nils-herrmann/clean_scirepeval huggingface dataset:
Perception of Average Value in Multiclass Scatterplots

The visual system can make highly efficient aggregate judgements about a set of objects, with speed roughly independent of the number of objects considered. While there is a rich literature on these mechanisms and their ramifications for visual summarization tasks, this prior work rarely considers more complex tasks requiring multiple judgements over long periods of time, and has not considered certain critical aggregation types, such as the localization of the mean value of a set of points. In this paper, we explore these questions using a common visualization task as a case study: relative mean value judgements within multi-class scatterplots. We describe how the perception literature provides a set of expected constraints on the task, and evaluate these predictions with a large-scale perceptual study with crowd-sourced participants. Judgements are no harder when each set contains more points, redundant and conflicting encodings, as well as additional sets, do not strongly affect performance, and judgements are harder when using less salient encodings. These results have concrete ramifications for the design of scatterplots.
Deep Neural Solver for Math Word Problems

This paper presents a deep neural solver to automatically solve math word problems. In contrast to previous statistical learning approaches, we directly translate math word problems to equation templates using a recurrent neural network (RNN) model, without sophisticated feature engineering. We further design a hybrid model that combines the RNN model and a similarity-based retrieval model to achieve additional performance improvement. Experiments conducted on a large dataset show that the RNN model and the hybrid model significantly outperform stateof-the-art statistical learning methods for math word problem solving.
Context-Based Pedestrian Path Prediction

We present a novel Dynamic Bayesian Network for pedestrian path prediction in the intelligent vehicle domain. The model incorporates the pedestrian situational awareness, situation criticality and spatial layout of the environment as latent states on top of a Switching Linear Dynamical System (SLDS) to anticipate changes in the pedestrian dynamics. Using computer vision, situational awareness is assessed by the pedestrian head orientation, situation criticality by the distance between vehicle and pedestrian at the expected point of closest approach, and spatial layout by the distance of the pedestrian to the curbside. Our particular scenario is that of a crossing pedestrian, who might stop or continue walking at the curb. In experiments using stereo vision data obtained from a vehicle, we demonstrate that the proposed approach results in more accurate path prediction than only SLDS, at the relevant short time horizon (1 s), and slightly outperforms a computationally more demanding state-of-the-art method.
Dex: a semantic-graph differencing tool for studying changes in large code bases

This paper describes an automated tool called Dex (difference extractor) for analyzing syntactic and semantic changes in large C-language code bases. It is applied to patches obtained from a source code repository, each of which comprises the code changes made to accomplish a particular task. Dex produces summary statistics characterizing these changes for all of the patches that are analyzed. Dex applies a graph differencing algorithm to abstract semantic graphs (ASGs) representing each version. The differences are then analyzed to identify higher-level program changes. We describe the design of Dex, its potential applications, and the results of applying it to analyze bug fixes from the Apache and GCC projects. The results include detailed information about the nature and frequency of missing condition defects in these projects.
Botnet in DDoS Attacks: Trends and Challenges

Threats of distributed denial of service (DDoS) attacks have been increasing day-by-day due to rapid development of computer networks and associated infrastructure, and millions of software applications, large and small, addressing all varieties of tasks. Botnets pose a major threat to network security as they are widely used for many Internet crimes such as DDoS attacks, identity theft, email spamming, and click fraud. Botnet based DDoS attacks are catastrophic to the victim network as they can exhaust both network bandwidth and resources of the victim machine. This survey presents a comprehensive overview of DDoS attacks, their causes, types with a taxonomy, and technical details of various attack launching tools. A detailed discussion of several botnet architectures, tools developed using botnet architectures, and pros and cons analysis are also included. Furthermore, a list of important issues and research challenges is also reported.
Power Assist Wear Driven with Pneumatic Rubber Artificial Muscles

In the coming advanced age society, an innovative technology to assist the activities of daily living of elderly and disabled people and the heavy work in nursing is desired. To develop such a technology, an actuator safe and friendly for human is required. It should be small, lightweight and has to provide a proper softness. A pneumatic rubber artificial muscle is available as such actuators. We have developed some types of pneumatic rubber artificial muscles and applied them to wearable power assist devices. A wearable power assist device is equipped to the human body to assist the muscular force, which supports activities of daily living, rehabilitation, heavy working, training and so on. In this paper, some types of pneumatic rubber artificial muscles developed in our laboratory are introduced. Further, two kinds of wearable power assist devices driven with the rubber artificial muscles are described. Some evaluations can clarify the effectiveness of pneumatic rubber artificial muscle for such an innovative human assist technology.
Complexity and Philosophy

