nils-herrmann/clean_scirepeval huggingface dataset:
Rule Based Expert System for Diagnosis of Neuromuscular Disorders

-------------------------------------------------------------------ABSTRACT-----------------------------------------------------------------In this paper, we discuss the implementation of a rule based expert system for diagnosing neuromuscular diseases. The proposed system is implemented as a rule based expert system in JESS for the diagnosis of Cerebral Palsy, Multiple Sclerosis, Muscular Dystrophy and Parkinson’s disease. In the system, the user is presented with a list of questionnaires about the symptoms of the patients based on which the disease of the patient is diagnosed and possible treatment is suggested. The system can aid and support the patients suffering from neuromuscular diseases to get an idea of their disease and possible treatment for the disease.
Factors associated with weaning practices in term infants: a prospective observational study in Ireland.

The WHO (2001) recommends exclusive breast-feeding and delaying the introduction of solid foods to an infant's diet until 6 months postpartum. However, in many countries, this recommendation is followed by few mothers, and earlier weaning onto solids is a commonly reported global practice. Therefore, this prospective, observational study aimed to assess compliance with the WHO recommendation and examine weaning practices, including the timing of weaning of infants, and to investigate the factors that predict weaning at ≤ 12 weeks. From an initial sample of 539 pregnant women recruited from the Coombe Women and Infants University Hospital, Dublin, 401 eligible mothers were followed up at 6 weeks and 6 months postpartum. Quantitative data were obtained on mothers' weaning practices using semi-structured questionnaires and a short dietary history of the infant's usual diet at 6 months. Only one mother (0.2%) complied with the WHO recommendation to exclusively breastfeed up to 6 months. Ninety-one (22.6%) infants were prematurely weaned onto solids at ≤ 12 weeks with predictive factors after adjustment, including mothers' antenatal reporting that infants should be weaned onto solids at ≤ 12 weeks, formula feeding at 12 weeks and mothers' reporting of the maternal grandmother as the principal source of advice on infant feeding. Mothers who weaned their infants at ≤ 12 weeks were more likely to engage in other sub-optimal weaning practices, including the addition of non-recommended condiments to their infants' foods. Provision of professional advice and exploring antenatal maternal misperceptions are potential areas for targeted interventions to improve compliance with the recommended weaning practices.
Toward a Wearable Sensor for Eating Detection

Researchers strive to understand eating behavior as a means to develop diets and interventions that can help people achieve and maintain a healthy weight, recover from eating disorders, or manage their diet and nutrition for personal wellness. A major challenge for eating-behavior research is to understand when, where, what, and how people eat. In this paper, we evaluate sensors and algorithms designed to detect eating activities, more specifically, when people eat. We compare two popular methods for eating recognition (based on acoustic and electromyography (EMG) sensors) individually and combined. We built a data-acquisition system using two off-the-shelf sensors and conducted a study with 20 participants. Our preliminary results show that the system we implemented can detect eating with an accuracy exceeding 90.9% while the crunchiness level of food varies. We are developing a wearable system that can capture, process, and classify sensor data to detect eating in real-time.
VHDL Implementation For a Fuzzy Logic Controller

This paper describes the implementation for a basic fuzzy logic controller in Very High speed integrated-circuit Hardware-Description Language (VHDL). It is not intended as an introduction to fuzzy logic control methodology; instead, we try to demonstrate the implementation of a fuzzy logic controller through the use of the VHDL code. Use of the hardware description language (HDL) in the application is suitable for being implemented into an Application Specific Integrated Circuit (ASIC) and Field Programmable Gate Array (FPGA). The main advantages of using the HDL approach are rapid prototyping, and allowing usage of powerful synthesis tools such as Xilinx ISE, Synosys, Mentor Graphic, or Cadence to be targeted easily and efficiently.
Barriers to the adoption and use of personal health record systems

Personal health records (PHR) have enormous potential to improve both documentation of health information and patient care. The adoption of these systems, however, has been relatively slow. In this work, we used a multi-method approach to evaluate PHR systems. We interviewed potential end users---clinicians and patients---and conducted evaluations with patients and caregivers as well as a heuristic evaluation with HCI experts. In these studies, we focused on three PHR systems: Google Health, Microsoft HealthVault, and WorldMedCard. Our results demonstrate that both usability concerns and socio-cultural influences are barriers to PHR adoption and use. In this paper, we present those results as well as reflect on how both PHR designers and developers might address these issues now and throughout the design cycle.
Intelligent Control of Unmanned Aerial Vehicles for Improved Autonomy

