nils-herrmann/clean_scirepeval huggingface dataset:
The Effects of Shopping Orientations , Online Trust and Prior Online Purchase Experience toward Customers ’ Online Purchase Intention

The advancement of the World Wide Web has resulted in the creation of a new form of retail transactionselectronic retailing (e-tailing) or web-shopping. Thus, customers’ involvements in online purchasing have become an important trend. As such, it is vital to identify the determinants of the customer online purchase intention. The aim of this research is to evaluate the impacts of shopping orientations, online trust and prior online purchase experience to the customer online purchase intention. A total of 242 undergraduate information technology students from a private university in Malaysia participated in this research. The findings revealed that impulse purchase intention, quality orientation, brand orientation, online trust and prior online purchase experience were positively related to the customer online purchase intention.
Algorithms for image processing and computer vision

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Automated Software Test Data Generation

Abstracr-Test data generation in program testing is the process of identifying a set of test data which satisfies given testing criterion. Most of the existing test data generators 161, [It], [lo],  [30] use symbolic evaluation to derive test data. However, in practical programs this technique frequently requires complex algebraic manipulations, especially in the presence of arrays. In this paper we present an alternative approach of test data generation which is based on actual execution of the program under test, function minimization methods, and dynamic data flow analysis. Test data are developed for the program using actual values of input variables. When the program is executed, the program execution flow is monitored. If during program execution an undesirable execution flow is observed (e.g., the “actual” path does not correspond to the selected control path) then function minimization search algorithms are used to automatically locate the values of input variables for which the selected path is traversed. In addition, dynamic data Bow analysis is used to determine those input variables responsible for the undesirable program behavior, leading to significant speedup of the search process. The approach of generating test data is then extended to programs with dynamic data structures, and a search method based on dynamic data flow analysis and backtracking is presented. In the approach described in this paper, values of array indexes and pointers are known at each step of program execution, and this approach exploits this information to overcome difficulties of array and pointer handling; as a result, the effectiveness of test data generation can be significantly improved.
Exploring Models and Data for Image Question Answering

This work aims to address the problem of imagebased question-answering (QA) with new models and datasets. In our work, we propose to use neural networks and visual semantic embeddings, without intermediate stages such as object detection and image segmentation, to predict answers to simple questions about images. Our model performs 1.8 times better than the only published results on an existing image QA dataset. We also present a question generation algorithm that converts image descriptions, which are widely available, into QA form. We used this algorithm to produce an order-of-magnitude larger dataset, with more evenly distributed answers. A suite of baseline results on this new dataset are also presented.
Learning Text Similarity with Siamese Recurrent Networks

This paper presents a deep architecture for learning a similarity metric on variablelength character sequences. The model combines a stack of character-level bidirectional LSTM’s with a Siamese architecture. It learns to project variablelength strings into a fixed-dimensional embedding space by using only information about the similarity between pairs of strings. This model is applied to the task of job title normalization based on a manually annotated taxonomy. A small data set is incrementally expanded and augmented with new sources of variance. The model learns a representation that is selective to differences in the input that reflect semantic differences (e.g., “Java developer” vs. “HR manager”) but also invariant to nonsemantic string differences (e.g., “Java developer” vs. “Java programmer”).
Solid State Drives: The Beginning of the End for Current Practice in Digital Forensic Recovery?

