Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed Pricing Policy for Cloud-Assisted Body-to-Body Networks with Optimal QoS and Energy Considerations

Cloud-Assisted Wireless Body Area Networks (WBANs) is one of the key emerging technology for medical applications in the era of Internet-of-Things. In cloud-assisted WBANs, the WBANs are able to access the cloud resources, which are geographically close to the local Access Points (APs). However, in the presence of poor radio link-quality between cloud-assisted WBANs and APs, energy consumption and service delay of the network dynamically changed. Therefore, in order to solve this problem, we propose an energy-efficient Body-to-Body (B2B) communications among coexisting WBANs to relay the information of the patients to nearest APs to provide real-time healthcare services. On the other hand, the healthcare services enabled for cloud-assisted WBANs is popularly known as Healthcare-as-a-Service (He-aaS). In He-aaS, it is very challenging to finalize a price agreement between WBANs and Health-Cloud Service Providers (H-CSPs) as WBANs follow a heterogeneous architecture. The existing pricing mechanisms, which are constrained to the homogeneous applications, are not compatible to He-aaS. We propose an optimal and distributed pricing policy for He-aaS in order to increase the profit level of each WBAN with the consideration of the optimal QoS and energy. Analysis of the proposed algorithms and the inferences of the results validates the usefulness of the proposed energy-efficient B2B communication and distributed pricing policy. Extensive results indicate that our proposed schemes are able to efficiently utilize limited resource with optimal QoS and energy consideration.
Levelset Method with Multi-Speed-Function and Its Application in Segmentation of Medical Images

All of the former level set algorithms have only one level set function and only one speed function, and it is a complex procedure to minimize the energy function during the evolvement of the zero-level-set. Furthermore, there are a lot of problems in this single speed function. In this paper, a new multi-level-set algorithm with multiplicate speed functions is proposed according to the different properties of different objects: Different level set functions are constructed in different regions, and so are different speed functions accordingly; many zero-level-sets are evolved at the same time and act on one another in order to segment. This method not only enhances the accuracy of segmentation, but also solves the bounder gap problem well, which is quite a puzzle for single level set algorithm. Perfect results are achieved when this method is applied to segment the MR and CT images.
Towards a Socio-technical Ontology Engineering Methodology

The development of the semantic web technology has enabled the integration of existing systems and resources. The implementation can be seen such as in the Linked Open Data initiatives, semantic search and semantic web portal. Ontology has been playing the crucial role for the implementation of the semantic web technology, but the development of ontology itself has lagged behind. Methodologies have been developed for ontology engineering, but most of them focused on the technical rather than the socio-technical aspect. Only a few has approached socio-technically but only partially. This article will describe aspects towards a socio-technical approach in ontology engineering methodology more comprehensively. This approach is important because of the complexity and the social nature of the ontology building process. A comparative study will expose the plus and minus points of methods used within the existing methodologies from the socio-technical point of view. Then a workbench will be proposed that take socio-technical aspect within the ontology engineering methodology into account.
A Comparison of Different Punctuation Prediction Approaches in a Translation Context

We test a series of techniques to predict punctuation and its effect on machine translation (MT) quality. Several techniques for punctuation prediction are compared: language modeling techniques, such as n-grams and long shortterm memories (LSTM), sequence labeling LSTMs (unidirectional and bidirectional), and monolingual phrase-based, hierarchical and neural MT. For actual translation, phrase-based, hierarchical and neural MT are investigated. We observe that for punctuation prediction, phrase-based statistical MT and neural MT reach similar results, and are best used as a preprocessing step which is followed by neural MT to perform the actual translation. Implicit punctuation insertion by a dedicated neural MT system, trained on unpunctuated source and punctuated target, yields similar results.
Improving R and ArcGIS integration

We discuss a new approach for integrating R with ArcGIS. Later this year Esri plans to release an open source R package that provides a solution inside the application process for passing data between ArcGIS and R. Using this new methodology, the researchers can easily build geoprocessing tools that wrap R scripts. This new methodology will potentially support a community of people who develop and share R-based geoprocessing tools for ArcGIS.
A framework for designing natural object detector in video

This paper presents a framework for detecting natural objects in video. We analyze each key frame of video to determine if there is any interesting natural object inside. After key frames are extracted by our shot boundary detection (SBD) system, natural object detection in a key frame is done through following such a general pipeline. First, a novel representation CF*IRF (color frequency and inverse region frequency) is developed to detect all possible regions of the target object. Then those regions are validated by texture features and other color features. Finally the shape and location information of the regions give the final answer of the present of the target object. In this paper we also discuss the problem of search task's evaluation. A set of criteria is proposed, by which different search tasks can be classified and analyzed.
Using BLE to Pre-empt Transitions between Smart Indoor Spaces

