Miracle-dz/UniFAR-FTUs huggingface dataset:
Collaborative delay-aware scheduling in peer-to-peer UGC video sharing

We have recently witnessed an explosion of user-generated content (UGC) sharing, particularly video clips, as the new killer Internet application. Given the sheer amount of resource demands, the peer-to-peer (or peer-assisted) model has been suggested for this new service scenario. There are however a series of unique challenges from the UGC videos to be addressed, in particular, their significantly shorter lengths. As such, any delay, even being minor as compared to those for conventional movie-like videos, will be perceptually amplified. Given the much more stringent delay requirement, the UGC video sharing thus calls for sophisticated scheduling to provide quality playback. In this paper, we propose a novel collaborative delay-aware scheduling (CODAS) that is customized for the short UGC videos. CODAS improves playback quality and reduces server workload, through adaptive prioritization of data requests and tighter collaboration between peer suppliers and the server. We present detailed design and optimization of CODAS, particularly the synergy policies in different zones of a shrinking window. We evaluate it through extensive trace-driven simulations and PlanetLab prototype experiments, and the results show the great improvement over the state-of-the-art solutions.
MAV navigation through indoor corridors using optical flow

Safe navigation through corridors plays a major role in the autonomous use of Micro Aerial Vehicles (MAVs) in indoor environments. In this paper, we present an approach for wall collision avoidance using a depth map based on optical flow from on board camera images. An omnidirectional fisheye camera is used as a primary sensor, while IMU data is needed for compensating rotational effects of the optical flow. The here presented approach is designed for safely maneuvering a helicopter through an indoor corridor. Results based on real images taken in a corridor with textured walls are shown at the end of this paper.
Code-switching as a Strategy for the Decolonisation of Learning: A Case of One University in Swaziland

Abstract Although the call for the decolonisation of learning and an African renaissance has been a common trend in research recently, less attention has been paid to code-switching as a strategy for decolonising learning at universities. This article explores code-switching as a strategy for decolonising learning at a private university in Swaziland. The article addresses two questions: 1) What are students’ experiences of code-switching during Englishmedium lectures? 2) What are the students’ perceptions of code-switching during Englishmedium lectures? Data were collected using a mixed methods, single case study among a purposive sample of 30 first-year Bachelor of Education in Mathematics students at a private university in Swaziland. The findings indicated that students code switch to solidify pedagogical content presented in English during lectures. The findings also indicated that students employ code-switching as a strategy for breaking linguistic barriers imposed by the English language. Finally, the findings indicated that students describe code-switching as an invaluable strategy for decolonising learning at university. The study contributed in a unique way to knowledge because it is the first of this kind to explore decolonisation at a Swazi university—hence it does not only add to the growing body of literature—but also pioneers scholarship on the decolonisation of learning at Swazi universities. The article concludes by suggesting a student-centred approach to learning that embraces code-switching as a strategy for decolonising learning at the university.
Transcriptional Regulation and Adaptation to a High-Fiber Environment in Bacillus subtilis HH2 Isolated from Feces of the Giant Panda

In the giant panda, adaptation to a high-fiber environment is a first step for the adequate functioning of intestinal bacteria, as the high cellulose content of the gut due to the panda's vegetarian appetite results in a harsh environment. As an excellent producer of several enzymes and vitamins, Bacillus subtilis imparts various advantages to animals. In our previous study, we determined that several strains of B. subtilis isolated from pandas exhibited good cellulose decomposition ability, and we hypothesized that this bacterial species can survive in and adapt well to a high-fiber environment. To evaluate this hypothesis, we employed RNA-Seq technology to analyze the differentially expressed genes of the selected strain B. subtilis HH2, which demonstrates significant cellulose hydrolysis of different carbon sources (cellulose and glucose). In addition, we used bioinformatics software and resources to analyze the functions and pathways of differentially expressed genes. Interestingly, comparison of the cellulose and glucose groups revealed that the up-regulated genes were involved in amino acid and lipid metabolism or transmembrane transport, both of which are involved in cellulose utilization. Conversely, the down-regulated genes were involved in non-essential functions for bacterial life, such as toxin and bacteriocin secretion, possibly to conserve energy for environmental adaptation. The results indicate that B. subtilis HH2 triggered a series of adaptive mechanisms at the transcriptional level, which suggests that this bacterium could act as a probiotic for pandas fed a high-fiber diet, despite the fact that cellulose is not a very suitable carbon source for this bacterial species. In this study, we present a model to understand the dynamic organization of and interactions between various functional and regulatory networks for unicellular organisms in a high-fiber environment.
Waveform features for tree identification

