nils-herrmann/clean_scirepeval huggingface dataset:
Self-Presentation 2.0: Narcissism and Self-Esteem on Facebook

Online social networking sites have revealed an entirely new method of self-presentation. This cyber social tool provides a new site of analysis to examine personality and identity. The current study examines how narcissism and self-esteem are manifested on the social networking Web site Facebook.com . Self-esteem and narcissistic personality self-reports were collected from 100 Facebook users at York University. Participant Web pages were also coded based on self-promotional content features. Correlation analyses revealed that individuals higher in narcissism and lower in self-esteem were related to greater online activity as well as some self-promotional content. Gender differences were found to influence the type of self-promotional content presented by individual Facebook users. Implications and future research directions of narcissism and self-esteem on social networking Web sites are discussed.
Building applied natural language generation systems

In this article, we give an overview of Natural Language Generation (nlg) from an applied system-building perspective. The article includes a discussion of when nlg techniques should be used; suggestions for carrying out requirements analyses; and a description of the basic nlg tasks of content determination, discourse planning, sentence aggregation, lexicalization, referring expression generation, and linguistic realisation. Throughout, the emphasis is on established techniques that can be used to build simple but practical working systems now. We also provide pointers to techniques in the literature that are appropriate for more complicated scenarios.
Forgetting Is Regulated through Rac Activity in Drosophila

Initially acquired memory dissipates rapidly if not consolidated. Such memory decay is thought to result either from the inherently labile nature of newly acquired memories or from interference by subsequently attained information. Here we report that a small G protein Rac-dependent forgetting mechanism contributes to both passive memory decay and interference-induced forgetting in Drosophila. Inhibition of Rac activity leads to slower decay of early memory, extending it from a few hours to more than one day, and to blockade of interference-induced forgetting. Conversely, elevated Rac activity in mushroom body neurons accelerates memory decay. This forgetting mechanism does not affect memory acquisition and is independent of Rutabaga adenylyl cyclase-mediated memory formation mechanisms. Endogenous Rac activation is evoked on different time scales during gradual memory loss in passive decay and during acute memory removal in reversal learning. We suggest that Rac's role in actin cytoskeleton remodeling may contribute to memory erasure.
Event Correlation: Language and Semantics

300.twolf 3000 102 2945 102 2945 Hardware CPU: Intel(R) Xeon(R) CPU 5150 @ 2.66GHz CPU MHz: 2660 FPU: Integrated CPU(s) enabled: 4 cores, 2 chips, 2 cores/chip CPU(s) orderable: 1,2 chip(s) Parallel: No Primary Cache: 32KB(I) + 32KB(D) on chip, per core Secondary Cache: 4096KB(I+D) on chip, per chip, shared L3 Cache: N/A Other Cache: N/A Memory: 4 x 1024MB ECC FB-DIMM DDR2-667MHz Disk Subsystem: 1 x 250GB SATA HDD Other Hardware: Software  Red Hat Enterprise Linux 4 Update 3 EM64T Compiler: Intel C++ Compiler 9.1.042 for EM64T File System: ext3 System State: Runlevel 3
FACTOR STRUCTURE OF THE “ ATTITUDES TOWARD RESEARCH ” SCALE 6

Students at the undergraduate level usually tend to view research methods courses negatively. However, an understanding of these attitudes is necessary to help instructors facilitate the learning of research for their students, by enabling them to create more positive attitudes toward such courses. The aim of this study is to describe the development of an “attitudes toward research” scale and verify the dimensions of attitudes toward research among undergraduate students enrolled in introductory research courses. The basic hypothesis of this research study is that the concept of attitudes is multidimensional in nature. The sample of the study consisted of 226 students who had completed a research methods course. Based on a factor analysis, five factors of student attitudes toward research were identified. These were the factors of usefulness of research, anxiety, affect indicating positive feelings about research, life relevancy of research to the students’ daily lives, and difficulty of research.
The Behavioural Paths to Wellbeing : An Exploratory Study to Distinguish Between Hedonic and Eudaimonic Wellbeing From an Activity Perspective