One of the abiding concerns of relatives of people with intellectual disability who are approaching adulthood, and those who care for senior citizens with cognitive impairment, relates to scheduling and organisation of daily activities such as commuting, medication plans and appointments. With appropriate software, information can be collected and harvested to support ID service users or aging people who have lost part of their cognitive skill. One key enabler in this system is the smartphone that has become part of modern life. Although the GPS capabilities of smartphones work well for outside locations, indoor positioning is still problematic and indoor location is a vital awareness component in ambient assistance. This study considers the use of Bluetooth Low Energy (BLE ) Received Signal Strength Indicators (RSSI) from either a smart phone or beacon to assist with user embarkation and transition between spaces. The full scope of the study considers the impact of phone orientation, whether a phone is in the hand of the user, how performance can be impacted by presence of typical office furniture and the impact of different phone models. In this preliminary version we focus on differences between whether the phone is in the hand of the user or on a table top and how RSSI information can be used to develop services that predict and assist with behaviour of users as they move between locations.
Letting a Neural Network Decide Which Machine Translation System to Use for Black-Box Fuzzy-Match Repair

While systems using the Neural Network-based Machine Translation (NMT) paradigm achieve the highest scores on recent shared tasks, phrase-based (PBMT) systems, rule-based (RBMT) systems and other systems may get better results for individual examples. Therefore, combined systems should achieve the best results for MT, particularly if the system combination method can take advantage of the strengths of each paradigm. In this paper, we describe a system that predicts whether a NMT, PBMT or RBMT will get the best Spanish translation result for a particular English sentence in DGT-TM 20161. Then we use fuzzy-match repair (FMR) as a mechanism to show that the combined system outperforms individual systems in a black-box machine translation setting.
Real-Time Redundancy for the 1.3 GHz Master Oscillator of the European-XFEL

Many modern large-scale facilities, like European X-ray Free Electron Laser (E-XFEL), require precise synchronisation, often down to femtosecond level. Even a very short interruption or an excessive glitch in the reference signal might break the precise time relations between subsystems. In such event, a time-consuming resynchronization process is required that renders the facility not available for the users until it is completed. Therefore, such events are highly undesirable. In this paper, we present an autonomous redundancy solution for the European-XFEL's master oscillator that will guarantee a continuous delivery of the high-quality reference signal even in case of most of the potential failures. The concept and implementation are presented, as well as results from testing in the laboratory environment.
A Web Information Extraction Method Based on Ontology

Web information extraction is a very important and difficulty research subject which involves lots of fields, such as artificial intelligence, machinery learning, etc. As a modeling tool in describing the concept model of information systems at the semantic and knowledge level, ontology is widely used in many areas of computer science in recent years. A new method using ontology to extract valuable information from web documents was proposed in this paper. Firstly, according to the characteristics of the websites and web pages, the text content of web pages was extracted by locating the pages’ regional position. Secondly, on the basis of the traditional vector space model as well as the domain ontology, the concept vectors were generated according to the weightings of the concept vectors combining with the level structure feature of the ontology. Thus, the instances of ontology knowledge base were created semi-automatically, and the text of non-structured web page was turned into semantic structured information which can be understood by the machine.
Wine Quality Evaluation Using Machine Learning Algorithms

There are many prediction systems available for problems like stock exchange, medical diagnosis, insurance calculation, etc. Wine Quality is one area where there is a big opportunity to recommend a good quality of wine to users based on their preferences as well as in historical data. This paper describes the work to learn and assess whether a given wine sample is of good quality or not. The use of machine learning techniques specifically the linear regression with stochastic gradient descent were explored, and the features that perform well on this classification were engineered. The main aim is to develop a cost-effective system to acquire knowledge using data analysis through machine learning algorithms to predict the quality of wine in a better way.
Archives des publications du CNRC

We describe the system entered by the National Research Council Canada in the SemEval-2014 L2 writing assistant task. Our system relies on a standard Phrase-Based Statistical Machine Translation trained on generic, publicly available data. Translations are produced by taking the already translated part of the sentence as ﬁxed context. We show that translation systems can address the L2 writing assistant task, reaching out-of-ﬁve word-based accuracy above 80 percent for 3 out of 4 language pairs. We also present a brief analysis of remaining errors.
A New Railyard Safety Approach for Detection and Tracking of Personnel and Dynamic Objects Using Software-Defined Radar

