nils-herrmann/clean_scirepeval huggingface dataset:
Longitudinal Analysis of the Role of Perceived Self-Efficacy for Self-Regulated Learning in Academic Continuance and Achievement

The present study examined the developmental course of perceived efficacy for self-regulated learning and its contribution to academic achievement and likelihood of remaining in school in a sample of 412 Italian students (48% males and 52% females ranging in age from 12 to 22 years). Latent growth curve analysis revealed a progressive decline in self-regulatory efficacy from junior to senior high school, with males experiencing the greater reduction. The lower the decline in self-regulatory efficacy, the higher the high school grades and the greater the likelihood of remaining in high school controlling for socioeconomic status. Reciprocal cross-lagged models revealed that high perceived efficacy for self-regulated learning in junior high school contributed to junior high school grades and self-regulatory efficacy in high school, which partially mediated the relation of junior high grades on high school grades and the likelihood of remaining in school. Socioeconomic status contributed to high school grades only mediationally through junior high grades and to school drop out both directly and mediationally through junior high grades.
Pig Identification Based on MXNet

We tried the challenge of pig identification on MXNet, which is the new Amazon’s deep learning framework. By using the feature-based transfer learning method with fine-tuning, the features of the pre-trained Convolutional Neural Network(CNN) model were stitched together with the random weight output layer, and then input Target data and update the new fine-tuning model with a lower learning rate by back-propagation strategy. The results show that this method can achieve high enough accuracy in less time and is more efficient than directly constructing a new CNN model. In addition, we also tried data-augmentation methods to scale up the original dataset to improve accuracy.
Factors Affecting the Successful Implementation of ICT Projects in Government

A government is a huge and complex organisation, whose operations and strategic focus could be greatly enhanced by the well focussed application of Information and Communication Technologies (ICT) to support improvements in productivity, management effectiveness and ultimately, the quality of services offered to citizens. While the benefits of ICT in government cannot be disputed, there are several concerns about its success as well as the strategies to be adopted in implementation of systems in various countries. In this paper, the characteristic challenges that developing nations face, which make ICT implementation in government fail to succeed are identified and synthesised. The paper presents results of literature review of case studies from both developed and developing countries and preliminary studies grounded in the Kenya e-Government reality. The key factors are identified, synthesised and categorised under common broad categories. This results in a rich picture of ICT implementation experience that helps to identify possible solutions. A descriptive framework for categorising key factors in ICT implementation in government illustrated with references to the literature is proposed. The input variables are categorised into factors for success (drivers and enablers), and factors for failure (barriers and inhibitors). The output variables are categorized into organisational and technological benefits. Finally, an action for success is proposed. This action includes suggestions for increasing the impact of factors for success while reducing the impact of factors for failure and use of available good practice.
Human-in-the-Loop Challenges for Entity Matching: A Midterm Report

Entity matching (EM) has been a long-standing challenge in data management. In the past few years we have started two major projects on EM (Magellan and Corleone/Falcon). These projects have raised many human-in-the-loop (HIL) challenges. In this paper we discuss these challenges. In particular, we show how these challenges forced us to revise our solution architecture, from a typical RDBMS-style architecture to a very human-centric one, in which human users are first-class objects driving the EM process, using tools at pain-point places. We discuss how such solution architectures can be viewed as combining "tools in the loop" with "human in the loop". Finally, we discuss lessons learned which can potentially be applied to other problem settings. We also hope that more researchers will investigate EM, as it can be a rich "playground" for HIL research.
How Well Sentence Embeddings Capture Meaning

Several approaches for embedding a sentence into a vector space have been developed. However, it is unclear to what extent the sentence's position in the vector space reflects its semantic meaning, rather than other factors such as syntactic structure. Depending on the model used for the embeddings this will vary -- different models are suited for different down-stream applications. For applications such as machine translation and automated summarization, it is highly desirable to have semantic meaning encoded in the embedding. We consider this to be the quality of semantic localization for the model -- how well the sentences' meanings coincides with their embedding's position in vector space. Currently the semantic localization is assessed indirectly through practical benchmarks for specific applications.
 In this paper, we ground the semantic localization problem through a semantic classification task. The task is to classify sentences according to their meaning. A SVM with a linear kernel is used to perform the classification using the sentence vectors as its input. The sentences from subsets of two corpora, the Microsoft Research Paraphrase corpus and the Opinosis corpus, were partitioned according to their semantic equivalence. These partitions give the target classes for the classification task. Several existing models, including URAE, PV--DM and PV--DBOW, were assessed against a bag of words benchmark.
Deep Learning Based Forecasting of Critical Infrastructure Data

