Miracle-dz/UniFAR-FTUs huggingface dataset:
The Design of Organic User Interfaces: Shape, Sketching and Hypercontext

With the emergence of flexible display technologies, it will be necessary for interface designers to move beyond flat interfaces and to contextualize interaction in an object’s physical shape. Grounded in early explorations of organic user interfaces (OUIs), this paper examines the evolving relationship between industrial and interaction designs and examines how not only what we design is changing, but how we design too. First, we discuss how (and why) to better support the design of OUIs: how supporting sketching, a fundamental activity of many design fields, is increasingly critical and why a ‘hypercontextualized’approachtotheirdesigncanreducethedrawbacksmetwheneverydayobjects become interactive. Finally, underlying both these points is the maturation of technology to that of a computational material; when interactive hardware is seamlessly melded into an object’s shape, the ‘computer’disappears and is better seen as a basic design material that, incidentally, happens to have interactive behavior. STUDY HIGHLIGHTS • The art of user interface design is on the cusp of a revolutionary change. • Flexible display materials dramatically alter how computer interfaces can be designed. • Technology is moving towards that of a computational material.
Design and evaluation of a limited two-way speech translator

We present a limited speech translation system for English and colloquial Levantine Arabic, which we are currently developing as part of the DARPA Babylon program. The system is intended for question/answer communication between an English-speaking operator and an Arabic-speaking subject. It uses speech recognition to convert a spoken English question into text, and plays out a pre-recorded speech file corresponding to the Arabic translation of this text. It then uses speech recognition to convert the Arabic reply into Arabic text, and does information extraction on this text to find the answer content, which is rendered into English. A novel aspect of our work is its use of a statistical classifier to extract information content from the Arabic text. We present evaluation results for both individual components and the end-to-end system.
Bivariate Empirical Mode Decomposition for Unbalanced Real-World Signals

The bivariate empirical mode decomposition (BEMD) algorithm employs uniform sampling on a circle to perform projections in multiple directions, in order to calculate the local mean of a bivariate signal. However, this approach is adequate only for equal powers in both the data channels within a bivariate signal, and results in suboptimal performance for data channels exhibiting power imbalance, a typical case in practice. To that end, we exploit second-order bivariate statistical properties to introduce a nonuniform sampling scheme for data adaptive selection of the projection directions. In this way, the resulting nonuniformly sampled BEMD (NS-BEMD) algorithm provides a more accurate time-frequency representation of bivariate data than standard BEMD, for the same number of projections. The advantages of the proposed approach are demonstrated in case studies on BEMD for correlated data channels, selection of optimal noise power in noise-assisted BEMD, and for speed estimation using Doppler radar.
An Empirical Study on User Access Control in Online Social Networks

In recent years, access control in online social networks has attracted academia a considerable amount of attention. Previously, researchers mainly studied this topic from a formal perspective. On the other hand, how users actually use access control in their daily social network life is left largely unexplored. This paper presents the first large-scale empirical study on users' access control usage on Twitter and Instagram. Based on the data of 150k users on Twitter and 280k users on Instagram collected consecutively during three months in New York, we have conducted both static and dynamic analysis on users' access control usage. Our findings include: female users, young users and Asian users are more concerned about their privacy; users who enable access control setting are less active and have smaller online social circles; global events and important festivals can influence users to change their access control setting. Furthermore, we exploit machine learning classifiers to perform an access control setting prediction. Through experiments, the predictor achieves a fair performance with the AUC equals to 0.70, indicating whether a user enables her access control setting or not can be predicted to a certain extent.
Peace, democratization and reconciliation in textbooks and educational media: Ninth International Conference on Textbooks and Educational Media

―Beginning teachers must learn to think about the appropriate resource base for teaching and learning, pedagogy and curriculum decisions. ‖ In their synthesis of textbooks and teacher education Loewenberg-Ball & Feiman-Nemser (2005) suggest that resourcing lessons is a fundamental aspect of teaching and that student teachers must be aware of the breadth and depth of teaching and learning resources. This paper presents the findings of research on textbooks and teacher education. Using the theoretical frame of Textbook Pedagogy the paper presents research on how student teachers come to mediate the use of textbooks, worksheets and learning resource artefacts. The research used a case study approach to profile the role that teacher education plays in how teaching and learning resources are used by teachers and the way they enculturate students into their use in the classroom.
Hybrid clustering algorithm

The paper presents a new graph based clustering algorithm. Traditional clustering algorithms have the drawback that it takes large number of iterations in order to come up with the desired number of clusters. The advantage of this approach is that the size of the dataset is reduced using graph based clustering approach and the required number of clusters is generated using K means algorithm. The proposed algorithm consists of two phases, the first phase being constructing the graph and de-associating the graphs into connected sub graphs which denote the number of sub groups within the data. In the second phase in order to group the sub graphs that are close to each other K means algorithm is employed.
Vision Based Obstacle Detection Module for a Wheeled Mobile Robot