In a typical railyard environment, a myriad of large and dynamic objects pose significant risks to railyard workers. Unintentional falls, trips and collisions with dynamic rolling stock due to distractions or lack of situational awareness are an unfortunate reality in modern railyards. The challenges of current technologies in detecting and tracking multiple differently-sized mobile objects in situations such as i) one-on-one, ii) many-to-one, iii) one-to-many, iv) blind spot, and v) interfering/non-interfering separation creates the possibility for reduction or loss of situational awareness in this fast-paced environment. The simultaneous tracking of assets with different size, velocity and material composition in different working and environmental conditions can only be accomplished through joint infrastructure-based asset discovery and localization sensors that cause no interference or impediment to the railyard workers, and which are capable of detecting near-misses as well. Our team is investigating the design and performance of such a solution, and is currently focusing on the innovative usage of lightweight low-cost RADAR under different conditions that are expected to be encountered in railyards across North America. We are employing Ancorteks 580-AD Software Defined RADAR (SDRadar) system, which operates at the license-free frequency of 5.8 GHz and with a vari∗Address all correspondence to this author. ety of different configuration options that make it well-suited for generalized object tracking. The challenges, however, stem from the unique interplay between tracking large metallic objects such as railcars, locomotives, and trucks, as well as smaller objects such as railyard workers, in particular their robust discernment from each other. Our design‘s higher-level system can interact with the lower-level SDRadar design to change the parameters in real-time to detect and track large objects over significant distances. The algorithm optimally adjusts waveform, sweep time and sample rate based on one or multiple detected object crosssections and subsequently alters these parameters to be able to discern other objects from them that are in close proximity. We also use an ensemble method to determine the velocity and distance of target objects to accurately track the subject and larger objects at a distance. The methodology has been field-tested with several test cases in a multitude of weather and lighting conditions. We have also tested the proper height, azimuth and elevation angles for positioning our SDRadar to alleviate the risk of blind spots and enhancing the detection and tracking capabilities of our algorithm. The approach has outperformed our previous tests using visual and acoustic sensors for detection and tracking railroad workers in terms of accuracy and operating flexibility. In this paper, we discuss the details of our proposed approach and present our results from the field tests. Proceedings of the 2018 Joint Rail Conference JRC2018 April 18-20, 2018, Pittsburgh, PA, USA
Propositional Knowledge Representation and Reasoning in Restricted Boltzmann Machines.

While knowledge representation and reasoning are considered the keys for human-level artificial intelligence, connectionist networks have been shown successful in a broad range of applications due to their capacity for robust learning and flexible inference under uncertainty. The idea of representing symbolic knowledge in connectionist networks has been well-received and attracted much attention from research community as this can establish a foundation for integration of scalable learning and sound reasoning. In previous work, there exist a number of approaches that map logical inference rules with feed-forward propagation of artificial neural networks (ANN). However, the discriminative structure of an ANN requires the separation of input/output variables which makes it difficult for general reasoning where any variables should be inferable. Other approaches address this issue by employing generative models such as symmetric connectionist networks, however, they are difficult and convoluted. In this paper we propose a novel method to represent propositional formulas in restricted Boltzmann machines which is less complex, especially in the cases of logical implications and Horn clauses. An integration system is then developed and evaluated in real datasets which shows promising results.
Effective Router Assisted Congestion Control for SDN

Router Assisted Congestion Control (RACC) was designed to improve endto- end congestion control performance by using prior knowledge on network condition. However, the traditional Internet does not provide such information, which makes this approach is not feasible to deliver. Our paper addresses this network information deficiency issue by proposing a new congestion control method that works on the Software Defined Network (SDN) framework. We call this proposed method as PACEC (Path Associativity Centralized Congestion Control). In SDN, global view of the network information contains the network topology including link properties (i.e., type, capacity, power consumption, etc.). PACEC uses this information to determine the feedback signal, in order for the source to start sending data at a high rate and to quickly reach fair-share rate. The simulation shows that the efficiency and fairness of PACEC are better than Transmission Control Protocol (TCP) and Rate Control Protocol (RCP).
Metaheuristic Approach for Efficient Feature Selection: A Data Classification Perspective