Intelligent monitoring and control of critical infrastructure such as electric power grids, public water utilities and transportation systems produces massive volumes of time series data from heterogeneous sensor networks. Time Series Forecasting (TSF) is essential for system safety and security, and also for improving the efficiency and quality of service delivery. Being highly dependent on various external factors, the observed system behavior is usually stochastic, which makes the next value prediction a tricky and challenging task that usually needs customized methods. In this paper we propose a novel deep learning based framework for time series analysis and prediction by ensembling parametric and nonparametric methods. Our approach takes advantage of extracting features at different time scales, which improves accuracy without compromising reliability in comparison with the state-of-the-art methods. Our experimental evaluation using real-world SCADA data from a municipal water management system shows that our proposed method outperforms the baseline methods evaluated here.
Implications of buyer decision theory for design of e-commerce websites

In the rush to open their website, e-commerce sites too often fail to support buyer decision making and search, resulting in a loss of sale and the customer’s repeat business. This paper reviews why this occurs and the failure of many B2C and B2B website executives to understand that appropriate decision support and search technology can’t be fully bought off-the-shelf. Our contention is that significant investment and effort is required at any given website in order to create the decision support and search agents needed to properly support buyer decision making. We provide a framework to guide such effort (derived from buyer behavior choice theory); review the open problems that e-catalog sites pose to the framework and to existing search engine technology; discuss underlying design principles and guidelines; validate the framework and guidelines with a case study; and discuss lessons learned and steps needed to better support buyer decision behavior in the future. Future needs are also pinpointed.
Document Summarization Using Conditional Random Fields

Desktop users commonly work on multiple tasks. The TaskTracer system provides a convenient, lowcost way for such users to define a hierarchy of tasks and to associate resources with those tasks. With this information, TaskTracer then supports the multi-tasking user by configuring the computer for the current task. To do this, it must detect when the user switches the task and identify the user’s current task at all times. This problem of “task switch detection” is a special case of the general problem of change-point detection. It involves monitoring the behavior of the user and predicting in real time when the user moves from one task to another. We present a framework that analyzes a sequence of observations to detect task switches. First, a classifier is trained discriminatively to predict the current task based only on features extracted from the window in focus. Second, multiple single-window predictions (specifically, the class probability estimates) are combined to obtain more reliable predictions. This paper studies three such combination methods: (a) simple voting, (b) a likelihood ratio test that assesses the variability of the task probabilities over the sequence of windows, and (c) application of the Viterbi algorithm under an assumed task transition cost model. Experimental results show that all three methods improve over the single-window predictions and that the Viterbi approach gives the best results.
User privacy risk calculator

User privacy is becoming an issue on the Internet due to common data breaches and various security threats. Services tend to require private user data in order to provide more personalized content and users are typically unaware of potential risks to their privacy. This paper proposes a risk calculator based on a feedforward neural network that will provide users with an ability to calculate risks to their privacy. The proposed calculator is evaluated on a set of real world example scenarios. Furthermore, to give more insight into privacy issues, each estimated risk is explained by several real life scenarios that might happen if the observed parameters are obtained by an attacker. In turn, this should raise user awareness and knowledge about privacy issues on the Internet.
A touch-less fingerphoto recognition system for mobile hand-held devices

Fingerphoto is an image of a human finger obtained with the help of an ordinary camera. Its acquisition is convenient and does not require any particular biometric scanner. The high degree of freedom in finger positioning introduces challenges to its recognition. This paper proposes a fingerphoto based human authentication system for mobile hand-held devices by using a non-conventional scale-invariant features. The system utilizes built-in camera of the mobile devices to acquire biometric sample and therefore, eliminates the dependence over specifics scanners. It can successfully handle some of the issues like orientation, rotation and lack of registration at the time of matching. It has achieved CRR of 96.67% and EER of 3.33% which is better than any other system available in the literature.
Improved Training of Generative Adversarial Networks Using Representative Features