Interest in full-waveform airborne laser scanning (ALS) data has significantly increased with the release of waveform digitizers by commercial vendors. Despite the recent widespread availability of full-waveform data, the full potential of this type of data has yet to be realised. Some of the most promising applications for waveform data can be found in various fields of forestry, in which ALS data can aid in understanding single-tree characteristics. Waveform data can provide both vertical and horizontal information on forests. In this article, we study the feasibility of using full waveform data for tree identification. This study also considers the applicability of methods designed for use with conventional data, the possibility of generating methods that could use considerably denser point clouds extracted from full-waveforms, as well as the applicability of single descriptive or distinct waveform characteristics for tree species classification and tree parameter extraction. In addition, waveform data is compared with terrestrial close-range images. Superimposing waveform data on registered close-range images offers an excellent opportunity for understanding the waveform in greater detail.
3D model-based continuous emotion recognition

We propose a real-time 3D model-based method that continuously recognizes dimensional emotions from facial expressions in natural communications. In our method, 3D facial models are restored from 2D images, which provide crucial clues for the enhancement of robustness to overcome large changes including out-of-plane head rotations, fast head motions and partial facial occlusions. To accurately recognize the emotion, a novel random forest-based algorithm which simultaneously integrates two regressions for 3D facial tracking and continuous emotion estimation is constructed. Moreover, via the reconstructed 3D facial model, temporal information and user-independent emotion presentations are also taken into account through our image fusion process. The experimental results show that our algorithm can achieve state-of-the-art result with higher Pearson's correlation coefficient of continuous emotion recognition in real time.
Deriving Semantic Sensor Metadata from Raw Measurements

Sensor network deployments have become a primary source of big data about the real world that surrounds us, measuring a wide range of physical properties in real time. With such large amounts of heterogeneous data, a key challenge is to describe and annotate sensor data with high-level metadata, using and extending models, for instance with ontologies. However, to automate this task there is a need for enriching the sensor metadata using the actual observed measurements and extracting useful meta-information from them. This paper proposes a novel approach of characterization and extraction of semantic metadata through the analysis of sensor data raw observations. This approach consists in using approximations to represent the raw sensor measurements, based on distributions of the observation slopes, building a classification scheme to automatically infer sensor metadata like the type of observed property, integrating the semantic analysis results with existing sensor networks metadata.
A Novel VENOM Attack Identification Mechanism in Cloud Virtualization Environment

This paper investigates the security issue of virtualization in the cloud computing. We focus on how to identify the VENOM attack in the cloud-computing environment, and how to protect the hypervisor from this VENOM attack. Firstly, we have implemented VENOM vulnerability in the environment of QEMU/KVM and tried to identify its behaviors (action) in the cloud. Secondly, we also tried to protect the hypervisor, which is the most vulnerability part for virtualization environment. The proposed mechanism provides identification of the VENOM attack and lock the FDC port (0x3f5), which is responsible to send I/O command to the hypervisor.
Teleworkbench: validating robot programs from simulation to prototyping with minirobots

This paper describes a Demo showing the role of the Teleworkbench in the validation process of a multi-agent system, e.g., a trac management system. In the Demo, we show the capability of the Teleworkbench in seamlessly bridging the simulation and experimentation with real robots. During experiments, important information is logged for analysis purpose. Additionally, a graphical user interface enables geographically distributed users to perform some levels of interactivity, e.g., watch the video or command the robots.
Neural Network versus Econometric Models in Forecasting Inflation

Artificial neural network modeling has recently attracted much attention as a new technique for estimation and forecasting in economics and finance. The chief advantages of this new approach are that such models can usually find a solution for very complex problems, and that they are free from the assumption of linearity that is often adopted to make the traditional methods tractable. In this paper we compare the performance of Back-Propagation Artificial Neural Network (BPN) models with the traditional econometric approaches to forecasting the inflation rate. Of the traditional econometric models we use a structural reduced form model, an ARIMA model, a vector autoregressive model, and a Bayesian vector autoregression model. We compare each econometric model with a hybrid BPN models which uses the same set of variables. Dynamic forecasts are compared for three different horizons: one, three and twelve months ahead. Root mean squared errors and mean absolute errors are used to compare quality of forecasts. The results show the hybrid BPN models able to forecast as well as all the traditional econometric methods, and to outperform them in some cases.
Autocrine regulation of TGF-&bgr;1-induced cell migration by exocytosis of ATP and activation of P2 receptors in human lung cancer cells