Digital evidence is increasingly relied upon in computer forensic examinations and legal proceedings in the modern courtroom. The primary storage technology used for digital information has remained constant over the last two decades, in the form of the magnetic disc. Consequently, investigative, forensic, and judicial procedures are well-established for magnetic disc storage devices (Carrier, 2005). However, a paradigm shift has taken place in technology storage and complex, transistor-based devices for primary storage are now increasingly common. Most people are aware of the transition from portable magnetic floppy discs to portable USB transistor flash devices, yet the transition from magnetic hard drives to solid-state drives inside modern computers has so far attracted very little attention from the research community. Here we show that it is imprudent and potentially reckless to rely on existing evidence collection processes and procedures, and we demonstrate that conventional assumptions about the behaviour of storage media are no longer valid. In particular, we demonstrate that modern storage devices can operate under their own volition in the absence of computer instructions. Such operations are highly destructive of traditionally recoverable data. This can contaminate evidence; can obfuscate and make validation of digital evidence reports difficult; can complicate the process of live and dead analysis recovery; and can complicate and frustrate the post recovery forensic analysis. Our experimental findings demonstrate that solid-state drives (SSDs) have the capacity to destroy evidence catastrophically under their own volition, in the absence of specific instructions to do so from a computer.
Evolutionary Structural Analysis of the Bitcoin Network

We model the Bitcoin transaction graph in small time slices using several different models, including one of our own. In order to gauge the effectiveness of each model, we compare some of the attributes of the generated graphs, like diameter, clustering, etc. While our model captures some features of the Bitcoin graph, none of the models fully capture its complexity. We also present a possible algorithm for detection of money laundering, and show that it does detect abnormalities present in Bitcoin but not present in our models.
State-Regularized Recurrent Neural Networks

Recurrent neural networks are a widely used class of neural architectures. They have, however, two shortcomings. First, it is difficult to understand what exactly they learn. Second, they tend to work poorly on sequences requiring long-term memorization, despite having this capacity in principle. We aim to address both shortcomings with a class of recurrent networks that use a stochastic state transition mechanism between cell applications. This mechanism, which we term stateregularization, makes RNNs transition between a finite set of learnable states. We evaluate stateregularized RNNs on (1) regular languages for the purpose of automata extraction; (2) nonregular languages such as balanced parentheses, palindromes, and the copy task where external memory is required; and (3) real-word sequence learning tasks for sentiment analysis, visual object recognition, and language modeling. We show that state-regularization (a) simplifies the extraction of finite state automata modeling an RNN’s state transition dynamics; (b) forces RNNs to operate more like automata with external memory and less like finite state machines; (c) makes RNNs have better interpretability and explainability.
Composable Planning with Attributes

The tasks that an agent will need to solve often are not known during training. However, if the agent knows which properties of the environment are important then, after learning how its actions affect those properties, it may be able to use this knowledge to solve complex tasks without training specifically for them. Towards this end, we consider a setup in which an environment is augmented with a set of user defined attributes that parameterize the features of interest. We propose a method that learns a policy for transitioning between “nearby” sets of attributes, and maintains a graph of possible transitions. Given a task at test time that can be expressed in terms of a target set of attributes, and a current state, our model infers the attributes of the current state and searches over paths through attribute space to get a high level plan, and then uses its low level policy to execute the plan. We show in 3D block stacking, gridworld games, and StarCraft that our model is able to generalize to longer, more complex tasks at test time by composing simpler learned policies.
The Role of Machine Vision for Intelligent Vehicles

Humans assimilate information from the traffic environment mainly through visual perception. Obviously, the dominant information required to conduct a vehicle can be acquired with visual sensors. However, in contrast to most other sensor principles, video signals contain relevant information in a highly indirect manner and hence visual sensing requires sophisticated machine vision and image understanding techniques. This paper provides an overview on the state of research in the field of machine vision for intelligent vehicles. The functional spectrum addressed covers the range from advanced driver assistance systems to autonomous driving. The organization of the article adopts the typical order in image processing pipelines that successively condense the rich information and vast amount of data in video sequences. Data-intensive low-level “early vision” techniques first extract features that are later grouped and further processed to obtain information of direct relevance for vehicle guidance. Recognition and classification schemes allow to identify specific objects in a traffic scene. Recently, semantic labeling techniques using convolutional neural networks have achieved impressive results in this field. High-level decisions of intelligent vehicles are often influenced by map data. The emerging role of machine vision in the mapping and localization process is illustrated at the example of autonomous driving. Scene representation methods are discussed that organize the information from all sensors and data sources and thus build the interface between perception and planning. Recently, vision benchmarks have been tailored to various tasks in traffic scene perception that provide a metric for the rich diversity of machine vision methods. Finally, the paper addresses computing architectures suited to real-time implementation. Throughout the paper, numerous specific examples and real world experiments with prototype vehicles are presented.
An integrated framework for project portfolio selection