Background/Objectives: Feature selection has been an active research area in pattern recognition, statistics, and data mining communities. The main objective of the proposed feature selection method is to choose a subset of input variables by eliminating features with little or no predictive information using Meta -heuristic approach. Methods/Statistical Analysis: The presented method uses genetic algorithm for selecting the optimal feature subset from the datasets. Findings: The purpose of this method is to reduce the dimension of the original thereby improves classification accuracy of the selected feature subsets. The experiment performed with various standard dataset revealed that the proposed method is superior to most of the existing feature selection methods in terms of feature subset selection, classification accuracy and running time.
Updating an International Medical Device Standard: A Process for Audible Alarms

Many human factors and ergonomics problems are associated with clinical alarms, usaually referred to as ‘alarm fatigue’. Among these problems is the nature of auditory signals used to attract attention, as these signals are often difficult to learn, easily confusable, and sometimes prone to masking. Symptomatic of this problem is the poor quality of the audible alarms associated with a global medical device safety standard, IEC 60601-1-8. A project aimed at improving and updating these sounds according to best practice is being carried out. This paper charts the progress of this venture and summarizes the results and the published papers which present those results.
Customization lite [operating systems]

There has been a great deal of interest in recent years in developing operating systems that can be customized to meet the performance and functionality needs of particular applications while being extensible to support new policies, new implementations and new interfaces. We describe a structuring technique, called building-block composition, that we are employing for this purpose. The customizability that this technique provides to untrusted applications is, while large, less than that provided by some other techniques. However, it results in a more extensible and maintainable system, is easier for untrusted applications to use, and provides a better foundation for overall improved performance, particularly for multiprocessors.
XSEDE Cloud Survey Report

A National Science Foundation-sponsored cloud user survey was conducted from September 2012 to April 2013 by the XSEDE Cloud Integration Investigation Team to better understand how cloud is used across a wide variety of scientific fields and the humanities, arts, and social sciences. Data was collected from 80 cloud users from around the globe. The project descriptions in this report illustrate the potential of cloud in accelerating research, enhancing collaboration, and enriching education. Cloud users provided extensive data on core usage, preferred storage, bandwidth, etc. and described cloud benefits and limitations for their specific use cases. Educators, research administrators, CIOs, and research computing practitioners may find value in this data when considering the use and/or deployment of public, private, or hybrid clouds to complement current cyberinfrastructure.
Presence management and bluetooth naming on situated displays

This paper describes a user study that investigated to what extent the display of Bluetooth presence and device names on a public screen changes people usage of Bluetooth and alters social practices in a particular context. In this work, the utilization of Bluetooth device naming extended beyond identity representation and introduced the use of a simple interaction mechanism in which the system can recognize parts of the Bluetooth device name as explicit instructions to trigger the generation of content from the web on a interactive public display. The user study, which involved the deployment of a fully functional prototype in a University bar, generally addressed the suitability of these techniques and the type of social practices that emerged. However, in this paper, we particularly focus on how the naming of the devices was utilized as a communication tool. In fact, the results from the analysis of usage logs and in­situ group interviews suggest that people creatively appropriated the interaction techniques employed and these techniques were effective in their ability to sustain situated interaction and self disclosure around the public display. Implications of our findings to the design of further functionalities are also pointed out.
Estimation of similarity between functions extracted from x86 executable files

Comparison of functions is required in various domains of software engineering. In most domains, comparison is done using source code, but in some domains, such as license violation or malware analysis, only binary code is available. The goal of this paper is to evaluate whether the existing solution meant for ARM architecture can be applied to x86 architecture. The existing solution encompasses multiple approaches, but for the purpose of this paper three representative approaches are implemented; two are based on machine learning, and the third does not require previous knowledge. Results show that the best recalls obtained for the first ten positions on both architectures are comparable and do not differ significantly. The results confirm that adaptation of all approaches of the existing solution is not only possible but also promising and represent adequate basis for future research.
Face Recognition across Pose using ELM Framework

This paper proposes the use of Extreme Learning Machine regression and classification framework to recognize face across pose. ELM regression framework is used to generate virtual frontal view from its corresponding side view. Kernel version of ELM is used for the non-linear mapping estimation between frontal and its corresponding non frontal view. Non Linear estimation using proposed KELM is found to be satisfactory even in the case of reduced image coefficients. In addition to regression framework, this paper also explored few popular face features like LBP, HOG, appearance and entropy features with ELM classification framework. Combination of appearance and entropy feature set is found to give best classification accuracy among the considered feature set. Proposed experiments are conducted on subset of FERET database with images up to 45° pose variation.