This paper focuses on recent advances in the control of unmanned aerial vehicles on a single platform basis. Additionally it addresses the case when UAV assets are operating in groups or clusters intended to accomplish surveillance and reconnaissance, rescue, forest fire detection or other critical missions. The scope of the paper is to suggest challenges in UAV control for extreme performance and reliability – both of which are necessary attributes of vehicle autonomy. Intelligent control technologies aimed at stability augmentation and performance enhancement are addressed. Topics for multiple UAVs are discussed dealing with mission planning, cooperative control and vehicle cooperation when UAV teams are performing missions in urban or other environments.
Fast Human Detection Combining Range Image Segmentation and Local Feature Based Detection

This paper proposes a human detection method that combines range image segmentation and human detection based on image local features. The method uses a stereo vision system called Subtraction Stereo, which extracts a range image of foreground regions. An extracted range image is segmented for each object by Mean Shift Clustering. Human detection based on local features is applied to each segment of foreground regions to detect humans. In this process, regions to scan a detection window for extracting local features are restricted. In addition, the size of the detection window is obtained using the distance information of a range image and camera parameters. Therefore, processing time and false detection can be reduced. Joint HOG features are used as the image local features. When applying the Joint HOG based human detection, occlusion of multiple humans is considered in construction of a classifier and in integration of detection windows, which improves the detection performance for the occluded humans. The proposed method is evaluated by experiments comparing with the method using Joint HOG features only. 11fps fast human detection is achieved.
Service resizing for quick DDoS mitigation in cloud computing environment

Current trends in distributed denial of service (DDoS) attacks show variations in terms of attack motivation, planning, infrastructure, and scale. “DDoS-for-Hire” and “DDoS mitigation as a Service” are the two services, which are available to attackers and victims, respectively. In this work, we provide a fundamental difference between a “regular” DDoS attack and an “extreme” DDoS attack. We conduct DDoS attacks on cloud services, where having the same attack features, two different services show completely different consequences, due to the difference in the resource utilization per request. We study various aspects of these attacks and find out that the DDoS mitigation service’s performance is dependent on two factors. Gaurav Somani gaurav@curaj.ac.in Manoj Singh Gaur gaurms@mnit.ac.in Dheeraj Sanghi dheeraj@cse.iitk.ac.in Mauro Conti conti@math.unipd.it Rajkumar Buyya rbuyya@unimelb.edu.au 1 Central University of Rajasthan, Rajasthan, India 2 Malaviya National Institute of Technology, Rajasthan, India 3 Indian Institute of Technology, Kanpur, India 4 University of Padua, Padua, Italy 5 The University of Melbourne, Parkville, Australia One factor is related to the severity of the “resource-race” with the victim web-service. Second factor is “attack cooling down period” which is the time taken to bring the service availability post detection of the attack. Utilizing these two important factors, we propose a supporting framework for the DDoS mitigation services, by assisting in reducing the attack mitigation time and the overall downtime. This novel framework comprises of an affinity-based victim-service resizing algorithm to provide performance isolation, and a TCP tuning technique to quickly free the attack connections, hence minimizing the attack cooling down period. We evaluate the proposed novel techniques with real attack instances and compare various attack metrics. Results show a significant improvement to the performance of DDoS mitigation service, providing quick attack mitigation. The presence of proposed DDoS mitigation support framework demonstrated a major reduction of more than 50% in the service downtime.
cvpaper.challenge in 2015 - A review of CVPR2015 and DeepSurvey

The “cvpaper.challenge” is a group composed of members from AIST,  Tsukuba that aims to systematically summarize papers on computer vision, pattern recognition, and related fields. For this particular review, we focused on reading the ALL 602 conference papers presented at the CVPR2015, the premier annual computer vision event held in June 2015, in order to grasp the trends in the field. Further, we are proposing “DeepSurvey” as a mechanism embodying the entire process from the reading through all the papers, the generation of ideas, and to the writing of paper.
Flexible electronics under strain : a review of mechanical characterization and durability enhancement strategies

Flexible electronics incorporate all the functional attributes of conventional rigid electronics in formats that have been altered to survive mechanical deformations. Understanding the evolution of device performance during bending, stretching, or other mechanical cycling is, therefore, fundamental to research efforts in this area. Here, we review the various classes of flexible electronic devices (including power sources, sensors, circuits and individual components) and describe the basic principles of device mechanics. We then review techniques to characterize the deformation tolerance and durability of these flexible devices, and we catalogue and geometric designs that are intended to optimize electronic systems for maximum flexibility.
CloudExp: A comprehensive cloud computing experimental framework