The task of selecting project portfolios is an important and recurring activity in many organizations. There are many techniques available to assist in this process, but no integrated framework for carrying it out. This paper simpli®es the project portfolio selection process by developing a framework which separates the work into distinct stages. Each stage accomplishes a particular objective and creates inputs to the next stage. At the same time, users are free to choose the techniques they ®nd the most suitable for each stage, or in some cases to omit or modify a stage if this will simplify and expedite the process. The framework may be implemented in the form of a decision support system, and a prototype system is described which supports many of the related decision making activities.
Fusion4D: real-time performance capture of challenging scenes

We contribute a new pipeline for live multi-view performance capture, generating temporally coherent high-quality reconstructions in real-time. Our algorithm supports both incremental reconstruction, improving the surface estimation over time, as well as parameterizing the nonrigid scene motion. Our approach is highly robust to both large frame-to-frame motion and topology changes, allowing us to reconstruct extremely challenging scenes. We demonstrate advantages over related real-time techniques that either deform an online generated template or continually fuse depth data nonrigidly into a single reference model. Finally, we show geometric reconstruction results on par with offline methods which require orders of magnitude more processing time and many more RGBD cameras.
Mental Toughness and Individual Differences in Learning, Educational and Work Performance, Psychological Well-being, and Personality: A Systematic Review

Mental toughness (MT) is an umbrella term that entails positive psychological resources, which are crucial across a wide range of achievement contexts and in the domain of mental health. We systematically review empirical studies that explored the associations between the concept of MT and individual differences in learning, educational and work performance, psychological well-being, personality, and other psychological attributes. Studies that explored the genetic and environmental contributions to individual differences in MT are also reviewed. The findings suggest that MT is associated with various positive psychological traits, more efficient coping strategies and positive outcomes in education and mental health. Approximately 50% of the variation in MT can be accounted for by genetic factors. Furthermore, the associations between MT and psychological traits can be explained mainly by either common genetic or non-shared environmental factors. Taken together, our findings suggest a 'mental toughness advantage' with possible implications for developing interventions to facilitate achievement in a variety of settings.
Hardware for Machine Learning : Challenges and Opportunities ( Invited Paper )

Machine learning plays a critical role in extracting meaningful information out of the zetabytes of sensor data collected every day. For some applications, the goal is to analyze and understand the data to identify trends (e.g., surveillance, portable/wearable electronics); in other applications, the goal is to take immediate action based the data (e.g., robotics/drones, self-driving cars, smart Internet of Things). For many of these applications, local embedded processing near the sensor is preferred over the cloud due to privacy or latency concerns, or limitations in the communication bandwidth. However, at the sensor there are often stringent constraints on energy consumption and cost in addition to throughput and accuracy requirements. Furthermore, flexibility is often required such that the processing can be adapted for different applications or environments (e.g., update the weights and model in the classifier). In many applications, machine learning often involves transforming the input data into a higher dimensional space, which, along with programmable weights, increases data movement and consequently energy consumption. In this paper, we will discuss how these challenges can be addressed at various levels of hardware design ranging from architecture, hardware-friendly algorithms, mixed-signal circuits, and advanced technologies (including memories and sensors).
Building Large Machine Reading-Comprehension Datasets using Paragraph Vectors