Navigation in mobile robotic ambit is a methodology that allows guiding a mobile robot (MR) to accomplish a mission through an environment with obstacles in a good and safe way, and it is one of themost challenging competence required of theMR. The success of this task requires a good coordination of the four main blocks involved in navigation: perception, localization, cognition, and motion control. The perception block allows the MR to acquire knowledge about its environment using sensors. The localization block must determine the position of the MR in the environment. Using the cognition block the robot will select a strategy for achieving its goals. The motion control block contains the kinematic controller, its objective is to follow a trajectory described by its position (Siegwart & Nourbakhsh, 2004). The MR should possess an architecture able to coordinate the on board navigation elements in order to achieve correctly the different objectives specified in the mission with efficiency that can be carried out either in indoor or outdoor environments. In general, global planning methods complemented with local methods are used for indoor missions since the environments are known or partially known; whereas, for outdoormissions local planning methods are more suitable, becoming global planning methods, a complement because of the scant information of the environment. In previous work, we developed a path planning method for wheeled MR navigation using a novel proposal of ant colony optimization named SACOdm (Simple Ant Colony Optimization with distance (d) optimization, and memory (m) capability), considering obstacle avoidance into a dynamic environment (Porta et al., 2009). In order to evaluate the algorithm we used virtual obstacle generation, being indispensable for real world application to have a way of sensing the environment. There are several kind of sensors, broadly speaking they can be classified as passive and active sensors. Passive sensors measure the environmental energy that the sensor receives, in this classification some examples are microphones, tactile sensors, and vision based sensors. Active sensors, emit energy into the environment with the purpose of measuring the environmental reaction. It is common that an MR have several passive and/or active sensors; in our MR, for example, the gear motors use optical quadrature encoders, it uses a high precision GPS for localization, and two video cameras to implement a stereoscopic vision system for object recognition and localization of obstacles for map building and map reconstruction. 3
Approche de minimisation de la consommation d'énergie dans les réseaux de capteurs sans fil

In this work, we proposed a solution to minimize the consumption of energy in a sensor network in order to extend the lifetime of our network. The idea of the solution is based on the idea of points of articulation in a graph. From the initial graph that models the communication in the network, We determine the points of articulation and we keep in the graph only the nodes which are points of articulation. The other nodes are put in standby to save the overall power of the network. The simulation results clearly showed the efficiency of our solution in terms of energy and lifetime.
Gene Selection Using Locality Sensitive Laplacian Score

Gene selection based on microarray data, is highly important for classifying tumors accurately. Existing gene selection schemes are mainly based on ranking statistics. From manifold learning standpoint, local geometrical structure is more essential to characterize features compared with global information. In this study, we propose a supervised gene selection method called locality sensitive Laplacian score (LSLS), which incorporates discriminative information into local geometrical structure, by minimizing local within-class information and maximizing local between-class information simultaneously. In addition, variance information is considered in our algorithm framework. Eventually, to find more superior gene subsets, which is significant for biomarker discovery, a two-stage feature selection method that combines the LSLS and wrapper method (sequential forward selection or sequential backward selection) is presented. Experimental results of six publicly available gene expression profile data sets demonstrate the effectiveness of the proposed approach compared with a number of state-of-the-art gene selection methods.
Compressed Learning: A Deep Neural Network Approach

Compressed Learning (CL) is a joint signal processing and machine learning framework for inference from a signal, using a small number of measurements obtained by linear projections of the signal. In this paper we present an end-to-end deep learning approach for CL, in which a network composed of fully-connected layers followed by convolutional layers perform the linear sensing and non-linear inference stages. During the training phase, the sensing matrix and the non-linear inference operator are jointly optimized, and the proposed approach outperforms state-of-the-art for the task of image classification. For example, at a sensing rate of 1% (only 8 measurements of 28 X 28 pixels images), the classification error for the MNIST handwritten digits dataset is 6.46% compared to 41.06% with state-of-the-art.
Scenario Based Simulation Study of Ad Hoc Routing Protocol's Behavior in Wireless Sensor Networks

