nils-herrmann/clean_scirepeval huggingface dataset:
Allocation and scheduling of conditional task graph in hardware/software co-synthesis

This paper introduces an allocation and scheduling algorithm that efficiently handles conditional execution in multi-rate embedded system. Control dependencies are introduced into the task graph model. We propose a mutual exclusion detection algorithm that helps the scheduling algorithm to exploit the resource sharing. Allocation and scheduling are performed simultaneously to take advantage of the resource sharing among those mutual exclusive tasks. The algorithm is fast and efficient,and so is suitable to be used in the inner loop of our hardware/software co-synthesis framework which must call the scheduling routine many times.
Integrated Medical Workflow for Augmented Reality Applications

Augmented Reality (AR) and Virtual Reality (VR) have the potential to improve surgical interventions and image-guided therapy by providing clinicians with interactive three-dimensional visualizations in all phases of treatment. However, experimental AR research applications typically focus on a single medical procedure and do not attempt to cover the whole clinical workflow from preoperative to intra-operative and post-operative support. In the scope of the EU-funded Marie Curie research project ARIS*ER (Augmented Reality in Surgery), we are investigating a more complete workflow support with AR technology. This paper discusses a component-oriented software architecture which specifically aims at addressing the needs of medical AR, in particular strategies for ensuring uniform application quality throughout the medical workflow. We discuss the management of off-line and on-line image acquisition methods and methods for consistent management of modeling, processing and visualization of medical datasets throughout the medical workflow. Finally, we present three different medical application examples which illustrate the use of the framework. CR Categories: D.2.6 [Programming Environments]: Graphical environments—Integrated environmentsInteractive environmentsProgrammer workbench H.5.1 [Multimedia Information Systems]: Artificial, augmented, and virtual realities— [I.3.7]: ThreeDimensional Graphics and Realism—Virtual reality
Extracting sclera features for cancelable identity verification

In this paper, we propose a novel sclera template generation, manipulation, and matching scheme for cancelable identity verification. Essentially, a region indicator matrix is generated based on an angular grid reference frame. For binary feature template generation, a random matrix and a local binary patterns (LBP) operator are utilized. Subsequently, the template is manipulated by user-specific random sequence attachment and bit shifting. Finally, matching is performed by a normalized Hamming distance comparison. Some experimental results on UBIRIS v1 database are included with discussion.
Interaction of tomato lycopene and ketosamine against rat prostate tumorigenesis.

Prior investigations on the beneficial effect of dietary processed tomato products and lycopene on prostate cancer risk suggested that lycopene may require the presence of other constituents to exert its chemopreventive potential. We investigated whether ketosamines, a group of carbohydrate derivatives present in dehydrated tomato products, may interact with lycopene against prostate tumorigenesis. One ketosamine, FruHis, strongly synergized with lycopene against proliferation of the highly metastatic rat prostate adenocarcinoma MAT-LyLu cell line in vitro. The FruHis/lycopene combination significantly inhibited in vivo tumor formation by MAT-LyLu cells in syngeneic Copenhagen rats. Energy-balanced diets, supplemented with tomato paste, tomato powder, or tomato paste plus FruHis, were fed to Wistar-Unilever rats (n = 20 per group) treated with N-nitroso-N-methylurea and testosterone to induce prostate carcinogenesis. Survival from carcinogenesis was lowest in the control group (median survival time, 40 weeks) and highest in the group fed the tomato paste/FruHis diet (51 weeks; P = 0.004, versus control). The proportions of dying rats with macroscopic prostate tumors in the control, tomato paste, tomato powder, and tomato paste/FruHis groups were 63% (12 of 19), 39% (5 of 13), 43% (6 of 14), and 18% (2 of 11), respectively. FruHis completely blocked DNA oxidative degradation at >250 micromol/L in vitro, whereas neither ascorbate nor phenolic antioxidants from tomato were effective protectors in this assay. FruHis, therefore, may exert tumor-preventive effect through its antioxidant activity and interaction with lycopene.
GruMon: fast and accurate group monitoring for heterogeneous urban spaces