The science of complexity is based on a new way of thinking that stands in sharp contrast to the philosophy underlying Newtonian science, which is based on reductionism, determinism, and objective knowledge. This paper reviews the historical development of this new world view, focusing on its philosophical foundations. Determinism was challenged by quantum mechanics and chaos theory. Systems theory replaced reductionism by a scientifically based holism. Cybernetics and postmodern social science showed that knowledge is intrinsically subjective. These developments are being integrated under the header of “complexity science”. Its central paradigm is the multi-agent system. Agents are intrinsically subjective and uncertain about their environment and future, but out of their local interactions, a global organization emerges. Although different philosophers, and in particular the postmodernists, have voiced similar ideas, the paradigm of complexity still needs to be fully assimilated by philosophy. This will throw a new light on old philosophical issues such as relativism, ethics and the role of the subject.
A Dynamic MapReduce Scheduler for Heterogeneous Workloads

MapReduce is an important programming model for building data centers containing ten of thousands of nodes. In a practical data center of that scale, it is a common case that I/O-bound jobs and CPU-bound jobs, which demand different resources, run simultaneously in the same cluster. In the MapReduce framework, parallelization of these two kinds of job has not been concerned. In this paper, we give a new view of the MapReduce model, and classify the MapReduce workloads into three categories based on their CPU and I/O utilization. With workload classification, we design a new dynamic MapReduce workload predict mechanism, MR-Predict, which detects the workload type on the fly. We propose a Triple-Queue Scheduler based on the MR-Predict mechanism. The Triple-Queue scheduler could improve the usage of both CPU and disk I/O resources under heterogeneous workloads. And it could improve the Hadoop throughput by about 30% under heterogeneous workloads.
A centralized trust model approach for cloud computing

In the IT world of corporate networking, how businesses store and compute data is starting to shift from in-house servers to the cloud. However, some enterprises are still hesitant to make this leap to the cloud because of their information security and data privacy concerns. Enterprises that want to invest into this service need to feel confident that the information stored on the cloud is secure. Due to this need for confidence, trust is one of the major qualities that cloud service providers (CSPs) must build for cloud service users (CSUs). To do this, a model that all CSPs can follow must exist to establish a trust standard in the industry. If no concrete model exists, the future of cloud computing will be stagnant. This paper presents a new trust model that involves all the cloud stakeholders such as CSU, CSP, and third-party auditors. Our proposed trust model is objective since it involves third-party auditors to develop unbiased trust between the CSUs and the CSPs. Furthermore, to support the implementation of the proposed trust model, we rank CSPs according to the trust-values obtained from the trust model. The final score for each participating CSP will be determined based on the third-party assessment and the feedback received from the CSUs.
Mining Email Content for Author Identification Forensics

We describe an investigation into e-mail content mining for author identification, or authorship attribution, for the purpose of forensic investigation. We focus our discussion on the ability to discriminate between authors for the case of both aggregated e-mail topics as well as across different e-mail topics. An extended set of e-mail document features including structural characteristics and linguistic patterns were derived and, together with a Support Vector Machine learning algorithm, were used for mining the e-mail content. Experiments using a number of e-mail documents generated by different authors on a set of topics gave promising results for both aggregated and multi-topic author categorisation.
Plant classification using group of features

Increasing world population and limited food resources, has made it inevitable to apply the benefits of modern technology to improve the efficiency of agricultural fields. Automatic plant type identification process is crucial not only to industries related to food production but also to environmentalists and related authorities. It increases productivity, contributes to a better understanding of the relationship between environmental factors and healthy crops. It is expected to reduce the labor costs for farmers and increase the operation speed and accuracy. In this paper, we propose a method to classify the type of plants in a video sequence. Our approach utilizes feature fusion together with color and texture features and support vector machine is used for classification. A variety of feature extraction techniques are employed in W-B, R-G and B-Y color spaces to extract color and textural features. Principal component analysis and t-distributed stochastic neighbor embedding methods are employed for dimension reduction. The performance of the approach is tested on dataset collected through a government supported project, TARBIL, for which over 1200 agro-stations are placed throughout Turkey. 5-fold cross validation technique as well as random test samples are used to test the accuracy of the system.
Big data Analytics in the Education Sector: Needs, Opportunities and Challenges