This paper investigates and undertakes simulation based study of Adhoc routing protocols in Wireless Sensor Networks. For our simulation based study we have considered three different scenarios where a Adhoc routing protocol can be applied, namely a) Phenom nodes with Mobility, Sensor nodes with mobility while the SINK node is stationary b) Phenom nodes with Mobility, Sensor nodes are stationary and Sink node is stationary c) Phenom nodes are stationary, Sensor nodes are stationary and SINK node is stationary. We have considered AODV and DSR as the Adhoc routing protocols for comparison in these scenarios. We wanted to consider a table driven routing protocol like DSDV but it was not able to scale beyond 80 nodes in the very first scenario itself. As we wanted uniformity across all the scenarios we opted out of table driven routing protocols like DSDV, even though DSDV could have been used in the third scenario. Finally, through our analysis we show the various intricacies that are associated with Adhoc routing protocols when used in a sensor environment. We also list the various problems we faced while simulating the routing protocols in each of the scenarios.
Multiple Target Tracking by Learning-Based Hierarchical Association of Detection Responses

We propose a hierarchical association approach to multiple target tracking from a single camera by progressively linking detection responses into longer track fragments (i.e., tracklets). Given frame-by-frame detection results, a conservative dual-threshold method that only links very similar detection responses between consecutive frames is adopted to generate initial tracklets with minimum identity switches. Further association of these highly fragmented tracklets at each level of the hierarchy is formulated as a Maximum A Posteriori (MAP) problem that considers initialization, termination, and transition of tracklets as well as the possibility of them being false alarms, which can be efficiently computed by the Hungarian algorithm. The tracklet affinity model, which measures the likelihood of two tracklets belonging to the same target, is a linear combination of automatically learned weak nonparametric models upon various features, which is distinct from most of previous work that relies on heuristic selection of parametric models and manual tuning of their parameters. For this purpose, we develop a novel bag ranking method and train the crucial tracklet affinity models by the boosting algorithm. This bag ranking method utilizes the soft max function to relax the oversufficient objective function used by the conventional instance ranking method. It provides a tighter upper bound of empirical errors in distinguishing correct associations from the incorrect ones, and thus yields more accurate tracklet affinity models for the tracklet association problem. We apply this approach to the challenging multiple pedestrian tracking task. Systematic experiments conducted on two real-life datasets show that the proposed approach outperforms previous state-of-the-art algorithms in terms of tracking accuracy, in particular, considerably reducing fragmentations and identity switches.
Brain Machine Interface for physically retarded people using colour visual tasks

A Brain Machine Interface is a communication system which connects the human brain activity to an external device bypassing the peripheral nervous system and muscular system. It provides a communication channel for the people who are suffering with neuromuscular disorders such as amyotrophic lateral sclerosis, brain stem stroke, quadriplegics and spinal cord injury. In this paper, a simple BMI system based on EEG signal emanated while visualizing of different colours has been proposed. The proposed BMI uses the color visual tasks and aims to provide a communication through brain activated control signal for a system from which the required task operation can be performed to accomplish the needs of the physically retarded community. The ability of an individual to control his EEG through the colour visualization enables him to control devices. The EEG signal is recorded from 10 voluntary healthy subjects using the noninvasive scalp electrodes placed over the frontal, parietal, motor cortex, temporal and occipital areas. The obtained EEG signals were segmented and then processed using an elliptic filter. Using spectral analysis, the alpha, beta and gamma band frequency spectrum features are obtained for each EEG signals. The extracted features are then associated to different control signals and a neural network model using back propagation algorithm has been developed. The proposed method can be used to translate the colour visualization signals into control signals and used to control the movement of a mobile robot. The performance of the proposed algorithm has an average classification accuracy of 95.2%.
Teacher, team, and school change through reciprocal learning

Abstract This paper documents the first year of a qualitative case study investigating the experiences of reciprocal learning teams of teachers within a small, rural secondary school. The purpose of this study was to examine how teachers experienced collaborative professional development (PD) and how their experiences contributed to developing the culture and structure of their school. Collected data were analyzed using an iterative process of coding, categorizing, and abstracting data. Three themes emerged: (a) self- and co-regulated learning in teams invigorated collegial relationships and contributed to a sense of agency toward change at the school level; (b) differentiating support for collaborative inquiry among teachers and within teams is critical; and (c) creating structures to support collaborative inquiry among teachers engages teachers in their ongoing professional learning. This case study of a rural secondary school demonstrates how deep and substantial school change can happen when teachers co-construct and co-regulate their PD.
TALEN/CRISPR-mediated engineering of a promoterless anti-viral RNAi hairpin into an endogenous miRNA locus