Hedonic wellbeing and eudaimonic wellbeing are two prevailing approaches to wellbeing. However, remarkably little research has distinguished them from an activity perspective; the knowledge of behavioural paths for achieving these two wellbeings is poor. This study first clarified the behavioural contents of the two approaches through a bottom-up method and then analysed the representativeness of activities to indicate to what extent activities contributed to wellness. We found that the paths to hedonic wellbeing and eudaimonic wellbeing overlapped and differed from each other. Furthermore, this study explained why hedonic activity differed from eudaimonic activity by analysing activity characteristics. We found that people reported higher frequency, sensory experience, and affective experience in hedonic activity, whereas they reported higher intellectual experience, behavioural experience, and spiritual experience in eudaimonic activity. Finally, we explored the behavioural pattern of wellbeing pursuit in both an unthreatening situation and a threatening situation. We found that the overlap between the two approaches increased in the threatening situation. Moreover, people in the threatening situation tended to score lower on all characteristics except frequency relative to those in the unthreatening situation. It seemed that the behavioural pattern in the threatening situation was less effective than its equivalent in the unthreatening situation.
Using Lanchester Attrition Laws for Combat Prediction in StarCraft

Smart decision making at the tactical level is important for Artificial Intelligence (AI) agents to perform well in the domain of real-time strategy (RTS) games. Winning battles is crucial in RTS games, and while humans can decide when and how to attack based on their experience, it is challenging for AI agents to estimate combat outcomes accurately. A few existing models address this problem in the game of StarCraft but present many restrictions, such as not modeling injured units, supporting only a small number of unit types, or being able to predict the winner of a fight but not the remaining army. Prediction using simulations is a popular method, but generally slow and requires extensive coding to model the game engine accurately. This paper introduces a model based on Lanchester’s attrition laws which addresses the mentioned limitations while being faster than running simulations. Unit strength values are learned using maximum likelihood estimation from past recorded battles. We present experiments that use a StarCraft simulator for generating battles for both training and testing, and show that the model is capable of making accurate predictions. Furthermore, we implemented our method in a StarCraft bot that uses either this or traditional simulations to decide when to attack or to retreat. We present tournament results (against top bots from 2014 AIIDE competition) comparing the performances of the two versions, and show increased winning percentages for our method.
A dataset for activity recognition in an unmodified kitchen using smart-watch accelerometers

Activity recognition from smart devices and wearable sensors is an active area of research due to the widespread adoption of smart devices and the benefits it provide for supporting people in their daily lives. Many of the available datasets for fine-grained primitive activity recognition focus on locomotion or sports activities with less emphasis on real-world day-to-day behavior. This paper presents a new dataset for activity recognition in a realistic unmodified kitchen environment. Data was collected using only smart-watches from 10 lay participants while they prepared food in an unmodified rented kitchen. The paper also providing baseline performance measures for different classifiers on this dataset. Moreover, a deep feature learning system and more traditional statistical features based approaches are compared. This analysis shows that - for all evaluation criteria - data-driven feature learning allows the classifier to achieve best performance compared with hand-crafted features.
A Methodology for Privacy-Aware IoT-Forensics

The Internet of Things (IoT) brings new challenges to digital forensics. Given the number and heterogeneity of devices in such scenarios, it bring extremely difficult to carry out investigations without the cooperation of individuals. Even if they are not directly involved in the offense, their devices can yield digital evidence that might provide useful clarification in an investigation. However, when providing such evidence they may leak sensitive personal information. This paper proposes PRoFIT; a new model for IoT-forensics that takes privacy into consideration by incorporating the requirements of ISO/IEC 29100:2011 throughout the investigation life cycle. PRoFIT is intended to lay the groundwork for the voluntary cooperation of individuals in cyber crime investigations.
The Use of Mobile Devices in Aiding Dietary Assessment and Evaluation

There is a growing concern about chronic diseases and other health problems related to diet including obesity and cancer. The need to accurately measure diet (what foods a person consumes) becomes imperative. Dietary intake provides valuable insights for mounting intervention programs for prevention of chronic diseases. Measuring accurate dietary intake is considered to be an open research problem in the nutrition and health fields. In this paper, we describe a novel mobile telephone food record that will provide an accurate account of daily food and nutrient intake. Our approach includes the use of image analysis tools for identification and quantification of food that is consumed at a meal. Images obtained before and after foods are eaten are used to estimate the amount and type of food consumed. The mobile device provides a unique vehicle for collecting dietary information that reduces the burden on respondents that are obtained using more classical approaches for dietary assessment. We describe our approach to image analysis that includes the segmentation of food items, features used to identify foods, a method for automatic portion estimation, and our overall system architecture for collecting the food intake information.
Forensic carving of network packets and associated data structures

