Miracle-dz/UniFAR-FTUs huggingface dataset:
Proposal of Instantaneous Power-Line Frequency Synchronized Superimposed Chart for Communications Quality Evaluation of broadband PLC System

Power line communications (PLC) is a communication technology that uses a power-line as a transmission medium. Previous studies have shown that connecting an AC adapter such as a mobile phone charger to the power-line affects signal quality. Therefore, in this paper, the authors analyze the influence of chargers on inter-computer communications using packet capture to evaluate communications quality. The analysis results indicate the occurrence of a short duration in which packets are not detected once in a half period of the power-line supply: named communication forbidden time. For visualizing the communication forbidden time and for evaluating the communications quality of the inter-computer communications using PLC, the authors propose an instantaneous powerline frequency synchronized superimposed chart and its plotting algorithm. Further, in order to analyze accurately, the position of the communication forbidden time can be changed by altering the initial burst signal plotting position. The difference in the chart, which occurs when the plotting start position changes, is also discussed. We show analysis examples using the chart for a test bed data assumed an ideal environment, and show the effectiveness of the chart for analyzing PLC inter-computer communications. key words: PLC, packet capture analysis, visualization, communication quality evaluation
Towards the implementation of a countrywide K-12 learning analytics initiative in Uruguay

ABSTRACT The present work describes the challenges faced during the development of a countrywide Learning Analytics study and tool focused on tracking and understanding the trajectories of Uruguayan students during their first three years of secondary education. Due to the large scale of the project, which covers an entire national educational system, several challenges and constraints were faced during its conception and development. Examples of key challenges were: to understand the different nuances of the educational system, to satisfy ethical and legal requirements without narrowing down the scope and potential novelty of the initiative and to deal with integration and inconsistencies in the databases. This paper presents the design decisions and solutions found to address or mitigate the problems found as a contribution to facilitate similar large-scale projects. Three main experiments using data mining were conducted and their results are also described, pointing out the feasibility of finding meaningful patterns that can be used by educational authorities to improve their decision making and foster public educational policies. Finally, the paper describes the use of the data and the findings of the study to create an interactive visual tool to explore the relationship between student variables, performance and persistence in the system.
Cuckoo: Opportunistic MapReduce on Ephemeral and Heterogeneous Cloud Resources

Cloud infrastructures are generally over-provisioned for handling load peaks and node failures. However, the drawback of this approach is that a large portion of data center resources remains unused. In this paper, we propose a framework that leverages unused resources of data centers, which are ephemeral by nature, to run MapReduce jobs. Our approach allows: i) to run efficiently Hadoop jobs on top of heterogeneous Cloud resources, thanks to our data placement strategy, ii) to predict accurately the volatility of ephemeral resources, thanks to the quantile regression method, and iii) for avoiding the interference between MapReduce jobs and co-resident workloads, thanks to our reactive QoS controller. We have extended Hadoop implementation with our framework and evaluated it with three different data center workloads. The experimental results show that our approach divides Hadoop job execution time by up to 7 when compared to the standard Hadoop implementation.
Improving recommender systems with machine learning and social media

This thesis studies the opportunity to utilize posts from social media in recommender systems. Recommender systems are used to help users find content they are interested in when there are many alternatives, and is very common in streaming services. One of the problems of recommender systems is called the cold-start problem, where the system does not have enough information about an item or a user to make accurate recommendations. We want to mitigate this problem by gathering information from Twitter. Twitter is an extensive source of information about real-time events, but because of considerable noise it can be difficult to find the information relevant for a recommender system. In this thesis, we present a system capable of gathering and organizing information from Twitter in a way that makes it usable for a movie recommender system. This is done by filtering tweets about new movies through a classifier based on an artificial neural network that sorts movie-related tweets from non-movie-related tweets. After finding relevant tweets, our system can tell movie titles apart by clustering the data in such a way that clusters are assigned a movie title and a sentiment. The solution gathers data from an unstructured source and organizes the data in such a way that makes it usable by a recommender system. The solution is evaluated against previous research concerning both clustering, sentiment analysis and classification. The results indicate that social media and Twitter in particular is a useful, extensive source than can be used to mitigate the cold-start problem of recommender systems.
Accuracy Versus Readability