Successful RNAi applications depend on strategies allowing robust and persistent expression of minimal gene silencing triggers without perturbing endogenous gene expression. Here, we propose a novel avenue which is integration of a promoterless shmiRNA, i.e. a shRNA embedded in a micro-RNA (miRNA) scaffold, into an engineered genomic miRNA locus. For proof-of-concept, we used TALE or CRISPR/Cas9 nucleases to site-specifically integrate an anti-hepatitis C virus (HCV) shmiRNA into the liver-specific miR-122/hcr locus in hepatoma cells, with the aim to obtain cellular clones that are genetically protected against HCV infection. Using reporter assays, Northern blotting and qRT-PCR, we confirmed anti-HCV shmiRNA expression as well as miR-122 integrity and functionality in selected cellular progeny. Moreover, we employed a comprehensive battery of PCR, cDNA/miRNA profiling and whole genome sequencing analyses to validate targeted integration of a single shmiRNA molecule at the expected position, and to rule out deleterious effects on the genomes or transcriptomes of the engineered cells. Importantly, a subgenomic HCV replicon and a full-length reporter virus, but not a Dengue virus control, were significantly impaired in the modified cells. Our original combination of DNA engineering and RNAi expression technologies benefits numerous applications, from miRNA, genome and transgenesis research, to human gene therapy.
Effects of defensive pressure on basketball shooting performance

Reasons behind successful team performance were explained through analysing the effects of defensive pressure on basketball shooting performance. Team success, shooting efficiency and defensive opposition were analysed from 3344 shooting attempts with Wilcoxon signed ranks tests, Friedman tests and odds ratio calculations. Results of the analysis identified significant differences between winners and losers for close 2 point and 3 point shooting percentages. The increased level of defensive pressure had influential effect on the shooting performance and the game outcome. It was concluded that winning teams achieved more effective shooting percentages as the consequence of better team cooperation, because players could work out more opened scoring opportunities without any active defensive presence. The other occasion of the higher shooting percentages was the level of defensive performance because winners more times forced their opponent to try under maximal defensive pressure. After all, perhaps the most important difference between winning and losing teams was that winners could exploit their scoring opportunities more effectively, not only without defensive opposition but also from the hardest situations under high level of defensive pressure also.
Invariant-Based Augmented Reality on Mobile Phones

A calibration-free augmented reality based on affine invariant is firstly formulated by tensor method. This approach does not use the calibration parameters of the camera and the 3D locations of the environment’s object, and can realize the augmentation of virtual objects. Meanwhile, a new approach to resolving occlusion problem in augmented reality is presented. Based on an arm-optimized implementation of the Scale Invariant Feature Transform (SIFT) algorithm developed by David Lowe and Random Sample Consensus (RANSAC) algorithm, the point correspondences in any two views are determined. According to the invariant for two views and these point correspondences, the occluding contours can be transferred to any views, so the occlusion is resolved. Some typical experiments show that the approach of invariant-based augmented reality is feasible on mobile phones.
Limits of the Web Log Analysis Artifacts

In this technical paper, we estimate instrumental limits of an unexacting comparison of results reported in different Web log studies. We consider sensitivity of results of log analysis to 4 controllable factors: a log sampling technique, an observation period and two cut-off variables peculiar to the Web log analysis (a LAN cut-off to exclude local area networks clients and a temporal cut-off to detect temporal search sessions). It is shown that 3 of these factors may lead to multiple differences in the case of marginal values of the factors whereas an effect of usual factors combinations is limited by 30%. These limits overcover differences between the results of Excite and AltaVista studies.
Interferon Regulatory Factor 4 Contributes to Transformation of v-Rel-Expressing Fibroblasts

ABSTRACT The avian homologue of the interferon regulatory factor 4 (IRF-4) and a novel splice variant lacking exon 6, IRF-4ΔE6, were isolated and characterized. Chicken IRF-4 is expressed in lymphoid organs, less in small intestine, and lungs. IRF-4ΔE6 mRNA, though less abundant than full-length IRF-4, was detected in lymphoid tissues, with the highest levels observed in thymic cells. IRF-4 is highly expressed in v-Rel-transformed lymphocytes, and the expression of IRF-4 is increased in v-Rel- and c-Rel-transformed fibroblasts relative to control cells. The expression of IRF-4 from retrovirus vectors morphologically transformed primary fibroblasts, increased their saturation density, proliferation, and life span, and promoted their growth in soft agar. IRF-4 and v-Rel cooperated synergistically to transform fibroblasts. The expression of IRF-4 antisense RNA eliminated formation of soft agar colonies by v-Rel and reduced the proliferation of v-Rel-transformed cells. v-Rel-transformed fibroblasts produced interferon 1 (IFN1), which inhibits fibroblast proliferation. Infection of fibroblasts with retroviruses expressing v-Rel resulted in an increase in the mRNA levels of IFN1, the IFN receptor, STAT1, JAK1, and 2′,5′-oligo(A) synthetase. The exogenous expression of IRF-4 in v-Rel-transformed fibroblasts decreased the production of IFN1 and suppressed the expression of several genes in the IFN transduction pathway. These results suggest that induction of IRF-4 expression by v-Rel likely facilitates transformation of fibroblasts by decreasing the induction of this antiproliferative pathway.