Miracle-dz/UniFAR-FTUs huggingface dataset:
A feasibility study using interactive commercial off-the-shelf computer gaming in upper limb rehabilitation in patients after stroke.

BACKGROUND Commercial off-the-shelf computer gaming devices have been making inroads into the rehabilitation arena, with the objective of making therapeutic exercise fun and contextual. One such device is the Nintendo Wii. Published clinical studies evaluating its acceptance, potential benefits and side-effects in the rehabilitation of patients with post-stroke weakness are few in number. OBJECTIVE The aim of this study is to assess the feasibility of using the Nintendo Wii as an adjunct to conventional rehabilitation of patients with post-stroke upper limb weakness. METHODS Twenty rehabilitation inpatients within 3 months after a stroke with upper limb weakness received 6 sessions of upper limb exercises via a Nintendo Wii over 2 weeks in addition to conventional rehabilitation. Outcome measures include a questionnaire, Fugl-Meyer Assessment of Upper Limb Motor Function and visual analogue scale of upper limb pain. RESULTS A total of 16 subjects completed the study. All 16 found Nintendo Wii gaming enjoyable and comparable to, if not better than, conventional therapy. There were small but statistically significant improvements in the Fugl-Meyer Assessment and Motricity Index scores. CONCLUSION Nintendo Wii appears to be a feasible adjunctive device to augment conventional therapy in a cohort of subacute stroke patients with moderate impairments of upper limb strength and function.
A FINITE-DIFFERENCE APPROXIMATION OF THE PRIMITIVE QUATIONS

The hexagonal grid based on a partition of the icosahedron has distinct geometrical qualities for the mapping of a sphere and also presents some indexing difficulties. The applicability of this grid to the primitive equations of fluid dynamics is demonstrated, and a finite-difference approximation of these equations is proposed. The basic variables are the mass fluxes from one hexagonal cell t o the next through their common boundary. This scheme conserves the total mass, the total momentum, and the total kinetic energy of the fluid as well as the total -squared vorticity of a nondivergent flow. A computational test was performed using a hexagonal grid to describe space periodic waves on a nonrotating plane. The systematic variation of total kinetic and potential energy is less than 10-5 after 1,000 time steps.
Managing Information Quality in Virtual Communities of Practice

This paper examines why the rise of computermediated communication, driven by dramatically lowered cost, creates new structural problems from an information quality standpoint. We review how the new economics of information enable a new paradigm of collaboration. Spearheaded by the Open Source community, this evolutionary approach to collaborative content creation is a way to address information quality in virtual communities of practice. Based on experience gained in community projects, a few simple steps toward improving the quality of information in virtual communities are presented and illustrated.
An efficient dynamic energy-aware application mapping algorithm for multicore processors

We propose and analyse an efficient energy-aware application mapping algorithm for dynamic multicore systems. Computer systems integrate more and more processor cores in a single chip due to the limitation of frequency. Not only desktop and server processors, but also mobile processors are equipped with eight or even ten cores. Modern applications have increased focus on parallel computation due to the multicore trend. Mapping applications to the processor without optimization can lead to performance loss and system efficiency degradation. The energy efficiency of multicore systems is especially important for battery-driven devices. We investigate the energy consumption of data transmission in multicore chips. The consumption is divided into router energy and link energy. We discuss the total energy required for transferring data across nodes in a multicore processor. Based on the energy model and application model, we propose an energy-aware mapping algorithm which maps applications in two different ways.We evaluate the proposed algorithm with other algorithms by using synthetic and real applications. The synthetic workloads proved that the proposed algorithm can be executed dynamically with a negligible time overhead, while the energy consumption reduced significantly. Results from four applications show that the power consumption of the proposed algorithm has reduced by 20.2% and 13.4% compared with the first fit and incremental algorithms respectively.
We Can Deliver Messages to Far Vehicles

Vehicular ad hoc networks (VANETs) enable vehicles to communicate with each other but require efficient and robust routing protocols for their success. In this paper, we exploit the infrastructure of roadside units (RSUs) to efficiently and reliably route packets in VANETs. Our system operates by using vehicles to carry and forward messages from a source vehicle to a nearby RSU and, if needed, route these messages through the RSU network and, finally send them from an RSU to the destination vehicle. Our system is mostly critical for users who are far apart and want to communicate using their vehicles' onboard units. Many recent paradigms, like social networks, will greatly benefit from a system like ours to enable users on the road to exchange different types of data. We evaluate the performance of our system using the ns2 simulation platform and compare our scheme to existing solutions. The results prove the feasibility and efficiency of our scheme.
Probing intergalactic radiation fields during cosmic reionization through gamma-ray absorption

