Miracle-dz/UniFAR-FTUs huggingface dataset:
Delay-Limited Capacity for Broadcast Channels

In this paper, we consider an OFDM downlink situation, where the information is sent from only one transmitter to several receivers. For such a scenario the delay-limited capacity is investigated. We show that the delay-limited capacity is equal to zero when one Rayleigh channel is assigned to each user, and that it is greater than zero in case of the broadcast model where n users share n independent channels simultaneously. Furthermore, the case of more than one channel per user is considered. For an OFDM situation with many channels, it turns out that sharing all channels by all users outperforms the exclusive assignment of channels to individual users. However, the shared channel use is worse in case of very few channels.
The impact of inquiry learning on students' ability to analyze data and draw conclusions

Previous research suggests that involving students in real world inquiry projects improves their understanding of science content. Some particular features that inquiry teachers use include being a facilitator, modeling inquiry, encouraging student thinking, and engaging students in self-directed learning in which students solve problems, hypothesize, interpret data, create experiments, and explain findings. The primary focus of this study was to determine the impact of inquiry learning on sixth grade students’ ability to analyze science data and draw conclusions. The 5E’s inquiry teaching model was used during the research. The 5E’s include engage, explore, explain, elaborate and evaluate. They are a series of steps when lesson planning that involves creating excitement, asking questions and designing ways to answer questions, sharing information and then taking investigations a step further or to the next level. Activities that were appropriate for Earth Science were selected. Each successive inquiry activity utilized a gradual release of inquiry components. The outcomes of a five week inquiry unit were compared to the results of a five week traditional teaching unit. The results of this study suggested both traditional and inquiry teaching are important to develop wellrounded science students. Inquiry teaching improves students’ ability to apply science skills to data and analysis and drawing conclusions tasks. This produces strong science thinkers. Traditional teaching improves students’ ability to demonstrate data analysis and drawing conclusions skills on traditional tests. Because students need all of these skills, both teaching methods should continue to be an integral part of teaching.
Improvement of Rating Methodology

Importance The article focuses on rating methodologies, their drawbacks and improvement methods. Objectives The research generalizes the new approach to the rating methodology adjusting it for the Brusov – Filatova – Orekhova modern theory of capital cost and capital structure in relation to corporations of any age. Methods The research is based on the Brusov – Filatova – Orekhova modern theory of capital cost and capital structure (BFO theory). Results The article analyzes methodological and systemic drawbacks of the existing credit ratings of non-financial issuers. We incorporate financial ratios used for rating into the general case of the BFO theory. The article also examines how the Weighted Average Cost of Capital depends on coverage and leverage ratios. Conclusions and Relevance The proposed approach will make ratings more accurate and unbiased. The findings may prove useful to international and Russian rating agencies to evaluate the solvency of issuers.
Performance Evaluation of HARQ Technique with UMTS Turbo Code

The hybrid automatic repeat request technique (HARQ) represents the error control principle which combines an error correcting code and automatic repeat request procedure (ARQ), within the same transmission system. In this paper, using Monte Carlo simulation process, the characteristics of HARQ technique are determined, for the case of the Universal Mobile Telecommunication System (UMTS) turbo code. Keywords — Automatic repeat request (ARQ), Hybrid automatic repeat request (HARQ), Monte Carlo simulation, UMTS Turbo code.
Efficient Traceable Authorization Search System for Secure Cloud Storage

Secure search over encrypted remote data is crucial in cloud computing to guarantee the data privacy and usability. To prevent unauthorized data usage, fine-grained access control is necessary in multi-user system. However, authorized user may intentionally leak the secret key for financial benefit. Thus, tracing and revoking the malicious user who abuses secret key needs to be solved imminently. In this paper, we propose an escrow free traceable attribute based multiple keywords subset search system with verifiable outsourced decryption (EF-TAMKS-VOD). The key escrow free mechanism could effectively prevent the key generation centre (KGC) from unscrupulously searching and decrypting all encrypted files of users. Also, the decryption process only requires ultra lightweight computation, which is a desirable feature for energy-limited devices. In addition, efficient user revocation is enabled after the malicious user is figured out. Moreover, the proposed system is able to support flexible number of attributes rather than polynomial bounded. Flexible multiple keyword subset search pattern is realized, and the change of the query keywords order does not affect the search result. Security analysis indicates that EF-TAMKS-VOD is provably secure. Efficiency analysis and experimental results show that EF-TAMKS-VOD improves the efficiency and greatly reduces the computation overhead of users’ terminals.
Distributed Optimization in Electric Power Systems: Partitioning, Communications, and Synchronization

