Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient SimRank Computation via Linearization

SimRank, proposed by Jeh and Widom, provides a good similarity measure that has been successfully used in numerous applications. While there are many algorithms proposed for computing SimRank, their computational costs are very high. In this paper, we propose a new computational technique, "SimRank linearization," for computing SimRank, which converts the SimRank problem to a linear equation problem. By using this technique, we can solve many SimRank problems, such as single-pair compuation, single-source computation, all-pairs computation, top k searching, and similarity join problems, efficiently.
Learning algorithm for color recognition of license plates

To improve accuracy and adaptability, this paper presents a learning algorithm for color recognition of license plates. For three components of the hue-saturation-value (HSV) color space, different membership functions were defined to calculate their fuzzy degrees. Through the weighted fusion of the three membership degrees, a single map was produced to be the classification function for color recognition, and the final decision is based on the integrated map. Thresholds of membership functions, weight vectors of membership degrees and classification thresholds were all learned by the proposed learning algorithm, according to the classification error minimization inductive principle. Experiments were conducted on two different test sets. The overall accuracies of the proposed algorithm are 97.70% and 96.20%, respectively. The experimental results show that the proposed algorithm can learn the appropriate thresholds and weights from the training images, which are consistent with the practical application environments. Thus it improves the accuracy and adaptability of the color recognition algorithm and can meet the requirements of the practical engineering applications.
Enhancing delay-based packet forwarding schemes in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have been recognized as a key technology in shaping tomorrow's world. To assure reliable and cost-effective forwarding of data, opportunistic schemes were explored. In this paper, we evaluate the performance of delay-based opportunistic schemes in WSNs and then propose a mechanism to overcome their drawbacks. The proposed idea has the merits of minimizing the time for time-critical applications and/or the cost for energy-critical applications. The idea was implemented in TinyOS and evaluated in TOSSIM. Simulation results prove its capacities in enhancing delay-based schemes with virtually no extra cost in computing demand, memory usage and communication overhead.
EM-Based joint symbol and blur estimation for 2D barcode

Decoding a severely blurred 2D barcode can be considered as a special case of blind image restoration issue. In this paper, we propose an appropriate system model which includes the original image with the particularities related to barcode, the blur and the observed image. We develop an unsupervised algorithm that jointly estimates the blur and detects the symbols using the maximum likelihood (ML) criterion. Besides, we show that when taking into account the spatial properties of the barcode, the prohibitive complexity of the ML algorithm can be reduced without degrading its performance. Simulation results show that the algorithm performs accurate estimation of the blur and achieves good performance for symbol detection which is close to that obtained with supervised algorithm.
Structure-Based Virtual Screening for Drug Discovery: Principles, Applications and Recent Advances

Structure-based drug discovery (SBDD) is becoming an essential tool in assisting fast and cost-efficient lead discovery and optimization. The application of rational, structure-based drug design is proven to be more efficient than the traditional way of drug discovery since it aims to understand the molecular basis of a disease and utilizes the knowledge of the three-dimensional structure of the biological target in the process. In this review, we focus on the principles and applications of Virtual Screening (VS) within the context of SBDD and examine different procedures ranging from the initial stages of the process that include receptor and library pre-processing, to docking, scoring and post-processing of topscoring hits. Recent improvements in structure-based virtual screening (SBVS) efficiency through ensemble docking, induced fit and consensus docking are also discussed. The review highlights advances in the field within the framework of several success studies that have led to nM inhibition directly from VS and provides recent trends in library design as well as discusses limitations of the method. Applications of SBVS in the design of substrates for engineered proteins that enable the discovery of new metabolic and signal transduction pathways and the design of inhibitors of multifunctional proteins are also reviewed. Finally, we contribute two promising VS protocols recently developed by us that aim to increase inhibitor selectivity. In the first protocol, we describe the discovery of micromolar inhibitors through SBVS designed to inhibit the mutant H1047R PI3Kα kinase. Second, we discuss a strategy for the identification of selective binders for the RXRα nuclear receptor. In this protocol, a set of target structures is constructed for ensemble docking based on binding site shape characterization and clustering, aiming to enhance the hit rate of selective inhibitors for the desired protein target through the SBVS process.
The Neural Basis of Vocal Pitch Imitation in Humans

