Miracle-dz/UniFAR-FTUs huggingface dataset:
Large Crowd Count Based on Improved SURF Algorithm

This paper uses an analysis of Speeded up Robust Feature (SURF), based on the method of Linear Interpolation for camera distortion calibration, for high-density crowd counting. The eigenvalues are built on the Gray Level Co-occurrence Matrix (GLCM) features and the SURF features. Though the method of linear interpolation, weight values are interpolated to reduce the error, which is caused by camera distortion calibration. The optimized crowd’s feature vector can be got then. Through the method of support vector regression, the crowd’s number can be forecast by training model. The experiment result shows that the method of this paper has a higher accuracy than the previous methods.
Pseudo-Haptic Weight: Changing the Perceived Weight of Virtual Objects By Manipulating Control-Display Ratio

In virtual reality, the lack of kinesthetic feedback often prevents users from experiencing the weight of virtual objects. Control-to-display (C/D) ratio manipulation has been proposed as a method to induce weight perception without kinesthetic feedback. Based on the fact that lighter (heavier) objects are easier (harder) to move, this method induces an illusory perception of weight by manipulating the rendered position of users' hands---increasing or decreasing their displayed movements. In a series of experiments we demonstrate that C/D-ratio induces a genuine perception of weight, while preserving ownership over the virtual hand. This means that such a manipulation can be easily introduced in current VR experiences without disrupting the sense of presence. We discuss these findings in terms of estimation of physical work needed to lift an object. Our findings provide the first quantification of the range of C/D-ratio that can be used to simulate weight in virtual reality.
A Novel Approach to ECG Classification Based upon Two-Layered HMMs in Body Sensor Networks

This paper presents a novel approach to ECG signal filtering and classification. Unlike the traditional techniques which aim at collecting and processing the ECG signals with the patient being still, lying in bed in hospitals, our proposed algorithm is intentionally designed for monitoring and classifying the patient's ECG signals in the free-living environment. The patients are equipped with wearable ambulatory devices the whole day, which facilitates the real-time heart attack detection. In ECG preprocessing, an integral-coefficient-band-stop (ICBS) filter is applied, which omits time-consuming floating-point computations. In addition, two-layered Hidden Markov Models (HMMs) are applied to achieve ECG feature extraction and classification. The periodic ECG waveforms are segmented into ISO intervals, P subwave, QRS complex and T subwave respectively in the first HMM layer where expert-annotation assisted Baum-Welch algorithm is utilized in HMM modeling. Then the corresponding interval features are selected and applied to categorize the ECG into normal type or abnormal type (PVC, APC) in the second HMM layer. For verifying the effectiveness of our algorithm on abnormal signal detection, we have developed an ECG body sensor network (BSN) platform, whereby real-time ECG signals are collected, transmitted, displayed and the corresponding classification outcomes are deduced and shown on the BSN screen.
Visual and Olfactory Floral Cues of Campanula (Campanulaceae) and Their Significance for Host Recognition by an Oligolectic Bee Pollinator

Oligolectic bees collect pollen from a few plants within a genus or family to rear their offspring, and are known to rely on visual and olfactory floral cues to recognize host plants. However, studies investigating whether oligolectic bees recognize distinct host plants by using shared floral cues are scarce. In the present study, we investigated in a comparative approach the visual and olfactory floral cues of six Campanula species, of which only Campanula lactiflora has never been reported as a pollen source of the oligolectic bee Ch. rapunculi. We hypothesized that the flowers of Campanula species visited by Ch. rapunculi share visual (i.e. color) and/or olfactory cues (scents) that give them a host-specific signature. To test this hypothesis, floral color and scent were studied by spectrophotometric and chemical analyses, respectively. Additionally, we performed bioassays within a flight cage to test the innate color preference of Ch. rapunculi. Our results show that Campanula flowers reflect the light predominantly in the UV-blue/blue bee-color space and that Ch. rapunculi displays a strong innate preference for these two colors. Furthermore, we recorded spiroacetals in the floral scent of all Campanula species, but Ca. lactiflora. Spiroacetals, rarely found as floral scent constituents but quite common among Campanula species, were recently shown to play a key function for host-flower recognition by Ch. rapunculi. We conclude that Campanula species share some visual and olfactory floral cues, and that neurological adaptations (i.e. vision and olfaction) of Ch. rapunculi innately drive their foraging flights toward host flowers. The significance of our findings for the evolution of pollen diet breadth in bees is discussed.
Four cases of radical hysterectomy with acute normovolemic hemodilution despite low preoperative hematocrit values.