To integrate large volumes of renewables and use electricity more efficiently, many industrial trials are on-going around the world that aim to realize decentralized or hierarchical control of renewable and distributed energy resources, flexible loads and monitoring devices. As the cost and complexity involved in the centralized communications and control infrastructure may be prohibitive in controlling millions of these distributed energy resources and devices, distributed optimization methods are expected to become much more prevalent in the operation of future electric power systems, as they have the potential to address this challenge and can be applied to various applications such as optimal power flow, state estimation, voltage control, and many others. While many distributed optimization algorithms are developed mathematically, little effort has been reported so far on how these methods should actually be implemented in real-world large-scale systems. The challenges associated with this include identifying how to decompose the overall optimization problem, what communication infrastructures can support the information exchange among subproblems, and whether to coordinate the updates of the subproblems in a synchronous or asynchronous manner. This research is dedicated to developing mathematical tools to address these issues, particularly for solving the non-convex optimal power flow problem. As the first part of this thesis, we develop a partitioning method that defines the boundaries of regions when applying distributed algorithms to a power system. This partitioning method quantifies the computational couplings among the buses and groups the buses with large couplings into one region. Through numerical experiments, we show that the developed spectral partitioning approach is the key to achieving fast convergence of distributed optimization algorithms on large-scale systems. After the partitioning of the system is defined, one needs to determine whether the communications among neighboring regions are supported. Therefore, as the second part of this thesis, we propose models for centralized and distributed communications infrastructures and study the impact of communication delays on the efficiency of distributed optimization algorithms through network simulations. Our findings suggest that the centralized communications infrastructure can be prohibitive for distributed optimization and cost-effective
A Fused Context-Aware Neural Model for Item Recommendation

The user reviews have been widely adopted for user rating prediction. However, the existing review-based methods mainly derive the latent features for the users and items in an independent and static manner. That is, a single static feature vector is used for a user to identify her preference on all candidate items without considering the particular characteristics of each item. In this paper, we propose a fused context-aware neural model for rating prediction by using reviews and rating scores simultaneously, named FCAN. FCAN consists of two learning components: context-based feature learning and interaction-based feature learning. In context-based feature learning, the dynamic user latent features and item latent features are extracted through the convolution operations and nonlinearities supercharged by the neural treatment. In this sense, the relevant features from both the user review and item review on the basis of the user-item pair are identified for rating prediction. Moreover, introducing context-based features increases interpretability in rating prediction. However, the features learnt in context-based learning component is only review-driven and may not be comprehensive. We further extract complementary latent features by using the user-item rating scores in interaction-based feature learning component. With a dynamic linear fusion mechanism, FCAN can better capture the user preference for rating prediction using both the reviews and the rating scores. Experiments on five real-world datasets show that FCAN achieves significantly better predication accuracy than state-of-the-art review-based alternatives.
Can memory training help improve interpreting quality? A case report in China

ABSTRACT Interpreting, especially simultaneous interpreting, is a complex cognitive processing task that has special requirements for the interpreter’s memory resources. To date, interpreting scholars have not yet reached a consensus about the interrelationship between memory and interpreting quality, but it is generally acknowledged that memory is one of the essential elements that may affect the interpreting process as well as the final interpreting quality. Against this background, this experimental study examines the interplay between memory training and interpreting quality with reference to the language combination of Chinese and English. By exercising different memory training methods among beginning and advanced interpreting learners, the present study comes to the following conclusions: (1) Memory training exerted significant favourable effect on the interpreting learners’ performance. (2) Memory allocation training played a more important role than memory capacity training in the learners’ development. (3) Memory capacity training was more beneficial for the interpreting learners at the beginning phase, while memory allocation training proved to be more helpful as the interpreting learners built up more experience in interpreting learning. Therefore, an effective coordination of memory capacity and memory allocation training is necessary in interpreting teaching in order to achieve a comprehensive impact on the interpreting learning outcome.
Performance Analysis of Mesh-based NoC’s on Routing Algorithms

The advent of System-on-Chip (SoCs), has brought about a need to increase the scale of multi-core chip networks. Bus Based communications have proved to be limited in terms of performance and ease of scalability, the solution to both bus – based and Point-to-Point (P2P) communication systems is to use a communication infrastructure called Network-on-Chip (NoC). Performance of NoC depends on various factors such as network topology, routing strategy and switching technique and traffic patterns. In this paper, we have taken the initiative to compile together a comparative analysis of different Network on Chip infrastructures based on the classification of routing algorithm, switching technique, and traffic patterns. The goal is to show how varied combinations of the three factors perform differently based on the size of the mesh network, using NOXIM, an open source SystemC Simulator of mesh-based NoC. The analysis has shown tenable evidence highlighting the novelty of XY routing algorithm.
Design of II Impedance Matching Networks