Vocal imitation is a phenotype that is unique to humans among all primate species, and so an understanding of its neural basis is critical in explaining the emergence of both speech and song in human evolution. Two principal neural models of vocal imitation have emerged from a consideration of nonhuman animals. One hypothesis suggests that putative mirror neurons in the inferior frontal gyrus pars opercularis of Broca's area may be important for imitation. An alternative hypothesis derived from the study of songbirds suggests that the corticostriate motor pathway performs sensorimotor processes that are specific to vocal imitation. Using fMRI with a sparse event-related sampling design, we investigated the neural basis of vocal imitation in humans by comparing imitative vocal production of pitch sequences with both nonimitative vocal production and pitch discrimination. The strongest difference between these tasks was found in the putamen bilaterally, providing a striking parallel to the role of the analogous region in songbirds. Other areas preferentially activated during imitation included the orofacial motor cortex, Rolandic operculum, and SMA, which together outline the corticostriate motor loop. No differences were seen in the inferior frontal gyrus. The corticostriate system thus appears to be the central pathway for vocal imitation in humans, as predicted from an analogy with songbirds.
When multi-touch meets streaming

With the advent of mobile devices with large displays, it is intuitive and natural for users to interact with an application on a mobile device using multi-touch gestures. In this paper, we propose that these multi-touch gestures can be streamed on-the-fly among multiple participating users, making it possible to engage users in a collaborative or competitive experience. Such multi-touch streams, featuring very low streaming bit rates, can be rendered on receivers to precisely reconstruct the states of an application. We present the challenges, system framework, embedded algorithm design, and real-world evaluation of TouchTime, a new system that has been designed from scratch to facilitate the streaming of multi-touch gestures among multiple users. By seamlessly combining local computation on mobile devices and services from the "cloud," we explore the design space of suitable mechanisms to represent and packetize multi-touch gestures, and of practical protocols to transport concurrent live multi-touch streams over the Internet. Specifically, we propose an auction-based reflector selection algorithm to achieve the minimal end-to-end delay in a live multi-touch streaming session. To demonstrate TouchTime, we have developed a new real-world music composition application --- called MusicScore --- using the Apple iPad Programming SDK, and used it as our running example and experimental testbed to evaluate our design choices and implementation of TouchTime.
Overview of compression and packet loss effects in speech biometrics

An overview is presented of compression and packet loss effects in speech biometrics. These new problems appear particularly in recent applications of biometrics over mobile or Internet networks. The influence of speech compression on speaker recognition performance in mobile networks is investigated. In a first experiment, it is found that the use of GSM coding degrades the performance. In a second experiment, the features for the speaker recognition system are calculated directly from the information available in the encoded bit stream. It is found that a low LPC order in GSM coding is responsible for most performance degradations. A speaker recognition system was obtained which is equivalent in performance to the original one which decodes and reanalyses speech before performing recognition. The joint packet loss and compression effects over IP networks are also studied. It is experimentally demonstrated that the adverse effects of packet loss alone are negligible, while the encoding of speech, particularly at a low bit rate, coupled with packet loss, can reduce the verification accuracy considerably.
Hybridizing GMDH and least squares SVM support vector machine for forecasting tourism demand.

In this paper, we proposed a novel hybrid group method of data handling least squares support vector machine (GLSSVM) algorithm, which combines the theory a group method of data handling (GMDH) with the least squares support vector machine (LSSVM). With the GMDH is used to determine the inputs of LSSVM method and the LSSVM model which works as time series forecasting. The aim of this study is to examine the feasibility of the hybrid model in tourism demand forecasting by comparing it with GMDH and LSSVM model. The tourist arrivals to Johor Malaysia during 1970 to 2008 were employed as the data set. The comparison of modeling results demonstrate that the hybrid model outperforms than two other nonlinear approaches GMDH and LSSVM models.
Exploring representations of Black masculinity and emasculation on NBC’s Parenthood

ABSTRACT Here, representations of Black masculinity and emasculation are explored on NBC’s Parenthood. We question whether depictions of Black masculinity represented through Parenthood’s character, Alex, are empowering or stereotypical; how do they reflect, destabilize, and/or reinforce heteronormative depictions of Black community members? Two seasons were analyzed using Owen’s thematic analysis. Ultimately, Black males are portrayed as being societal failures, criminals, violent, and hypersexualized through his character. This embodiment ultimately preserves long-held images of Black men as “Toms, coons … bucks … as oversexed, aggressive, violent, animalistic, or emasculated” (Jackson, R. L. II. 2006. Scripting the black masculine body: Identity, discourse, and racial politics in popular media. Albany, NY: State University of New York Press). Practical recommendations and suggestions for researchers and media practitioners are provided.
Coupled Ecosystem/Supply Chain Modelling of Fish Products from Sea to Shelf: The Peruvian Anchoveta Case