The study was approved by the institutional ethics committee at our institution with all patients giving their written, informed consent. Four patients from a continuing study with preoperative diagnosis of carcinoma of the cervix are presented. They had a preoperative Hct of ,33%, were ASA physical status I or II, were without cardiovascular, pulmonary, or hormonal dysfunctions, and were scheduled for radical hysterectomy. They fasted for 10 h, and neither preoperative bowel cleansing nor any preoperative infusions were administered before measurement. After the induction of general anesthesia and insertion of central and arterial catheters, baseline measurements of plasma volume (PV), Hct, and RCV were performed. (For measuring procedures, see below.) These baseline measurements showed that all four patients had low preoperative Hct values because of extraordinarily large PVs rather than small RCVs. Therefore, ANH to a target Hct of 20% was performed. A median of 1,300 mL of blood was removed and simultaneously replaced by 1,550 mL of colloid (5% albumin solution or 6% hydroxyethylstarch). After completion of ANH and a steady-state interval of 30 min without any further infusions, simultaneous measurements of PV, Hct, and RCV were performed. After these measurements, the surgical procedure began. The intraoperative transfusion trigger for beginning retransfusion was a Hct of 16%. After having reached this Hct value, the fraction of inspired oxygen was switched from 0.5 to 1.0. All autologous blood was retransfused in every patient late in surgery when major blood loss had ceased. Postoperative measurements of PV, Hct, and RCV were performed immediately after closure of the abdominal wall during a period of stable anesthesia without obvious blood loss. Preoperative PV and Hct measurements before ANH and postoperative measurements were performed in duplicate in a time interval of 30 min without any infusions.
Models for Genetic Diversity Generated by Negative Binomial Point Processes

We develop a model based on a generalised Poisson-Dirichlet distribution for the analysis of genetic diversity, and illustrate its use on microsatellite data for the genus Dasyurus (the quoll, a marsupial carnivore listed as near-threatened in Australia). Our class of distributions, termed $PD_\alpha^{(r)}$, is constructed from a negative binomial point process, generalizing the usual one-parameter $PD_\alpha$ model, which is constructed from a Poisson point process. Both models have at their heart a Stable$(\alpha)$ process, but in $PD_\alpha^{(r)}$, an extra parameter $r>0$ adds flexibility, analogous to the way the negative binomial distribution allows for "overdispersion" in the analysis of count data. A key result obtained is a generalised version of Ewens' sampling formula for $PD_\alpha^{(r)}$. We outline the theoretical basis for the model, and, for the quolls data, estimate the parameters $\alpha$ and r by least squares, showing how the extra parameter r aids in the interpretability of the data by comparison with the standard $PD_\alpha$ model. The methods potentially have implications for the management and conservation of threatened populations.
Transport Coefficients from Large Deviation Functions

We describe a method for computing transport coefficients from the direct evaluation of large deviation functions. This method is general, relying on only equilibrium fluctuations, and is statistically efficient, employing trajectory based importance sampling. Equilibrium fluctuations of molecular currents are characterized by their large deviation functions, which are scaled cumulant generating functions analogous to the free energies. A diffusion Monte Carlo algorithm is used to evaluate the large deviation functions, from which arbitrary transport coefficients are derivable. We find significant statistical improvement over traditional Green–Kubo based calculations. The systematic and statistical errors of this method are analyzed in the context of specific transport coefficient calculations, including the shear viscosity, interfacial friction coefficient, and thermal conductivity.
Density evolution analysis of LDPC codes with different receivers on impulsive noise channels

In conventional communication systems, the additive noise at the receiver is usually assumed to be Gaussian distributed. However, this assumption is not always valid and examples of non-Gaussian distributed noise include power line channels, underwater acoustic channels and man-made interference. Therefore it is important to design a receiver to mitigate the effects of impulsive noise. This paper proposes a new low-complexity receiver design that closely approximates the optimal log-likelihood ratio demapper and performs well when the channel varies from slightly impulsive to very impulsive. Furthermore, we present a density evolution analysis and simulation results of (3, 6) LDPC code on additive impulsive noise channels, with symmetric alpha-stable probability density functions, employing different receivers to overcome the impulsive nature of the channels. Each receiver calculates or approximates the log-likelihood ratios of the received symbols, depending on whether they are optimal or sub-optimal respectively. The threshold signal-to-noise ratios of the LDPC code are derived to determine the start of the waterfall region of the bit-error rate performance and simulation results are presented to validate our density evolution analysis.
Denoising and compression in wavelet domain via projection onto approximation coefficients

We describe a new filtering approach in the wavelet domain for image denoising and compression, based on the projections of details subbands coefficients (resultants of the splitting procedure, typical in wavelet domain) onto the approximation subband coefficients (much less noisy). The new algorithm is called Projection Onto Approximation Coefficients (POAC). As a result of this approach, only the approximation subband coefficients and three scalars are stored and/or transmitted to the channel. Besides, with the elimination of the details subbands coefficients, we obtain a bigger compression rate. Experimental results demonstrate that our approach compares favorably to more typical methods of denoising and compression in wavelet domain.
Unsupervised Ontology Generation from Unstructured Text. CRESST Report 827.

