Miracle-dz/UniFAR-FTUs huggingface dataset:
In search of directionality effectsin the translation processand in the end product

This article tackles directionality as one of the most contentious issues in translation studies, still without solid empirical footing. The research presented here shows that, to understand directionality effects on the process of translation and its end product, performance in L2 → L1 and L1 → L2 translation needs to be compared in a specific setting in which more factors than directionality are considered—especially text type. For 26 professional translators who participated in an experimental study, L1 → L2 translation did not take significantly more time than L2 → L1 translation and the end products of both needed improvement from proofreaders who are native speakers of the target language. A close analysis of corrections made by the proofreaders shows that different aspects of translation quality are affected by directionality. A case study of two translators who produced high quality L1 → L2 translations reveals that their performance was affected more by text type than by directionality.
Manifold Mixup: Learning Better Representations by Interpolating Hidden States

Deep networks often perform well on the data distribution on which they are trained, yet give incorrect (and often very confident) answers when evaluated on points from off of the training distribution. This is exemplified by the adversarial examples phenomenon but can also be seen in terms of model generalization and domain shift. Ideally, a model would assign lower confidence to points unlike those from the training distribution. We propose a regularizer which addresses this issue by training with interpolated hidden states and encouraging the classifier to be less confident at these points. Because the hidden states are learned, this has an important effect of encouraging the hidden states for a class to be concentrated in such a way so that interpolations within the same class or between two different classes do not intersect with the real data points from other classes. This has a major advantage in that it avoids the underfitting which can result from interpolating in the input space. We prove that the exact condition for this problem of underfitting to be avoided by Manifold Mixup is that the dimensionality of the hidden states exceeds the number of classes, which is often the case in practice. Additionally, this concentration can be seen as making the features in earlier layers more discriminative. We show that despite requiring no significant additional computation, Manifold Mixup achieves large improvements over strong baselines in supervised learning, robustness to single-step adversarial attacks, semi-supervised learning, and Negative Log-Likelihood on held out samples.
Consumer perceptions, understanding, and expectations of Advanced Driver Assistance Systems (ADAS) and vehicle automation

Consumers are faced with an increasingly complex decision process as novel safety technologies become commonplace in new vehicles. Consumers’ knowledge of these systems is potentially limited given the recent introduction and constant evolution of ADAS. We examined consumers’ understanding and perceptions of ADAS and vehicle automation in a national survey. Our analysis focused on consumers’ understanding of how certain driving tasks that can be automated (e.g., steering, braking, navigation, etc.) maps onto proposed levels of vehicle automation. Additionally, we report what sources of information and methods of education consumers prefer to utilize when learning about new safety systems. These data can inform new driver training, public policy and transportation goals of how best to educate the motoring public about new vehicle technologies and the capabilities of next-generation automated vehicles as well as facilitate public acceptance of higher levels of vehicle automation.
A Comparative Study of Temporary and Ongoing Teams on e-Environment

Background: Virtual teams are more and more common since the internet and mobile revolutions. Organizations are facing new challenges due to the lack of interpersonal relationships between the members of these teams. This study examines common team processes in an e-environment, relying upon two virtual team types: temporary and ongoing teams. Literature review: The aim of the review is to provide an overview of the challenges of such teams and to understand the complexity of team processes in such an environment. Research questions: 1. Does the type of virtual team have an impact on the quality of common team processes? 2. Can criteria be identified to determine which type of virtual team is more appropriate for given tasks? Research methodology: A quantitative test was conducted to compare the mean gaps between temporary and ongoing teams in trust, communication, and five collaborative processes. Then, by employing qualitative thematic analysis, we constructed a conceptual model to understand the reasons for these mean gaps. Results: Primary findings indicate that all but one of the tested processes achieved higher levels in ongoing teams rather than in temporary ones. Also, the more the collaborative process entails complex activities involving social bonds, the greater the gaps between the two team types. Conclusion: At the initiation of a virtual team, it would be best to focus on projects limited to elementary activities. If more complex team activities are anticipated, virtual team members should work together for an extended period.
Improving Students’ Creative Thinking Ability Through Problem Based Learning Models on Stoichiometric Materials

