Miracle-dz/UniFAR-FTUs huggingface dataset:
Automated Categorization of Wikipedia Pages

The automatic categorization of documents is often approached from a natural language processing (NLP) perspective in which both linguistic and non-linguistic features specific to each document are used as inputs to a classifier which categorizes each document in isolation (e.g. see [1]). However, this approach neglects the fact that in many instances, these documents are related to each other in some way – for instance through citations, references or hyperlinks – and these relations can provide useful information about the categories of each of the documents. For instance, a set of documents that all reference one another may be more likely to be of the same category than a set of documents that reference disjoint sets of other documents. As such, using network features – features that pertain to a document, the documents it links to, and the relations between them – may improve classification performance.
Evaluating and Architecting System dependabilitY

We present a system architect and administrator's view of system dependability issues and research suggestions. The dependability problem has been extensively studied by operators and architects, and most simple failures have known solutions. However, many operators and architects are not aware of many of the available solutions or the scope of the dependability problem. More complex failures where multiple unrelated failure events cause an overall outage are becoming a more prevalent part of medium and large systems dependability in the field. Examples of these baroque multiple failure outages are presented. Implications of these two major issues on benchmarking, research, and system architecture are discussed.
A TMS320C40 based Speech Recognition System for Embedded Applications

This paper describes a prototype implementation of a speech recognition system for embedded applications. The recognition system is comprised of a feature extractor and a classifier. The feature extractor is based on a 64-point Fast Fourier Transformation (FFT); the classifier is based on discrete-density Hidden Markov Models (HMM) with a variable codebook size. Training as well as classification are implemented using the Viterbi algorithm. The prototype is implemented on a digital signal processor (DSP) of type TMS320C40 from Texas Instruments. The recognition rate and the performance are experimentally evaluated using a test vocabulary of 20 words. keywords: automatic speech recognition, Hidden Markov Models, TMS320C40
Utilizing ICT to prevent loneliness and social isolation of the elderly. A literature review

Ageing of the population is a worldwide phenomenon. Numerous ICT-based solutions have been developed for elderly care but mainly connected to the physiological and nursing aspects in services for the elderly. Social work is a profession that should pay attention to the comprehensive wellbeing and social needs of the elderly. Many people experience loneliness and depression in their old age, either as a result of living alone or due to a lack of close family ties and reduced connections with their culture of origin, which results in an inability to participate actively in community activities (Singh & Misra, 2009). Participation in society would enhance the quality of life. With the development of information technology, the use of technology in social work practice has risen dramatically. The aim of this literature review is to map out the state of the art of knowledge about the usage of ICT in elderly care and to figure out research-based knowledge about the usability of ICT for the prevention of loneliness and social isolation of elderly people. The data for the current research comes from the core collection of the Web of Science and the data searching was performed using Boolean? The searching resulted in 216 published English articles. After going through the topics and abstracts, 34 articles were selected for the data analysis that is based on a multi approach framework. The analysis of the research approach is categorized according to some aspects of using ICT by older adults from the adoption of ICT to the impact of usage, and the social services for them. This literature review focused on the function of communication by excluding the applications that mainly relate to physical nursing. The results show that the so-called ‘digital divide’ still exists, but the older adults have the willingness to learn and utilise ICT in daily life, especially for communication. The data shows that the usage of ICT can prevent the loneliness and social isolation of older adults, and they are eager for technical support in using ICT. The results of data analysis on theoretical frames and concepts show that this research field applies different theoretical frames from various scientific fields, while a social work approach is lacking. However, a synergic frame of applied theories will be suggested from the perspective of social work.
Identification of Interactions in the NMD Complex Using Proximity-Dependent Biotinylation (BioID)

Proximity-dependent trans-biotinylation by the Escherichia coli biotin ligase BirA mutant R118G (BirA*) allows stringent streptavidin affinity purification of proximal proteins. This so-called BioID method provides an alternative to the widely used co-immunoprecipitation (co-IP) to identify protein-protein interactions. Here, we used BioID, on its own and combined with co-IP, to identify proteins involved in nonsense-mediated mRNA decay (NMD), a post-transcriptional mRNA turnover pathway that targets mRNAs that fail to terminate translation properly. In particular, we expressed BirA* fused to the well characterized NMD factors UPF1, UPF2 and SMG5 and detected by liquid chromatography-coupled tandem mass spectrometry (LC-MS/MS) the streptavidin-purified biotinylated proteins. While the identified already known interactors confirmed the usefulness of BioID, we also found new potentially important interactors that have escaped previous detection by co-IP, presumably because they associate only weakly and/or very transiently with the NMD machinery. Our results suggest that SMG5 only transiently contacts the UPF1-UPF2-UPF3 complex and that it provides a physical link to the decapping complex. In addition, BioID revealed among others CRKL and EIF4A2 as putative novel transient interactors with NMD factors, but whether or not they have a function in NMD remains to be elucidated.
Iterative Pose Computation from Line Correspondences

