nils-herrmann/clean_scirepeval huggingface dataset:
Immune Checkpoint Blockade in Cancer Therapy.

Immunologic checkpoint blockade with antibodies that target cytotoxic T lymphocyte-associated antigen 4 (CTLA-4) and the programmed cell death protein 1 pathway (PD-1/PD-L1) have demonstrated promise in a variety of malignancies. Ipilimumab (CTLA-4) and pembrolizumab (PD-1) are approved by the US Food and Drug Administration for the treatment of advanced melanoma, and additional regulatory approvals are expected across the oncologic spectrum for a variety of other agents that target these pathways. Treatment with both CTLA-4 and PD-1/PD-L1 blockade is associated with a unique pattern of adverse events called immune-related adverse events, and occasionally, unusual kinetics of tumor response are seen. Combination approaches involving CTLA-4 and PD-1/PD-L1 blockade are being investigated to determine whether they enhance the efficacy of either approach alone. Principles learned during the development of CTLA-4 and PD-1/PD-L1 approaches will likely be used as new immunologic checkpoint blocking antibodies begin clinical investigation.
Automatic Features for Essay Scoring - An Empirical Study

Essay scoring is a complicated processing requiring analyzing, summarizing and judging expertise. Traditional work on essay scoring focused on automatic handcrafted features, which are expensive yet sparse. Neural models offer a way to learn syntactic and semantic features automatically, which can potentially improve upon discrete features. In this paper, we employ convolutional neural network (CNN) for the effect of automatically learning features, and compare the result with the state-of-art discrete baselines. For in-domain and domain-adaptation essay scoring tasks, our neural model empirically outperforms discrete models.
Vectorization of line drawing image based on junction analysis

Converting a scanned or shot line drawing image into a vector graph can facilitate further editand reuse, making it a hot research topic in computer animation and image processing. Besides avoiding noiseinfluence, its main challenge is to preserve the topological structures of the original line drawings, such as linejunctions, in the procedure of obtaining a smooth vector graph from a rough line drawing. In this paper, wepropose a vectorization method of line drawings based on junction analysis, which retains the original structureunlike done by existing methods. We first combine central line tracking and contour tracking, which allowsus to detect the encounter of line junctions when tracing a single path. Then, a junction analysis approachbased on intensity polar mapping is proposed to compute the number and orientations of junction branches.Finally, we make use of bending degrees of contour paths to compute the smoothness between adjacent branches,which allows us to obtain the topological structures corresponding to the respective ones in the input image.We also introduce a correction mechanism for line tracking based on a quadratic surface fitting, which avoidsaccumulating errors of traditional line tracking and improves the robustness for vectorizing rough line drawings.We demonstrate the validity of our method through comparisons with existing methods, and a large amount ofexperiments on both professional and amateurish line drawing images. 本文提出一种基于交叉点分析的线条矢量化方法, 克服了现有方法难以保持拓扑结构的不足。通过中心路径跟踪和轮廓路径跟踪相结合的方式, 准确检测交叉点的出现提出一种基于极坐标亮度映射的交叉点分析方法, 计算交叉点的分支数量和朝向; 利用轮廓路径的弯曲角度判断交叉点相邻分支间的光顺度, 从而获得与原图一致的拓扑结构。
A survey on engineering approaches for self-adaptive systems

The complexity of information systems is increasing in recent years, leading to increased effort for maintenance and configuration. Self-adaptive systems (SASs) address this issue. Due to new computing trends, such as pervasive computing, miniaturization of IT leads to mobile devices with the emerging need for context adaptation. Therefore, it is beneficial that devices are able to adapt context. Hence, we propose to extend the definition of SASs and include context adaptation. This paper presents a taxonomy of self-adaptation and a survey on engineering SASs. Based on the taxonomy and the survey, we motivate a new perspective on SAS including context adaptation.
Remark on algorithm 659: Implementing Sobol's quasirandom sequence generator

An algorithm to generate Sobol' sequences to approximate integrals in up to 40 dimensions has been previously given by Bratley and Fox in Algorithm 659. Here, we provide more primitive polynomials and "direction numbers" so as to allow the generation of Sobol' sequences to approximate integrals in up to 1111 dimensions. The direction numbers given generate Sobol' sequences that satisfy Sobol's so-called Property A.
Reading and controlling human brain activation using real-time functional magnetic resonance imaging

Understanding how to control how the brain's functioning mediates mental experience and the brain's processing to alter cognition or disease are central projects of cognitive and neural science. The advent of real-time functional magnetic resonance imaging (rtfMRI) now makes it possible to observe the biology of one's own brain while thinking, feeling and acting. Recent evidence suggests that people can learn to control brain activation in localized regions, with corresponding changes in their mental operations, by observing information from their brain while inside an MRI scanner. For example, subjects can learn to deliberately control activation in brain regions involved in pain processing with corresponding changes in experienced pain. This may provide a novel, non-invasive means of observing and controlling brain function, potentially altering cognitive processes or disease.
Magic Mirror: A Virtual Fashion Consultant

