nils-herrmann/clean_scirepeval huggingface dataset:
A building block approach to sensornet systems

We present a building block approach to hardware platform design based on a decade of collective experience in this area, arriving at an architecture in which general-purpose modules that require expertise to de sign and incorporate commonly-used functionality are integrated with application-specific carriers that satisfy the unique sensing, power supply, and mechanical constraints of an application. Of course, modules are widespread, but our focus is far less on the performance of any individual module and far more on an overall architecture that supports the prototype, pilot, and production stages of design, and preserves the artifacts and learnings accumulated along the way.
 We present heuristics for partitioning functionality between modules and carriers, and identify guidelines for their interconnection. Our approach advocates exporting a wide electrical interface, eliminating the system bus, and supporting many physical interconnect options for modules and carriers. We evaluate this approach by constructing a family of general-purpose modules and application-specific carriers that achieve a high degree of reuse despite very different application requirements. We show that this approach shortens platform development time-to-result for novice graduate students, making custom platforms broadly accessible.
A Critical Review of Recurrent Neural Networks for Sequence Learning

Countless learning tasks require dealing with sequential data. Image captioning, speech synthesis, and music generation all require that a model produce outputs that are sequences. In other domains, such as time series prediction, video analysis, and musical information retrieval, a model must learn from inputs that are sequences. Interactive tasks, such as translating natural language, engaging in dialogue, and controlling a robot, often demand both capabilities. Recurrent neural networks (RNNs) are connectionist models that capture the dynamics of sequences via cycles in the network of nodes. Unlike standard feedforward neural networks, recurrent networks retain a state that can represent information from an arbitrarily long context window. Although recurrent neural networks have traditionally been difficult to train, and often contain millions of parameters, recent advances in network architectures, optimization techniques, and parallel computation have enabled successful large-scale learning with them. In recent years, systems based on long short-term memory (LSTM) and bidirectional (BRNN) architectures have demonstrated ground-breaking performance on tasks as varied as image captioning, language translation, and handwriting recognition. In this survey, we review and synthesize the research that over the past three decades first yielded and then made practical these powerful learning models. When appropriate, we reconcile conflicting notation and nomenclature. Our goal is to provide a selfcontained explication of the state of the art together with a historical perspective and references to primary research.
Sensation seeking and internet dependence of Taiwanese high school adolescents

The present study examined excessive Internet use of Taiwanese adolescents and a psychological aspect of users, sensation seeking, thus to differentiate motivation of Internet dependents and non-dependents. Seven hundred and fifty three Taiwanese high school students were selected using cluster sampling and 88 of them were categorized as Internet dependent users. Results indicated that Internet dependents spent more time on-line than non-dependents. While Internet dependents perceived significantly more negative Internet influences on daily routines, school performance, and parental relation than non-dependents, both Internet dependents and non-dependents viewed Internet use as enhancing peer relations. Making friends through the Internet has become a popular activity among adolescents, potentially leading to its excessive use. Internet dependents scored significantly higher on overall sensation seeking and disinhibition than Internet non-dependents. However, both groups did not differ in the life experience seeking subscale and thrill and adventure seeking subscale. This finding contradicts that of Lavin, Marvin, McLarney, Nola, and Scott [ (2000) 425]. Possible reasons for this discrepancy and for the relation between Internet dependence and disinhibition in Taiwanese adolescents are also discussed.
Privacy attacks in social media using photo tagging networks: a case study with Facebook

Social-networking users unknowingly reveal certain kinds of personal information that malicious attackers could profit from to perpetrate significant privacy breaches. This paper quantitatively demonstrates how the simple act of tagging pictures on the social-networking site of Facebook could reveal private user attributes that are extremely sensitive. Our results suggest that photo tags can be used to help predicting some, but not all, of the analyzed attributes. We believe our analysis make users aware of significant breaches of their privacy and could inform the design of new privacy-preserving ways of tagging pictures on social-networking sites.
Automatic Symbolic Traffic Scene Analysis Using Belief Networks

Automatic symbolic tra c scene analysis is essen tial to many areas of IVHS Intelligent Vehicle Highway Systems Tra c scene information can be used to optimize tra c ow during busy pe riods identify stalled vehicles and accidents and aid the decision making of an autonomous vehi cle controller Improvements in technologies for machine vision based surveillance and high level symbolic reasoning have enabled us to develop a system for detailed reliable tra c scene analysis The machine vision component of our system em ploys a contour tracker and an a ne motionmodel based on Kalman lters to extract vehicle trajec tories over a sequence of tra c scene images The symbolic reasoning component uses a dynamic be lief network to make inferences about tra c events such as vehicle lane changes and stalls In this pa per we discuss the key tasks of the vision and reasoning components as well as their integration into a working prototype
Pin: building customized program analysis tools with dynamic instrumentation