We discuss expectations for the absorption of high-energy gamma-rays by γγ pair production with intergalactic radiation fields (IRFs) at very high redshifts (z∼ 5–20), and the prospects thereof for probing the cosmic reionization era. For the evolving IRF, a semi-analytical model incorporating both Population II and Population III stars is employed, which is consistent with a wide variety of existing high-z observations including quasi-stellar object spectral measurements, Wilkinson Microwave Anisotropy Probe Thomson depth constraints, near-infrared source count limits, etc. We find that the ultraviolet IRF below the Lyman edge energy with intensities in the range of a few times 10−19 erg cm−2 s−1 Hz−1 sr−1 can cause appreciable attenuation above ∼12 GeV at z∼ 5, down to ∼6–8 GeV at z≳ 8–10. This may be observable in the spectra of blazars or gamma-ray bursts by the Fermi Gamma-ray Space Telescope or next-generation facilities such as the Cherenkov Telescope Array, Advanced Gamma-ray Imaging System or 5@5, providing invaluable insight into early star formation and cosmic reionization.
Design and Analysis of Prototype Hardware for Secret Sharing Using 2-D Image Processing

Information security has been a major concern with the striking advancement in communication and information techniques. Details of significance such as corporate data, secret message and private details have to be shielded from any unauthorized handling or spiteful approaches. Protection of such valuable information over insecure networks can be accomplished by monitoring or filtering of all packets in the data hiding technology. Images can be used for covert sharing of data. Any type of data can be practically hidden inside any image, without detectably affecting the quality of the image. This can be done by substituting some information of the image with the secret information in carefully chosen ways. The image can then be sent by electronic mail, where it appears as a casual attachment. The receiver can get the secret data by applying reverse transform. Hardware modeling of this process can impart portability and improve the speed of the same. This paper discusses the design and analysis of prototype hardware to perform secret sharing using 2-D Image Processing.
Hilbert Transform, Analytic Signal, and Modulation Analysis for Graph Signal Processing

We propose Hilbert transform (HT) and analytic signal (AS) construction for signals over graphs. This is motivated by the popularity of HT, AS, and modulation analysis in conventional signal processing, and the observation that complementary insight is often obtained by viewing conventional signals in the graph setting. Our definitions of HT and AS use a conjugate-symmetry-like property exhibited by the graph Fourier transform (GFT). We show that a real graph signal (GS) can be represented using smaller number of GFT coefficients than the signal length. We show that the graph HT (GHT) and graph AS (GAS) operations are linear and shift-invariant over graphs. Using the GAS, we define the amplitude, phase, and frequency modulations for a graph signal (GS). Further, we use convex optimization to develop an alternative definition of envelope for a GS. We illustrate the proposed concepts by showing applications to synthesized and real-world signals. For example, we show that the GHT is suitable for anomaly detection/analysis over networks and that GAS reveals complementary information in speech signals.
Using data-driven prediction methods in a hedonic regression problem

The traditional studies about hedonic prices apply simple functional forms such as linear or linearity transformable structures. Nowadays, it’s known in the literature the importance of introducing non-linearity to improve the models’ explanatory capacity. In this work we apply data-driven methods to carry out the hedonic regression. These methods don’t impose any a priori assumption about the functional form. We use the nearest neigbors technique as non-parametric method and neural networks and genetic algorithms both as semi-parametric methods. Neural Networks have already been employed to the specific hedonic regression problem but, to the authors’ knowledge, this is the first time that a genetic algorithm is employed. The empirical results that we have obtained demonstrate the usefulness of applying data driven models in the estimation of hedonic price functions. They can improve traditional parametric models in terms of out-of-sample R2.
Communication Channel Management for Overlay Connectivity Maintenance

A fundamental problem for both structured and unstructured peer-to-peer networks is how to maintain connected the topology of a network in the presence of processes that, possibly concurrently, join and leave the network. In this paper we firstly define a model of the computation well-suited to analyze connectivity maintenance among processes carrying out a distributed computation considering unbounded concurrency and infinite participation. Secondly upon this model we provide a specification of the connectivity maintenance problem. We finally present a protocol that guarantees connectivity maintenance by arranging processes of the computation on a tree. The protocol handles both joins and leaves concurrently and actively (i.e., some piece of code is executed by a leving/joining process interacting with its neighbors in the topology).
Norovirus Outbreak Caused by a New Septic System in a Dolomite Aquifer