What should I wear? We present Magic Mirror, a virtual fashion consultant, which can parse, appreciate and recommend the wearing. Magic Mirror is designed with a large display and Kinect to simulate the real mirror and interact with users in augmented reality. Internally, Magic Mirror is a practical appreciation system for automatic aesthetics-oriented clothing analysis. Specifically, we focus on the clothing collocation rather than the single one, the style (aesthetic words) rather than the visual features. We bridge the gap between the visual features and aesthetic words of clothing collocation to enable the computer to learn appreciating the clothing collocation. Finally, both object and subject evaluations verify the effectiveness of the proposed algorithm and Magic Mirror system.
Obfuscation using Encryption

Protecting source code against reverse engineering and theft is an important problem. The goal is to carry out computations using confidential algorithms on an untrusted party while ensuring confidentiality of algorithms. This problem has been addressed for Boolean circuits known as ‘circuit privacy’. Circuits corresponding to real-world programs are impractical. Well-known obfuscation techniques are highly practicable, but provide only limited security, e.g., no piracy protection. In this work, we modify source code yielding programs with adjustable performance and security guarantees ranging from indistinguishability obfuscators to (non-secure) ordinary obfuscation. The idea is to artificially generate ‘misleading’ statements. Their results are combined with the outcome of a confidential statement using encrypted selector variables. Thus, an attacker must ‘guess’ the encrypted selector variables to disguise the confidential source code. We evaluated our method using more than ten programmers as well as pattern mining across open source code repositories to gain insights of (micro-)coding patterns that are relevant for generating misleading statements. The evaluation reveals that our approach is effective in that it successfully preserves source code confidentiality.
Big Data Stream Learning with SAMOA

Big data is flowing into every area of our life, professional and personal. Big data is defined as datasets whose size is beyond the ability of typical software tools to capture, store, manage and analyze, due to the time and memory complexity. Velocity is one of the main properties of big data. In this demo, we present SAMOA (Scalable Advanced Massive Online Analysis), an open-source platform for mining big data streams. It provides a collection of distributed streaming algorithms for the most common data mining and machine learning tasks such as classification, clustering, and regression, as well as programming abstractions to develop new algorithms. It features a pluggable architecture that allows it to run on several distributed stream processing engines such as Storm, S4, and Samza. SAMOA is written in Java and is available at http://samoa-project.net under the Apache Software License version 2.0.
An overview of research on "passive" brain-computer interfaces for implicit human-computer interaction

This paper surveys existing and past research on brain-computer interfaces (BCI) for implicit human-computer interaction. A novel way of using BCI has indeed emerged, which proposes to use BCI in a less explicit way : the so-called “passive” BCI. Implicit BCI or passive BCI refers to BCI in which the user does not try to control his brain activity. Thus the brain activity is assimilated to an input and can be used to adapt the application to the user’s mental state. In this paper, we first study “implicit interaction” in general and recall its main applications. Then, we make a survey of existing and past research on brain-computer interfaces for implicit human-computer interaction. It seems indeed that BCI can be used in many applications in an implicit way, such as for adaptive automation, affective computing, or for video games. In such applications, BCI based on implicit interaction was often reported to improve performance of either the system or the user, or to introduce novel capacities based on mental states.
Relative localization and communication module for small-scale multi-robot systems

We characterize and improve an existing infrared relative localization/communication module used to find range and bearing between robots in small-scale multi-robot systems. Modifications to the algorithms of the original system are suggested which offer better performance. A mathematical model which accurately describes the system is presented and allows us to predict the performance of modules with augmented sensorial capabilities. Finally, the usefulness of the module is demonstrated in a multi-robot self-localization task using both a realistic robotic simulator and real robots, and the performance is analyzed
Distractor Generation with Generative Adversarial Nets for Automatically Creating Fill-in-the-blank Questions

Distractor generation is a crucial step for fill-in-the-blank question generation. We propose a generative model learned from training generative adversarial nets (GANs) to create useful distractors. Our method utilizes only context information and does not use the correct answer, which is completely different from previous Ontology-based or similarity-based approaches. Trained on the Wikipedia corpus, the proposed model is able to predict Wiki entities as distractors. Our method is evaluated on two biology question datasets collected from Wikipedia and actual college-level exams. Experimental results show that our context-based method achieves comparable performance to a frequently used word2vec-based method for the Wiki dataset. In addition, we propose a second-stage learner to combine the strengths of the two methods, which further improves the performance on both datasets, with 51.7% and 48.4% of generated distractors being acceptable.
A time-varying subjective quality model for mobile streaming videos with stalling events