Academic institutions are now drawing attention in finding methods for making effective learning process, for identifying learner’s achievements and weakness, for tracing academic progress and also for predicting future performance. People’s increased expectation for accountability and transparency makes it necessary to implement big data analytics in the educational institution. But not all the educationalist and administrators are ready to take the challenge. So, it is now obvious to know about the necessity and opportunity as well as challenges of implementing big data analytics. This paper will describe the needs, opportunities and challenges of implementing big data analytics in the education sector.
You are where you tweet: a content-based approach to geo-locating twitter users

We propose and evaluate a probabilistic framework for estimating a Twitter user's city-level location based purely on the content of the user's tweets, even in the absence of any other geospatial cues. By augmenting the massive human-powered sensing capabilities of Twitter and related microblogging services with content-derived location information, this framework can overcome the sparsity of geo-enabled features in these services and enable new location-based personalized information services, the targeting of regional advertisements, and so on. Three of the key features of the proposed approach are: (i) its reliance purely on tweet content, meaning no need for user IP information, private login information, or external knowledge bases; (ii) a classification component for automatically identifying words in tweets with a strong local geo-scope; and (iii) a lattice-based neighborhood smoothing model for refining a user's location estimate. The system estimates k possible locations for each user in descending order of confidence. On average we find that the location estimates converge quickly (needing just 100s of tweets), placing 51% of Twitter users within 100 miles of their actual location.
Matching with Shape Contexts

We introduce a new shape descriptor, the shape context , for measuring shape similarity and recovering point correspondences. The shape context describes the coarse arrangement of the shape with respect to a point inside or on the boundary of the shape. We use the shape context as a vector-valued attribute in a bipartite graph matching framework. Our proposed method makes use of a relatively small number of sample points selected from the set of detected edges; no special landmarks or keypoints are necessary. Tolerance and/or invariance to common image transformations are available within our framework. Using examples involving both silhouettes and edge images, we demonstrate how the solution to the graph matching problem provides us with correspondences and a dissimilarity score that can be used for object recognition and similarity-based retrieval.
Ring Loss: Convex Feature Normalization for Face Recognition

We motivate and present Ring loss, a simple and elegant feature normalization approach for deep networks designed to augment standard loss functions such as Softmax. We argue that deep feature normalization is an important aspect of supervised classification problems where we require the model to represent each class in a multi-class problem equally well. The direct approach to feature normalization through the hard normalization operation results in a non-convex formulation. Instead, Ring loss applies soft normalization, where it gradually learns to constrain the norm to the scaled unit circle while preserving convexity leading to more robust features. We apply Ring loss to large-scale face recognition problems and present results on LFW, the challenging protocols of IJB-A Janus, Janus CS3 (a superset of IJB-A Janus), Celebrity Frontal-Profile (CFP) and MegaFace with 1 million distractors. Ring loss outperforms strong baselines, matches state-of-the-art performance on IJB-A Janus and outperforms all other results on the challenging Janus CS3 thereby achieving state-of-the-art. We also outperform strong baselines in handling extremely low resolution face matching.
Losing Control: On the Effectiveness of Control-Flow Integrity under Stack Attacks

Adversaries exploit memory corruption vulnerabilities to hijack a program's control flow and gain arbitrary code execution. One promising mitigation, control-flow integrity (CFI), has been the subject of extensive research in the past decade. One of the core findings is that adversaries can construct Turing-complete code-reuse attacks against coarse-grained CFI policies because they admit control flows that are not part of the original program. This insight led the research community to focus on fine-grained CFI implementations. In this paper we show how to exploit heap-based vulnerabilities to control the stack contents including security-critical values used to validate control-flow transfers. Our investigation shows that although program analysis and compiler-based mitigations reduce stack-based vulnerabilities, stack-based memory corruption remains an open problem. Using the Chromium web browser we demonstrate real-world attacks against various CFI implementations: 1)~against CFI implementations under Windows 32-bit by exploiting unprotected context switches, and 2)~against state-of-the-art fine-grained CFI implementations (IFCC and VTV) in the two premier open-source compilers under Unix-like operating systems. Both 32 and 64-bit x86 CFI checks are vulnerable to stack manipulation. Finally, we provide an exploit technique against the latest shadow stack implementation.
Smart Gas Level Monitoring, Booking & Gas Leakage Detector over IoT

This journal explains about the most common problem experienced in our day-to-day lives that is regarding GAS container going empty. We bring this paper to create awareness about the reducing weight of the gas in the container, and to place a gas order using IOT. The gas booking/order is being done with the help IOT and that the continuous weight measurement is done using a load cell which is interfaced with a Microcontroller (to compare with an ideal value). For ease it is even has a been added with an RF TX & Rx modules which will give the same information. When it comes it to security of the kit as well as gas container we have an MQ-2(gas sensor), LM 35(temperature sensor), which will detect the surrounding environment for any chance of error. When ever any change is subjected in any of the sensors (load cell, LM35, Mq-2) a siren (60db) is triggered.
A unifying framework for vector-valued manifold regularization and multi-view learning