This paper presents a method for estimating the position and orientation of a camera with respect to a known 3-D object from line correspondences. The main idea of the method is to estimate a pose with either a weak perspective or a paraperspective camera model and to improve this pose iteratively. At convergence the result is compatible with a perspective camera model. This iterative improvement of a linear (affine) camera model has already been used for points but has never been extended to lines. Known methods which compute pose from line correspondences deal with a set of nonlinear equations which are solved either in closed-form or using minimization techniques. These methods have to deal with multiple solutions. In contrast our method starts with a solution which is very close to the true solution and converges in very few iterations (typically three to five iterations). The rank analysis of the linear system to be solved at each iteration allows us to characterize geometric configurations which defeat the algorithm.
A Low Temperature Sensitivity Switched-Capacitor Current Reference

A current reference with low temperature sensitivity based on a switched-capacitor technique has been developed. The implementation is targeted for a 0. 18µ µ µ µ CMOS process. HSPICE simulations using level 49 models valid over a wide temperature range were used to verify the design. The simulation results predict variations of less than 0.029% over a temperature range of -40 ° ° ° °C to 125 °C.
A novel approach for feature selection based on MapReduce for biomarker discovery

Scale feature selection is one of the most important fields in the big data domain that can solve real data problems, such as bioinformatics, when it is necessary to process huge amount of data. The efficiency of existing feature selection algorithms significantly downgrades, if not totally inapplicable, when data size exceeds hundreds of gigabytes, because most feature selection algorithms are designed for centralized computing architecture. For that distributed computing techniques, such as MapReduce can be applied to handle very large data. Our approach is to scale the existing method for feature selection, Kmeans clustering and Signal to Noise Ratio (SNR) combined with optimization technique as Binary Particle Swarm Optimization (BPSO). The proposed method is divided into two stages. In the first stage, we have used parallel Kmeans on MapReduce for clustering features, and then we have applied iterative MapReduce that implement parallel SNR ranking for each cluster, after we have selected the top ranked feature from each cluster. The top scored features from each cluster are gathered and a new feature subset is generated. In the second stage the new feature subset is used as input to the novel BPSO proposed based on MapReduce and optimized feature subset is being produced. The proposed method is implemented in a distributed environment, and its efficiency is illustrated through analyzing practical problems such as biomarker discovery.
A Model for Ubiquitous Care of Noncommunicable Diseases

The ubiquitous computing, or ubicomp, is a promising technology to help chronic diseases patients managing activities, offering support to them anytime, anywhere. Hence, ubicomp can aid community and health organizations to continuously communicate with patients and to offer useful resources for their self-management activities. Communication is prioritized in works of ubiquitous health for noncommunicable diseases care, but the management of resources is not commonly employed. We propose the UDuctor, a model for ubiquitous care of noncommunicable diseases. UDuctor focuses the resources offering, without losing self-management and communication supports. We implemented a system and applied it in two practical experiments. First, ten chronic patients tried the system and filled out a questionnaire based on the technology acceptance model. After this initial evaluation, an alpha test was done. The system was used daily for one month and a half by a chronic patient. The results were encouraging and show potential for implementing UDuctor in real-life situations.
Sparsely-Connected Neural Networks: Towards Efficient VLSI Implementation of Deep Neural Networks

Recently deep neural networks have received considerable attention due to their ability to extract and represent high-level abstractions in data sets. Deep neural networks such as fully-connected and convolutional neural networks have shown excellent performance on a wide range of recognition and classification tasks. However, their hardware implementations currently suffer from large silicon area and high power consumption due to the their high degree of complexity. The power/energy consumption of neural networks is dominated by memory accesses, the majority of which occur in fully-connected networks. In fact, they contain most of the deep neural network parameters. In this paper, we propose sparsely-connected networks, by showing that the number of connections in fully-connected networks can be reduced by up to 90% while improving the accuracy performance on three popular datasets (MNIST, CIFAR10 and SVHN). We then propose an efficient hardware architecture based on linear-feedback shift registers to reduce the memory requirements of the proposed sparsely-connected networks. The proposed architecture can save up to 90% of memory compared to the conventional implementations of fully-connected neural networks. Moreover, implementation results show up to 84% reduction in the energy consumption of a single neuron of the proposed sparsely-connected networks compared to a single neuron of fully-connected neural networks.
Resilience and the role of arts and culture-based activities in mature industrial districts