The Q-based method of ll impedance matching network design is studied systematically. A more practical definition of the loaded quality factor Q than that used hitherto is adopted. Designable conditions and design formulas based on the loaded Q are analytically demonstrated. Accurate explicit expressions of network frequency responses and harmonic rejection in term of the loaded Q are established and a method of determining the loaded Q for the required harmonic attenuation is developed. We also formulat,e all t,olerance and parasitic sensitivities and their relations to the loaded Q.
Practical Aspects of Declarative Languages : 13th International Symposium, PADL 2011, Austin, TX, USA, January 24-25, 2011. Proceedings

This book constitutes the refereed proceedings of the 13th International Symposium on Practical Aspects of Declarative Languages, PADL 2011, held in Austin, TX, USA, in January 2011, co-located with POPL 2011, the Symposium on Principles of Programming Languages. The 17 revised full papers presented together with one application paper were carefully reviewed and selected from 40 submissions. The volume features a variety of contributions ranging from message-passing and mobile networks, concurrent and parallel programming, event processing and reactive programming, profiling and portability in Prolog, constraint programming, grammar combinators, belief set merging and work on new language extensions and tools
Revealing Groups of Semantically Close Textual Documents by Clustering: Problems and Possibilities

The chapter introduces clustering as a family of algorithms that can be successfully used to organize text documents into groups without prior knowledge of these groups. The chapter also demonstrates using unsupervised clustering to group large amount of unlabeled textual data (customer reviews written informally in five natural languages) so it can be used later for further analysis. The attention is paid to the process of selecting clustering algorithms, their parameters, methods of data preprocessing, and to the methods of evaluating the results by a human expert with an assistance of computers, too. The feasibility has been demonstrated by a number of experiments with external evaluation using known labels and expert validation with an assistance of a computer. It has been found that it is possible to apply the same procedures, including clustering, cluster validation, and detection of topics and significant words for different natural languages with satisfactory results.
The Distributed Workflow Management System - Flow Agent

While mainframe or 2-tier client/server system have serious problems in flexibility and scalability for the large-scale business processes,3-tier client/server architecture and object-oriented system modeling which construct business process on service components seem to bring software system some scalability.As enabling infrastructure for object-oriented methodology,distributed WFMS(Workflow Management System)can flexibly describe business rules among autonomous service tasks,and support scalability of large-scale business process.But current distributed WFMS still have difficulty to manage a large number of distributed tasks,the Multi-Task DOmain'architecture of FlowAgent will try to solve this problem,and bring a dynamic and distributed environment for task-scheduling.
Implementation and pretest of the trip purpose detection

This deliverable provides an overview over the current status of the work in Work Package 4 “Automated Travel Mode and Trip Purpose Detection”. The deliverable is focused on Task 4.3 Development and implementation of trip purpose imputation. The goal of this task is to implement the detection of trip purposes using temporal and spatial patterns observed in the GPS data. That includes timings and durations of activities and frequencies of visited places. In this deliverable we introduce trip purpose identification using random forests, a machine- learning approach based on decision trees. Analysis is done using GPS and accelerometer data collected by 156 participants, taking part in a one-week travel survey in Switzerland completed in 2012. Results show that random forests provide robust trip purpose classification. For ensemble runs, a share of correct predictions between 80 and 85 % was achieved. Different setups of the classifier are possible, and sometimes required by the application context. The classifier’s training set and its input variables (feature set) are defined in various ways. Four relevant setups are tested here.
Input Stage With Double Functionality Applied To A High Performance Three-phase Ups

This work presents a topological configuration that allows the input stage of a double conversion UPS to be used as a three-phase rectifier and a DC/DC voltage boost converter in normal and backup operating mode, respectively. Thus, the use of a specific circuit for the discharge of the battery bank, which would be rated for UPS nominal power, is avoided. The operation analysis of the proposed input stage is presented, as well as the mathematical modeling and the control system that guarantee the applicability in the input power factor correction, DC bus voltage control and for the DC bus capacitors voltage balance. Hardware-in-the-loop results are acquired for a 20 kVA three-phase UPS to validate the developed analysis besides to demonstrate the good performance of the system.
Squib: On Phonology and prelexical mechanisms of the language acquisition