Ontologies are a vital component of most knowledge acquisition systems, and recently there has been a huge demand for generating ontologies automatically since manual or supervised techniques are not scalable. In this paper, we introduce OntoMiner , a rule-based, iterative method to extract and populate ontologies from unstructured or free text. OntoMiner transforms text into a graph structure called a textGraph in which nodes are candidate terms and words from the text and edges are grammatical, semantic, and categorical relations between nodes. OntoMiner iteratively uses graph pattern rules over the textGraphs to mine ontological information and at the end of each iteration, based on the newly found information, OntoMiner improves the existing ontology. Our preliminary experiments indicate that OntoMiner achieves up to 93.4% accuracy, which to our knowledge exceeds the accuracy levels of previous work.
A Novel Matrix Norm Based Gaussian Kernel for Feature Extraction of Images

Gaussian kernel is widely used in Support Vector Machines and many other kernel methods, and it is most often deemed to provide a local measure of similarity between vectors, which causes large storage requirements and large computational effort for transforming images to vectors owing to its viewing images as vectors. A novel matrix norm based Gaussian kernel (M-Gaussian kernel) which views images as matrices is proposed to solve the problem. Experiments conducted on ORL face database show the effectiveness of the proposed M-Gaussian kernel.
Establishing the Uniqueness of the Human Voice for Security Applications

Establishing the individuality (uniqueness) of a person’s voice is a necessary precursor to the successful development and deployment of security-related voice applications, such as speaker verification and speaker identification systems. Due to the large world population, however, the task of establishing the uniqueness of a person’s voice is difficult, and one that has not yet been demonstrated. Our approach is to transform a many-class problem into a dichotomous one by using, as pattern classifier features, the distances between measurements extracted from utterances by the same and by different speakers. The resulting statistically inferable model allows us to experiment with a relatively small sample of the population and then to generalize the results to the entire population. This dichotomous model has previously been used to establish the individuality of handwriting and of fingerprints.
ReCollection: a Disposal/Formal Requirement-Based Tool to Support Sustainable Collection Making

Modern Information Science deals with tasks which include classifying, searching and browsing large numbers of digital objects. The problem today is that our computerized tools are poorly adapted to our needs as they are often too formal: we illustrate this matter in the rst section of this article with the example of multimedia collections. We then propose a software tool, ReCollection, for dealing with digital collections in a less formal and more sustainable manner. Finally, we explain how our software design is strongly backed up by both artistic and psychological knowledge concerning the ancient human activity of collecting, which we will see can be described as a metaphor for categorization in which two irreducible cognitive modes are at play: aspectual similarity and spatiotemporal proximity.
Merging ratio images based realistic object class re-rendering

The merging ratio image (MRI) for realistic object class re-rendering is presented and applied to human face image. We focus on the Lambertian object class and utilize a uniform ratio image scheme - merging ratio image - to merge the expression ratio image, ageing ratio image, and illumination ratio image (quotient image), which exhibits a new facial model quality. Given a single face image and some other photorealistic face examples with distinct attributes, such as aged wrinkles, expressive feature motions, and certain lighting conditions, we generate expressive expressions, natural ageing, rejuvenating and varying illuminations with MRI rendering technique. Experimental results demonstrate the attractive properties of our method with some face examples from MPI Caucasian face database and AI&R Asian face database.
The re-infection rate of high-risk HPV and the recurrence rate of vulvar intraepithelial neoplasia (VIN) usual type after surgical treatment

Summary Background VIN usual type appears to be related to the HPV’s oncogenic types. The aim of this prospective multicenter study was to evaluate the re-infection rate of high-risk HPV and the recurrence rate of VIN usual type after surgical treatment. Material/Methods The study enrolled 103 women affected by VIN usual type. They underwent wide local excision by CO2 laser. The patients were investigated by clinical evaluation and HPV DNA test 6 months after surgical treatment, and then were followed-up at 12, 18, 24, and 36 months. The recurrences were treated with re-excision. Results The rate of HPV infection after surgical treatment was 34% at 6 months, 36.9% at 12 months, 40% at 18 months, 41.7% at 24 months and 44.7% at 36 months. The mean time from HPV infection to the development of VIN was 18.8 months. Conclusions HPV testing in the follow-up of VIN usual type patients might be useful for identifying those patients with a higher risk of recurrence after surgical treatment, although more studies are needed. These preliminary data suggest that the test, in addition to clinical examination, can improve the efficacy of the follow-up.
Overcoming Distractions during Transitions from Break to Work using a Conversational Website-Blocking System