Over-the-top mobile video streaming is invariably influenced by volatile network conditions which cause playback interruptions (stalling events), thereby impairing users’ quality of experience (QoE). Developing models that can accurately predict users’ QoE could enable the more efficient design of quality-control protocols for video streaming networks that reduce network operational costs while still delivering high-quality video content to the customers. Existing objective models that predict QoE are based on global video features, such as the number of stall events and their lengths, and are trained and validated on a small pool of ad hoc video datasets, most of which are not publicly available. The model we propose in this work goes beyond previous models as it also accounts for the fundamental effect that a viewer’s recent level of satisfaction or dissatisfaction has on their overall viewing experience. In other words, the proposed model accounts for and adapts to the recency, or hysteresis effect caused by a stall event in addition to accounting for the lengths, frequency of occurrence, and the positions of stall events factors that interact in a complex way to affect a user’s QoE. On the recently introduced LIVE-Avvasi Mobile Video Database, which consists of 180 distorted videos of varied content that are afflicted solely with over 25 unique realistic stalling events, we trained and validated our model to accurately predict the QoE, attaining standout QoE prediction performance.
Factors influencing clients in the selection of offshore software outsourcing vendors: An exploratory study using a systematic literature review

Offshore software development outsourcing is a modern business strategy for developing high quality software at low cost.  The objective of this research paper is to identify and analyse factors that are important in terms of the competitiveness of vendor organisations in attracting outsourcing projects.  We performed a systematic literature review (SLR) by applying our customised search strings which were derived from our research questions. We performed all the SLR steps, such as the protocol development, initial selection, final selection, quality assessment, data extraction and data synthesis.  We have identified factors such as cost-saving, skilled human resource, appropriate infrastructure, quality of product and services, efficient outsourcing relationships management, and an organisation’s track record of successful projects which are generally considered important by the outsourcing clients. Our results indicate that appropriate infrastructure, cost-saving, and skilled human resource are common in three continents, namely Asia, North America and Europe. We identified appropriate infrastructure, cost-saving, and quality of products and services as being common in three types of organisations (small, medium and large). We have also identified four factors-appropriate infrastructure, cost-saving, quality of products and services, and skilled human resource as being common in the two decades (1990–1999 and 2000–mid 2008).  Cost-saving should not be considered as the driving factor in the selection process of software development outsourcing vendors. Vendors should rather address other factors in order to compete in as sk the OSDO business, such and services.
A New Local Adaptive Thresholding Technique in Binarization

Image binarization is the process of separation of pixel values into two groups, white as background and black as foreground. Thresholding plays a major in binarization of images. Thresholding can be categorized into global thresholding and local thresholding. In images with uniform contrast distribution of background and foreground like document images, global thresholding is more appropriate. In degraded document images, where considerable background noise or variation in contrast and illumination exists, there exists many pixels that cannot be easily classified as foreground or background. In such cases, binarization with local thresholding is more appropriate. This paper describes a locally adaptive thresholding technique that removes background by using local mean and mean deviation. Normally the local mean computational time depends on the window size. Our technique uses integral sum image as a prior processing to calculate local mean. It does not involve calculations of standard deviations as in other local adaptive techniques. This along with the fact that calculations of mean is independent of window size speed up the process as compared to other local thresholding techniques.
Evaluating GPU Passthrough in Xen for High Performance Cloud Computing

With the advent of virtualization and Infrastructure-as-a-Service (IaaS), the broader scientific computing community is considering the use of clouds for their technical computing needs. This is due to the relative scalability, ease of use, advanced user environment customization abilities clouds provide, as well as many novel computing paradigms available for data-intensive applications. However, there is concern about a performance gap that exists between the performance of IaaS when compared to typical high performance computing (HPC) resources, which could limit the applicability of IaaS for many potential scientific users. Most recently, general-purpose graphics processing units (GPGPUs or GPUs) have become commonplace within high performance computing. We look to bridge the gap between supercomputing and clouds by providing GPU-enabled virtual machines (VMs) and investigating their feasibility for advanced scientific computation. Specifically, the Xen hypervisor is utilized to leverage specialized hardware-assisted I/O virtualization and PCI passthrough in order to provide advanced HPC-centric Nvidia GPUs directly in guest VMs. This methodology is evaluated by measuring the performance of two Nvidia Tesla GPUs within Xen VMs and comparing to bare-metal hardware. Results show PCI passthrough of GPUs within virtual machines is a viable use case for many scientific computing workflows, and could help support high performance cloud infrastructure in the near future.
SECURITY CHALLENGES , ISSUES AND THEIR SOLUTIONS FOR VANET

