of Neural Action Potentials Using Rapid Multiplexing Directly at the Electrodes

Neural recording systems that interface with implanted microelectrodes are used extensively in experimental neuroscience and neural engineering research. Interface electronics that are needed to amplify, filter, and digitize signals from multichannel electrode arrays are a critical bottleneck to scaling such systems. This paper presents the design and testing of an electronic architecture for intracortical neural recording that drastically reduces the size per channel by rapidly multiplexing many electrodes to a single circuit. The architecture utilizes mixed-signal feedback to cancel electrode offsets, windowed integration sampling to reduce aliased high-frequency noise, and a successive approximation analog-to-digital converter with small capacitance and asynchronous control. Results are presented from a 180 nm CMOS integrated circuit prototype verified using in vivo experiments with a tungsten microwire array implanted in rodent cortex. The integrated circuit prototype achieves <0.004 mm2 area per channel, 7 µW power dissipation per channel, 5.6 µVrms input referred noise, 50 dB common mode rejection ratio, and generates 9-bit samples at 30 kHz per channel by multiplexing at 600 kHz. General considerations are discussed for rapid time domain multiplexing of high-impedance microelectrodes. Overall, this work describes a promising path forward for scaling neural recording systems to numbers of electrodes that are orders of magnitude larger.
Modelling and Analysis of Characteristics of a Piezoelectric-Actuated Micro-/Nano Compliant Platform Using Bond Graph Approach

The piezoelectric-actuated flexure-based compliant platform is commonly adopted in many fields of micro and nanotechnology. In this paper, bond graph modeling, and kinematic and dynamic characteristics of a piezoelectric-actuated micro-/nano compliant platform system are investigated. During modeling, the bond graph model of the piezoelectric actuator (PZT) is derived by considering both the electrical domain and the mechanical domain. Considering the compliances of flexure hinges and elastic linkages, as well as the input ends, the bond graph model for the bridge-type displacement amplification mechanism in the compliant platform is established by combining pseudo-rigid-body (PRB) model theory and elastic beam theory. Based on the interactions between the PZT subsystem and compliant platform subsystem, the kinematic performance of the proposed compliant platform system is evaluated through both computer simulations and experimental tests. Furthermore, the frequency responses, dynamic responses and load capacity of the compliant platform system are studied. This paper explores a new modeling method for a piezoelectric-actuated compliant platform system, which can provide an effective solution when analyzing the micro-/nano system.
MorAz: an Open-source Morphological Analyzer for Azerbaijani Turkish

MorAz is an open-source morphological analyzer for Azerbaijani Turkish. The analyzer is available through both as a website for interactive exploration and as a RESTful web service for integration into a natural language processing pipeline. MorAz implements the morphology of Azerbaijani Turkish in two-level using Helsinki finite-state transducer and wraps the analyzer with python scripts in a Django instance.
A new similarity measure for automatic text categorization based on vector space model

Text classification is the process of assigning a predefine class or category to an anonymous text based on its content. It is an important task in text mining. Several Text classification algorithms were developed for natural languages, such as English, Chinese and Dutch. However, the number of related works for Arabic is limited. In this research, we will attempt to generalize the method to compute category representative vectorand propose a new similarity measure(referred to, hereafter, as origin-similarity) based on aVector Space Model to classify Arabic documents and compare proposed method with well-known similarity techniques.The measurement used a dataset that consists of 250 Arabictextsindependently classified into five classes: art and culture, economics, politics, society, and sport. The experimental findings show that Arabic text classification using VSMprovides the best results and could attribute the category of a text with an accuracy of 91 %.
Prism: Multiple spline regression with regularization, dimensionality reduction, and feature selection

Prism uses a combination of statistical methods to conduct spline-based multiple regression. Prism conducts this regression using regularization, dimensionality reduction, and feature selection, through a combination of smoothing spline regression, PCA, and RVR/LASSO. Smoothing splines can be used to model non-parametric relationships using piece-wise cubic functions (Wahba and Wold 1975; Fox 2000). Relevance vector regression (RVR) refers to application of a relevance vector machine (RVM) to a regression problem; broadly, RVM is similar to multiple linear regression with regularization, using automatic relevance determination for feature selection (Tipping 2000). RVM shares many commonalities with SVM, and is implemented as a special case of a Sparse Bayesian framework (Tipping 2001; Tipping and Faul 2003).
Normative Tensions in Shared Augmented Reality

