Miracle-dz/UniFAR-FTUs huggingface dataset:
ASAP Adaptive QoS Support with Reduced Reservation Overhead in MANETs

Existing QoS approaches for mobile ad hoc networks focus on optimizing end-to-end bandwidth allocation with respect to, e.g., node mobility, link-failures and packet-loss. Typically this is achieved through over-reservation. This is specially true in loosely connected networks or in high mobility scenarios where reservations take place only on part of the path because no end-to-end connection is available. In this paper, we propose ASAP, an Adaptive ReServation and Pre-Allocation Protocol for mobile ad hoc networks. ASAP provides high service quality and minimizes wasted reservations. It does so by using a simple soft/hard reservation mechanism and a flexible signaling schema.
Pyramidal Normal Map Integration for Real-time Photometric Stereo

This paper presents a real-time photometric stereo method designed for online 3D telecommunications. This method requires only consumer grade hardware such as a webcam and a GPU. The lighting conditions are controlled from the computer screen and the photometric reconstruction is performed on the GPU. Our method reaches real-time rendering thanks to a pyramidal integration of the normal maps based on an iterative scheme. Moreover, we propose a method to relax the constraint on the synchronization between the light source controller and the camera.
Exploring the experience of facilitating self-management with minority ethnic stroke survivors: a qualitative study of therapists' perceptions

Abstract Purpose: The utility of self-management with people from minority ethnic backgrounds has been questioned, resulting in the development of culturally specific tools. Yet, the use of stroke specific self-management programmes is underexplored in these high risk groups. This article presents the experience of stroke therapists in using a stroke specific self-management programme with stroke survivors from minority ethnic backgrounds. Methods: 26 stroke therapists with experience of using the self-management programme with stroke survivors from minority ethnic backgrounds participated in semi-structured interviews. These were audio recorded, transcribed verbatim and analysed thematically. Results: Three themes were identified. One questioned perceived differences in stroke survivors interaction with self-management based on ethnicity. The other themes contrasted with this view demonstrating two areas in which ethnic and cultural attributes were deemed to influence the self-management process both positively and negatively. Aspects of knowledge of health, illness and recovery, religion, family and the professionals themselves are highlighted. Conclusions: This study indicates that ethnicity should not be considered a limitation to the use of an individualized stroke specific self-management programme. However, it highlights potential facilitators and barriers, many of which relate to the capacity of the professional to effectively navigate cultural and ethnic differences. Implications for Rehabilitation Stroke therapists suggest that ethnicity should not be considered a barrier to successful engagement with a stroke specific self-management programme. Health, illness and recovery beliefs along with religion and the specific role of the family do however need to be considered to maximize the effectiveness of the programme. A number of the facilitators and barriers identified are not unique to stroke survivors from ethnic minority communities, nor shared by all. The therapists skills at negotiating identified barriers to self-management are highlighted as an area for further development.
Detecting Illegal Drug Usage in Social Media using Support Vector Machines and Deep Neural Networks

Our objective in this project is to develop a system for identifying probable illegal drug users and distributors using social media. The primary structure of our approach can be separated very generally into two classification problems, one for identifying indications of drug use in text, and another to identify illegal drugs and drug paraphernalia in images. The broader impacts of this project are quite straightforward in the direct application of law enforcement. It would also provide potentially useful insight into central hubs of traffic through geo-tagged information. Within the field of computer science this project will require the creation of an tagged image database, which will have broader uses as an evaluation metric for various object recognition systems. Additionally any advancements made in object detection would be applicable to many diverse projects requiring such capabilities.
Evaluation of an Adaptive PI Rate Controller for Congestion Control in Wireless Ad-Hoc/Sensor Networks

From operating systems to cell phones and web servers to PDAs, more products and systems depend on TCP for network interconnection everywhere and anytime. Congestion control is a key feature of TCP. TCP performance could have significant impact on the network life for wireless ad-hoc/sensor networks, as higher TCP throughput and lower retransmissions could reduce resource consumption. This paper evaluates the feasibility of an adaptive Proportional and Integral (PI) rate control algorithm in wireless ad-hoc/sensor networks. Performance evaluation and comparison of this adaptive rate controller with TCP New Reno have been conducted by means of simulation. The results obtained from the simulations show that the adaptive PI rate control algorithm can improve the overall average throughput for static scenarios and the collision level at the MAC layer is also reduced, which could have a considerable impact on energy consumption.
Improving Anti-spam Engine with Large Imbalanced Dataset Using Information Retrieval Technology