Sustainability assessment of food supply chains is relevant for global sustainable development. A framework is proposed for analysing fishfood (fish products for direct human consumption) supply chains with local or international scopes. It combines a material flow model (including an ecosystem dimension) of the supply chains, calculation of sustainability indicators (environmental, socio-economic, nutritional), and finally multi-criteria comparison of alternative supply chains (e.g. fates of landed fish) and future exploitation scenarios. The Peruvian anchoveta fishery is the starting point for various local and global supply chains, especially via reduction of anchoveta into fishmeal and oil, used worldwide as a key input in livestock and fish feeds. The Peruvian anchoveta supply chains are described, and the proposed methodology is used to model them. Three scenarios were explored: status quo of fish exploitation (Scenario 1), increase in anchoveta landings for food (Scenario 2), and radical decrease in total anchoveta landings to allow other fish stocks to prosper (Scenario 3). It was found that Scenario 2 provided the best balance of sustainability improvements among the three scenarios, but further refinement of the assessment is recommended. In the long term, the best opportunities for improving the environmental and socio-economic performance of Peruvian fisheries are related to sustainability-improving management and policy changes affecting the reduction industry. Our approach provides the tools and quantitative results to identify these best improvement opportunities.
Automated construction of a cycle-approximate transaction level model of a memory controller

Transaction level (TL) models are key to early design exploration, performance estimation and virtual prototyping. Their speed and accuracy enable early and rapid System-on-Chip (SoC) design evaluation and software development. Most devices have only register transfer level (RTL) models that are too complex for SoC simulation. Abstracting these models to TL ones, however, is a challenging task, especially when the RTL description is too obscure or not accessible. This work presents a methodology for automatically creating a TL model of an RTL memory controller component. The device is treated as a black box and a multitude of simulations is used to obtain results, showing its timing behavior. The results are classified into conditional probability distributions, which are reused within a TL model to approximate the RTL timing behavior. The presented method is very fast and highly accurate. The resulting TL model executes approximately 1200 times faster, with a maximum measured average timing offset error of 7.66%.
Fall Detection Using Location Sensors and Accelerometers

The rapid aging of the world's population is driving the development of pervasive solutions for elder care. These solutions, which often involve fall detection with accelerometers, are accurate in laboratory conditions but can fail in some real-life situations. To overcome this, the authors present the Confidence system, which detects falls mainly with location sensors. A user wears one to four tags. By detecting tag locations with sensors, the system can recognize the user's activity, such as falling and then lying down afterward, as well as the context in terms of the location in the home. The authors used a scenario consisting of events difficult to recognize as falls or nonfalls to compare the Confidence system with accelerometer-based fall-detection methods, some augmented with context data from a location sensor. The methods that used context information were approximately 30 percent more accurate than those that did not. The Confidence system was also successfully validated in a real-life setting with elderly users.
Improving computer interaction for older adults

A point and click interface can present difficulties for older adults (particularly those with physical impairments) who may not easily be able to operate a standard input device such as a mouse or trackball. The use of gestural commands, via a multitouch touchscreen device, is an alternative and direct method of interacting with an application. This research investigates whether gestural commands on a multitouch touchscreen device can make interfaces easier to use, learn and remember for older adults.
Ubiquitous Stream Programming to Facilitate the Migration to Multicore Architectures

The goal of the StreamIt project is to become the language of choice for streaming applications, which include multimedia codes, as well as cryptography, networking and security, and some forms of scientific computation. StreamIt provides a high level stream language and a stream-aware compiler that leverages domain specific language constructs to deliver high performance from high levels of abstraction. We believe that StreamIt will lead to a dramatic improvement in programmer productivity, program robustness, portability, and performance scalability on commodity multicore architectures. A key component of the research is to also provide seamless integration with other programming practices, because large applications have various properties that may require different language abstractions. This paper outlines a methodology that allows StreamIt programs to run as part of the X10 virtual machine. X10 is one of three general purpose languages under development from vendors designing large scale par-
Modification of Deposited, Size-Selected MoS2 Nanoclusters by Sulphur Addition: An Aberration-Corrected STEM Study