Despite the success of generative adversarial networks (GANs) for image generation, the trade-off between visual quality and image diversity remains a significant issue. This paper achieves both aims simultaneously by improving the stability of training GANs. The key idea of the proposed approach is to implicitly regularize the discriminator using representative features. Focusing on the fact that standard GAN minimizes reverse Kullback-Leibler (KL) divergence, we transfer the representative feature, which is extracted from the data distribution using a pre-trained autoencoder (AE), to the discriminator of standard GANs. Because the AE learns to minimize forward KL divergence, our GAN training with representative features is influenced by both reverse and forward KL divergence. Consequently, the proposed approach is verified to improve visual quality and diversity of state of the art GANs using extensive evaluations.
Internet of things security: challenges and perspectives

No one can deny that the Internet of Things (IOT) will revolutionize our daily thanks to its many benefits in order to improve and simplify people's lives. Us any new technology the internet of things has a number of problems that prevents it to reach its full potential. Security is the major issues that face the IOT. In this article, we discuss about the Internet of things and its security challenges. We also discuss some major attacks and solutions that can be implemented against these attacks. Finally, the paper presents future directions for securing the IoT.
Sustainable construction: construction and demolition waste reconsidered.

Construction activity in Europe has increased substantially in the past decade. Likewise, there has also been a commensurate rise in the generation of construction and demolition waste (C&DW). This, together with the fact that in many European countries the rate of recycling and reuse of C&DW is still quite low has engendered a serious environmental problem and a motivation to develop strategies and management plans to solve it. Due to its composition, there is a significant potential to reuse and/or recycle C&DW, and thereby, contribute to improving the sustainability of construction and development, but practical procedures are not yet widely known or practiced in the construction industry. This article (a) summarizes the different applications that are presently practiced to optimize the recovery and/or application of C&DW for reuse, and (b) proposes various measures and strategies to improve the processing of this waste. The authors suggest that to enhance environmental effectiveness, a conscious and comprehensive C&DW management plan should be implemented in each jurisdiction. More precisely, this study presents a holistic approach towards C&DW management, through which environmental benefits can be achieved through the application of new construction methods that can contribute to sustainable growth.
Chinese Poetry Generation with a Working Memory Model

As an exquisite and concise literary form, poetry is a gem of human culture. Automatic poetry generation is an essential step towards computer creativity. In recent years, several neural models have been designed for this task. However, among lines of a whole poem, the coherence in meaning and topics still remains a big challenge. In this paper, inspired by the theoretical concept in cognitive psychology, we propose a novel Working Memory model for poetry generation. Different from previous methods, our model explicitly maintains topics and informative limited history in a neural memory. During the generation process, our model reads the most relevant parts from memory slots to generate the current line. After each line is generated, it writes the most salient parts of the previous line into memory slots. By dynamic manipulation of the memory, our model keeps a coherent information flow and learns to express each topic flexibly and naturally. We experiment on three different genres of Chinese poetry: quatrain, iambic and chinoiserie lyric. Both automatic and human evaluation results show that our model outperforms current state-ofthe-art methods.
A trust-semantic fusion-based recommendation approach for e-business applications

a r t i c l e i n f o Collaborative Filtering (CF) is the most popular recommendation technique but still suffers from data sparsi-ty, user and item cold-start problems, resulting in poor recommendation accuracy and reduced coverage. This study incorporates additional information from the users' social trust network and the items' semantic domain knowledge to alleviate these problems. It proposes an innovative Trust–Semantic Fusion (TSF)-based recommendation approach within the CF framework. Experiments demonstrate that the TSF approach significantly outperforms existing recommendation algorithms in terms of recommendation accuracy and coverage when dealing with the above problems. A business-to-business recommender system case study validates the applicability of the TSF approach. Recommender systems are considered the most popular forms of web personalization and have become a promising and important research topic in information sciences and decision support systems Recommender systems are used to either predict whether a particular user will like a particular item or to identify a set of k items that will be of interest to a certain user, and have been used in different web-based applications including e-business, e-learning and e-tourism [8,22,31]. Currently, Collaborative Filtering (CF) is probably the most known and commonly used recommendation approach in recommender systems. CF works by collecting user ratings for items in a given domain and computing similarities between users or between items in order to produce recommendations [1,31]. CF can be further divided into user-based and item-based CF approaches. In user-based CF approach, a user will receive recommendations of items that similar users liked. In item-based CF approach, a user will receive recommendations of items that are similar to the ones that the user liked in the past . Despite their popularity and success, the CF-based approaches still suffer from some major limitations ; these include data sparsity, cold-start user and cold-start item problems [1,3,36,37]. The data sparsity problem occurs when the number of available items increases and the number of ratings in the rating matrix is insufficient for generating accurate predictions. When the ratings obtained are very small compared to the number of ratings that are needed to be predicted, a recommender system becomes unable to locate similar neighbors and produces poor recommendations. The cold-start (CS) user problem, which is also known as the new user problem, affects users who have none, or a small number of ratings. When the number of rated items is small for the CS user, …
Dynapack: space-time compression of the 3D animations of triangle meshes with fixed connectivity