Vehicular Ad hoc Networks (VANETs) are the promising approach to provide safety and other applications to the drivers as well as passengers. It becomes a key component of the intelligent transport system. A lot of works have been done towards it but security in VANET got less attention. In this article, we have discussed about the VANET and its technical and security challenges. We have also discussed some major attacks and solutions that can be implemented against these attacks. We have compared the solution using different parameters. Lastly we have discussed the mechanisms that are used in the solutions.
Tamper resistance mechanisms for secure embedded systems

Security is a concern in the design of a wide range of embedded systems. Extensive research has been devoted to the development of cryptographic algorithms that provide the theoretical underpinnings of information security. Functional security mechanisms, such as security protocols, suitably employ these mathematical primitives in order to achieve the desired security objectives. However, functional security mechanisms alone cannot ensure security, since most embedded systems present attackers with an abundance of opportunities to observe or interfere with their implementation, and hence to compromise their theoretical strength. This paper surveys various tamper or attack techniques, and explains how they can be used to undermine or weaken security functions in embedded systems. Tamper-resistant design refers to the process of designing a system architecture and implementation that is resistant to such attacks. We outline approaches that have been proposed to design tamper-resistant embedded systems, with examples drawn from recent commercial products.
Doppler radar vital sign detection with random body movement cancellation based on adaptive phase compensation

This paper presents a Doppler radar sensor system with camera-aided random body movement cancellation (RBMC) techniques for noncontact vital sign detection. The camera measures the subject's random body motion that is provided for the radar system to perform RBMC and extract the uniform vital sign signals of respiration and heartbeat. Three RBMC strategies are proposed: 1) phase compensation at radar RF front-end, 2) phase compensation for baseband complex signals, and 3) movement cancellation for demodulated signals. Both theoretical analysis and radar simulation have been carried out to validate the proposed RBMC techniques. An experiment was carried out to measure a subject person who was breathing normally but randomly moving his body back and forth. The experimental result reveals that the proposed radar system is effective for RBMC.
Cloud SLAs: present and future

The variability in the service level agreements (SLAs) of cloud providers prompted us to ask the question how do the SLAs compare and how should the SLAs be defined for future cloud services. We break down a cloud SLA into easy to understand components and use it to compare SLAs of public cloud providers. Our study indicates that none of the surveyed cloud providers offer any performance guarantees for compute services and leave SLA violation detection to the customer. We then provide guidance on how SLAs should be defined for future cloud services.
A Novel Low-Ringing Monocycle Picosecond Pulse Generator Based on Step Recovery Diode

This paper presents a high-performance low-ringing ultra-wideband monocycle picosecond pulse generator, formed using a step recovery diode (SRD), simulated in ADS software and generated through experimentation. The pulse generator comprises three parts, a step recovery diode, a field-effect transistor and a Schottky diode, used to eliminate the positive and negative ringing of pulse. Simulated results validate the design. Measured results indicate an output waveform of 1.88 peak-to-peak amplitude and 307ps pulse duration with a minimal ringing of -22.5 dB, providing good symmetry and low level of ringing. A high degree of coordination between the simulated and measured results is achieved.
Multi-layered Gesture Recognition with Kinect

This paper proposes a novel multi-layered gesture recognition method with Kinect. We explore the essential linguistic characters of gestures: the components concurrent character and the sequential organization character, in a multi-layered framework, which extracts features from both the segmented semantic units and the whole gesture sequence and then sequentially classifies the motion, location and shape components. In the first layer, an improved principle motion is applied to model the motion component. In the second layer, a particle-based descriptor and a weighted dynamic time warping are proposed for the location component classification. In the last layer, the spatial path warping is further proposed to classify the shape component represented by unclosed shape context. The proposed method can obtain relatively high performance for one-shot learning gesture recognition on the ChaLearn Gesture Dataset comprising more than 50, 000 gesture sequences recorded with Kinect.
Goal striving, need satisfaction, and longitudinal well-being: the self-concordance model.

An integrative model of the conative process, which has important ramifications for psychological need satisfaction and hence for individuals' well-being, is presented. The self-concordance of goals (i.e., their consistency with the person's developing interests and core values) plays a dual role in the model. First, those pursuing self-concordant goals put more sustained effort into achieving those goals and thus are more likely to attain them. Second, those who attain self-concordant goals reap greater well-being benefits from their attainment. Attainment-to-well-being effects are mediated by need satisfaction, i.e., daily activity-based experiences of autonomy, competence, and relatedness that accumulate during the period of striving. The model is shown to provide a satisfactory fit to 3 longitudinal data sets and to be independent of the effects of self-efficacy, implementation intentions, avoidance framing, and life skills.