Real-time monitoring of groups and their rich contexts will be a key building block for futuristic, group-aware mobile services. In this paper, we propose GruMon, a fast and accurate group monitoring system for dense and complex urban spaces. GruMon meets the performance criteria of precise group detection at low latencies by overcoming two critical challenges of practical urban spaces, namely (a) the high density of crowds, and (b) the imprecise location information available indoors. Using a host of novel features extracted from commodity smartphone sensors, GruMon can detect over 80% of the groups, with 97% precision, using 10 minutes latency windows, even in venues with limited or no location information. Moreover, in venues where location information is available, GruMon improves the detection latency by up to 20% using semantic information and additional sensors to complement traditional spatio-temporal clustering approaches. We evaluated GruMon on data collected from 258 shopping episodes from 154 real participants, in two large shopping complexes in Korea and Singapore. We also tested GruMon on a large-scale dataset from an international airport (containing ≈37K+ unlabelled location traces per day) and a live deployment at our university, and showed both GruMon's potential performance at scale and various scalability challenges for real-world dense environment deployments.
Polynomial-time probabilistic reasoning with partial observations via implicit learning in probability logics

Standard approaches to probabilistic reasoning require that one possesses an explicit model of the distribution in question. But, the empirical learning of models of probability distributions from partial observations is a problem for which efficient algorithms are generally not known. In this work we consider the use of bounded-degree fragments of the “sum-of-squares” logic as a probability logic. Prior work has shown that we can decide refutability for such fragments in polynomial-time. We propose to use such fragments to decide queries about whether a given probability distribution satisfies a given system of constraints and bounds on expected values. We show that in answering such queries, such constraints and bounds can be implicitly learned from partial observations in polynomial-time as well. It is known that this logic is capable of deriving many bounds that are useful in probabilistic analysis. We show here that it furthermore captures key polynomial-time fragments of resolution. Thus, these fragments are also quite expressive.
Genetic diversity of Plasmodium falciparum isolates from uncomplicated malaria cases in Ghana over a decade

Genotyping malaria parasites to assess their diversity in different geographic settings have become necessary for the selection of antigenic epitopes for vaccine development and for antimalarial drug efficacy or resistance investigations. This study describes the genetic diversity of Plasmodium falciparum isolates from uncomplicated malaria cases over a ten year period (2003–2013) in Ghana using the polymorphic antigenic marker, merozoite surface protein 2 (msp2). Archived filter paper blood blots from children aged nine years and below with uncomplicated malaria collected from nine sites in Ghana were typed for the presence of the markers. A total of 880 samples were genotyped for msp2 for the two major allelic families, FC27 and 3D7, using nested polymerase chain reaction (PCR). The allele frequencies and the multiplicity of infection were determined for the nine sites for five time points over a period of ten years, 2003–2004, 2005–2006, 2007–2008, 2010 and 2012–2013 malaria transmission seasons. The number of different alleles detected for the msp2 gene by resolving PCR products on agarose gels was 14. Both of the major allelic families, 3D7 and FC27 were common in all population samples. The highest multiplicity of infection (MOI) was observed in isolates from Begoro (forest zone, rural site): 3.31 for the time point 2007–2008. A significant variation was observed among the sites in the MOIs detected per infection (Fisher's exact test, P < 0.001) for the 2007 isolates and also at each of the three sites with data for three different years, Hohoe, P = 0.03; Navrongo, P < 0.001; Cape Coast, P < 0.001. Overall, there was no significant difference between the MOIs of the three ecological zones over the years (P = 0.37) and between the time points when data from all sites were pooled (P = 0.40). The diversity and variation between isolates detected using the msp2 gene in Ghanaian isolates were observed to be profound; however, there was homogeneity throughout the three ecological zones studied. This is indicative of gene flow between the parasite populations across the country probably due to human population movements (HPM).
RED: Reinforced Encoder-Decoder Networks for Action Anticipation

Action anticipation aims to detect an action before it happens. Many real world applications in robotics and surveillance are related to this predictive capability. Current methods address this problem by first anticipating visual representations of future frames and then categorizing the anticipated representations to actions. However, anticipation is based on a single past frame’s representation, which ignores the history trend. Besides, it can only anticipate a fixed future time. We propose a Reinforced Encoder-Decoder (RED) network for action anticipation. RED takes multiple history representations as input and learns to anticipate a sequence of future representations. One salient aspect of RED is that a reinforcement module is adopted to provide sequence-level supervision; the reward function is designed to encourage the system to make correct predictions as early as possible. We test RED on TVSeries, THUMOS-14 and TV-Human-Interaction datasets for action anticipation and achieve state-of-the-art performance on all datasets.
Fast Event-based Corner Detection