Septic systems that are built in compliance with regulations are generally not expected to be the cause of groundwater borne disease outbreaks, especially in areas with thick vadose zones. However, this case study demonstrates that a disease outbreak can occur in such a setting and outlines the combination of epidemiological, microbiological, and hydrogeological methods used to confirm the source of the outbreak. In early June 2007, 229 patrons and employees of a new restaurant in northeastern Wisconsin were affected by acute gastroenteritis; 6 people were hospitalized. Epidemiological case‐control analysis indicated that drinking the restaurant's well water was associated with illness (odds ratio = 3.2, 95% confidence interval = 0.9 to 11.4, P = 0.06). Microbiological analysis (quantitative reverse transcription‐polymerase chain reaction) measured 50 genomic copies per liter of norovirus genogroup I in the well water. Nucleotide sequencing determined the genotype as GI.2 and further showed the identical virus was present in patrons' stool specimens and in the septic tank. Tracer tests using dyes injected at two points in the septic system showed that effluent was traveling from the tanks (through a leaking fitting) and infiltration field to the well in 6 and 15 d, respectively. The restaurant septic system and well (85‐m deep, in a fractured dolomite aquifer) both conformed to state building codes. The early arrival of dye in the well, which was 188 m from the septic field and located beneath a 35‐m thick vadose zone, demonstrates that in highly vulnerable hydrogeological settings, compliance with regulations may not provide adequate protection from fecal pathogens.
Modeling and Control of Fluidic Robotic Joints with natural compliance

This paper reports on basic properties, dynamic modeling, pressure and position control of a newly developed fluidic actuator with rotary elastic chambers (REC-actuator), intended for robots working in human environment. This actuator with natural compliance can be operated by gas or oil. Pressure control of the actuator in pneumatic realization is designed using fast on-off valves, suitable for lightweight robotic joints. A phenomenological model of the actuator is considered, in which spring, damper and torque generating element are connected in parallel. The joint stiffness and damping as functions of pressures in the actuator chambers are identified in free oscillations experiments at constant pressures. The torque-generating element is further identified in a separate series of step responses experiments, using thereby previously obtained stiffness and damping. A sliding mode control law is applied for the position control of the naturally compliant actuator.
Operons Are a Conserved Feature of Nematode Genomes

The organization of genes into operons, clusters of genes that are co-transcribed to produce polycistronic pre-mRNAs, is a trait found in a wide range of eukaryotic groups, including multiple animal phyla. Operons are present in the class Chromadorea, one of the two main nematode classes, but their distribution in the other class, the Enoplea, is not known. We have surveyed the genomes of Trichinella spiralis, Trichuris muris, and Romanomermis culicivorax and identified the first putative operons in members of the Enoplea. Consistent with the mechanism of polycistronic RNA resolution in other nematodes, the mRNAs produced by genes downstream of the first gene in the T. spiralis and T. muris operons are trans-spliced to spliced leader RNAs, and we are able to detect polycistronic RNAs derived from these operons. Importantly, a putative intercistronic region from one of these potential enoplean operons confers polycistronic processing activity when expressed as part of a chimeric operon in Caenorhabditis elegans. We find that T. spiralis genes located in operons have an increased likelihood of having operonic C. elegans homologs. However, operon structure in terms of synteny and gene content is not tightly conserved between the two taxa, consistent with models of operon evolution. We have nevertheless identified putative operons conserved between Enoplea and Chromadorea. Our data suggest that operons and “spliced leader” (SL) trans-splicing predate the radiation of the nematode phylum, an inference which is supported by the phylogenetic profile of proteins known to be involved in nematode SL trans-splicing.
2 . 0 Does consciousness have a function ?

A major challenge for the successful naturalization of consciousness lies in locating its biological function, or functions. Although common sense suggests that conscious experience has many important functional roles in our lives, experiments and theoretical arguments challenge these everyday intuitions. Many human behaviors can occur in the absence of consciousness, and the natural world contains many creatures capable of engaging in complex behaviors, at least some of which may be doing so entirely without consciousness (e.g., mollusks, microorganisms). Yet, without any defensible function, consciousness is in danger of losing its legitimacy as an object of scientific study (Chalmers 1996; Humphrey 2002).
The role of stakeholders' expectations in predicting information systems implementation outcomes

