Miracle-dz/UniFAR-FTUs huggingface dataset:
Translation, Sentiment and Voices: A Computational Model to Translate and Analyse Voices from Real-Time Video Calling

With internet quickly becoming an easy access to many, voice calling over internet is slowly gaining momentum. Individuals has been engaging in video communication across the world in different languages. The decade saw the emergence of language translation using neural networks as well. With more data being generated in audio and visual forms, there has become a need and a challenge to analyse such information for many researchers from academia and industry. The availability of video chat corpora is limited as organizations protect user privacy and ensure data security. For this reason, an audio-visual communication system (VidALL) has been developed and audio-speeches were extracted. To understand human nature while answering a video call, an analysis was conducted where polarity and vocal intensity were considered as parameters. Simultaneously, a translation model using a neural approach was developed to translate English sentences to French. Simple RNN-based and Embedded-RNN based models were used for the translation model. BLEU score and target sentence comparators were used to check sentence correctness. Embedded-RNN showed an accuracy of 88.71 percentage and predicted correct sentences. A key finding suggest that polarity is a good estimator to understand human emotion.
wMAN: Weakly-supervised Moment Alignment Network for Text-based Video Segment Retrieval

Given a video and a sentence, the goal of weakly-supervised video moment retrieval is to locate the video segment which is described by the sentence without having access to temporal annotations during training. Instead, a model must learn how to identify the correct segment (i.e. moment) when only being provided with video-sentence pairs. Thus, an inherent challenge is automatically inferring the latent correspondence between visual and language representations. To facilitate this alignment, we propose our Weakly-supervised Moment Alignment Network (wMAN) which exploits a multi-level co-attention mechanism to learn richer multimodal representations. The aforementioned mechanism is comprised of a Frame-By-Word interaction module as well as a novel Word-Conditioned Visual Graph (WCVG). Our approach also incorporates a novel application of positional encodings, commonly used in Transformers, to learn visual-semantic representations that contain contextual information of their relative positions in the temporal sequence through iterative message-passing. Comprehensive experiments on the DiDeMo and Charades-STA datasets demonstrate the effectiveness of our learned representations: our combined wMAN model not only outperforms the state-of-the-art weakly-supervised method by a significant margin but also does better than strongly-supervised state-of-the-art methods on some metrics.
Quantification of 3D spatial correlations between state variables and distances to the grain boundary network in full-field crystal plasticity spectral method simulations

Deformation microstructure heterogeneities play a pivotal role during dislocation patterning and interface network restructuring. Thereby, they affect indirectly how the microstructure recrystallizes. Given this relevance, it has become common practice to study the evolution of deformation microstructure heterogeneities with 3D experiments and full-field crystal plasticity computer simulations including tools such as the spectral method. Quantifying material point to grain or phase boundary distances, though, is a practical challenge with spectral method crystal plasticity models because these discretize the material volume rather than mesh explicitly the grain and phase boundary interface network. This limitation calls for specific data post-processing methods to quantify the spatial correlations between state variable values at each material point and the points’ corresponding distance to the closest grain or phase boundary. This work contributes to the development of advanced such post-processing routines. Specifically, two grain reconstruction and three distancing methods are developed for solving above challenge. The individual strengths and limitations of these methods surplus the efficiency of their parallel implementation is assessed with an exemplary Düsseldorf Advanced Material Simulation Kit large scale crystal plasticity study. We apply the new tool to assess the evolution of subtle stress and disorientation gradients towards grain boundaries.
Dependence correction of multiple tests with applications to sparsity

The present paper establishes new multiple procedures for simultaneous testing of a large number of hypotheses under dependence. Special attention is devoted to experiments with rare false hypotheses. This sparsity assumption is typically for various genome studies when a portion of remarkable genes should be detected. The aim is to derive tests which control the false discovery rate (FDR) always at finite sample size. The procedures are compared for the set up of dependent and independent $p$-values. It turns out that the FDR bounds differ by a dependency factor which can be used as a correction quantity. We offer sparsity modifications and improved dependence tests which generalize the Benjamini-Yekutieli test and adaptive tests in the sense of Storey. As a byproduct, an early stopped test is presented in order to bound the number of rejections. The new procedures perform well for real genome data examples.
Service Embedding in IoT Networks