This study aims to determine the improvement of students’ creative thinking skills through the Problem Based Learning model on Stoichiometry material. The population in this study were all students of class XI IPA, Senior High School in Tulang Bawang Barat Regency, in the even semester of 2017/2018 school year. The sampling technique used was cluster random sampling and obtained a sample of class as the experimental class and the control class. This research method is quasi-experimental with Pretest-Posttest Control Group Design. The increase in students’ creative thinking skills is measured based on the average n-Gain score of students. The results showed that in the experimental class, the average value of n-Gain students’ creative thinking skills was 0.78 (high criteria) and the control class, the average value of n-Gain students’ creative thinking skills was 0.52 (medium criteria). Based on the results of this study it can be concluded that students’ creative thinking skills on Stoichiometry material, increased through the Problem Based Learning models
Emotional Utility and Recall of the Facebook News Feed

We report a laboratory study (N=53) in which participants browsed their own Facebook news feeds for 10-15 minutes, choosing exactly when to quit, and later rated the overall emotional utility of the episode before attempting to recall threads. Finally, the emotional utility of each encountered thread was rated while looking over a recording of the interaction. We report that Facebook browsing was, overall, an emotionally positive experience; that recall of threads exhibited classic primacy and recency serial order effects; that recalled threads were both more positive and more valenced (less neutral) on average, than forgotten threads; and that overall emotional valence judgments were predicted, statistically, by the peak and end thread judgments. We find no evidence that local quit decisions were driven by the emotional utility of threads. In the light of these findings, we discuss the suggestion that emotional utility might partly explain the attractiveness of reading the news feed, and that an emotional memory bias might further increase the attractiveness of the newsfeed in prospect.
A Citizen-Sensing-Based Digital Service for the Analysis of On-Site Post-Earthquake Messages

The effectiveness of disaster response depends on the correctness and timeliness of data regarding the location and the impact of the event. These two issues are critical when the data come from citizens’ tweets, since the automatic classification of disaster-related tweets suffers from many shortcomings. In this paper, we explore an approach based on participatory sensing (i.e., a subset of mobile crowdsourcing that emphasizes the active and intentional participation of citizens to collect data from the place where they live or work). We operate with the hypothesis of a “friendly world”, that is by assuming that after a calamitous event, in the survivors prevails the feeling of helping those who suffer. The extraction, from the Twitter repository, of the few tweets relevant to the event of interest has a long processing time. With the aggravating circumstance in the phase that follows a severe earthquake, the elaboration of tweets clashes with the need to act promptly. Our proposal allows a huge reduction of the processing time. This goal is reached by introducing a service and a mobile app, the latter is an intermediate tool between Twitter and the citizens, suitable to assist them to write structured messages that act as surrogates of tweets. The article describes the architecture of the software service and the steps involved in the retrieval, from the Twitter server, of the messages coming from citizens living in the places hit by the earthquake; moreover, it details the storage of those messages into a geographical database and their processing using SQL.
A study of the determinants of postgraduate students’ satisfaction of using online research databases

In today’s technology-enriched environment, online research databases have become an indispensable tool in learning and research activities. Libraries worldwide have incorporated them as an integral part of their current service delivery. This study intends to determine whether the technology satisfaction model (TSM) validly measures postgraduate students’ satisfaction of using online research databases in an academic library. It further explores the relationships among antecedents within the model. To measure the components of the TSM, the stratified random sampling strategy was used to conduct a survey with 300 postgraduate students from six faculties of COMSATS University Islamabad (CUI) in Pakistan. The data were statistically analysed using the Rasch model and structural equation modelling (SEM). The results of the study illustrate direct and indirect associations among the exogenous, endogenous and mediating variables of the TSM, with the studied postgraduate students’ computer self-efficacy playing a crucial role in influencing their satisfaction. The TSM shows that 69% of the variance in the postgraduate students’ satisfaction can be explained by perceived usefulness, computer self-efficacy and perceived ease of use, which confirm that the TSM has a strong predictive power and is viable for measuring endogenous variable. The findings also make a significant contribution to the testing of databases for academics, service providers and researchers in higher education.
Humans Vs. Algorithms: Assessment of Security Risks Posed by Facial Morphing to Identity Verification at Border Control

Facial morphing, if applied to a biometric portrait intended for an identity document application, compromises further identity verification by means of the issued document. An electronic machine readable travel document is a prime target of a face morphing attack because a successful attack allows a wanted criminal for illicit border crossing. The open question is whether human examiners and algorithms can be fooled only by professionally created manual morphs or even by automatically generated morphs with evident visual artifacts. In this paper, we introduce a border control simulation to examine the ability of humans in recognizing morphed passport photographs as well as in mismatching morphed passport photographs against "live" faces of travelers. The error rates of humans are compared with those of algorithms to emphasize the necessity for computer-aided support of border guards.
Potentials of Egg Shell and Snail Shell Powder in Sorghum Beer Clarification