This study aims at providing scientific arguments against the phenomena that appear in the Bible translation especially the extreme poles of literal and free translation versions. These two contradictory poles can be reconciled so that the literal products are not only accurate but also readable and vice versa the products of free translation version are not only readable but has a high level of accuracy. Data is in the form of terms taken from a famous biblical text of English – Indonesian translation entitled “The Sermon on the Mount” from two different versions namely literal and free translations. This study reveals that the reconciliation process of the two extreme poles can be done through the selection of proper translation procedure and by applying the chosen procedure correctly. Based on the finding, the meeting point of literal and free translation versions lies on the literal procedure, whereas the method of choosing correct procedure in literal translation can be done by way of starting from literal procedure to the left and from literal procedure to the right for the free translation version.
A Survey on 3D Virtual Object Manipulation: From the Desktop to Immersive Virtual Environments

Interactions within virtual environments often require manipulating 3D virtual objects. To this end, researchers have endeavoured to find efficient solutions using either traditional input devices or focusing on different input modalities, such as touch and mid‐air gestures. Different virtual environments and diverse input modalities present specific issues to control object position, orientation and scaling: traditional mouse input, for example, presents non‐trivial challenges because of the need to map between 2D input and 3D actions. While interactive surfaces enable more natural approaches, they still require smart mappings. Mid‐air gestures can be exploited to offer natural manipulations mimicking interactions with physical objects. However, these approaches often lack precision and control. All these issues and many others have been addressed in a large body of work. In this article, we survey the state‐of‐the‐art in 3D object manipulation, ranging from traditional desktop approaches to touch and mid‐air interfaces, to interact in diverse virtual environments. We propose a new taxonomy to better classify manipulation properties. Using our taxonomy, we discuss the techniques presented in the surveyed literature, highlighting trends, guidelines and open challenges, that can be useful both to future research and to developers of 3D user interfaces.
Real-Time Bearing Remaining Useful Life Estimation Based on the Frozen Convolutional and Activated Memory Neural Network

Bearings are widely used in rotating machinery, such as aircraft engines and wind turbines. In this paper, we proposed a new data-driven method called frozen convolution and activated memory network (FCAMN) for bearing remaining useful life (RUL) estimation based on the deep neural network. The proposed method is composed of two parts: the multi-scale convolutional neural network is carried out to pre-train the raw data to directly obtain the global and local features, and the second step is accomplished by the convolutional-memory neural network, which enables to connect the convolutional layer with the long-short-time-memory layer together to predict the continuous bearing RUL. Compared with the traditional networks, the proposed network can additionally extract both the global and local information on the vertical feature axis and the associated context information on the horizontal time axis. The experiments are conducted to prove that the proposed method requires fewer training samples and outperforms other methods in RUL estimation.
Prototype of Parking Finder Application for Intelligent Parking System

This paper explains the design and fabrication of an intelligent, user-friendly parking system developed in the United Arab Emirates. The need for a smart parking system was assessed by conducting a survey on the current parking issues. This paper elaborates on the hardware and software realization of the proposed parking system developed for motorists to locate vacant parking using mobile application. The various types of vehicle detection sensors available in the market have been evaluated for the implementation of the system. The main objective of this paper is to build a prototype intelligent parking system with maximum accuracy. The goal of the project was to control the detection modules wirelessly through a customized mobile application, allowing ease of operation and maintenance. While the users can enjoy better comfort and safety, the focus is to create a self-reliable, and ecologically sustainable system while reducing searching time, fuel wastage resulting in lower carbon footprint. The mobile application has been developed using Android Studio, and the results are presented in this paper.
Mining Spatiotemporal Diffusion Network: A New Framework of Active Surveillance Planning

Infectious diseases pose a constant and serious threat to human life. One way to prevent infectious disease spread is through active surveillance: monitoring patients to discover disease incidences before they get out of hand. However, active surveillance can be difficult to implement, especially when the monitored area is vast and resources are limited. Incidences of infectious disease that arrive with visitors from abroad are a further challenge. When faced with imported incidences and a large region to monitor, it is critical that public health authorities precisely allocate their sparse resources to high-priority areas to maximize the efficacy of active surveillance. In this paper, the difficulties of active surveillance are considered, and we offer a computational framework to address these challenges by modeling and mining the spatiotemporal patterns of infectious risks from heterogeneous data sources. Malaria is used as an empirical case study (with real-world data) to validate our proposed method and enhance our findings.
Software Piracy Detection Model Using Ant Colony Optimization Algorithm

