Miracle-dz/UniFAR-FTUs huggingface dataset:
Mining Qualitative Behavioral Data from Quantitative Data: A Case Study from the Gender HCI Project

Recent research has shown that gender differences exist that influ- ence the ways that males and females work with problem-solving software. These gender differences may put females at a disadvantage in competing for jobs requiring these skills. Earlier research has shown the existence of gender differences in confidence that affects feature usage and adoption; however these findings have raised new questions. We are seeking answers to these questions through qualitative methods. The case study we present here documents our methodology and may be used as a guide for others embarking on similar quali- tative analyses.
Data Collection through Vehicular Sensor Networks by using TCDGP

Now a days Many car manufacturers are planning to install wireless connectivity equipment in their vehicles to enable communications with "roadside base station" and also between vehicles, for the purposes of safety, driving assistance, and entertainment. One distinct feature is that vehicles are highly mobile, with speed up to 30 m/s, though their mobility patterns are more predictable than those of nodes in Mobile Ad-hoc Networks (MANET) due to the constraints imposed by road, speed limits, and commuting habits. Therefore, these networks require specific solutions and identify a novel research area, i.e., Vehicular Ad-hoc Networks (VANET). In this paper, we focus on a particular VSN architecture, where the ad hoc network is operated by a telecommunication/service provider to combine non-valuable individual sensed data and extract from them effective feedbacks about the situation of the road in a geographical area. In operated VSNs, providers tend to reduce the traffic load on their network, using the free-frequency communication medium (IEEE 802.11p, for example). To do so, we propose TCDGP (Tree Clustered Data Gathering Protocol), a cross layer protocol based on hierarchical and geographical data collection, aggregation and dissemination mechanisms. We analyze the performances of our solution using a simulation environment and realistic mobility models. We demonstrate the feasibility of such solution and show that TCDGP offers the operator precious information without overloading his network.
Performance Analysis of Digital Signal Processors Using SMV Benchmark

Unlike general-purpose processors, digital signal processors (DSP processors) are strongly application-dependent. To meet the needs for diverse applications, a wide variety of DSP processors based on different architectures ranging from the traditional to VLIW have been introduced to the market over the years. The functionality, performance, and cost of these processors vary over a wide range. In order to select a processor that meets the design criteria for an application, processor performance is usually the major concern for digital signal processing (DSP) application developers. Performance data are also essential for the designers of DSP processors to improve their design. Consequently, several DSP performance benchmarks have been proposed over the past decade or so. However, none of these benchmarks seem to have included recent new DSP applications. In this paper, we use a new benchmark that we recently developed to compare the performance of popular DSP processors from Texas Instruments and StarCore. The new benchmark is based on the Selectable Mode Vocoder (SMV), a speech-coding program from the recent third generation (3G) wireless voice applications. All benchmark kernels are compiled by the compilers of the respective DSP processors and run on their simulators. Weighted arithmetic mean of clock cycles and arithmetic mean of code size are used to compare the performance of five DSP processors. In addition, we studied how the performance of a processor is affected by code structure, features of processor architecture and optimization of compiler. The extensive experimental data gathered, analyzed, and presented in this paper should be helpful for DSP processor and compiler designers to meet their specific design goals.
A Novel Cooperative Relaying Network Scheme with Inter-Relay Data Exchange

In this paper, we propose a novel scheme of cooperative relaying network based on data exchange between relays before forwarding their received data to destination. This inter-relay data exchange step is done during an additional middle-slot in order to enhance the transmit signals from relays to the destination under low transmit power condition. To reduce the propagation errors between relays as well as the required transmit power during this data exchange, only the relay possessing the highest SNR is engaged into exchanging data by forwarding its received signal to the other relays. As for the remaining non-selected relays, i.e. with low SNR, the transmitted signal is estimated by using both signals received separately at different time slots (i.e., 1st and 2nd slot) from source and the 'best' selected relay, respectively, emulating virtual antenna array where appropriate weights for the antenna array are developed. In addition, we investigate distributed transmit beamforming and maximum ratio combining at the relays and the destination, respectively, to combine coherently the received signals. At the relay optimal location and for low SNR condition, the proposed method has significant better outage behavior and average throughput than conventional methods using one or two time slots for transmission.
Optimizing HARQ with Rateless Codes for Wireless Multicast under Strict Delay-Bandwidth Constraints