Aim: Turbidity is one of the integral parameters used to ascertain beer quality and clarity. This has been achieved through a combination of filtration processes and utilization of kieselghur as filter aid. Kieselghur on the other hand is expensive and not readily available; therefore there is need to find potential filter aids from locally available materials. The aim of this research is to determine the effectiveness of egg shell powder and snail shell powder in sorghum beer clarification. Study Design: This study was made to fit into using a combination of T-test and one way Analysis of Variance. Place and Duration of Study: The research was carried out at Department of Food Science and Technology Laboratory, Federal University of Technology, Owerri, Nigeria, between May 2018 and November 2018. Methodology: Sorghum grains were malted by adoption of barley malting protocols while snail shells and egg shells obtained from the market were processed into powders. Temperature programmed infusion mashing method was used to produce wort from sorghum malt that was germinated for 2 days and 3 days. The resulting worts were fermented using Saccharomyces carlsbergensis to obtain sorghum beer and the beer was clarified with kieselghur, egg shell powder and snail shell powder. Turbidity and other quality analyses were carried out on the sorghum wort, sorghum beer and clarified sorghum beer. Results: The results showed that beers clarified with Kieselghur had better clarity than egg shell powder and snail shell powder clarified sorghum beers. Egg shell powder showed better clarification potentials than snail shell powder. There is a correlation between total solids and turbidity. Germination period significantly influenced the turbidity of sorghum beer. Conclusion: This study showed that egg shell powder can serve as potential filter aid in beverage clarification.
Sobolev Descent

We study a simplification of GAN training: the problem of transporting particles from a source to a target distribution. Starting from the Sobolev GAN critic, part of the gradient regularized GAN family, we show a strong relation with Optimal Transport (OT). Specifically with the less popular *dynamic* formulation of OT that finds a path of distributions from source to target minimizing a "kinetic energy". We introduce Sobolev descent that constructs similar paths by following gradient flows of a critic function in a kernel space or parametrized by a neural network. In the kernel version, we show convergence to the target distribution in the MMD sense. We show in theory and experiments that regularization has an important role in favoring smooth transitions between distributions, avoiding large gradients from the critic. This analysis in a simplified particle setting provides insight in paths to equilibrium in GANs.
MEASURING SOFTWARE COMPLEXITY USING NEURAL NETWORKS

Measuring the software complexity is an important task in the management of software projects. In the recent years, many researchers have paid much attention to this challenging task due to the commercial importance of software projects. In the literature, there are some software metrics and estimation models to measure the complexity of software. However, we still need to introduce novel models of software metrics to obtain more accurate results regarding software complexity. In this paper, we will show that neural networks can be used as an alternative method for estimation of software complexity metrics. We use a neural network of three layers with a single hidden layer and train this network by using distinct training algorithms to determine the accuracy of software complexity. We compare our results of software complexity obtained by using neural networks with those calculated by Halstead model. This comparison shows that the difference between our estimated results obtained by Bayesian Regularization Algorithm with 10 hidden neurons and Halstead calculated results of software complexity is less than 2%, implying the effectiveness of our proposed method of neural networks in estimating software complexity.
Evaluation of Transfer Learning Techniques for Classification and Localization of Marine Animals

The objective is to evaluate methods for simultaneous classification and localization towards a better size estimate of marine animals in still images. Marine animals in such images vary in orientations and size. It is challenging to create a bounding box that predicts the shape of the object. We compare axis-aligned and rotatable bounding box techniques for size estimation.
Energy-Efficient Edge Computing Service Provisioning for Vehicular Networks: A Consensus ADMM Approach

In vehicular networks, in-vehicle user equipment (UE) with limited battery capacity can achieve opportunistic energy saving by offloading energy-hungry workloads to vehicular edge computing nodes via vehicle-to-infrastructure links. However, how to determine the optimal portion of workload to be offloaded based on the dynamic states of energy consumption and latency in local computing, data transmission, workload execution and handover, is still an open issue. In this paper, we study the energy-efficient workload offloading problem and propose a low-complexity distributed solution based on consensus alternating direction method of multipliers. By incorporating a set of local variables for each UE, the original problem, in which the optimization variables of UEs are coupled together, is transformed into an equivalent general consensus problem with separable objectives and constraints. The consensus problem can be further decomposed into a bunch of subproblems, which are distributed across UEs and solved in parallel simultaneously. Finally, the proposed solution is validated based on a realistic road topology of Beijing, China. Simulation results have demonstrated that significant energy saving gain can be achieved by the proposed algorithm.
Ensemble Network Intrusion Detection Model Based on Classification & Clustering for Dynamic Environment