Internet enables information to be accessible anytime and anywhere. This scenario creates an environment whereby information can be easily copied. Easy access to the internet is one of the factors which contribute towards piracy in Malaysia as well as the rest of the world. According to a survey conducted by Compliance Gap BSA Global Software Survey in 2013 on software piracy, found out that 43 percent of the software installed on PCs around the world was not properly licensed, the commercial value of the unlicensed installations worldwide was reported to be $62.7 billion. Piracy can happen anywhere including universities. Malaysia as well as other countries in the world is faced with issues of piracy committed by the students in universities. Piracy in universities concern about acts of stealing intellectual property. It can be in the form of software piracy, music piracy, movies piracy and piracy of intellectual materials such as books, articles and journals. This scenario affected the owner of intellectual property as their property is in jeopardy. This study has developed a classification model for detecting software piracy. The model was developed using a swarm intelligence algorithm called the Ant Colony Optimization algorithm. The data for training was collected by a study conducted in Universiti Teknologi MARA (Perlis). Experimental results show that the model detection accuracy rate is better as compared to J48 algorithm.
Temporality in link prediction

This article describes research into the discovery and modelling of emergent temporal phenomena in social networks. It summarizes experimental results that bring together two views in contemporary science: Bayesian analysis and link prediction, to enhance the current understanding of emergent temporal patterns in social network analysis (SNA), particularly in value creation through social connectedness—an important, and growing, discipline within management science. Traditional link prediction methods use the values of metrics in a graph to determine where new links are likely to arise, and little work has been done on analyzing long-term graph trends. We have found that existing graph generation models are unrealistic in their prediction, and can be complemented through the use of temporal metrics, in the study of some networks. To date, no temporal information has been used in link prediction research, thereby excluding valuable temporal trends that emerge in sociogram sequences and also lowering the accuracy of the link prediction. We extracted information from the Pussokram online dating network dataset, and 9,939 cases of each class were formed. Logistic regression in the Weka data mining system was used to perform link prediction. Our results show that temporal metrics are an extremely valuable new contribution to link prediction, and should be used in future applications. In addition to using metrics to measure the local behaviors of participants in social networks, we used Bayesian networks to model the interrelationships between the metrics as local behaviors and links forming between individuals as emergent behaviors (social complexity). We also explored how the temporality in Link Prediction: Understanding Social Complexity E:CO issue Vol. 11 No. 1 2009 pp. 69-83
Small-size pedestrian detection in large scene based on fast R-CNN

Pedestrian detection is a canonical sub-problem of object detection with high demand during recent years. Although recent deep learning object detectors such as Fast/Faster R-CNN have shown excellent performance for general object detection, they have limited success for small size pedestrian detection in large-view scene. We study that the insufficient resolution of feature maps lead to the unsatisfactory accuracy when handling small instances. In this paper, we investigate issues involving Fast R-CNN for pedestrian detection. Driven by the observations, we propose a very simple but effective baseline for pedestrian detection based on Fast R-CNN, employing the DPM detector to generate proposals for accuracy, and training a fast R-CNN style network to jointly optimize small size pedestrian detection with skip connection concatenating feature from different layers to solving coarseness of feature maps. And the accuracy is improved in our research for small size pedestrian detection in the real large scene.
Adaptive $p$-Persistent LBT for Unlicensed LTE: Performance Analysis and Optimization

As an innovative technology, the licensed assisted access (LAA) for unlicensed LTE has been recently standardized by the 3rd generation partnership project (3GPP) protocols. The primary issue for LAA deployment is how to friendly and fairly coexist with the existing Wi-Fi networks in the same unlicensed spectrum. In this paper, we propose and analyze an adaptive <inline-formula><tex-math notation="LaTeX">$p$</tex-math></inline-formula>-persistent channel access scheme for LAA, namely <inline-formula><tex-math notation="LaTeX">$p$</tex-math></inline-formula>-LAA, to balance the tradeoff between the throughput and fairness for the coexistence network. To deal with the asynchronous channel access problem, we first introduce a heterogenous superframe (HS) to facilitate the performance analysis. Based on the HS, we then derive the saturated normalized throughput of the proposed <inline-formula><tex-math notation="LaTeX">$p$</tex-math></inline-formula>-LAA protocol. Furthermore, the channel access probability of the <inline-formula><tex-math notation="LaTeX">$p$</tex-math></inline-formula>-LAA protocol is optimized to achieve the balance between the throughput and fairness. Finally, the accuracy of our theoretical analysis is verified by sufficient Monte Carlo simulations and the results show that the proposed <inline-formula><tex-math notation="LaTeX">$p$</tex-math></inline-formula>-LAA protocol can achieve a better throughput and fairness performance than the conventional LAA protocol.
Research on Somatosensory Interaction Based on Convolutional Neural Network