ABSTRACT In this paper, we build on the results of previous research on how industrial districts (IDs) well-endowed with innovation capabilities fall into decline, and sometimes react against it. Major challenges may bring about deep crises. In particular, mature IDs stuck into the cognitive core and the institutional frames, which have featured their past growth, have often weak or slow reactions to such crises, demonstrating lack of adaptability and low resilience. However, lock-ins can be avoided or overcome by the activation and integration of a multiplicity of secondary know-how nuclei. The present paper combines some conceptualizations on such ID dynamics with the roles of their cultural backgrounds, and the possible activation of place-based arts and culture-based activities.
POP beyond SODA, reaching the new horizon of service cooperation

As we have been gaining more experiences in services provision, online services are becoming increasingly complex. They have moved from simple service provision and invocation to very sophisticated service interaction and cooperation. As presented in this paper, service cooperation will be a promising computation model to achieve overall goals beyond individual capabilities. Based on the supreme wide spread of service-oriented development (SODA), the process-oriented platform (POP), with favourable flexibility derived from late binding, will be the optimum approach to this end. The PI production developed by us is such a system implementation, in which architecture, components, and functionalities are also introduced in detail. We believe that the service cooperation paradigm is a hopeful solution to future service evolution, and the PI system will be an instructive explorer to reach this new horizon
Determinants of powered two-wheelers virtual lane width in urban arterials

The present paper aims at modeling the dynamic width of the virtual lane of the Powered Two-Wheelers (PTWs) and revealing the critical factors that may influence it, such as the speed of the motorcycles, the existence of a heavy vehicle and so on. All possible manners of PTW maneuvering in an urban arterial (2 lanes per direction of travel) were identified and then data were collected from real measurements using advanced video analysis techniques. Further, statistical models were developed in order to reveal the relation between the virtual lane's width and several determinants such as PTW speeds, lane headways, traffic mix and so on. Results showed that the virtual lane width depended on the speed of the PTW, the existence of a platoon, the existence of a heavy vehicle and the headways in both lanes. Moreover, results revealed the need to address the human behavior and the cognitive aspects of driving during the modeling.
The Modified McCall ’ s Quality Framework applied to Software Safety

Software has become responsible for most of the critical functions of complex systems. Software safety is the notion that software will execute within a system context without contributing to hazards. Software for safety-critical systems must deal with the hazards identified by safety analysis in order to make the system safe. Software safety is a composite of many criteria. Existing software quality models like McCall’s and Boehm’s and ISO 9126 are inadequate in addressing the software safety issues of real time safety-critical embedded systems. At present there does not exist any standard model that comprehensively addresses the factors, criteria and metrics (FCM) approach of the quality models in respect of software safety. This paper proposes a new model for software safety based on the McCall’s software quality model that specifically identifies the criteria corresponding to software safety in safety critical applications. This framework is then applied to a prototype safety-critical system viz. a software–controlled Road Traffic Control System (RTCS) commonly used in city traffic, to validate its utility.
Big Data Platforms as a Service: Challenges and Approach

Infrastructure-as-a-Service has revolutionized the manner in which users commission computing infrastructure. Coupled with Big Data platforms (Hadoop, Cassandra), IaaS has democratized the ability to store and process massive datasets. For users that need to customize or create new Big Data stacks, however, readily available solutions do not yet exist. Users must first acquire the necessary cloud computing infrastructure, and manually install the prerequisite software. For complex distributed services this can be a daunting challenge. To address this issue, we argue that distributed services should be viewed as a single application consisting of virtual machines. Users should no longer be concerned about individual machines or their internal organization. To illustrate this concept, we introduce Cloud-Get, a distributed package manager that enables the simple installation of distributed services in a cloud computing environment. Cloud-Get enables users to instantiate and modify distributed services, including Big Data services, using simple commands. Cloud-Get also simplifies creating new distributed services via standardized package definitions.
Search Strategies in Inter-Domain Traffic Engineering