Summary TGF-&bgr;1 plays a key role in cancer progression through induction of various biological effects, including cell migration. Extracellular nucleotides, such as ATP, released from cells play a role in signaling through activation of P2 receptors. We show here that exocytosis of ATP followed by activation of P2 receptors play a key role in TGF-&bgr;1-induced actin remodeling associated with cell migration. Treatment with TGF-&bgr;1 facilitated migration of human lung cancer A549 cells, which was blocked by pretreatment with ecto-nucleotidase and P2 receptor antagonists. ATP and P2 agonists facilitated cell migration. TGF-&bgr;1-induced actin remodeling, which contributes to cell migration, was also suppressed by pretreatment with ecto-nucleotidase and P2 receptor antagonists. Knockdown of P2X7 receptor suppressed TGF-&bgr;1-induced migration and actin remodeling. These results indicate the involvement of TGF-&bgr;1-induced ATP release in cell migration, at least in part, through activation of P2X7 receptors. TGF-&bgr;1 caused release of ATP from A549 cells within 10 minutes. Both ATP-enriched vesicles and expression of a vesicular nucleotide transporter (VNUT) SLC17A9, which is responsible for exocytosis of ATP, were found in cytosol of A549 cells. TGF-&bgr;1 failed to induce release of ATP from SLC17A9-knockdown cells. TGF-&bgr;1-induced cell migration and actin remodeling were also decreased in SLC17A9-knockdown cells. These results suggest the importance of exocytosis of ATP in cell migration. We conclude that autocrine signaling through exocytosis of ATP and activation of P2 receptors is required for the amplification of TGF-&bgr;1-induced migration of lung cancer cells.
Problem Solving Using the HEX Family

The HEX formalism has been designed as an extension of answer set programs that oﬀers an abstract interface to access external sources of information and computation, such as the World Wide Web or description logics reasoners. The generic nature makes the extension powerful, which has been exploited in diﬀerent ways: as an end user problem solving language, as a backend formalism, or as the basis of a richer formalism with possibly increased expressiveness. The increasing spread of HEX is paralleled with the frequently asked questions of what HEX is, and how it can be used for problem solving. In this paper, we aim to answer these questions; we consider diﬀerent scenarios and provide a methodology for applying HEX from a user perspective. Furthermore, we brieﬂy present a collection of applications based on HEX or derived from it, including sample snippets from associated HEX programs.
On trust and influence: A computational red teaming game theoretic perspective

The concept of trust has attracted the attention of many researchers over the years who studied the impact of trust in many domains. Trust is a ubiquitous concept. It is pervasive in every aspect of our life, from interpersonal relationships to national defence and security applications. However, despite the vast literature on trust, we are not close enough to mastering the dynamics of trust. One reason is that if we define procedural steps for trust, we simultaneously define steps for deception; thus, we simply define a vacuous cycle. Another reason is that, the dynamics of trust change as the world changes. But how can we then study trust? This paper connects the interdisciplinary literature to synthesize a Computational Red Teaming (CRT) based model of trust that defines opportunities whereby computational intelligence techniques, more specifically, evolutionary game theory researchers, can contribute to this vastly growing research area. We offer a position on the topic by reviewing games for trust and introduce a new theoretic game to study influence and transfer of trust.
Reliable Classifiers in ROC Space

The performance of a classifier can be improved by abstaining on uncertain instance classifications. The transformation from the original Receiver Operator Characteristic (ROC) curve to the curve obtained by abstention is provided. We include proofs on dominance of this new ROC curve to aid classifier selection and to show the effectiveness of the approach. For specific cost and class distributions we provide an approach in ROC space to transform a classifier into a new one that has a desired precision per class.
Self-Organization , Writ Large Anthony Chemero

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Knowledge on Japanese People's Beliefs in the Anime Natsume Yuujinchou

Japanese people's trust is a belief inherited from time immemorial and is still maintained today. This can be seen from popular works that still raise traditional Japanese values. One of them is the anime Natsume Yuujinchou raising the values of Japanese people's beliefs about gods or Us and the life of the unseen. This study was about to describe the beliefs of Japanese people found in the anime Natsume Yuujinchou. The research method used to explore Japanese people's trust is a qualitative descriptive method. The approach used is literary anthropology. Found five concepts of Japanese people's beliefs from the anime, namely, 1) trust in the existence of a god; 2) trust in inanimate objects and can live if entered by the spirit; 3) belief in vengeance or punishment invisibly; 4) belief in the manifestations of kindness and evil from the spirit; 5) belief in souls (spirits) or spirits (spirits) in plants or animals The conclusions of this study are Japanese people's beliefs contained in the Natsume Yuujinchou anime in the form of supernatural concepts, soul concepts (souls) and spirits (spirits), and the concept of retaliation (tatari).
Growth Phase-Dependent Expression of Drug Exporters in Escherichia coli and Its Contribution to Drug Tolerance