Anti-spam technology always employs machine learning to identify spam emails. Unfortunately, the email samples used to establish machine learning models are always not in a ideal status: there are too many spam emails compared with normal ones, which may lead to biased machine learning models and unsatisfactory performance in prediction. Besides, there are too many email samples, which lead to unaffordable resource consuming to run machine learning training process and thus difficult for human engineers to sort. In this paper, we proposed an information retrieval technology based approach to compress and balance the training data set . The key breakthrough here is to shrink and balance the training data set by removing similar data using information retrieval technology. Experiments show anti-spam classifier can have better performance with a much smaller and balanced training data set by applying this approach?
Exploiting various information for knowledge element relation recognition

Knowledge element relation recognition is to mine intrinsic and hidden relations, i.e., preorder, analogy and illustration from knowledge element set, which can be used in knowledge organization and knowledge navigation system. This paper focuses on what information is employed to recognize knowledge element relations. First, a formal definition of knowledge element and the types of relation are given. Next, an algorithm for knowledge element sort is proposed to gain the sequence number of knowledge element. Then, information of term, type, distance, knowledge element relation level and document level is selected to represent candidate relation instances. Evaluation on the four data sets related to “computer” discipline, using Support Vector Machines, shows that term, type and distance features contribute to most of the performance improvement, and incorporation of all features can achieve excellent performance of relation recognition, whose F1 Micro-averaged measure is above 83%.
Automatic extraction of semantic colors in sports video

Color has been widely used in sports video analysis. Previous techniques, however require color models from prior information or user interaction, and do not address the problem of how to automatically form color models from a video in an arbitrary sports setting. In this paper, we propose an automatic technique for extracting color models of the playing surface and the team uniforms, which can be used in higher-level processes such as tracking and recognition. Unlike most previous methods, our approach is capable of handling multi-colored patterns like striped uniforms and playing fields. Multiple forms of color processing are used to analyze video frame content, which are then used iteratively to refine the color models. The results of our color modeling technique have been applied to shot classification, and experiments on videos of different sports have verified our approach.
Bladder tumour-derived somatic TSC1 missense mutations cause loss of function via distinct mechanisms

More than 50% of transitional cell carcinomas of the bladder show loss of heterozygosity of a region spanning the TSC1 locus at 9q34 and mutations of TSC1 have been identified in 14.5% of tumours. These comprise nonsense mutations, splicing mutations, small deletions and missense mutations. Missense mutations are only rarely found in the germline in TSC disease. Therefore, we have examined six somatic missense mutations found in bladder cancer to determine whether these result in loss of function. We describe loss of function via distinct mechanisms. Five mutations caused mutually exclusive defects at mRNA and protein levels. Of these, two mutations caused pre-mRNA splicing errors that were predicted to result in premature protein truncation and three resulted in markedly reduced stability of exogenous TSC1 protein. Primary tumours with aberrant TSC1 pre-mRNA splicing were confirmed as negative for TSC1 expression by immunohistochemistry. Expression was also significantly reduced in a tumour with a TSC1 missense mutation resulting in diminished protein half-life. A single TSC1 missense mutation identified in a tumour with retained heterozygosity of the TSC1 region on chromosome 9 caused an apparently TSC2- and mTOR-independent localization defect of the mutant protein. We conclude that although TSC1 missense mutations do not play a major role in causation of TSC disease, they represent a significant proportion of somatic loss of function mutations in bladder cancer.
Construction of OSS-based E-Learning cloud in China

In China, e-Learning has grown into a widely accepted way of learning, and open-source software has been developed and used in many fields as well. With the huge growth of users, services, education contents and resources, the e-learning consumers and providers both have requirements to reduce the operating, development and maintenance costs. OSS-based cloud computing framework just can satisfy the requirements. In this paper, the current situation of e-learning system and their requirements in China is reviewed, a layered architecture for the e-Learning cloud is proposed, and the open-source solutions through the experiment of building a cloud computing running environment with Hadoop and converting Sakai to a SaaS application are presented. At last, the impact of open source and cloud computing to e-learning is summarized.
Language, Categorization, and Convention

Linguistic meaning is a convention. This article investigates how such conventions can arise for color categories in populations of simulated "agents". The method uses concepts from evolutionary game theory: A language game where agents assign names to color patches and is played repeatedly by members of a population. The evolutionary dynamics employed make minimal assumptions about agents' perceptions and learning processes. Through various simulations it is shown that under different kinds of reasonable conditions involving outcomes of individual games, the evolutionary dynamics push populations to stationary equilibria, which can be interpreted as achieving shared population meaning systems. Optimal population agreement for meaning is characterized through a mathematical formula, and the simulations presented reveal that for a wide variety of situations, optimality is achieved.
Design of a power amplifier based on GaN HEMTs at Ka-band