This paper presents a general vector-valued reproducing kernel Hilbert spaces (RKHS) formulation for the problem of learning an unknown functional dependency between a structured input space and a structured output space, in the Semi-Supervised Learning setting. Our formulation includes as special cases Vector-valued Manifold Regularization and Multi-view Learning, thus provides in particular a unifying framework linking these two important learning approaches. In the case of least square loss function, we provide a closed form solution with an efficient implementation. Numerical experiments on challenging multi-class categorization problems show that our multi-view learning formulation achieves results which are comparable with state of the art and are significantly better than single-view learning.
Dengue and the risk of urban yellow fever reintroduction in São Paulo State, Brazil.

To propose a mathematical method for the estimation of the Basic Reproduction Number, R0, of urban yellow fever in a dengue-infested area.



The method is based on the assumption that, as the same vector (Aedes aegypti) causes both infections, all the quantities related to the mosquito, estimated from the initial phase of dengue epidemic, could be applied to yellow fever dynamics. It is demonstrated that R0 for yellow fever is, on average, 43% lower than that for dengue. This difference is due to the longer dengue viremia and its shorter extrinsic incubation period.



In this study the analysis was expanded to the epidemiological situation of dengue in S o Paulo in the year 2001. The total number of dengue cases increased from 3,582 in 2000 to 51,348 in 2001. It was then calculated R0 for yellow fever for every city which have shown R0 of dengue greater than 1. It was also estimated the total number of unprotected people living in highly risky areas for urban yellow fever.



Currently there is a great number of non-vaccinated people living in Aedes aegypti infested area in the state of São Paulo.
Design of a 9-bit UART module based on Verilog HDL

Universal Asynchronous Receiver Transmitter (UART) is widely used in data communication process especially for its advantages of high reliability, long distance and low cost. In this paper, we present the design of 9-bit UART modules based on Verilog HDL. This design features automatic address identification in the character itself. We have implemented the VLSI design of the module and pass data between the proposed 9-bit UART module with a host CPU. The design consists of receiver module, transmitter module, prescaler module and asynchronous FIFOs. We have explained the functions of each individual sub-modules and how the design works in simulation.
On blind signatures and perfect crimes

David Chaum has introduced the idea of blind signamres, an extension of the concept of digital signatures, as a way to protect the identity and privacy of a user in electronic payment and service networks. Blind signatures also prevent so-called 'dossier creation " about users by organizations. While the concept of blind signatures still allows authorities to distinguish between valid and false data, it prevents these authoriries from connecting specific data or actions to specific users. With the growing emphasis on the protection of the privacy of user data and user actions in electronic systems, blind signatures seem to be a perfect solution. This paper however, discusses a problemaric aspect of blind signatures, showing that this perfect solution can potentially lead to perfect crime. We use a real crime case as an example.
Research into Cyberbullying: Student Perspectives on Cybersafe Learning Environments

This paper reports a qualitative study designed to investigate the issues of cybersafety and cyberbullying and report how students are coping with them. Through discussion with 74 students, aged from 10 to 17, in focus groups divided into three age levels, data were gathered in three schools in Victoria, Australia, where few such studies had been set. Social networking sites and synchronous chat sites were found to be the places where cyberbullying most commonly occurred, with email and texting on mobile phones also used for bullying. Grades 8 and 9 most often reported cyberbullying and also reported behaviours and internet contacts that were cybersafety risks. Most groups preferred to handle these issues themselves or with their friends rather then alert parents and teachers who may limit their technology access. They supported education about these issues for both adults and school students and favoured a structured mediation group of their peers to counsel and advise victims.
Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus

Over the past decade, large-scale supervised learning corpora have enabled machine learning researchers to make substantial advances. However, to this date, there are no large-scale questionanswer corpora available. In this paper we present the 30M Factoid QuestionAnswer Corpus, an enormous question answer pair corpus produced by applying a novel neural network architecture on the knowledge base Freebase to transduce facts into natural language questions. The produced question answer pairs are evaluated both by human evaluators and using automatic evaluation metrics, including well-established machine translation and sentence similarity metrics. Across all evaluation criteria the questiongeneration model outperforms the competing template-based baseline. Furthermore, when presented to human evaluators, the generated questions appear comparable in quality to real human-generated questions.