Robust and powerful software instrumentation tools are essential for program analysis tasks such as profiling, performance evaluation, and bug detection. To meet this need, we have developed a new instrumentation system called Pin. Our goals are to provide easy-to-use, portable, transparent, and efficient instrumentation. Instrumentation tools (called Pintools) are written in C/C++ using Pin's rich API. Pin follows the model of ATOM, allowing the tool writer to analyze an application at the instruction level without the need for detailed knowledge of the underlying instruction set. The API is designed to be architecture independent whenever possible, making Pintools source compatible across different architectures. However, a Pintool can access architecture-specific details when necessary. Instrumentation with Pin is mostly transparent as the application and Pintool observe the application's original, uninstrumented behavior. Pin uses dynamic compilation to instrument executables while they are running. For efficiency, Pin uses several techniques, including inlining, register re-allocation, liveness analysis, and instruction scheduling to optimize instrumentation. This fully automated approach delivers significantly better instrumentation performance than similar tools. For example, Pin is 3.3x faster than Valgrind and 2x faster than DynamoRIO for basic-block counting. To illustrate Pin's versatility, we describe two Pintools in daily use to analyze production software. Pin is publicly available for Linux platforms on four architectures: IA32 (32-bit x86), EM64T (64-bit x86), Itanium®, and ARM. In the ten months since Pin 2 was released in July 2004, there have been over 3000 downloads from its website.
Zero-Shot Learning Through Cross-Modal Transfer

This work introduces a model that can recognize objects in images even if no training data is available for the objects. The only necessary knowledge about the unseen categories comes from unsupervised large text corpora. In our zero-shot framework distributional information in language can be seen as spanning a semantic basis for understanding what objects look like. Most previous zero-shot learning models can only differentiate between unseen classes. In contrast, our model can both obtain state of the art performance on classes that have thousands of training images and obtain reasonable performance on unseen classes. This is achieved by first using outlier detection in the semantic space and then two separate recognition models. Furthermore, our model does not require any manually defined semantic features for either words or images.
Wirelessly Automated Reconfigurable Antenna With Directional Selectivity

In this paper, a frequency reconfigurable antenna system with a directional selectivity is presented. The reconfiguration of the antenna’s radiation characteristics is wirelessly controlled through a mobile phone and a microcontroller. The antenna system is a multi-element radiator composed of four monopoles on top of a hexagonal ground plane, in addition to four rectangular microstrip two-elements antenna arrays. The monopoles and the microstrip arrays are designed to contribute to the communication requirements of the complete system. The position, orientation, and topology of the various antennas are optimized to achieve the required frequency reconfigurability in addition to the variable selective directivity performance. The whole system is fabricated and integrated in a single compact unit controlled by a microcontroller, four servo motors, and a Bluetooth module. The antenna system is tested and the measured results confirm the validity of the proposed design.
Challenges and Best Practices for Mobile Application Development: Review Paper

Over the last ten years or so, mobile devices technology has changed significantly, with these devices and operating systems becoming more sophisticated. These developments have led to a huge variety of mobile applications designed for mobile operating systems. These mobile applications are typically harder to design and build because of several factors such as screen size and limited processing power and so forth. Therefore, it is important to clearly identify the characteristics of mobile application development and the issues and challenges related to it, as well as, the key features that characterize a great mobile application which make them valuable and useful. This paper has reviewed existing literature of the challenge and best practices of mobile application development. This study contributes towards a great understanding of the characteristics of mobile application development process, examines real challenges faced and explores the best practices that can be effectively applied to improve the development of mobile application.
A Comprehensive Analysis of Knowledge Management Cycles

At present knowledge and its proper management became an essential issue for every organization. In the modern globalized world, organizations cannot survive in a sustainable way without efficient knowledge management. Knowledge management cycle (KMC) is a process of transforming information into knowledge within an organization, which explains how knowledge is captured, processed, and distributed in an organization. For the better performance organizations require a practical and coherent strategy and comprehensive KMC. The aim of this study is to examine the KMCs and how they are playing vital role for the development of organizations.
Kaizen programming