A GaN power amplifier which can be applied to Ka-band is simulated and studied in the paper. Based on typical AlGaN/GaN HEMT (high electron mobility transistor) pattern, the transistor model is set up by the Silvaco TCAD. The on-chip spiral inductor and metal-insulator-metal (MIM) capacitance are also simulated in HFSS respectively. Extracted from the modal above, s-parameters are used to design a power amplifier circuit. The final result shows that the output power can reach 21.4 dBm and PAE is 18% with 25 V bias voltage and 9 mA static output current at 35 GHz when the input power is 10 dBm.
Physical Layer Effect on MAC Performance in Ad-Hoc Wireless Networks

In wireless ad hoc networks, every device (node) has a transmission radius and may communicate successfully with any node lying within a disk of radius . However, a node also has an interference radius , which determines contention between links. The relation between the transmission and interference radius depends on the underlying physical layer. Pair-wise interference between two links in ad-hoc networks has been previously modeled by disk graphs, allowing the application of graph theoretic results. However, these geometric models do not accurately represent the physical layer. In this paper, geometric conditions for contention between links are derived by analyzing the physical layer and utilizing the relationship between and . This analysis results in geometric conditions significantly different from the existing disk graph models. A bound on MAC performance is derived based on the physical layer model, by specifying a graph coloring heuristic. This bound is very different from that derived for disk-graph models, thus demonstrating the importance of the physical layer when evaluating MAC performance.
Neural network controller for boost converter

This paper presents a neural network controller scheme for boost converters. This controller is designed to stabilize the output voltage of the boost converter and to improve its performance during transient operations. To investigate the effectiveness of the proposed controller, certain operations such as starting up and reference voltage variations are verified. The simulation results show that the proposed controller has an improved performance compared to the conventional PI controller method.
Energy Management for Smart Grids With Electric Vehicles Based on Hierarchical MPC

This paper presents an energy management system for smart grids with electric vehicles based on hierarchical model predictive control (HiMPC). The energy management system realizes load-frequency control (LFC), an economic operation and an electric vehicle integration into the smart grid. The main component is the HiMPC, which allows covering different time scales, regarding constraints (e.g. power ratings) and predictions (e.g. on renewable generation), as well as rejecting disturbances (e.g. due to fluctuating renewable generation) based on a systematic model- and optimization-based design. For the electric vehicle integration, an aggregator is proposed as link between HiMPC and individual vehicle. The aggregator in particular provides predictions to the HiMPC on the availability of electric vehicles for LFC based on the current mobility demand and the statistical mobility behavior of the vehicle users. Throughout the paper, the energy management system is evaluated for the smart grid of an intermediate city.
Personalized recommender systems integrating tags and item taxonomy

The social tags in Web 2.0 are becoming another important information source to profile users' interests and preferences to make personalized recommendations. To solve the problem of low information sharing caused by the free-style vocabulary of tags and the long tails of the distribution of tags and items, this paper proposes an approach to integrate the social tags given by users and the item taxonomy with standard vocabulary and hierarchical structure provided by experts to make personalized recommendations. The experimental results show that the proposed approach can effectively improve the information sharing and recommendation accuracy.
HOT HIGH-MASS ACCRETION DISK CANDIDATES

To better understand the physical properties of accretion disks in high-mass star formation, we present a study of a dozen high-mass accretion disk candidates observed at high spatial resolution with the Australia Telescope Compact Array (ATCA) in the high-excitation (4,4) and (5,5) lines of NH3. All of our originally selected sources were detected in both NH3 transitions, directly associated with CH3OH Class II maser emission and implying that high-excitation NH3 lines are good tracers of the dense gas components in hot-core-type targets. Only the one source that did not satisfy the initial selection criteria remained undetected. From the 11 mapped sources, six show clear signatures of rotation and/or infall motions. These signatures vary from velocity gradients perpendicular to the outflows, to infall signatures in absorption against ultracompact H ii regions, to more spherical infall signatures in emission. Although our spatial resolution is ∼1000 AU, we do not find clear Keplerian signatures in any of the sources. Furthermore, we also do not find flattened structures. In contrast to this, in several of the sources with rotational signatures, the spatial structure is approximately spherical with sizes exceeding 104 AU, showing considerable clumpy sub-structure at even smaller scales. This implies that on average typical Keplerian accretion disks—if they exist as expected—should be confined to regions usually smaller than 1000 AU. It is likely that these disks are fed by the larger-scale rotating envelope structure we observe here. Furthermore, we do detect 1.25 cm continuum emission in most fields of view. While in some cases weak cm continuum emission is associated with our targets, more typically larger-scale H ii regions are seen offset more than 10″ from our sources. While these H ii regions are unlikely to be directly related to the target regions, this spatial association nevertheless additionally stresses that high-mass star formation rarely proceeds in an isolated fashion but in a clustered mode.
Unsupervised Tracking of Stereoscopic Video Objects Employing Neural Networks Retraining