The Internet of Things (IoT) is the cornerstone of smart applications such as smart buildings, smart factories, home automation, and healthcare automation. These smart applications express their demands in terms of high-level requests. Application requests in service-oriented IoT architectures are translated into a business process (BP) workflow. In this paper, we model such a BP as a virtual network containing a set of virtual nodes and links connected in a specific topology. These virtual nodes represent the requested processing and locations where sensing and/or actuation are needed. The virtual links capture the requested communication requirements between nodes. We introduce a framework, optimized using mixed integer linear programming (MILP), that embeds the BPs from the virtual layer into a lower-level implementation at the IoT physical layer. We formulate the problem of finding the optimal set of IoT nodes and links to embed BPs into the IoT layer considering three objective functions: i) minimizing network and processing power consumption only, ii) minimizing mean traffic latency only, iii) minimizing a weighted combination of power consumption and traffic latency to study the trade-off between minimizing the power consumption and minimizing the traffic latency. We have established, as reference, a scenario where service embedding is performed to meet all the demands with no consideration to power consumption or latency. Compared to this reference scenario, our results indicate that the power savings achieved by our energy efficient embedding scenario is 42% compared with the energy-latency unaware service embedding (ELUSE) reference scenario, while our low latency embedding reduced the traffic latency by an average of 47% compared to the ELUSE scenario. Our combined energy efficient low latency service embedding approach achieved high optimality by jointly realizing 91% of the power and latency reductions obtained under the single objective of minimizing power consumption or latency.
A Closer Look at Data Bias in Neural Extractive Summarization Models

In this paper, we take stock of the current state of summarization datasets and explore how different factors of datasets influence the generalization behaviour of neural extractive summarization models. Specifically, we first propose several properties of datasets, which matter for the generalization of summarization models. Then we build the connection between priors residing in datasets and model designs, analyzing how different properties of datasets influence the choices of model structure design and training methods. Finally, by taking a typical dataset as an example, we rethink the process of the model design based on the experience of the above analysis. We demonstrate that when we have a deep understanding of the characteristics of datasets, a simple approach can bring significant improvements to the existing state-of-the-art model.
Rainbow independent sets in certain classes of graphs

Rainbow matchings in graphs and in hypergraphs have been studied extensively, one motivation coming from questions on matchings in 3‐partite hypergraphs, including questions on transversals in Latin squares. Matchings in graphs are independent sets in line graphs, so a natural problem is to extend the study to rainbow independent sets in general graphs. We study problems of the following form: given a class C ${\mathscr{C}}$ of graphs, how many independent sets of size n $n$ in a graph belonging to C ${\mathscr{C}}$ are needed to guarantee the existence of a rainbow set of size m $m$ ? A particularly interesting case is the class of graphs having a given upper bound on their maximum degree.
MIP-BOOST: Efficient and Effective L 0 Feature Selection for Linear Regression

Abstract Recent advances in mathematical programming have made mixed integer optimization a competitive alternative to popular regularization methods for selecting features in regression problems. The approach exhibits unquestionable foundational appeal and versatility, but also poses important challenges. Here, we propose MIP-BOOST, a revision of standard mixed integer programming feature selection that reduces the computational burden of tuning the critical sparsity bound parameter and improves performance in the presence of feature collinearity and of signals that vary in nature and strength. The final outcome is a more efficient and effective L 0 feature selection method for applications of realistic size and complexity, grounded on rigorous cross-validation tuning and exact optimization of the associated mixed integer program. Computational viability and improved performance in realistic scenarios is achieved through three independent but synergistic proposals. Supplementary materials including additional results, pseudocode, and computer code are available online.
Proposal and Evaluation of an Information Sharing Method for All-to-All Communication in Wireless Sensor Networks

Wireless sensor networks (WSNs) consist of a large number of small nodes with sensing, computation, and wireless communication capabilities. Since it is wasteful and redundant to deploy multiple and independent application-oriented WSNs consisting of dedicated nodes and devices in the same region, a multi-purpose WSN has been attracting researchers in recent years. In a multipurpose WSN, application or service-oriented networks are overlaid and they share physical nodes and devices. Each service network consists of nodes contributing to the application, e. g. nodes equipped with corresponding sensors or actuators and nodes which relay messages among them, and they exchange messages with each other to provide users with desired functions or services. Limiting message exchanges to nodes belonging to the same service network would help in saving energy and bandwidth to some extent. However, as the number of applications increases, concurrent multiple service networks dissipate energy and bandwidth. There have been many proposals for efficient information sharing in WSNs and they have advantages and disadvantages. Characteristics of information sharing methods differ from each other and their performance depends on several conditions such as the size of region and the node density. We conducted comprehensive evaluation of information sharing methods and found that a ring scheme was the most efficient among the six, whereas it had some disadvantages. Especially in a large scale network, the ratio of receiving node is lower, because each source node sends a data message in a short time and a node forwards a data message right after reception. On the contrary, none of methods could accomplish the perfect information sharing. In this thesis, we consider to improve a ring-based method to accomplish the perfect information sharing. Our proposal is based on a token-ring mechanism adopting multiple rings. It consists of three phases, i.e. area decomposition to decrease the size of ring for faster information sharing, ring construction for circular shape of ring, and scheduling for
N ° 2017-10 Tight Money-Tight Credit : Coordination Failure in the Conduct of Monetary and Financial Policies June 2017