Cloud computing is an emerging and fast-growing computing paradigm that has gained great interest from both industry and academia. Consequently, many researchers are actively involved in cloud computing research projects. One major challenge facing cloud computing researchers is the lack of a comprehensive cloud computing experimental tool to use in their studies. This paper introduces CloudExp, a modeling and simulation environment for cloud computing. CloudExp can be used to evaluate a wide spectrum of cloud components such as processing elements, data centers, storage, networking, Service Level Agreement (SLA) constraints, web-based applications, Service Oriented Architecture (SOA), virtualization, management and automation, and Business Process Management (BPM). Moreover, CloudExp introduces the Rain workload generator which emulates real workloads in cloud environments. Also, MapReduce processing model is integrated in CloudExp in order to handle the processing of big data problems.
To Err Is Human Building a Safer Health System

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Better Static Memory Management: Improving Region-Based Analysis of Higher-Order Languages

Static memory management replaces runtime garbage collection with compile-time annotations that make all memory allocation and deallocation explicit in a program. We improve upon the Tofte/Talpin region-based scheme for compile-time memory management[TT94]. In the Tofte/Talpin approach, all values, including closures, are stored in regions. Region lifetimes coincide with lexical scope, thus forming a runtime stack of regions and eliminating the need for garbage collection. We relax the requirement that region lifetimes be lexical. Rather, regions are allocated late and deallocated as early as possible by explicit memory operations. The placement of allocation and deallocation annotations is determined by solving a system of constraints that expresses all possible annotations. Experiments show that our approach reduces memory requirements significantly, in some cases asymptotically.
Failure mechanisms and closed reduction of a constrained tripolar acetabular liner.

Unlike traditional bipolar constrained liners, the Osteonics Omnifit constrained acetabular insert is a tripolar device, consisting of an inner bipolar bearing articulating within an outer, true liner. Every reported failure of the Omnifit tripolar implant has been by failure at the shell-bone interface (Type I failure), failure at the shell-liner interface (Type II failure), or failure of the locking mechanism resulting in dislocation of the bipolar-liner interface (Type III failure). In this report we present two cases of failure of the Omnifit tripolar at the bipolar-femoral head interface. To our knowledge, these are the first reported cases of failure at the bipolar-femoral head interface (Type IV failure). In addition, we described the first successful closed reduction of a Type IV failure.
Distance protection of transmission line with infeed based on real-time simulator

In this paper real-time simulation, analysis, and validation of the conventional distance relay based on MATLAB/simulink and Real-Time LABoratory (RT-LAB) is presented. In addition to the detailed model of six impedance measuring units of distance relay, power system model is implemented in Opal-RT's RT-LAB simulator. Some cases are highlighted to illustrate the modelling performance. Moreover, the effect of infeed current, which is an emerging issue for power system protection, using conventional distance relay in real-time environment is evaluated.
Finding consensus among words: lattice-based word error minimization

We describe a new algorithm for finding the hypothesis in a recognition lattice that is expected to minimize the word er ror rate (WER). Our approach thus overcomes the mismatch between the word-based performance metric and the standard MAP scoring paradigm that is sentence-based, and that can le ad to sub-optimal recognition results. To this end we first find a complete alignment of all words in the recognition lattice, identifying mutually supporting and competing word hypotheses . Finally, a new sentence hypothesis is formed by concatenating the words with maximal posterior probabilities. Experimental ly, this approach leads to a significant WER reduction in a large vocab ulary recognition task.
The limitations of optimization from samples

In this paper we consider the following question: can we optimize objective functions from the training data we use to learn them? We formalize this question through a novel framework we call optimization from samples (OPS). In OPS, we are given sampled values of a function drawn from some distribution and the objective is to optimize the function under some constraint. 
 While there are interesting classes of functions that can be optimized from samples, our main result is an impossibility. We show that there are classes of functions which are statistically learnable and optimizable, but for which no reasonable approximation for optimization from samples is achievable. In particular, our main result shows that there is no constant factor approximation for maximizing coverage functions under a cardinality constraint using polynomially-many samples drawn from any distribution. 
 We also show tight approximation guarantees for maximization under a cardinality constraint of several interesting classes of functions including unit-demand, additive, and general monotone submodular functions, as well as a constant factor approximation for monotone submodular functions with bounded curvature.
Generative Model for Heterogeneous Inference