This paper presents Kaizen Programming, an evolutionary tool based on the concepts of Continuous Improvement from Kaizen Japanese methodology. One may see Kaizen Programming as a new paradigm since, as opposed to classical evolutionary algorithms where individuals are complete solutions, in Kaizen Programming each expert proposes an idea to solve part of the problem, thus a solution is composed of all ideas together. Consequently, evolution becomes a collaborative approach instead of an egocentric one. An idea's quality (analog to an individual's fitness) is not how good it fits the data, but a measurement of its contribution to the solution, which improves the knowledge about the problem. Differently from evolutionary algorithms that simply perform trial-and-error search, one can determine, exactly, parts of the solution that should be removed or improved. That property results in the reduction in bloat, number of function evaluations, and computing time. Even more important, the Kaizen Programming tool, proposed to solve symbolic regression problems, builds the solutions as linear regression models - not linear in the variables, but linear in the parameters, thus all properties and characteristics of such statistical tool are valid. Experiments on benchmark functions proposed in the literature show that Kaizen Programming easily outperforms Genetic Programming and other methods, providing high quality solutions for both training and testing sets while requiring a small number of function evaluations.
An Introductory Study on Time Series Modeling and Forecasting

ACKNOWLEDGEMENT The timely and successful completion of the book could hardly be possible without the helps and supports from a lot of individuals. I will take this opportunity to thank all of them who helped me either directly or indirectly during this important work. First of all I wish to express my sincere gratitude and due respect to my supervisor Dr. R.K. Agrawal, Associate Professor SC & SS, JNU. I am immensely grateful to him for his valuable guidance, continuous encouragements and positive supports which helped me a lot during the period of my work. I would like to appreciate him for always showing keen interest in my queries and providing important suggestions. I also express whole hearted thanks to my friends and classmates for their care and moral supports. The moments, I enjoyed with them during my M.Tech course will always remain as a happy memory throughout my life. I owe a lot to my mother for her constant love and support. She always encouraged me to have positive and independent thinking, which really matter in my life. I would like to thank her very much and share this moment of happiness with her. Last but not the least I am also thankful to entire faculty and staff of SC & SS for their unselfish help, I got whenever needed during the course of my work. Time series modeling and forecasting has fundamental importance to various practical domains. Thus a lot of active research works is going on in this subject during several years. Many important models have been proposed in literature for improving the accuracy and effeciency of time series modeling and forecasting. The aim of this book is to present a concise description of some popular time series forecasting models used in practice, with their salient features. In this book, we have described three important classes of time series models, viz. the stochastic, neural networks and SVM based models, together with their inherent forecasting strengths and weaknesses. We have also discussed about the basic issues related to time series modeling, such as stationarity, parsimony, overfitting, etc. Our discussion about different time series models is supported by giving the experimental forecast results, performed on six real time series datasets. While fitting a model to a dataset, special care is taken to select the most parsimonious one. To evaluate forecast accuracy as well as to compare among different models …
A signal detection theory approach for camera tamper detection

Camera tamper detection is the ability to detect faults and operational failures in video surveillance cameras by analyzing the video. Researchers have increasingly focused on such techniques attributing to the ubiquitous deployment of large scale surveillance systems. In this paper, a signal detection theory approach is proposed to quantitatively analyze the information being captured by the camera and to detect tampers. Signal activity is used as a feature to measure the amount of information in the image. The distribution of features representing the normal operation of a camera are modeled as a Gaussian mixture model (GMM). The GMM is trained using synthetic data. To reduce the effects of noise, a Kalman filter is used to model changes in signal activity in the video. Experimental results show that the proposed approach out performed the state-of-the-art [13] in detecting tampered images with higher accuracy while generating lower false alarms.
An Asynchronous Parallel Stochastic Coordinate Descent Algorithm

We describe an asynchronous parallel stochastic coordinate descent algorithm for minimizing smooth unconstrained or separably constrained functions. The method achieves a linear convergence rate on functions that satisfy an essential strong convexity property and a sublinear rate (1/K) on general convex functions. Near-linear speedup on a multicore system can be expected if the number of processors is O(n) in unconstrained optimization and O(n) in the separable-constrained case, where n is the number of variables. We describe results from implementation on 40-core processors.
Feature-Rich Networks for Knowledge Base Completion