We present a dual contribution to the task of machine reading-comprehension: a technique for creating large-sized machine-comprehension (MC) datasets using paragraph-vector models; and a novel, hybrid neural-network architecture that combines the representation power of recurrent neural networks with the discriminative power of fully-connected multi-layered networks. We use the MC-dataset generation technique to build a dataset of around 2 million examples, for which we empirically determine the high-ceiling of human performance (around 91% accuracy), as well as the performance of a variety of computer models. Among all the models we have experimented with, our hybrid neuralnetwork architecture achieves the highest performance (83.2% accuracy). The remaining gap to the human-performance ceiling provides enough room for future model improvements.
Comparing and combining sentiment analysis methods

Several messages express opinions about events, products, and services, political views or even their author's emotional state and mood. Sentiment analysis has been used in several applications including analysis of the repercussions of events in social networks, analysis of opinions about products and services, and simply to better understand aspects of social communication in Online Social Networks (OSNs). There are multiple methods for measuring sentiments, including lexical-based approaches and supervised machine learning methods. Despite the wide use and popularity of some methods, it is unclear which method is better for identifying the polarity (i.e., positive or negative) of a message as the current literature does not provide a method of comparison among existing methods. Such a comparison is crucial for understanding the potential limitations, advantages, and disadvantages of popular methods in analyzing the content of OSNs messages. Our study aims at filling this gap by presenting comparisons of eight popular sentiment analysis methods in terms of coverage (i.e., the fraction of messages whose sentiment is identified) and agreement (i.e., the fraction of identified sentiments that are in tune with ground truth). We develop a new method that combines existing approaches, providing the best coverage results and competitive agreement. We also present a free Web service called iFeel, which provides an open API for accessing and comparing results across different sentiment methods for a given text.
Towards a theory of individual differences in statistical learning.

In recent years, statistical learning (SL) research has seen a growing interest in tracking individual performance in SL tasks, mainly as a predictor of linguistic abilities. We review studies from this line of research and outline three presuppositions underlying the experimental approach they employ: (i) that SL is a unified theoretical construct; (ii) that current SL tasks are interchangeable, and equally valid for assessing SL ability; and (iii) that performance in the standard forced-choice test in the task is a good proxy of SL ability. We argue that these three critical presuppositions are subject to a number of theoretical and empirical issues. First, SL shows patterns of modality- and informational-specificity, suggesting that SL cannot be treated as a unified construct. Second, different SL tasks may tap into separate sub-components of SL that are not necessarily interchangeable. Third, the commonly used forced-choice tests in most SL tasks are subject to inherent limitations and confounds. As a first step, we offer a methodological approach that explicitly spells out a potential set of different SL dimensions, allowing for better transparency in choosing a specific SL task as a predictor of a given linguistic outcome. We then offer possible methodological solutions for better tracking and measuring SL ability. Taken together, these discussions provide a novel theoretical and methodological approach for assessing individual differences in SL, with clear testable predictions.This article is part of the themed issue 'New frontiers for statistical learning in the cognitive sciences'.
Weakly Supervised Learning for Whole Slide Lung Cancer Image Classification

Histopathology image analysis serves as the gold standard for diagnosis of cancer and is directly related to the subsequent therapeutic treatment. However, pixel-wise delineated annotations on whole slide images (WSIs) are time-consuming and tedious, which poses difficulties in building a large-scale training dataset. How to effectively utilize available whole slide image-level label, which can be easily acquired, for deep learning is quite appealing. The main barrier on this task is due to the heterogeneous patterns in fine magnification level but only the WSIlevel labels are provided. Furthermore, a gigapixel scale WSI can not be easily analysed due to the immeasurable computational cost. In this paper, we propose a weakly supervised approach for fast and effective classification on whole slide lung cancer images. Our method takes advantage of a patch-based fully convolutional network for discriminative block retrieval. Furthermore, context-aware feature selection and aggregation strategies are proposed to generate globally holistic WSI descriptor. Extensive experiments demonstrate that our method outperforms state-of-the-art methods by a large margin with accuracy of 97.1%. In addition, we highlight that a small number of available coarse annotations can contribute to further accuracy improvement. We believe that deep learning has great potential to assist pathologists for histology image diagnosis in the near future.