Generative models (GMs) such as Generative Adversary Network (GAN) and Variational Auto-Encoder (VAE) have thrived these years and achieved high quality results in generating new samples. Especially in Computer Vision, GMs have been used in image inpainting, denoising and completion, which can be treated as the inference from observed pixels to corrupted pixels. However, images are hierarchically structured which are quite different from many real-world inference scenarios with non-hierarchical features. These inference scenarios contain heterogeneous stochastic variables and irregular mutual dependences. Traditionally they are modeled by Bayesian Network (BN). However, the learning and inference of BN model are NP-hard thus the number of stochastic variables in BN is highly constrained. In this paper, we adapt typical GMs to enable heterogeneous learning and inference in polynomial time. We also propose an extended autoregressive (EAR) model and an EAR with adversary loss (EARA) model and give theoretical results on their effectiveness. Experiments on several BN datasets show that our proposed EAR model achieves the best performance in most cases compared to other GMs. Except for black box analysis, we’ve also done a serial of experiments on Markov border inference of GMs for white box analysis and give theoretical results.
Defining patterns of genital injury from sexual assault: a review.

The forensic examination following rape has two primary purposes: to provide health care and to collect evidence. Physical injuries need treatment so that they heal without adverse consequences. The pattern of injuries also has a forensic significance in that injuries are linked to the outcome of legal proceedings. This literature review investigates the variables related to genital injury prevalence and location that are reported in a series of retrospective reviews of medical records. The author builds the case that the prevalence and location of genital injury provide only a partial description of the nature of genital trauma associated with sexual assault and suggests a multidimensional definition of genital injury pattern. Several of the cited studies indicate that new avenues of investigation, such as refined measurement strategies for injury severity and skin color, may lead to advancements in health care, forensic, and criminal justice science.
A Sequential Model for Classifying Temporal Relations between Intra-Sentence Events

We present a sequential model for temporal relation classification between intrasentence events. The key observation is that the overall syntactic structure and compositional meanings of the multi-word context between events are important for distinguishing among fine-grained temporal relations. Specifically, our approach first extracts a sequence of context words that indicates the temporal relation between two events, which well align with the dependency path between two event mentions. The context word sequence, together with a parts-of-speech tag sequence and a dependency relation sequence that are generated corresponding to the word sequence, are then provided as input to bidirectional recurrent neural network (LSTM) models. The neural nets learn compositional syntactic and semantic representations of contexts surrounding the two events and predict the temporal relation between them. Evaluation of the proposed approach on TimeBank corpus shows that sequential modeling is capable of accurately recognizing temporal relations between events, which outperforms a neural net model using various discrete features as input that imitates previous feature based models.
Learning Dynamic Embeddings from Temporal Interactions

Modeling a sequence of interactions between users and items (e.g., products, posts, or courses) is crucial in domains such as e-commerce, social networking, and education to predict future interactions. Representation learning presents an attractive solution to model the dynamic evolution of user and item properties, where each user/item can be embedded in a euclidean space and its evolution can be modeled by dynamic changes in its embedding. However, existing embedding methods either generate static embeddings, treat users and items independently, or are not scalable. Here we propose JODIE, a coupled recurrent model to jointly learn the dynamic embeddings of users and items from a sequence of user-item interactions. JODIE has three components. First, the update component updates the user and item embedding from each interaction using their previous embeddings with the two mutuallyrecursive Recurrent Neural Networks. Second, a novel projection component is trained to forecast the embedding of users at any future time. Finally, the prediction component directly predicts the embedding of the item in a future interaction. For models that learn from a sequence of interactions, traditional batching of training data can not be done due to complex user-user dependencies. Therefore, we present a novel batching algorithm called t-Batch that generates time-consistent batches that can be run in parallel, leading to massive speed-up. We conduct six experiments to validate JODIE on two prediction tasks—future interaction prediction and state change prediction— using four real-world datasets. We show that JODIE outperforms six state-of-the-art algorithms in these tasks by up to 22.4%. Moreover, we show that JODIE is highly scalable and up to 9.2× faster than comparable models. As an additional experiment, we illustrate that JODIE can predict student drop-out from courses up to five interactions in advance. ACM Reference Format: Srijan Kumar, Xikun Zhang, and Jure Leskovec.
A Component Architecture for High-Performance Scientific Computing

The Common Component Architecture (CCA) provides a means for software developers to manage the complexity of large-scale scientific simulations and to move toward a plug-and-play environment for high-performance computing. In the scientific computing context, component models also promote collaboration using independently developed software, thereby allowing particular individuals or groups to focus on the aspects of greatest interest to them. The CCA supports parallel and distributed computing as well as local high-performance connections between components in a language-independent manner. The design places minimal requirements on components and thus facilitates the integration of existing code into the CCA environment. The CCA model imposes minimal overhead to minimize the impact on application performance. The focus on high performance distinguishes the CCA from most other component models. The CCA is being applied within an increasing range of disciplines, including combustion research, global climate simulation, and computational chemistry. HPC Component Architecture 4