In a New Keynesian model with the BGG accelerator and risk shocks, we show that violations of Tinbergen's Rule and strategic interaction between economic authorities undermine the effectiveness of monetary and financial policies. Separate monetary and financial policy rules produce higher welfare than a monetary rule augmented with credit spreads. The latter yields a tight money-tight credit regime in which the interest rate responds too much to inflation and not enough to credit. Reaction curves for the policy-rule elasticities are nonlinear, which reflects shifts in these elasticities from strategic substitutes to complements. The Nash equilibrium is inferior to the Cooperative equilibrium, both are inferior to a first-best outcome, and both might produce tight money-tight credit regimes.
A Collision Avoidance Algorithm for Optimization-based Human Motion Prediction Based on Perceived Risk

Humans are able to plan movements around obstacles in a very efficient manner that almost seems effortless to the person. In digital human modeling (DHM), specifically optimization-based posture and motion prediction, the ability to computationally determine joint angle histories that effectively create motion around obstacles has been developed and employed widely. However, these methods are largely based on detecting spatial interference between objects and finding the optimal path with respect to a biomechanical cost function in an optimization problem to avoid the spatial interference. Although these methods result in a feasible path, they often times do not result in a realistic path, due to the lack of modeling for the cognitive bias that exists in real humans’ path planning in order to minimize a perceived risk. This cognitive bias is the tendency of people to grossly overestimate the low-probability of a collision between their hand and the obstacle. In turn, it results in serious sub-optimal performance, which has been documented by experimental studies in the literature. Therefore, a question arises: is it possible to account for the intentional sub-optimal human performance in a prediction model that optimizes performance? The world of cognitive science has been able to show that many biological tasks can be formulated in statistical terms, including path planning, which can account for cognitive bias. The perceived risk is formulated in terms of an economic loss or gain. This paper formulates the optimization based-motion prediction for a simple reach task around an obstacle in terms of statistical decision theory, where expected economic gains are optimized as well as biomechanical cost. The prediction models are developed in Matlab and solved using the optimization toolbox. The models are compared against motion capture data collected by a Motion Analysis System in which 6 subjects completed the simple reach task. Each subject completed ten trials in order to determine any effects due to learning as the subject became more experienced with the task.
Effect of medical nutrition combined with exercise intervention on the placental ischemic hypoxic injury and serum angiogenesis factors in patients with gestational hypertension

The incidence of gestational hypertension in pregnant women is about 9.4% in China, which is an idiopathic disease in pregnancy and one of the main reasons affecting maternal and child health in the perinatal period[1,2]. China nutrition and health survey in 2002 showed that in addition to the pregnant women in remote mountain village, the pregnant women in our country are in a state of excessive energy intake and weight gain, and overquick weight gain can significantly increase the incidence of hypertension during pregnancy; the deficiency of trace elements of pregnant women is very common in China, which is also one of the important causes of pregnancy complications[3-5]. Therefore, many scholars have thought at present that nutritional imbalance and lack of exercise are the direct causes of gestational hypertension in the pregnant women in China, and in addition to drug therapy, positive nutrition and exercise intervention is very necessary. At present, there is not much research on the effect of nutrition and exercise intervention on the condition of patients with gestational diabetes, and the change of illness in patients with gestational hypertension was explored in this study after increasing medical nutrition combined with exercise intervention in order to clarify the clinical value of the intervention, now reported as follows.
Development of a questionnaire onPrevention and Risk of Metabolic Syndrome among a rural population in Kelantan