- Anomaly detection is a critical issue in Network Intrusion Detection Systems (NIDSs). Most anomaly based NIDSs employ supervised algorithms, whose performances highly depend on attack-free training data. However, this kind of training data is difficult to obtain in real world network environment. Moreover, with changing network environment or services, patterns of normal traffic will be changed. This leads to high false positive rate of supervised NIDSs. Unsupervised outlier detection can overcome the drawbacks of supervised anomaly detection. Therefore, study apply one of the efficient data mining algorithms called ensemble network intrusion detection model based on classification & clustering. Without attack-free training data, ensemble clustering algorithm can detect outliers in datasets of network traffic. In this paper, study discuss model of anomaly-based network intrusion detection. In machine learning, a combination of classifiers, known as an ensemble classifier, often outperforms individual ones. While many ensemble approaches exist, it remains, however, a difficult task to find a suitable ensemble configuration for a particular dataset. This paper proposed method includes an ensemble feature selecting classifier, data mining classifier. The former consists of four classifiers using different sets of features and each of them employs a machine learning algorithm named - bagging-randomization -boosting and -stacking. The latter applies data mining technique to automatically extract computer users’ normal behavior from training network traffic data. The outputs of ensemble feature selecting classifier and data mining classifier are then fused together to get the final decision. The study proposes an ensemble-based that analysis of algorithm performance for intrusion detection. The method combines the output of four clustering methods to achieve an optimum selection. study then perform an extensive experimental evaluation of our proposed method using intrusion detection benchmark dataset, NSL-KDD.
Poverty incidence identification of cities and municipalities using convolutional Neural Network as applied to satellite imagery

Poverty remains to be a hindrance to national growth for many developing countries. This presents a problem in a country’s resource management and urban planning of its people. Furthermore, there is a big gap when it comes to data collection and monitoring in developing countries such that the government fails to prioritize this area of responsibility. The authors aim to bridge this gap by classifying wealth through the use of satellite images and emerging technology, particularly Convolutional Neural Networks. The researchers’ goal is to test whether AlexNet, a Convolutional Neural Network Architecture, can identify the poverty levels of municipalities in the Philippines, by estimating their poverty incidences, based on satellite images. With this research, it can measure a broader set of society growth indicators. Indicators such as the material of people’s houses, the size of their houses, their living conditions, access to roads and also how highly urbanized an area is, are considered important factors to indicate poverty. The results shows an accuracy of 0.84 and average precision-recall of 0.86 and 0.84, respectively. This study will give deeper insight to the government as to which areas to improve on in certain sectors given that a poverty incidence is high in a certain municipality.
Mixing of Hamiltonian Monte Carlo on strongly log-concave distributions 2: Numerical integrators

We obtain quantitative bounds on the mixing properties of the Hamiltonian Monte Carlo (HMC) algorithm with target distribution in d-dimensional Euclidean space, showing that HMC mixes quickly whenever the target log-distribution is strongly concave and has Lipschitz gradients. We use a coupling argument to show that the popular leapfrog implementation of HMC can sample approximately from the target distribution in a number of gradient evaluations which grows like d^1/2 with the dimension and grows at most polynomially in the strong convexity and Lipschitz-gradient constants. Our results significantly extend and improve on the dimension dependence of previous quantitative bounds on the mixing of HMC and of the unadjusted Langevin algorithm in this setting.
Restricted Access Window-Based Novel Service Differentiation Scheme for Group-Synchronized DCF

Group-synchronized distributed coordination function (GS-DCF) is a group-based medium access mechanism introduced in the IEEE 802.11ah draft standard. GS-DCF implements a novel channel access mechanism called restricted access window (RAW) which splits the channel time into various slots, devices into several groups, and assigns each slot to a group of devices. The Internet of Things is an ideal platform that captures the process and handles a large amount of data from the various heterogeneous applications with different channel access requirements. In this letter, we propose a novel RAW-based service differentiation scheme for GS-DCF. We propose a technique to allocate RAW slots to different applications based on their service requirements with minimal additional overhead. Analytical and simulation results show that the proposed scheme can effectively provide differentiated services in terms of throughput to the group of devices in GS-DCF.