Phonology is traditionally defined as the description of the grammatical competence of native speakers concerning the sound patterns of their language (Chomsky and Halle 1968). The field has subsequently been broadened to account for the abstract properties of phonology that are not specific to spoken language, but extend to the manual-visual modality in sign languages. A lot of attention has thus been devoted to the properties of the sensory-motor system of grammar - i.e. phonology - that are not specific to the auditory modality, that is, to spoken language, but extend to the visual modality. Abundant literature has explored this issue and showed that Sign Languages share many grammatical properties with spoken languages (among others, Brentari 1998; Nespor and Sandler 1999, Wilbur 1999; Sandler and Lillo-Martin 2006). This body of research has shown that parallel segmental and suprasegmental representations exist in the two linguistic modalities -- the auditory and the visual. DOI: 10.17074/2238-975X.2015v11n1p1
Climbing the Virtual Stepladder: Exploring the Reality of Virtual Worlds in Performance

The aim of this project was to undertake curiosity-based research on the possibilities that the current generation of virtual reality (VR) technology can offer for a movement-based artist. This focused on the interplay between real and virtual spaces, looking specifically at how the technology impacts on the roles of audience and performer. Investigation into how VR can offer interactive movement-based experiences, such as those using real objects within a virtual space, led to an examination of the somatic, conceptual and philosophical implications of this technology.
Applying Decision Tree Algorithms for Analyzing HS-VOSTS Questionnaire Results

Data mining and knowledge discovery techniques have shown to be effective in finding hidden underlying rules inside large database in an automated fashion. On the other hand, analyzing, assessing, and applying students` survey data are very important in science and engineering education because of various reasons such as quality improvement, engineering design process, innovative education, etc. Among those surveys, analyzing the students` views on science-technology-society can be helpful to engineering education. Because, although most researches on the philosophy of science have shown that science is one of the most difficult concepts to define precisely, it is still important to have an eye on science, pseudo-science, and scientific misconducts. In this paper, we report the experimental results of applying decision tree induction algorithms for analyzing the questionnaire results of high school students` views on science-technology-society (HS-VOSTS). Empirical results on various settings of decision tree induction on HS-VOSTS results from one South Korean university students indicate that decision tree induction algorithms can be successfully and effectively applied to automated knowledge discovery from students` survey data.
Ray RLlib: A Framework for Distributed Reinforcement Learning

Reinforcement learning (RL) training involves the deep nesting of highly irregular computation patterns, each of which typically exhibits opportunities for distributed computation. Current RL libraries offer parallelism at the level of the entire program, coupling all algorithm components together and making existing implementations difficult to scale, combine, and reuse. We argue for distributing RL components in a composable way by adapting algorithms for top-down hierarchical control, thereby encapsulating parallelism and resource requirements within short-running compute tasks. We demonstrate this principle by building RLlib on top of a task-based framework and show that we can implement a wide range of state-of-the art algorithms on top of a small set of general abstractions. These abstractions are key to composability and reuse in RLlib and do not come at the cost of performance---in our experiments, RLlib matches or exceeds the performance of highly optimized reference implementations. Ray RLlib is available as part of Ray at this https URL
DEVELOPING A MICROLEARNING STRATEGY WITH OR WITHOUT AN LMS

Microlearning, the learning strategy that focuses on delivering training and performance support in small focused chunks, has been a concept floating around in our industry since the lexicon of reusable learning objects was first introduced by Cisco to the learning space over fifteen years ago. Large on promise, this strategy has been short on benchmarkable solutions due to the large number of barriers encountered by learning practitioners taking on this approach. Although these barriers are diverse and tend to vary greatly from one organization to another, the most common obstacle for adopting microlearning has often been tied to the confusion around how to manage this type of content within a traditional LMS (Learning Management System) implementation. LMS-delivered content is most often composed of courses and modules that can run anywhere from minutes to hours in terms of seat time and requires participation in the somewhat formal confines of the platform. The advantages of typically more informal nature of microlearning content are in peril of losing some or all of their value if they require the same process for access. To assist learning practitioners in addressing this and other common barriers to microlearning implementation, this presentation will provide: strategies for leveraging your organization's learning transformation initiatives as a catalyst for introducing microlearning; examples of how to use common LCMS features to support role-based microlearning offerings; a pedagogical framework for disambiguating between appropriate learning modalities and appropriate content chunk sizes for all learning offerings; a sample decision framework to determine what types of content can or should be delivered directly from the LCMS/Digital Repository; and use cases presenting how xAPI can play a pivotal role in making the most of your microlearning offerings.