Inter-domain Traffic Engineering (TE) capabilities can significantly improve end-to-end network performance by allowing an ISP to route its traffic around congestion or failure occurring in other ISP networks. Despite its great importance, however, the state of the art in inter-domain TE is very primitive. The common practice is often “tweak and pray” – tweak BGP policy, in the hope of shedding traffic from, for example, an overloaded peering link. A major challenge for inter-domain TE is the level of uncertainty an ISP is facing. With many potentially competing ISPs, global coordination can be very difficult. As a result, a single ISP only has limited (and often inaccurate) knowledge about user demand, network topology, available network resource and routing policies in other domains that their traffic traverses. In this paper, we explicitly deal with such uncertainty by modeling inter-domain TE as search games. In such games, the network operator must balance loads across multiple paths, on which the available capacity is unknown and may change over time. We develop a set of novel algorithms for solving the search games based on binary congestion feedback from the network. These algorithms are very simple and naturally generalize conventional search strategies like binary search into higher dimensions. Simple analysis shows that they are near optimal in a static environment. Extensive simulations further demonstrate that they converge very quickly in a dynamic environment, are highly robust to measurement noise, and are quite stable, even in the presence of multiple competing players. Finally a key advantage of the proposed approach is that it can be potentially implemented in the context of the existing IP routing infrastructure, and requires no modification to either the routing protocols or the routers themselves.
Alpha-actinin binding kinetics modulate cellular dynamics and force generation

Significance In this study, we examine how proteins that cross-link actin filaments control certain biophysical aspects of living cells. We studied α-actinin-4 (ACTN4) a dimeric rod-shaped homodimer, of particular interest because mutations in its actin binding domain cause a human disease characterized by dysfunction of the kidney’s glomeruli; however, the mechanical impact of such mutations are unknown. We find that the human disease-causing K255E mutation in ACTN4 leads to a change in cellular biophysical properties, increasing the affinity for actin increases cellular forces and work, while decreasing cell movement. These observations describe the effects of variable cross-linking on cellular forces and dynamics, and reveal how pathology may arise mechanically from disruptive point mutations in cytoskeletal proteins. The actin cytoskeleton is a key element of cell structure and movement whose properties are determined by a host of accessory proteins. Actin cross-linking proteins create a connected network from individual actin filaments, and though the mechanical effects of cross-linker binding affinity on actin networks have been investigated in reconstituted systems, their impact on cellular forces is unknown. Here we show that the binding affinity of the actin cross-linker α-actinin 4 (ACTN4) in cells modulates cytoplasmic mobility, cellular movement, and traction forces. Using fluorescence recovery after photobleaching, we show that an ACTN4 mutation that causes human kidney disease roughly triples the wild-type binding affinity of ACTN4 to F-actin in cells, increasing the dissociation time from 29 ± 13 to 86 ± 29 s. This increased affinity creates a less dynamic cytoplasm, as demonstrated by reduced intracellular microsphere movement, and an approximate halving of cell speed. Surprisingly, these less motile cells generate larger forces. Using traction force microscopy, we show that increased binding affinity of ACTN4 increases the average contractile stress (from 1.8 ± 0.7 to 4.7 ± 0.5 kPa), and the average strain energy (0.4 ± 0.2 to 2.1 ± 0.4 pJ). We speculate that these changes may be explained by an increased solid-like nature of the cytoskeleton, where myosin activity is more partitioned into tension and less is dissipated through filament sliding. These findings demonstrate the impact of cross-linker point mutations on cell dynamics and forces, and suggest mechanisms by which such physical defects lead to human disease.
12-Month naturalistic outcomes of depressive disorders in Hong Kong’s primary care

Background. In Asia, the role of primary care physicians (PCPs) in mental health delivery is not clearly defined and what happens to patients following a depressive episode remains poorly understood. Objective. To examine the 12-month naturalistic outcomes of depression in primary care and the impact of PCP identification. Methods. A cohort study was conducted. A total of 10179 adults were consecutively recruited from the waiting rooms of 59 PCPs across Hong Kong to complete a survey which screened for depression. Blinded doctors provided data on their diagnosis and management; 539 screened-positive and 3819 screened-negative subjects consented to telephone follow-up at 3, 6 and 12 months. Study instruments included Patient Health Questionnaire-9, Centre for Epidemiologic Studies Depression Scale 20 and Short-Form Health Survey Version 2.0 (SF-12v2) and self-reported mental health and primary care service use. Results. 12-month remission rate was 60.31%. PCP detection had no association with remission. Identified patients had poorer health-related quality of life (HRQOL) at baseline but a faster rate of recovery in SF-12v2 mental component scores. PCP detection was associated with greater mental health service use at 12, 26 and 52 weeks, while GP consultation rates were only increased at 12 weeks. Conclusions. Over 1 year, ~60% of depressed patients experience symptom resolution, while 40% continue to suffer a chronic or remitting course of illness. Identification of depression by a PCP does not appear to affect remission of mood symptoms at 12 months, but is associated with a faster rate of recovery of HRQOL. PCP detection raises GP consultation rates temporarily however appears to enable more patients to access mental health services over 12 months.