Novel collaborative technologies afford new modes of behavior, which are often not regulated by established social norms. In particular, shared augmented reality (AR) - where multiple users can create, attach, and interact with the same virtual elements embedded into the physical environment - has the potential to interrupt current social norms of behavior. The objective of our study is to shed light on the ways in which shared AR challenges existing behavioral expectations. Using a simulated lab experimental design, we performed a study of users' interactions in a shared AR setting. Content analysis of participants' interviews reveals users' concerns over the preservation of their self- and social identity, as well as concerns related to personal space and the sense of psychological ownership over one's body and belongings. Our findings also point to the need for regulation of shared AR spaces and design of the technology's control mechanisms.
Phileas: A Simulation-based Approach for the Evaluation of Value-based Fog Services

Efficient design for Fog Computing applications need to consider the optimal use of the available resources at the edge and the Cloud, switching from one other depending on the current status, execution price, and user requirements. This is a complex task, as the optimization needs to consider the distributed and dynamic nature of the environment. There is the need to support investigation efforts by enabling researchers to experiment with Fog environments in a controlled and reproducible fashion. Unfortunately, most simulators do not implement a service model suited for Fog computing applications. This paper presents Phileas, a simulator that supports the definition of Fog services. Phileas allows to reenact the behavior of Fog services and evaluate different service policies and allocation strategies.
SINAI at IEST 2018: Neural Encoding of Emotional External Knowledge for Emotion Classification

In this paper, we describe our participation in WASSA 2018 Implicit Emotion Shared Task (IEST 2018). We claim that the use of emotional external knowledge may enhance the performance and the capacity of generalization of an emotion classification system based on neural networks. Accordingly, we submitted four deep learning systems grounded in a sequence encoding layer. They mainly differ in the feature vector space and the recurrent neural network used in the sequence encoding layer. The official results show that the systems that used emotional external knowledge have a higher capacity of generalization, hence our claim holds.
Research on Malicious JavaScript Detection Technology Based on LSTM

The attacker injects malicious JavaScript into web pages to achieve the purpose of implanting Trojan horses, spreading viruses, phishing, and obtaining secret information. By analyzing the existing researches on malicious JavaScript detection, a malicious JavaScript detection model based on LSTM (Long Short-Term Memory) is proposed. Features are extracted from the semantic level of bytecode, and the method of word vector is optimized. It can distinguish malicious JavaScript code and combat obfuscated code effectively. Experiments showed that the accuracy of detection model based on LSTM is 99.51%, and the F1-score is 98.37%, which is better than the existing model based on Random Forest and SVM algorithm.
An Enhanced Multi-Objective Biogeography-Based Optimization Algorithm for Automatic Detection of Overlapping Communities in a Social Network with Node Attributes

Community detection is one of the most important and interesting issues in social network analysis. In recent years, simultaneous considering of nodes' attributes and topological structures of social networks in the process of community detection has attracted the attentions of many scholars, and this consideration has been recently used in some community detection methods to increase their efficiencies and to enhance their performances in finding meaningful and relevant communities. But the problem is that most of these methods tend to find non-overlapping communities, while many real-world networks include communities that often overlap to some extent. In order to solve this problem, an evolutionary algorithm called MOBBO-OCD, which is based on multi-objective biogeography-based optimization (BBO), is proposed in this paper to automatically find overlapping communities in a social network with node attributes with synchronously considering the density of connections and the similarity of nodes' attributes in the network. In MOBBO-OCD, an extended locus-based adjacency representation called OLAR is introduced to encode and decode overlapping communities. Based on OLAR, a rank-based migration operator along with a novel two-phase mutation strategy and a new double-point crossover are used in the evolution process of MOBBO-OCD to effectively lead the population into the evolution path. In order to assess the performance of MOBBO-OCD, a new metric called alpha_SAEM is proposed in this paper, which is able to evaluate the goodness of both overlapping and non-overlapping partitions with considering the two aspects of node attributes and linkage structure. Quantitative evaluations reveal that MOBBO-OCD achieves favorable results which are quite superior to the results of 15 relevant community detection algorithms in the literature.
RSMTool: collection of tools building and evaluating automated scoring models

RSMTool is a collection of tools for researchers working on the development of automated scoring engines for written and spoken responses in an educational context. The main purpose of the tool is to simplify the integration of educational measurement recommendations into the model building process and to allow the researchers to rapidly and automatically generate comprehensive evaluations of the scoring model.
Challenges for Reliable and Large Scale Evaluation of Android Malware Analysis

Since Android became the first smartphone operating system, malware developers have put large efforts to craft new threats uploaded to the Google Play store and other third market places. Companies and researchers now include in their activities the analysis of malware targeting smartphones. Most of the time, the problem that is addressed consists in deciding if an application should be considered as a malware or not. Nevertheless, once a malware is tagged as a malicious application, users that have been infected ask for more technical explanations about the threat they have been exposed to. Dissecting a malware requires a lot of efforts for a security analyst to be conducted and companies are in demand of new tools for automatizing the analysis.
Exact ICL maximization in a non-stationary time extension of latent block model for dynamic networks