Event cameras offer many advantages over standard frame-based cameras, such as low latency, high temporal resolution, and a high dynamic range. They respond to pixellevel brightness changes and, therefore, provide a sparse output. However, in textured scenes with rapid motion, millions of events are generated per second. Therefore, stateof-the-art event-based algorithms either require massive parallel computation (e.g., a GPU) or depart from the event-based processing paradigm. Inspired by frame-based pre-processing techniques that reduce an image to a set of features, which are typically the input to higher-level algorithms, we propose a method to reduce an event stream to a corner event stream. Our goal is twofold: extract relevant tracking information (corners do not suffer from the aperture problem) and decrease the event rate for later processing stages. Our event-based corner detector is very efficient due to its design principle, which consists of working on the Surface of Active Events (a map with the timestamp of the latest event at each pixel) using only comparison operations. Our method asynchronously processes event by event with very low latency. Our implementation is capable of processing millions of events per second on a single core (less than a micro-second per event) and reduces the event rate by a factor of 10 to 20.
A highly efficient system for automatic face region detection in MPEG video

-Human faces provide a useful cue in indexing video content . In this paper, we present a highly efficient system that can rapidly detect human face regions in MPEG video sequences. The underlying algorithm takes the inverse quantized DCT coefficients of MPEG video as the input, and outputs the locations of the detected face regions. The algorithm consists of three stages, where chrominance, shape, and frequency information are used respectively. By detecting faces directly in the compressed domain, there is no need to carry out the inverse DCT transform, so that the algorithm can run faster than the real time. In our experiments, the algorithm detected 85-92% of the faces in three test sets, including both intra-frame and inter-frame coded image frames from news video. The average run time ranges from 13 to 33 milliseconds per frame. The algorithm can be applied to JPEG unconstrained images or motion JPEG video as well.
Formulation of deep reinforcement learning architecture toward autonomous driving for on-ramp merge

Multiple automakers have in development or in production automated driving systems (ADS) that offer freeway-pilot functions. This type of ADS is typically limited to restricted-access freeways only, that is, the transition from manual to automated modes takes place only after the ramp merging process is completed manually. One major challenge to extend the automation to ramp merging is that the automated vehicle needs to incorporate and optimize long-term objectives (e.g. successful and smooth merge) when near-term actions must be safely executed. Moreover, the merging process involves interactions with other vehicles whose behaviors are sometimes hard to predict but may influence the merging vehicle's optimal actions. To tackle such a complicated control problem, we propose to apply Deep Reinforcement Learning (DRL) techniques for finding an optimal driving policy by maximizing the long-term reward in an interactive environment. Specifically, we apply a Long Short-Term Memory (LSTM) architecture to model the interactive environment, from which an internal state containing historical driving information is conveyed to a Deep Q-Network (DQN). The DQN is used to approximate the Q-function, which takes the internal state as input and generates Q-values as output for action selection. With this DRL architecture, the historical impact of interactive environment on the long-term reward can be captured and taken into account for deciding the optimal control policy. The proposed architecture has the potential to be extended and applied to other autonomous driving scenarios such as driving through a complex intersection or changing lanes under varying traffic flow conditions.
Prevalence of Depression among Undergraduate Students : Gender and Age Differences

Depressive disorders are the most typical disease affecting many different factors of humanity. University students may be at increased risk of depression owing to the pressure and stress they encounter. Therefore, the purpose of this study is comparing the level of depression among male and female athletes and non-athletes undergraduate student of private university in Esfahan, Iran. The participants in this research are composed of 400 male and female athletes as well as no-athletes Iranian undergraduate students. The Beck depression test (BDI) was employed to measure the degree of depression. T-test was used to evaluate the distinction between athletes and non-athletes at P≤0.05. The ANOVA was conducted to examine whether there was a relationship between level of depression among non-athletes and athletes. The result showed that the prevalence rate of depression among non-athlete male undergraduate students is significantly higher than that of athlete male students. The results also presented that level of depression among female students is much more frequent compared to males. This can be due to the fatigue and lack of energy that are more frequent among female in comparison to the male students. Physical activity was negatively related to the level of depression by severity among male and female undergraduate students. However, there is no distinct relationship between physical activity and level of depression according to the age of athlete and nonathlete male and female undergraduate students. This study has essential implications for clinical psychology due to the relationship between physical activity and prevalence of depression.
A high frequency power factor correction converter with soft switching