Work breaks--both physical and digital--play an important role in productivity and workplace wellbeing. Yet, the growing availability of digital distractions from online content can turn breaks into prolonged "cyberloafing". In this paper, we present UpTime, a system that aims to support workers' transitions from breaks back to work--moments susceptible to digital distractions. Combining a browser extension and chatbot, users interact with UpTime through proactive and reactive chat prompts. By sensing transitions from inactivity, UpTime helps workers avoid distractions by automatically blocking distracting websites temporarily, while still giving them control to take necessary digital breaks. We report findings from a 3-week comparative field study with 15 workers. Our results show that automatic, temporary blocking at transition points can significantly reduce digital distractions and stress without sacrificing workers' sense of control. Our findings, however, also emphasize that overloading users' existing communication channels for chatbot interaction should be done thoughtfully.
Designing Games with Procedural Content Generation: An Authorial Approach

This paper describes the design of a novel approach to procedural content generation, intent on supporting game design activities. The distinctive factor in this approach is that content generation is guided by a series of target experience indicators, which the designer can define freely according to his own agenda. We detail its underlying concepts and procedural logic, as well as its purported benefits, and outline early experiments in the design process of a prototype.
Like an intuitive and courteous butler: a proactive personal agent for task management

The ability to proactively offer assistance promises to make personal agents more helpful to their users. We characterize the properties desired of proactive behaviour by a personal assistant agent in the realm of task management, and present an extended agent cognition model that features a meta-level layer charged with identifying potentially helpful actions and determining when it is appropriate to perform them. The reasoning that answers these questions draws on a theory of proactivity that describes user desires and a model of helpfulness. Operationally, assistance patterns represent a compiled form of this knowledge, instantiating meta-cognition over the agent's beliefs about its user's activities as well as over world state. We have implemented the resulting generic framework for proactive goal generation and deliberation as part of a personal assistant agent in the desktop domain.
PeerLens: Peer-inspired Interactive Learning Path Planning in Online Question Pool

Online question pools like LeetCode provide hands-on exercises of skills and knowledge. However, due to the large volume of questions and the intent of hiding the tested knowledge behind them, many users find it hard to decide where to start or how to proceed based on their goals and performance. To overcome these limitations, we present PeerLens, an interactive visual analysis system that enables peer-inspired learning path planning. PeerLens can recommend a customized, adaptable sequence of practice questions to individual learners, based on the exercise history of other users in a similar learning scenario. We propose a new way to model the learning path by submission types and a novel visual design to facilitate the understanding and planning of the learning path. We conducted a within-subject experiment to assess the efficacy and usefulness of PeerLens in comparison with two baseline systems. Experiment results show that users are more confident in arranging their learning path via PeerLens and find it more informative and intuitive.
A power-efficient and high performance FPGA accelerator for convolutional neural networks: work-in-progress

Recently, FPGAs have been widely used in the implementation of hardware accelerators for Convolutional Neural Networks (CNN), especially on mobile and embedded devices. However, most of these existing accelerators are designed with the same concept as their ASIC counterparts, that is all operations from different CNN layers are mapped to the same hardware units and work in a multiplexed way. Although this approach improves the generality of these accelerators, it does not take full advantage of reconfigurability and customizability of FPGAs, resulting in a certain degree of computational efficiency degradation, which is even worse on the embedded platforms. In this paper, we propose an FPGA-based CNN accelerator with all the layers mapped to their own on-chip units, and working concurrently as a pipeline. A strategy which can find the optimized paralleling scheme for each layer is proposed to eliminate the pipeline stall and achieve high resource utilization. In addition, a balanced pruning-based method is applied on fully connected (FC) layers to reduce the computational redundancy. As a case study, we implement a widely used CNNs model, LeNet-5, on an embedded FPGA device, Xilinx Zedboard. It can achieve a peak performance of 39.78 GOP/s and the power efficiency with a value 19.6 GOP/s/W which outperforms previous approaches.
Clinical profiles of dementia with Lewy bodies with and without Alzheimer's disease‐like hypometabolism

It is well known that Alzheimer's disease (AD)‐type pathology is commonly present in dementia with Lewy bodies (DLB) brains and that the degree of AD‐type pathology has an influence on the clinical characteristics of DLB. Although significant hypometabolism in the temporoparietal/precuneus on [18F]fluoro‐d‐glucose (18F‐FDG) positron emission tomography (PET) scans is considered to support a diagnosis of AD, some DLB patients also exhibit this metabolic pattern. The clinical significance of the metabolic pattern on DLB remains unknown.