1,2 Guoping Tan, 3 Jing Xu, 4 Yueheng Li, 5 Lizhong Xu 1, First Author, *2,Corresponding Author,5 Institute of Communication and Information Systems, College of Computer and Information Engineering, Hohai University, Nanjing, China, E-mail: gptan@hhu.edu. cn Shanghai Research Center for Wireless Communications, Shanghai, China 4 College of Computer and Information Engineering, Hohai University, Nanjing, China doi : 10.4156/aiss.vol3. issue7.41
Rule Extraction from Dynamic Cell Structure Neural Networks by Modified LREX Algorithm-A Comparative Study

The semantic of neural networks is not explicit and they are considered as black box systems. There are many researches investigating the area of rule extraction by neural networks. In this paper, the eclectic approach of rule extraction from a dynamic cell structure (DCS) neural network is investigated. To do this, a modified version of LERX algorithm is used for rule generation. Empirical results show that the DCS performs better than other self-organizing maps, e.g. Kohonen and competitive neural networks in generation of effective rules. Accuracy of classification, separability of Voronoi regions and mean squared error (MSE) in regions are the measures in which DCS performs better than other networks.
CyberOrgs : A Model for Decentralized Resource Control in Multi-Agent Systems

Competition and collaboration among agents in a multi-agent system extends to consumption of computational resources. Management of resource sharing is critical to the performance of multi-agent computations. We describe CyberOrgs, a hierarchical model for resource sharing between and within multi-agent computations executing over a network of computers. We introduce programming abstractions derived from the model through a prototype implementation of CyberOrgs as an Actor program.
Sector based Multi-hop Clustering Protocol for Wireless Sensor Networks

Minimization of energy consumption is a critical design requirement for most wireless sensor networks (WSNs) applications. WSNs are composed of small, energy constrained sensor nodes. The random deployment of sensor nodes in many applications requires unattended operation. The primary goal of WSNs is how to collect sensed information in energy efficient way since the energy is limited. The network lifetime is extended by hierarchical network clustering. In these networks, the sensor field is partitioned into small regions called clusters. Some of the nodes in the network become cluster head (CH), which aggregates the data collected from their member nodes, and transmit it to the base station using multi-hop communication. Numerous clustering protocols have been proposed in the literature to minimize the energy consumption among the sensor nodes. In this paper, an energy efficient clustering protocol for WSNs with a primary goal of increasing the lifetime of the network is proposed. The protocol named as sector based multi-hop clustering (SBMC), which uses the concept of leveling, sectoring and multi-hop clustering. Dissemination of data from CH to base station is done in such a way that the energy loss in discovering a route is decreased to minimum. The additional computation for the leveling and sectoring are carried out in base station just at the beginning of the network setup. Therefore, the goal of energy efficiency without additional energy consumption of the distributed nodes after network setup process in proposed SBMC protocol is achieved.
A Method of SNS Topic Models Extraction Based on Self-Adaptively LDA Modeling

While SNS(Social Network Services) playing an increasingly important role in today's online world, SNA(Social Network Analysis) and text mining based on such communication are becoming more and more useful for a wide variety of applications. However, topic models, which have been widely used in information classification and retrieval, are not proper for some SNS such as microblogging. Moreover, It is also quite important but difficult to select topic number for a specific target. In this paper, first we present a new evaluation metric for topic models extraction from SNS dataset. Then, by combining LDA modeling with this metric, a self-adaptively LDA modeling method is proposed. Experiments were successfully performed, and the results show that the proposed LDA modeling method can self-adaptively reach the appropriate number of social topics without losing performance for microblogging-like dataset.
Towards Feature Selection in Networks

Traditional feature selection methods assume that the data are independent and identically distributed (i.i.d.). In real world, tremendous amounts of data are distributed in a network. Existing features selection methods are not suited for networked data because the i.i. d. assumption no longer holds. This motivates us to study feature selection in a network. In this paper, we present a supervised feature selection method based on Laplacian Regularized Least Squares (LapRLS) for networked data. We use linear regression to utilize the content information, and adopt graph regularization to consider the link information. The proposed feature selection method aims at selecting a subset of features such that the empirical error of LapRLS is minimized. The resultant optimization problem is a mixed integer programming, which is difficult to solve. It is relaxed into a L2,1-norm constrained LapRLS problem and solved by accelerated proximal gradient descent algorithm. Experiments on benchmark networked data sets show that the proposed feature selection method outperforms traditional feature selection method and the state-of-the-art learning-in-network approaches.
A PROPOSED QUERY-SENSITIVE SIMILARITY MEASURE FOR INFORMATION RETRIEVAL *