The number of metabolic syndrome is increasing, necessity an assessment of knowledge to avoid this population growing. The aims of this study were to develop and validate Malay questionnaire on knowledge of prevention and risk on metabolic syndrome. Methodology: Cross sectional study was done among 200 samples from three villages in Bachok, Kelantan by face-to-face interview. Twenty-eight items were developed by a group of public health experts. To assess test retest reliability, 62 from all respondents were re-interviewed for the second time after two weeks interval. Result: Respondents were aged between 18 to 84 years old. Majority of the subjects were Malay (98%) and female (89%). Two items from the original questionnaire were deleted due to low correlations and after being judge unduly influence item. The factor loading using principal component analysis with varimax rotation was ranged from 0.41 to 0.84. Internal consistency of the components using cronbach’s alpha was ranged from 0.403 to 0.809. Intra Class Correlation (ICC) test was performed for test-retest reliability with the coefficient at 0.95. Conclusion:An acceptable questionnaire was developed for measuringthe knowledge on prevention and risk of metabolic syndrome. The feasibility was present and the internal consistency and ICC were excellent. It can be used to explore the baseline of knowledge of metabolic syndrome before tailoring anyhealth educationor promotion for preventing metabolic syndrome in Malaysia. Index TermsMetabolic syndrome, validation, questionnaire, knowledge, factor analysis.
Artificial Intelligence – Making an Intelligent personal assistant

A bot in computing is an autonomous program on a network (especially the Internet) which can interact with systems or users. [ Simpson, J., and Weiner, E. (1989)] This document gives the description of how memory of an Artificial-Intelligence bot can be stored in an optimized way with a faster searching algorithm and how it can learn new things; the user wants the bot to learn. This paper gives the details of how a bot uses a an ordered tree data structure, called TRIE or a prefix tree to dynamically store the things it learns and what to reply when a person commands asks him something, with a little modification. Keywords— Bot, Python, Prefix Tree, Trie, Web Crawler, Web Indexing, Web Scraper, Web Spider.
Bi-directional piezoelectric transformer based converter driving dielectric electro active polymer actuators

The dielectric electro active polymer (DEAP) technology has some unique features in terms of flexibility and dynamics, it has a high potential in various applications and in achieving a lower production cost. However it has the drawback of requiring a very high operational voltage, as well of having a relative low electromechanical coupling. In this paper a piezoelectric transformer based DEAP actuator driver is presented, which not only is capable of generating high voltages, but also incorporates a bi-directional power stage that will provide better system efficiency. Due to the low electromechanical coupling, only a small fraction of the supplied electrical energy is converted to mechanical energy. The bi-directional power stage enables the recovery of the electrical stored energy in the actuator, improving the system efficiency significantly. The main achievements of this work are the demonstration of bi-directional functionality and energy recovery, as well as an evaluation of the efficiency.
GENERIC OBJECT RECOGNITION BASED ON CRF INCORPORATING BoF AS GLOBAL FEATURES

Generic object recognition using a computer has become a necessity in various fields, such as robot vision and image retrieval in recent years. Conventional methods use conditional random field (CRF) that recognizes the class of each region using the features extracted from the local regions and the class co-occurrence between the adjoining regions. However, there is a problem with CRF – it tends to fall into the local optimal recognition result because it uses only local features and the relationship of class co-occurrence between local regions. To solve this problem, we propose a method that recognizes generic objects by incorporating bag of features (BoF) into CRF as the global feature. An experiment on an image dataset of 21 classes resulted in, the proposed method achieving an improvement in the recognition rate of 5.8%.
Integration of Heterogeneous GML Sources

Geography Markup Language (GML), as being an XML Schema meta standard for geospatial data, is becoming widely adopted for both storage and exchange of data between organizations and systems. In particular it is suited for open and seamless exchange of geospatial data on the web. Application schemas are designed based upon a set of GML schemas, allowing for extensions, restrictions and substitution of the base types. Instance documents from different application domains may thus appear heterogeneous, and general-purpose tools are commonly depending upon complex configuration files to handle unknown dialects. We propose a simple dictionary approach enabling integration of differing GML instance documents. The dictionary is built by a cascading process: 1) If all schemas are available, they are analyzed to identify the origin of the specialized elements, or 2) else, if not all schemas are present, or if there are some missing schemas, the instance document(s) are analyzed using certain heuristics based on the base GML element structure, or, 3) if still some element relations are unresolved, the user is asked to assist the system by a manual mapping procedure. We also propose a non-intrusive strategy where we integrate features from differing sources with minimal tampering of the original data. The implemented proof-of-concept framework is far from complete. However, we demonstrate the main features by using the strategy to develop a simple SVG GML browser and apply on to heterogeneous GML data. The work is part of Project OneMap, a long term effort contributing to the fusion of standard web technologies and geographic content, often referred to as the GeoWeb.