We propose jointly modelling Knowledge Bases and aligned text with Feature-Rich Networks. Our models perform Knowledge Base Completion by learning to represent and compose diverse feature types from partially aligned and noisy resources. We perform experiments on Freebase utilizing additional entity type information and syntactic textual relations. Our evaluation suggests that the proposed models can better incorporate side information than previously proposed combinations of bilinear models with convolutional neural networks, showing large improvements when scoring the plausibility of unobserved facts with associated textual mentions.
Fast Ellipse Detection Algorithm Using Hough Transform on the GPU

GPUs (Graphics Processing Units) are specialized microprocessors that accelerate 3D or 2D graphics operations. Recent GPUs, which have many processing units connected with a global memory, can be used for general purpose parallel computation. To utilize the powerful computing ability, GPUs are widely used for general purpose computing. The main purpose of this paper is an ellipse detection algorithm with Hough transform. The feature of our algorithm is that to reduce computational time and space, the parameter spaces in the Hough transform are decomposed for each parameter and each parameter is computed in series. Also, we implemented our algorithm on a modern GPU system. The experimental results show that, for an input image with size of 2040$\times$2040, our GPU implementation can achieve a speedup factor of approximately 64 times over the sequential implementation without the GPU support.
Java Code Analysis and Transformation into AWS Lambda Functions

Software developers are faced with the issue of either adapting their programming model to the execution model (e.g. cloud platforms) or finding appropriate tools to adapt the model and code automatically. A recent execution model which would benefit from automated enablement is Function-as-a-Service. Automating this process requires a pipeline which includes steps for code analysis, transformation and deployment. In this paper, we outline the design and runtime characteristics of Podilizer, a tool which implements the pipeline specifically for Java source code as input and AWS Lambda as output. We contribute technical and economic metrics about this concrete ’FaaSification’ process by observing the behaviour of Podilizer with two representative Java software projects.
Trust management of services in cloud environments: Obstacles and solutions

Trust management is one of the most challenging issues in the emerging cloud computing area. Over the past few years, many studies have proposed different techniques to address trust management issues. However, despite these past efforts, several trust management issues such as identification, privacy, personalization, integration, security, and scalability have been mostly neglected and need to be addressed before cloud computing can be fully embraced. In this article, we present an overview of the cloud service models and we survey the main techniques and research prototypes that efficiently support trust management of services in cloud environments. We present a generic analytical framework that assesses existing trust management research prototypes in cloud computing and relevant areas using a set of assessment criteria. Open research issues for trust management in cloud environments are also discussed.
Robustness Analysis of Artificial Neural Networks and Support Vector Machine in Making Prediction

This This study aims to investigate the robustness of prediction model by comparing artificial neural networks (ANNs), and support vector machine (SVMs) model. The study employs ten years monthly data of six types of macroeconomic variables as independent variables and the average rate of return of one-month time deposit of Indonesian Islamic banks (RR) as dependent variable. Finally, the performance is evaluated through graph analysis, statistical parameters and accuracy rate measurement. This research found that ANNs outperforms SVMs empirically resulted from the training process and overall data prediction. This is indicating that ANNs model is better in the context of capturing all data pattern and explaining the volatility of RR.
Food vs Non-Food Classification

Automatic understanding of food is an important research challenge. Food recognition engines can provide a valid aid for automatically monitoring the patient's diet and food-intake habits directly from images acquired using mobile or wearable cameras. One of the first challenges in the field is the discrimination between images containing food versus the others. Existing approaches for food vs non-food classification have used both shallow and deep representations, in combination with multi-class or one-class classification approaches. However, they have been generally evaluated using different methodologies and data, making a real comparison of the performances of existing methods unfeasible. In this paper, we consider the most recent classification approaches employed for food vs non-food classification, and compare them on a publicly available dataset. Different deep-learning based representations and classification methods are considered and evaluated.
Patterns of cognitive appraisal in emotion.

There has long been interest in describing emotional experience in terms of underlying dimensions, but traditionally only two dimensions, pleasantness and arousal, have been reliably found. The reasons for these findings are reviewed, and integrating this review with two recent theories of emotions (Roseman, 1984; Scherer, 1982), we propose eight cognitive appraisal dimensions to differentiate emotional experience. In an investigation of this model, subjects recalled past experiences associated with each of 15 emotions, and rated them along the proposed dimensions. Six orthogonal dimensions, pleasantness, anticipated effort, certainty, attentional activity, self-other responsibility/control, and situational control, were recovered, and the emotions varied systematically along each of these dimensions, indicating a strong relation between the appraisal of one's circumstances and one's emotional state. The patterns of appraisal for the different emotions, and the role of each of the dimensions in differentiating emotional experience are discussed.