Document clustering has been widely used in information retrieval systems in order to improve the efficiency and also the effectiveness of ranked output systems using clustering hypothesis. Based on this hypothesis, documents relevant to a query tend to be highly similar in the context defined by the query. In this way, a pair of documents has an overall similarity (ignoring the query) and a specific similarity (similarity of a pair of documents given a query). A Query-Sensitive Similarity Measure (QSSM) is a mechanism to measure the similarity of two documents given a query. In this paper, in the first step, we identify the sources of information that may be used for this purpose. In the second step, we propose a QSSM based on these information sources. Finally, we propose a parametric QSSM that simultaneously makes use of the product and weighted sum to fuse the information from the identified sources. A genetic algorithm is used to learn the optimal values of parameters in this measure for a specific collection. The leave-one-out method is used to evaluate the proposed learning scheme. Our motivation for this is to see whether the learning scheme can perform significantly better than the measure proposed in the second step. Using several document collections, the performance of each measure is evaluated and the results are compared with other QSSMs proposed in the past research. Keywords– Query sensitive similarity measure, document clustering, genetic algorithm
Learning Based Frequency- and Time-Domain Inter-Cell Interference Coordination in HetNets

In this paper, we focus on inter-cell interference coordination (ICIC) techniques in heterogeneous network (HetNet) deployments, whereby macro- and picocells autonomously optimize their downlink transmissions with loose coordination. We model this strategic coexistence as a multi-agent system, aiming at joint interference management and cell association. Using tools from Reinforcement Learning (RL), agents (i.e., macro- and picocells) sense their environment and self-adapt based on local information to maximize their network performance. Specifically, we explore both time- and frequency domain ICIC scenarios and propose a two-level RL formulation. Here, picocells learn their optimal cell range expansion (CRE) bias and transmit power allocation, as well as appropriate frequency bands for multi-flow transmissions, in which a user equipment (UE) can be simultaneously served by two or more base stations (BSs) from macro- and pico-layers. To substantiate our theoretical findings, Long-Term Evolution Advanced (LTE-A) based system-level simulations are carried out in which our proposed approaches are compared with a number of baseline approaches, such as resource partitioning (RP), static CRE, and single-flow Carrier Aggregation (CA). Our proposed solutions yield substantial gains up to 125% compared to static ICIC approaches in terms of average UE throughput in the time domain. In the frequency domain, our proposed solutions yield gains up to 240% in terms of cell-edge UE throughput.
L’induction pour mieux comprendre le processus de réadaptation chez des individus ayant subi un traumatisme

La recherche qualitative et inductive est peu utilisee dans le domaine de la sante. Pourtant, ce type d’approche permet de saisir la complexite des interventions menees, notamment de readaptation, en se focalisant sur la realite des usagers des services et des intervenants. C’est pourquoi cette methode a ete privilegiee lorsque nous avons voulu comprendre l’impact d’une intervention inductive novatrice, le Fil d’Ariane, sur le processus de readaptation d’individus ayant subi un traumatisme. L’objectif de cette etude est ainsi double : 1) demontrer les apports de la recherche inductive pour mieux comprendre l’impact des interventions en readaptation; 2) mieux comprendre les effets d’une intervention inductive novatrice, le Fil d’Ariane. Pour ce faire, des entretiens semi-diriges avec des usagers et des intervenants ont ete realises. Ces entrevues revelent, entre autres, l’importance de l’accompagnement de l’usager par l’intervenant, d’un engagement profond et authentique de l’usager envers le Fil d’Ariane, de meme que d’un suivi personnalise pour l’usager. D’un autre cote, l’intervention exige des intervenants un haut niveau d’engagement, un temps de redaction important ainsi que des qualites d’ecoute et d’empathie.
Evolutionary Recovery of a Recombinant Viral Genome

It is well appreciated that the evolutionary divergence of genes and genomes from a common ancestor ultimately leads to incompatibilities if those genomes are hybridized. Far less is known about the ability and nature of compensatory evolution to yield the recovery of function in hybrid genomes. Here the major capsid gene of the bacteriophage T7 (40-kb dsDNA) was replaced with the homologous gene of either T3 or K11, each 22% different at the protein level from the T7 homolog. Initial fitness was moderately impaired for the T3 exchange, but the K11 exchange was not viable without a compensatory change in the T7 scaffolding protein. Subsequent adaptation of the transgenic phages led to nearly complete fitness recoveries. Compensatory changes were few, mostly in the transgene and its main interacting partner, the scaffolding protein gene. The large magnitude of fitness recovery with relatively few mutations suggests that the fitness costs of hybridizations and horizontal gene exchanges between moderately diverged genomes can potentially be short-lived through compensatory evolution.
A fast multi-object tracking algorithm by fusing color and motion information