An integrative model of IS implementation success is presented. The model focuses on stakeholders' expectations as predictors of IS implementation outcomes, and on the organization's competence in designing and implementing the system. The proposed framework is derived from the literature on IS implementation, especially those studies that explain and/or predict the outcomes of such a process. The framework attempts to integrate the existing research streams: factor studies, process studies and expectancy studies. The model uses a multiperspective approach by taking into consideration stakeholders' different views of these processes.
Privacy Time-Related Analysis in Business Protocols

To automate the analysis of service descriptions, [6] proposed a simple and expressive business protocol model (the specification of possible message exchange sequences) based on state machines, supporting rich timing constraints. Furthermore, developers of client applications need to be aware not only of functional aspects but also of non-functional aspects including privacy. In fact, the major concerns of a client are the disclosure of its personnel data conveyed during the message exchange. The aim of the paper is to study the ability of business protocol to handle the privacy and its time-related properties.
ORF-MOSAIC for adaptive control of a biomimetic arm

This study is an attempt to take advantage of a cerebellar model to control a biomimetic arm. The cerebellar controller is a modified MOSAIC model which adaptively controls the arm. We call this model ORF-MOSAIC (Organized by Receptive Fields MOdular Selection And Identification for Control). The arm features a musculoskeletal model which is controlled through muscle activations by means of optimization techniques. With as few as 16 modules, we were able to control the arm in a workspace of 30×30 cm. The system was able to adapt to an external field as well as handling new objects despite delays. The discussion section suggests that there are similarities between the microzones in the cerebellum and the modules of this new model.
Differential Adsorption of Occluded and Nonoccluded Insect-Pathogenic Viruses to Soil-Forming Minerals

ABSTRACT Soil represents the principal environmental reservoir of many insect-pathogenic viruses. We compared the adsorption and infectivity of one occluded and two nonoccluded viruses, Helicoverpa armigera single nucleopolyhedrovirus (HaSNPV) (Baculoviridae), Cricket paralysis virus (CrPV) (Dicistroviridae), and Invertebrate iridescent virus 6 (IIV-6) (Iridoviridae), respectively, in mixtures with a selection of soil-forming minerals. The relative infective titers of HaSNPV and CrPV were unchanged or slightly reduced in the presence of different minerals compared to their titers in the absence of the mineral. In contrast, the infective titer of IIV-6 varied according to the mineral being tested. In adsorption studies, over 98% of HaSNPV occlusion bodies were adsorbed by all the minerals, and a particularly high affinity was observed with ferric oxide, attapulgite, and kaolinite. In contrast, the adsorption of CrPV and IIV-6 differed markedly with mineral type, with low affinity to bentonites and high affinity to ferric oxide and kaolinite. We conclude that interactions between soil-forming minerals and insect viruses appear to be most important in nucleopolyhedroviruses, followed by invertebrate iridescent viruses, and least important in CrPV, which may reflect the ecology of these pathogens. Moreover, soils with a high content of iron oxides or kaolinite would likely represent highly effective reservoirs for insect-pathogenic viruses.
Social Interaction Based Audience Segregation for Online Social Networks

Online social networking is the latest craze that has captured the attention of masses, people use these sites to communicate with their friends and family. These sites offer attractive means of social interactions and communications, but also raise privacy concerns. This paper examines user’s abilities to control access to their personal information posted in online social networks. Online social networks lack common mechanism used by individuals in their real life to manage their privacy. The lack of such mechanism significantly affects the level of user control over their self presentation in online social networks. In this paper, we present social interaction based audience segregation model for online social networks. This model mimics real life interaction patterns and makes online social networks more privacy friendly. Our model uses type, frequency, and initiation factor of social interactions to calculate friendship strength. The main contribution of the model is that it considers set of all possible interactions among friends and assigns a numerical weight to each type of interaction in order to increase or decrease its contribution in calculation of friendship strength based on its importance in the development of relationship ties.
Early detection of hazardous weather conditions in Turkey with satellite images using Support Vector Machines and Artificial Neural Networks

The prediction of meteorological phenomena resulting from rainfall, is one of the most important elements of the minimization of the damages. Local meteorological radars works at regional base hence they cannot represent the parameters of precipitation. Because of insufficiency of the radar systems early detection, satellite images can be used to create decision systems. At this point firstly infrared satellite images will be classified and then comparative results will be discussed on experimental stage. In this work, we used Wavelet Transform applied to infrared satellite images to extract approximation coefficients. Size of these coefficients are reduced using Principal Component Analysis and classified through classification algorithms. Artificial Neural Network and Support Vector Machines are used for classification. As a result of the classification made with Artificial Neural Networks, we accomplished 84% prediction rate. With the classification of Support Vector Machines, we reached 93% prediction rate.