Molybdenum disulphide (MoS2) is an earth-abundant material which has several industrial applications and is considered a candidate for platinum replacement in electrochemistry. Size-selected MoS2 nanoclusters were synthesised in the gas phase using a magnetron sputtering, gas condensation cluster beam source with a lateral time-of-flight mass selector. Most of the deposited MoS2 nanoclusters, analysed by an aberration-corrected scanning transmission electron microscope (STEM) in high-angle annular dark field (HAADF) mode, showed poorly ordered layer structures with an average diameter of 5.5 nm. By annealing and the addition of sulphur to the clusters (by sublimation) in the cluster source, the clusters were transformed into larger, crystalline structures. Annealing alone did not lead to crystallization, only to a cluster size increase by decomposition and coalescence of the primary clusters. Sulphur addition alone led to a partially crystalline structure without a significant change in the size. Thus, both annealing and sulphur addition processes were needed to obtain highly crystalline MoS2 nanoclusters.
Advantages of diffuse light for horticultural production and perspectives for further research

Plants use diffuse light more efficiently than direct light, which is well established due to diffuse light penetrates deeper into the canopy and photosynthetic rate of a single leaf shows a non-linear response to the light flux density. Diffuse light also results in a more even horizontal and temporal light distribution in the canopy, which plays substantial role for crop photosynthesis enhancement as well as production improvement. Here we show some of the recent findings about the effect of diffuse light on light distribution over the canopy and its direct and indirect effects on crop photosynthesis and plant growth, and suggest some perspectives for further research which could strengthen the scientific understanding of diffuse light modulate plant processes and its application in horticultural production.
Can trainees’ perceptions of the delivery of the foundation subjects & R.E. in Primary Teacher Education be enhanced by using a Three-Lens Model of Delivery?

This paper reports on the findings of a small-scale research project, carried out by a team of teacher educators working in the Primary Foundation Subjects at a university in the North West of England. The team worked together to develop the Three-Lens Model, a teaching model that aimed both to provide a unification of subject delivery across the module whilst splitting subject delivery into three clear areas: subject-“ness” (subject identity), how to teach (pedagogy) and ideas of what to teach (toolbox). The paper presents a discussion of the perceived effectiveness of the Three-Lens Model. It gathers data from trainees across a variety of cohorts and programmes within the School of Education who were exposed to the model of delivery for one year and specifically questions how successful it was in providing a cohesive delivery structure and in raising the trainees’ perceptions of the pedagogical element of their training. The findings suggest that this was a largely successful delivery model which could potentially benefit other training providers.
New primary neoplasms of the central nervous system in survivors of childhood cancer: a report from the Childhood Cancer Survivor Study.

BACKGROUND Subsequent primary neoplasms of the central nervous system (CNS) have frequently been described as late events following childhood leukemia and brain tumors. However, the details of the dose-response relationships, the expression of excess risk over time, and the modifying effects of other host and treatment factors have not been well defined. METHODS Subsequent primary neoplasms of the CNS occurring within a cohort of 14,361 5-year survivors of childhood cancers were ascertained. Each patient was matched with four control subjects by age, sex, and time since original cancer diagnosis. Tumor site-specific radiation dosimetry was performed, and chemotherapy information was abstracted from medical records. Conditional logistic regression was used to estimate odds ratios (ORs), to calculate 95% confidence intervals (CIs), and to model the excess relative risk (ERR) as a function of radiation dose and host factors. For subsequent gliomas, standardized incidence ratios (SIRs) and excess absolute risks (EARs) were calculated based on Surveillance, Epidemiology, and End Results data. RESULTS Subsequent CNS primary neoplasms were identified in 116 individuals. Gliomas (n = 40) occurred a median of 9 years from original diagnosis; for meningiomas (n = 66), it was 17 years. Radiation exposure was associated with increased risk of subsequent glioma (OR = 6.78, 95% CI = 1.54 to 29.7) and meningioma (OR = 9.94, 95% CI = 2.17 to 45.6). The dose response for the excess relative risk was linear (for glioma, slope = 0.33 [95% CI = 0.07 to 1.71] per Gy, and for meningioma, slope = 1.06 [95% CI = 0.21 to 8.15] per Gy). For glioma, the ERR/Gy was highest among children exposed at less than 5 years of age. After adjustment for radiation dose, neither original cancer diagnosis nor chemotherapy was associated with risk. The overall SIR for glioma was 8.7, and the EAR was 19.3 per 10,000 person-years. CONCLUSIONS Exposure to radiation therapy is the most important risk factor for the development of a new CNS tumor in survivors of childhood cancers. The higher risk of subsequent glioma in children irradiated at a very young age may reflect greater susceptibility of the developing brain to radiation.