This paper proposes a fast algorithm for multiple targets tracking in complex environment of industrial workshop, which integrates the background modeling and the motion information. First, the probability density image is calculated based on histogram of color from each target object. Second, these probability density images are filtered according to background image obtained from previous background modeling. Third, the motion information is fused into its tracking process, and the optimal position is thus predicted. Finally, the algorithm removes the false targets in the previous frame from those images of color probability density, in order to avoid the disturbance to other targets in the later tracking procedure. The experimental results have demonstrated that the proposed new algorithm is capable of reducing background and similar objects disturbance and achieving real-time performance.
Applying morphology to English-Arabic statistical machine translation

We introduce two approaches to augmenting English-Arabic statistical machine translation (SMT) with linguistic knowledge. The first approach improves SMT by adding linguistically motivated syntactic features to particular phrases. These added features are based on the English syntactic information, namely part-of-speech tags and dependency parse trees. We achieved improvements of 0.2 and 0.6 in BLEU score on two different data sets over the state-of-the-art SMT baseline system. The second approach improves morphological agreement in machine translation output through post-processing. Our method uses the projection of the English dependency parse tree onto the Arabic sentence in addition to the Arabic morphological analysis in order to extract the agreement relations between words in the Arabic sentence. Afterwards, classifiers for individual morphological features are trained using syntactic and morphological information from both the source and target languages. The predicted morphological features are then used to generate the correct surface forms. Our method achieves a statistically significant improvement over the baseline system according to human evaluation.
Localization Using Radial Basis Function Networks and Signal Strength Fingerprints in WLAN

Fingerprinting localization techniques provide reliable location estimates and enable the development of location aware applications especially for indoor environments, where satellite based positioning is infeasible. In our approach we utilize Received Signal Strength (RSS) fingerprints collected in known locations and employ a Radial Basis Function (RBF) neural network to approximate the function that maps fingerprints to location coordinates. We present a clustering scheme to reduce the size and computational complexity of the RBF architecture and demonstrate the applicability of this approach in a real-world WLAN setup. Experimental results indicate that the RBF based method is an efficient approach to the location determination problem that outperforms existing techniques in terms of the positioning error.
Throughput modeling of TCP with slow-start and fast recovery

Despite the rich literature on modeling TCP, we find two common deficiencies with the existing approaches. First, none of the work gives sufficient treatment to slow-start, although almost all of them show that retransmission timeout events are common. Second, the probability that retransmission timeout occurs has been underestimated, because retransmission timeout is coupled with fast recovery but fast recovery has not been properly modeled in the previous work. In this paper, new analytical models for predicting the steady state throughput of TCP flows are proposed. All major TCP mechanisms, including slow-start, congestion avoidance, fast retransmit, and fast recovery, are jointly considered under both bursty and independent loss models. We show that our proposed throughput models capture TCP performance more accurately.
Cedar : Engineering Role-Based Adaptive User Interfaces for Enterprise Applications

Feature-bloated enterprise applications such as customer relationship management (CRM) and enterprise resource planning (ERP) are very large scale, encompassing millions of lines-of-code and thousands of user interfaces (UI). Also, these applications are sold as generic off-the-shelf products to be used by people with diverse needs in required featureset and backgrounds such as skills, culture, etc. Although several approaches have been proposed for adapting UIs to various user profiles, little work has focused on simplifying enterprise application UIs through engineering adaptive behavior. We define UI simplification as a mechanism for increasing usability through adaptive behavior by providing users with a minimal feature-set and an optimal layout based on their individual profile. In this paper we present Role-Based UI Simplification (RBUIS), a tool supported approach based on our CEDAR architecture for simplifying enterprise application UIs through engineering role-based adaptive behavior. RBUIS is integrated in our generic platform for developing adaptive model-driven enterprise UIs. Our approach is validated from the technical and enduser perspectives by applying it to developing a prototype enterprise application and user-testing the outcome. Author
A Digital Framework for Pulse Coded Neural Network Hardware with Bit-Serial Operation

This publication presents a digital framework for building up pulse coded neural networks with leaky integrate-and-fire neurons and static synapses as well as dynamic synapses. The system, including a novel communication infrastructure, is mainly focused on ASIC synthesis but also shows a small footprint on Virtex2(Pro) FPGAs. Its bit-serial operation has been verified by simulations.
An Adaptive Approach to Spam Filtering on a New Corpus

Motivated by the absence of rigorous experimentation in the area of spam filtering using realistic email data, we present a newly-assembled corpus of genuine and unsolicited (spam) email, dubbed GenSpam, to be made publicly available. We also propose an adaptive model for semi-structured document classification based on smoothed n-gram language modelling and interpolation, and report promising results when applying the classifier to the spam filtering problem using a specifically assembled test set to be released as part of the GenSpam corpus.