Somatosensory interaction is an important part of human-computer interaction. The core of somatosensory interaction must accurately obtain the 3D spatial information of the human body. This article uses an RGBD camera to acquire both color and depth images. We Perform 2D human pose estimation in color images using hourglass network with residual structure. At the same time, we use the sparse feature points to align the color map with the depth map in order to accurately map the detection results in color images to the corresponding depth images. The experimental results show that this method can accurately and quickly obtain the 3-dimensional posture information of the human body, which is an important guarantee for somatosensory interaction.
Understanding Effects of Autonomous Agent Timing on Human-Agent Teams Using Iterative Modeling, Simulation and Human-in-the-Loop Experimentation

Recent U.S. Air Force Research Laboratory strategy documents have suggested the need for research in human-agent teaming. Teaming supports a dynamic shift in roles between the human and the agent, depending upon human performance and mission needs. Further, because the performance of these agents will be highly dependent upon the state of the human and the mission, this strategy suggests the need for increased use of modeling to provide a broader understanding of the automated agents’ behavior. This thesis applies a combination of static modeling in SysML activity diagrams, dynamic modeling of human and agent behavior in IMPRINT, and human experimentation in a dynamic, event-driven environment. The dynamic models and human experiments are used to understand the effects of agent delay time on human behavior, performance, and workload, as well as team dynamics. The models and experiments illustrate that agent delay time has a significant effect upon team behavior, performance, and the roles assumed by the human and agent. Therefore, it is proposed that the consequences of agent timing are significant in the context of human agent teaming and that models, which incorporate the human and agent within a common modeling environment, can be useful in understanding this effect.
Sliding Control with Fuzzy Type-2 Controller of Wind Energy System Based on Doubly Fed Induction Generator

Received: 20 January 2019 Accepted: 18 March 2019 The wind system based on double-fed induction generator (DFIG) has become a very important source of energy. To ensure the proper functioning of this system, many improved control algorithms have been developed for the rotor side converter (RSC). This article presents the analysis and design of a double-fed induction generator (DFIG) control technique based on coupling the type-2 fuzzy logic control with the sliding mode control (SMC). For the design of this technique, a decoupled modeling of the DFIG with the orientation of its stator flow is presented. The main purpose of the proposed technique is to make a control to meter the quantities of the powers produced by DFIG which are injected into the electrical network and to reduce the phenomenon of chattering which depends on control by sliding mode. This command has allowed us to reduce the chattering phenomenon and improve the performance of the system in terms of speed monitoring and stator side powers regulation. Simulations are performed using MATLAB / Simulink to validate the effectiveness of the proposed control algorithm. The simulation results, obtained when applying this control strategy to the system, demonstrated the validity of the results and thus validated the high performance of this control technique.
Fast object recognition method from random measurements of compressive sensing camera

Based on the objects’ sparse features, the compressive sensing imaging system has the unique advantage of breaking the Nyquist sampling theorem, and the target image can be reconstructed from very few random coded observations. The system is characterized by simple coding and complex decoding. It is difficult to meet the increasing real-time requirements in application because of the large time consumption by the iterative optimization algorithms. Therefore, it is a powerful way to improve the efficiency by bypassing the complex reconstruction process and extracting the target information directly from the random measurement data. In this paper, based on MNIST handwritten digital character database as an example, the object recognition method from random measurements of compressive sensing camera is explored. Firstly, the training samples in the MNIST database are coded with the observation of the random Bernoulli measurement matrix. And then the K-nearest neighbor classifier is constructed on the standardized samples, the measurements in the same measurement matrix of the target sample are put in the classifier, given the target recognition results. The experimental results show that the average recognition rate is 82.8% under the sampling rate of 0.1, and the total time to process 500 images is 0.063s. In contrast, the experiment of the traditional method by first reconstructing and then recognizing is conducted, the average recognition rate is 84.3%，and the total time to process 500 images is 48.2s. The proposed method is close to the traditional strategy in recognition accuracy, but the computational efficiency has been greatly improved (765 times), with great practical value.
Wars, languages and the role(s) of interpreters

Translation and interpreting always consist of an adptation to local needs and local ears. If the military system is made up of a complex network of subsystems we can expect that interpreters were required to adapt to that complexity. With no theoretical preparation they utilised the tools that, according to their common sense, could help approach people from different languages as well as cultures. Interpreters were the gatekeepers in the communication sequence and, under military command but also based on their self social control, they alternated codes and strategies depending on circumstances, always trying to avoid getting drowned in an ocean of words or lost in translation.