Dynapack exploits space-time coherence to compress the consecutive frames of the 3D animations of triangle meshes of constant connectivity. Instead of compressing each frame independently (space-only compression) or compressing the trajectory of each vertex independently (time-only compression), we predict the position of each vertex v of frame f from three of its neighbors in frame f and from the positions of v and of these neighbors in the previous frame (space-time compression). We introduce here two extrapolating spacetime predictors: the ELP extension of the Lorenzo predictor, developed originally for compressing regularly sampled 4D data sets, and the Replica predictor. ELP may be computed using only additions and subtractions of points and is a perfect predictor for portions of the animation undergoing pure translations. The Replica predictor is slightly more expensive to compute, but is a perfect predictor for arbitrary combinations of translations, rotations, and uniform scaling. For the typical 3D animations that we have compressed, the corrections between the actual and predicted value of the vertex coordinates may be compressed using entropy coding down to an average ranging between 1.37 and 2.91 bits, when the quantization used ranges between 7 and 13 bits. In comparison, space-only compression yields a range of 1.90 to 7.19 bits per coordinate and time-only compressions yields a range of 1.77 to 6.91 bits per coordinate. The implementation of the Dynapack compression and decompression is trivial and extremely fast. It perform a sweep through the animation, only accessing two consecutive frames at a time. Therefore, it is particularly well suited for realtime and out-of-core compression, and for streaming decompression.
A Personalized Courseware Recommendation System Based on Fuzzy

With the rapid growth of computer and Internet technologies, e-learning has become a major trend in the computer assisted teaching and learning field currently. In past years, many researchers made effort in developing elearning systems with personalized learning mechanism to assist on-line learning. However, most of them focused on using learner’s behaviors, interests, and habits to provide personalized e-learning services. These systems usually neglected to concern if learner’s ability and the difficulty of courseware are matched each other. Generally, recommending an inappropriate courseware might result in learner’s cognitive overhead or disorientation during a learning process. To promote learning efficiency and effectiveness, this paper presents a personalized courseware recommendation system (PCRS) based on the proposed fuzzy item response theory (FIRT), which can recommend courseware with appropriate difficult level to learner through learner gives a fuzzy response of understanding percentage for the learned courseware. Therefore, this paper will deal with how to modify the traditional item response theory (IRT) to handle non-crisp fuzzy response and estimate learner’s ability via the revised estimating function of learner’s ability. Based on the courseware’s modeling requirement, this paper also proposes a courseware modeling process to determine the difficulty parameters of courseware and design the content of courseware for personalized courseware recommendation services. Experiment results show that applying the proposed fuzzy item response theory to Webbased learning can achieve personalized learning and help learners to learn more effectively and efficiently.
Cloaker: Hardware Supported Rootkit Concealment

Rootkits are used by malicious attackers who desire to run software on a compromised machine without being detected. They have become stealthier over the years as a consequence of the ongoing struggle between attackers and system defenders. In order to explore the next step in rootkit evolution and to build strong defenses, we look at this issue from the point of view of an attacker. We construct Cloaker, a proof-of-concept rootkit for the ARM platform that is non- persistent and only relies on hardware state modifications for concealment and operation. A primary goal in the design of Cloaker is to not alter any part of the host operating system (OS) code or data, thereby achieving immunity to all existing rootkit detection techniques which perform integrity, behavior and signature checks of the host OS. Cloaker also demonstrates that a self-contained execution environment for malicious code can be provided without relying on the host OS for any services. Integrity checks of hardware state in each of the machine's devices are required in order to detect rootkits such as Cloaker. We present a framework for the Linux kernel that incorporates integrity checks of hardware state performed by device drivers in order to counter the threat posed by rootkits such as Cloaker.