Using validated carving techniques, we show that popular operating systems (e.g. Windows, Linux, and OSX) frequently have residual IP packets, Ethernet frames, and associated data structures present in system memory from long-terminated network traffic. Such information is useful for many forensic purposes including establishment of prior connection activity and services used; identification of other systems present on the system’s LAN or WLAN; geolocation of the host computer system; and cross-drive analysis. We show that network structures can also be recovered from memory that is persisted onto a mass storage medium during the course of system swapping or hibernation. We present our network carving techniques, algorithms and tools, and validate these against both purpose-built memory images and a readily available forensic corpora. These techniques are valuable to both forensics tasks, particularly in analyzing mobile devices, and to cyber-security objectives such as malware analysis.
The Eucalyptus Open-Source Cloud-Computing System

Cloud computing systems fundamentally provide access to large pools of data and computational resources through a variety of interfaces similar in spirit to existing grid and HPC resource management and programming systems.  These types of systems offer a new programming target for scalable application developers and have gained popularity over the past few years.  However, most cloud computing systems in operation today are proprietary, rely upon infrastructure that is invisible to the research community, or are not explicitly designed to be instrumented and modified by systems researchers. In this work, we present Eucalyptus -- an open-source software framework for cloud computing that implements what is commonly referred to as Infrastructure as a Service (IaaS); systems that give users the ability to run and control entire virtual machine instances deployed across a variety physical resources. We outline the basic principles of the Eucalyptus design, detail important operational aspects of the system, and discuss architectural trade-offs that we have made in order to allow Eucalyptus to be portable, modular and simple to use on infrastructure commonly found within academic settings.  Finally, we provide evidence that Eucalyptus enables users familiar with existing Grid and HPC systems to explore new cloud computing functionality while maintaining access to existing, familiar application development software and Grid middle-ware.
Video-based face recognition using ensemble of haar-like deep convolutional neural networks

Growing number of surveillance and biometric applications seek to recognize the face of individuals appearing in the viewpoint of video cameras. Systems for video-based FR can be subjected to challenging operational environments, where the appearance of faces captured with video cameras varies significantly due to changes in pose, illumination, scale, blur, expression, occlusion, etc. In particular, with still-to-video FR, a limited number of high-quality facial images are typically captured for enrollment of an individual to the system, whereas an abundance facial trajectories can be captured using video cameras during operations, under different viewpoints and uncontrolled conditions. This paper presents a deep learning architecture that can learn a robust facial representation for each target individual during enrollment, and then accurately compare the facial regions of interest (ROIs) extracted from a still reference image (of the target individual) with ROIs extracted from live or archived videos. An ensemble of deep convolutional neural networks (DCNNs) named HaarNet is proposed, where a trunk network first extracts features from the global appearance of the facial ROIs (holistic representation). Then, three branch networks effectively embed asymmetrical and complex facial features (local representations) based on Haar-like features. In order to increase the discriminativness of face representations, a novel regularized triplet-loss function is proposed that reduces the intra-class variations, while increasing the inter-class variations. Given the single reference still per target individual, the robustness of the proposed DCNN is further improved by fine-tuning the HaarNet with synthetically-generated facial still ROIs that emulate capture conditions found in operational environments. The proposed system is evaluated on stills and videos from the challenging COX Face and Chokepoint datasets according to accuracy and complexity. Experimental results indicate that the proposed method can significantly improve performance with respect to state-of-the-art systems for video-based FR.
Towards a Research Framework on Requirements Prioritization

There exist a large number of approaches for prioritization of software requirements. Despite of several empirical studies, there is still a lack of evidence of which approaches that are to prefer, since different studies have resulted in different conclusions. Reasons may be due to differences in contexts, variables measured, and data sets used. This paper presents a research framework for studies about requirements prioritization, which aims to enable building a more consistent knowledge base and stronger evidence. The framework facilitates comparison, replication, and high-level analysis of prioritization approaches by proposing suitable variables to measure. The basis of the framework comes from a systematic review conducted on requirements prioritization techniques, and is further refined through literature studies of similar frameworks in related areas, and in a research workshop. The framework supports researchers in conducting and reporting prioritization studies, and supports practitioners in getting information about different approaches.
EIE: Efficient Inference Engine on Compressed Deep Neural Network