A novel approach is presented in this paper for improving the performance of neural network classifiers in video object tracking applications, based on a retraining procedure at the user level. The procedure includes (a) a retraining algorithm for adapting the network weights to the current conditions, (b) semantically meaningful object extraction which plays the role of the retraining set and (c) a decision mechanism for determining when network retraining should be activated. The retraining algorithm takes into consideration both the former and the current network knowledge in order to achieve good generalization and reduce retraining time. Object extraction is accomplished by utilizing depth information, provided by stereoscopic video and incorporating a multiresolution implementation of the Recursive Shortest Spanning Tree (RSST) segmentation algorithm. Finally the decision mechanism in this framework depends on a scene change detection algorithm. Results are presented which illustrate the performance of the proposed approach in real life experiments.
Design and fabrication of flexible rod meshes

We present a computational tool for fabrication-oriented design of flexible rod meshes. Given a deformable surface and a set of deformed poses as input, our method automatically computes a printable rod mesh that, once manufactured, closely matches the input poses under the same boundary conditions. The core of our method is formed by an optimization scheme that adjusts the cross-sectional profiles of the rods and their rest centerline in order to best approximate the target deformations. This approach allows us to locally control the bending and stretching resistance of the surface with a single material, yielding high design flexibility and low fabrication cost.
A canonical measure of mobility for mobile ad hoc networks

For assessing different routing protocols for MANETs, it is important to have some index or quantitative measure of mobility that is relevant to the performance of the network. In this paper, we proposed a mobility measure that is "canonical" in that it is flexible and consistent. It is flexible because one can customize the definition for relevant mobility using a remoteness function. It is consistent because the mobility measure has the same linear relationship to the link change rate for a wide range of mobility scenarios.
Energy Enhanced Base Station Controlled Dynamic Clustering Protocol for Wireless Sensor Networks

—The energy efficiency is the determinant aspect of the wireless sensor network life time. Various routing protocols are used to reduce the energy consumption and to improve the network lifetime. To support high scalability and better data aggregation, sensor nodes are often grouped into disjoint, non overlapping subsets called clusters. Clusters create hierarchical WSNs which incorporate efficient utilization of limited resources of sensor nodes and thus extends network lifetime. This paper discusses an improved clustering protocol to minimize the energy consumption and maximize the network lifetime. The simulation results show that the proposed algorithm reduces the overall energy consumption and improves the lifetime over its comparatives.
Alternative Splicing Modulates Ubx Protein Function in Drosophila melanogaster

The Drosophila Hox gene Ultrabithorax (Ubx) produces a family of protein isoforms through alternative splicing. Isoforms differ from one another by the presence of optional segments—encoded by individual exons—that modify the distance between the homeodomain and a cofactor-interaction module termed the “YPWM” motif. To investigate the functional implications of Ubx alternative splicing, here we analyze the in vivo effects of the individual Ubx isoforms on the activation of a natural Ubx molecular target, the decapentaplegic (dpp) gene, within the embryonic mesoderm. These experiments show that the Ubx isoforms differ in their abilities to activate dpp in mesodermal tissues during embryogenesis. Furthermore, using a Ubx mutant that reduces the full Ubx protein repertoire to just one single isoform, we obtain specific anomalies affecting the patterning of anterior abdominal muscles, demonstrating that Ubx isoforms are not functionally interchangeable during embryonic mesoderm development. Finally, a series of experiments in vitro reveals that Ubx isoforms also vary in their capacity to bind DNA in presence of the cofactor Extradenticle (Exd). Altogether, our results indicate that the structural changes produced by alternative splicing have functional implications for Ubx protein function in vivo and in vitro. Since other Hox genes also produce splicing isoforms affecting similar protein domains, we suggest that alternative splicing may represent an underestimated regulatory system modulating Hox gene specificity during fly development.
A Survey on Current Requirement Process Practices in Software Companies and Requirement Process Problems

Requirement engineering literature exemplifies the description of attributes and practicalities of the target system. Software requirements may ambit from high-level academic affirmation or restraint of a system to elaborated statistical functionalities. This paper exhibits the current practices of requirement process at Companies. A survey of ten companies is conducted with the assistance of definitive questionnaire. The results of the questionnaire is then compared and examined amongst all ten companies to discover what standards are being followed by the companies. An effort is made to evaluate the current criterions of Pakistani companies with respect to requirement engineering process.