ABSTRACT Drug exporters contribute to the intrinsic drug resistance in many organisms. Although there are at least 20 exporter genes in Escherichia coli, most of them apparently do not confer drug resistance in complex laboratory media except for the AcrAB, EmrE, and MdfA efflux systems. In this study, we comprehensively investigated the growth phase-dependent expression of drug exporter genes. The expression of acrAB, emrAB, emrD, emrE, emrKY, mdfA, and ydgFE is stable at moderate levels during any growth phase, whereas mdtEF promoter activity greatly increased with cell growth and reached the maximum level at the late stationary phase. The growth phase-dependent increase in mdtEF expression was also observed on quantitative reverse transcription-PCR analysis. As expected from the transporter expression, the stationary-phase cells actually showed MdtEF-dependent tolerance to drugs and toxic dyes. Growth phase-dependent elevation of mdtEF expression was found to be mediated by the stationary-phase σ factor rpoS and the RpoS-dependent signaling pathway, Hfq, GadY, and GadX. The induction level was decreased by tnaAB deletion, suggesting that indole sensing stimulates this process.
Asymptotically rigid mapping class groups and Thompson groups

We consider Thompson's groups from the perspective of mapping class groups of surfaces of infinite type. This point of view leads us to the braided Thompson groups, which are extensions of Thompson's groups by infinite (spherical) braid groups. We will outline the main features of these groups and some applications to the quantization of Teichm\"uller spaces. The chapter provides an introduction to the subject with an emphasis on some of the authors results.
A Computational Study of a Recreated G Protein-GEF Reaction Intermediate Competent for Nucleotide Exchange: Fate of the Mg Ion

Small G-proteins of the superfamily Ras function as molecular switches, interacting with different cellular partners according to their activation state. G-protein activation involves the dissociation of bound GDP and its replacement by GTP, in an exchange reaction that is accelerated and regulated in the cell by guanine-nucleotide exchange factors (GEFs). Large conformational changes accompany the exchange reaction, and our understanding of the mechanism is correspondingly incomplete. However, much knowledge has been derived from structural studies of blocked or inactive mutant GEFs, which presumably closely represent intermediates in the exchange reaction and yet which are by design incompetent for carrying out the nucleotide exchange reaction. In this study we have used comparative modelling to recreate an exchange-competent form of a late, pre-GDP-ejection intermediate species in Arf1, a well-characterized small G-protein. We extensively characterized three distinct models of this intermediate using molecular dynamics simulations, allowing us to address ambiguities related to the mutant structural studies. We observed in particular the unfavorable nature of Mg associated forms of the complex and the establishment of closer Arf1-GEF contacts in its absence. The results of this study shed light on GEF-mediated activation of this small G protein and on predicting the fate of the Mg ion at a critical point in the exchange reaction. The structural models themselves furnish additional targets for interfacial inhibitor design, a promising direction for exploring potentially druggable targets with high biological specificity.
Research on emotion recognition based on physiological signal and AuBT

Emotions play a significant role in human perception and decision making. Recent studies on emotion recognition are the subject of attention by researchers from different fields. By focusing in the four emotional states: happiness, anger, sadness and a neutral state which can be always found in daily life, this dissertation studies the Physiological signal based on emotion recognition technique. This paper uses a biological signal toolbox Augsburg (AuBT) analysis of physiological signals for emotion recognition. Augsburg Biosignal Toolbox (AuBT) is a toolbox for analyzing physiological signals written in Matlab. The result of experiment show that application of Augsburg biological signal toolbox gets the overall recognition rate of 87.5%, indicating that the Augsburg Biosignal Toolbox emotion recognition is feasible.
Multimodal Biometric System Using Speech and Signature Modalities

This paper discusses about Multimodal Biometric System which are used to overcome some of the problems of unimodal systems like noise in sensed data, intra-class variations, distinctiveness, and spoof attacks. Multimodal biometrics is the combination of two or more modalities such as signature and speech modalities. In this work an online signature verification system and speaker verification system are combined as these modalities are widely accepted and natural to produce. Although this combination of multimodal enhances security and accuracy, yet the complexity of the system increases due to increased number of features extracted out of the multiple samples and suffers from additional cost in terms of acquisition time. So these days the key issue is at what degree features are to be extracted and how the cost factor can be minimized, as the number of features increases the variability of the intra-personal samples due to greater lag times in between consecutive acquisitions of the sample also increases. Increase in variability of the system will further increase FAR. Thus to resolve these issues an effective fusion level and fusion mode is required. This paper presents a novel user authentication system based on a combined acquisition of online pen and speech signals.