State-of-the-art deep neural networks (DNNs) have hundreds of millions of connections and are both computationally and memory intensive, making them difficult to deploy on embedded systems with limited hardware resources and power budgets. While custom hardware helps the computation, fetching weights from DRAM is two orders of magnitude more expensive than ALU operations, and dominates the required power.
 Previously proposed 'Deep Compression' makes it possible to fit large DNNs (AlexNet and VGGNet) fully in on-chip SRAM. This compression is achieved by pruning the redundant connections and having multiple connections share the same weight. We propose an energy efficient inference engine (EIE) that performs inference on this compressed network model and accelerates the resulting sparse matrix-vector multiplication with weight sharing. Going from DRAM to SRAM gives EIE 120× energy saving; Exploiting sparsity saves 10×; Weight sharing gives 8×; Skipping zero activations from ReLU saves another 3×. Evaluated on nine DNN benchmarks, EIE is 189× and 13× faster when compared to CPU and GPU implementations of the same DNN without compression. EIE has a processing power of 102 GOPS working directly on a compressed network, corresponding to 3 TOPS on an uncompressed network, and processes FC layers of AlexNet at 1.88×104 frames/sec with a power dissipation of only 600mW. It is 24,000× and 3,400× more energy efficient than a CPU and GPU respectively. Compared with DaDianNao, EIE has 2.9×, 19× and 3× better throughput, energy efficiency and area efficiency.
Bridging Between Computer and Robot Vision Through Data Augmentation: A Case Study on Object Recognition

Despite the impressive progress brought by deep network in visual object recognition, robot vision is still far from being a solved problem. The most successful convolutional architectures are developed starting from ImageNet, a large scale collection of images of object categories downloaded from the Web. This kind of images is very different from the situated and embodied visual experience of robots deployed in unconstrained settings. To reduce the gap between these two visual experiences, this paper proposes a simple yet effective data augmentation layer that zooms on the object of interest and simulates the object detection outcome of a robot vision system. The layer, that can be used with any convolutional deep architecture, brings to an increase in object recognition performance of up to 7%, in experiments performed over three different benchmark databases. Upon acceptance of the paper, our robot data augmentation layer will be made publicly available.
Efficient Program Analyses Using Deductive and Semantic Methodologies

Program analysis is the process of gathering deeper insights about a source code and analysing them to resolve software problems of arbitrary complexity. The key challenge in program analysis is to keep it fast, precise and straightforward. This research focuses on three key objectives to achieve an efficient program analysis: (i) expressive data representation, (ii) optimised data structure and (iii) fast data processing mechanisms. State of the art technologies such as Resource Description Framework (RDF) as data representation format, triplestores as the storage &amp; processing layer, and datalog to represent program analysis rules are considered in our research. diagram(BDD) to be embedded in the triplestore. Additionally, an ontology is being designed to standardise the definitions of concepts and representation of the knowledge in the program analysis domain.
Benchmarking personal cloud storage

Personal cloud storage services are data-intensive applications already producing a significant share of Internet traffic. Several solutions offered by different companies attract more and more people. However, little is known about each service capabilities, architecture and -- most of all -- performance implications of design choices. This paper presents a methodology to study cloud storage services. We apply our methodology to compare 5 popular offers, revealing different system architectures and capabilities. The implications on performance of different designs are assessed executing a series of benchmarks. Our results show no clear winner, with all services suffering from some limitations or having potential for improvement. In some scenarios, the upload of the same file set can take seven times more, wasting twice as much capacity. Our methodology and results are useful thus as both benchmark and guideline for system design.
Human facial illustrations: Creation and psychophysical evaluation

We present a method for creating black-and-white illustrations from photographs of human faces. In addition an interactive technique is demonstrated for deforming these black-and-white facial illustrations to create caricatures which highlight and exaggerate representative facial features. We evaluate the effectiveness of the resulting images through psychophysical studies to assess accuracy and speed in both recognition and learning tasks. These studies show that the facial illustrations and caricatures generated using our techniques are as effective as photographs in recognition tasks. For the learning task we find that illustrations are learned two times faster than photographs and caricatures are learned one and a half times faster than photographs. Because our techniques produce images that are effective at communicating complex information, they are useful in a number of potential applications, ranging from entertainment and education to low bandwidth telecommunications and psychology research.
Parsing Algebraic Word Problems into Equations

This paper formalizes the problem of solving multi-sentence algebraic word problems as that of generating and scoring equation trees. We use integer linear programming to generate equation trees and score their likelihood by learning local and global discriminative models. These models are trained on a small set of word problems and their answers, without any manual annotation, in order to choose the equation that best matches the problem text. We refer to the overall system as Alges. We compare Alges with previous work and show that it covers the full gamut of arithmetic operations whereas Hosseini et al. (2014) only handle addition and subtraction. In addition, Alges overcomes the brittleness of the Kushman et al. (2014) approach on single-equation problems, yielding a 15% to 50% reduction in error.