The latent block model (LBM) is a flexible probabilistic tool to describe interactions between node sets in bipartite networks, but it does not account for interactions of time varying intensity between nodes in unknown classes. In this paper we propose a non stationary temporal extension of the LBM that clusters simultaneously the two node sets of a bipartite network and constructs classes of time intervals on which interactions are stationary. The number of clusters as well as the membership to classes are obtained by maximizing the exact complete-data integrated likelihood relying on a greedy search approach. Experiments on simulated and real data are carried out in order to assess the proposed methodology.
Learning to Flip the Bias of News Headlines

This paper introduces the task of “flipping” the bias of news articles: Given an article with a political bias (left or right), generate an article with the same topic but opposite bias. To study this task, we create a corpus with bias-labeled articles from all-sides.com. As a first step, we analyze the corpus and discuss intrinsic characteristics of bias. They point to the main challenges of bias flipping, which in turn lead to a specific setting in the generation process. The paper in hand narrows down the general bias flipping task to focus on bias flipping for news article headlines. A manual annotation of headlines from each side reveals that they are self-informative in general and often convey bias. We apply an autoencoder incorporating information from an article’s content to learn how to automatically flip the bias. From 200 generated headlines, 73 are classified as understandable by annotators, and 83 maintain the topic while having opposite bias. Insights from our analysis shed light on how to solve the main challenges of bias flipping.
Optimizing SMT Solving Strategies by Learning with an Evolutionary Process

This paper deals with program optimization, i.e., learning of more efficient programs. The programs we want to improve are Z3 solving strategies. Z3 is a SMT (SAT Modulo Theory) solver which is currently developed by Microsoft Research. We define strategy generators based on evolutionary processes. SMT solving strategies include various aspects that can affect the performance of a SMT solver dramatically. Each of these elements includes a huge amount of options which cannot be exploited without expert knowledge. We define a generic evolutionary algorithm based on genetic programming concepts. This strategy generation process aims at learning better strategies by successive improvements, using rules that can be combined in order to handle both structures and parameters of the strategies. We experiment 7 different strategies generators on 2 SMT logics (QF_LRA,QF_LIA). The results show that the learned strategies improve default strategies available in the solver.
DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences

Identification of drug-target interactions (DTIs) plays a key role in drug discovery. The high cost and labor-intensive nature of in vitro and in vivo experiments have highlighted the importance of in silico-based DTI prediction approaches. In several computational models, conventional protein descriptors have been shown to not be sufficiently informative to predict accurate DTIs. Thus, in this study, we propose a deep learning based DTI prediction model capturing local residue patterns of proteins participating in DTIs. When we employ a convolutional neural network (CNN) on raw protein sequences, we perform convolution on various lengths of amino acids subsequences to capture local residue patterns of generalized protein classes. We train our model with large-scale DTI information and demonstrate the performance of the proposed model using an independent dataset that is not seen during the training phase. As a result, our model performs better than previous protein descriptor-based models. Also, our model performs better than the recently developed deep learning models for massive prediction of DTIs. By examining pooled convolution results, we confirmed that our model can detect binding sites of proteins for DTIs. In conclusion, our prediction model for detecting local residue patterns of target proteins successfully enriches the protein features of a raw protein sequence, yielding better prediction results than previous approaches. Our code is available at https://github.com/GIST-CSBL/DeepConv-DTI.
Performance-driven facial animation

As computer graphics technique rises to the challenge of rendering lifelike performers, more lifelike performance is required. The techniques used to animate robots, arthropods, and suits of armor, have been extended to flexible surfaces of fur and flesh. Physical models of muscle and skin have been devised. But more complex databases and sophisticated physical modeling do not directly address the performance problem. The gestures and expressions of a human actor are not the solution to a dynamic system. This paper describes a means of acquiring the expressions of real faces, and applying them to computer-generated faces. Such an "electronic mask" offers a means for the traditional talents of actors to be flexibly incorporated in digital animations. Efforts in a similar spirit have resulted in servo-controlled "animatrons," high-technology puppets, and CG puppetry [1]. The manner in which the skills of actors and puppetteers as well as animators are accommodated in such systems may point the way for a more general incorporation of human nuance into our emerging computer media.The ensuing description is divided into two major subjects: the construction of a highly-resoved human head model with photographic texture mapping, and the concept demonstration of a system to animate this model by tracking and applying the expressions of a human performer.
Coverage and Cynicism: The AFRL Submission to the WMT 2018 Parallel Corpus Filtering Task

The WMT 2018 Parallel Corpus Filtering Task aims to test various methods of filtering a noisy parallel corpus, to make it useful for training machine translation systems. We describe the AFRL submissions, including their preprocessing methods and quality metrics. Numerical results indicate relative benefits of different options and show where our methods are competitive.