Power factor correction (PFC) converters are typically operated at low frequency to mitigate switching losses and to simplify control; this in turn requires large passive components. Soft switching techniques could permit higher frequency operation, but most soft-switched converters do not maintain high performance across the wide voltage and power ranges required for PFC applications. Here we present a PFC converter which enables high frequency operation by maintaining soft switching and by using a control scheme which requires no current sensing. These advantages are verified with a prototype which achieves power factors above 0.996 (THD < 10%) while maintaining ZVS across voltage and power for efficiencies ∼ 97 %. By using increased switching frequency (∼ 10x over conventional designs), this converter can take advantage of greatly reduced passive component values for power conversion and EMI filtering.
Traffic sign recognition with hierarchical Convolutional Neural Network

Convolutional Neural Network(CNN) becomes one of the most preferred deep learning method because of achieving superior success at solution of important problems of machine learning like pattern recognition, object recognition and classification. With CNN, high performance has been obtained in traffic sign recognition which is important for autonomous vehicles. In this work, two-stage hierarchical CNN structure is proposed. Signs are seperated into 9 main groups at the first stage by using structure similarity index. And then classes of each main group are subclassed with CNNs at the second stage. Performance of the network is measured on 43-classes GTSRB dataset and compared with other methods.
Artificial Intelligence Powered Banking Chatbot

Chat robot, a computer program that simulates human conversation, or chat, through artificial intelligence an intelligence chat bot will be used to give information or answers to any question asked by user related to bank. It is more like a virtual assistant, people feel like they are talking with real person. They speak the same language we do, can answer questions. In banks, at user care centres and enquiry desks, human is insufficient and usually takes long time to process the single request which results in wastage of time and also reduce quality of user service. The primary goal of this chat bot is user can interact with mentioning their queries in plain English and the chat bot can resolve their queries with appropriate response in return The proposed system would help duplicate the user utility experience with one difference that employee and yet get the queries attended and resolved. It can extend daily life, by providing solutions to help desks, telephone answering systems, user care centers. This paper defines the dataset that we have prepared from FAQs of bank websites, architecture and methodology used for developing such chatbot. Also this paper discusses the comparison of seven ML classification algorithm used for getting the class of input to chat bot.
Discrete double integrator consensus

A distributed double integrator discrete time consensus protocol is presented along with stability analysis. The protocol will achieve consensus when the communication topology contains at least a directed spanning tree. Average consensus is achieved when the communication topology is strongly connected and balanced, where average consensus for double integrator systems is discussed. For second order systems average consensus occurs when the information states tend toward the average of the current information states not their initial values. Lastly, perturbation to the consensus protocol is addressed. Using a designed perturbation input, an algorithm is presented that accurately tracks the center of a vehicle formation in a decentralized manner.
A novel paradigm for calculating Ramsey number via Artificial Bee Colony Algorithm

The Ramsey number is of vital importance in Ramsey's theorem. This paper proposed a novel methodology for constructing Ramsey graphs about R(3, 10), which uses Artificial Bee Colony optimization(ABC) to raise the lower bound of Ramsey number R(3, 10). The r(3, 10)-graph contains two limitations, that is, neither complete graphs of order 3 nor independent sets of order 10. To resolve these limitations, a special mathematical model is put in the paradigm to convert the problems into discrete optimization whose smaller minimizers are correspondent to bigger lower bound as approximation of inf R(3, 10). To demonstrate the potential of the proposed method, simulations are done to to minimize the amount of these two types of graphs. For the first time, four r(3, 9, 39) graphs with best approximation for inf R(3, 10) are reported in simulations to support the current lower bound for R(3, 10). The experiments' results show that the proposed paradigm for Ramsey number's calculation driven by ABC is a successful method with the advantages of high precision and robustness.