nils-herrmann/clean_scirepeval huggingface dataset:
LEARNING ENGLISH IS FUN VIA KAHOOT : STUDENTS ’ ATTITUDE , MOTIVATION AND PERCEPTIONS

This study is aimed to investigate primary level students’ attitude and motivation and perceptions in learning English using Kahoot game. According to current development in Malaysian education field, 21st century learning style is more focused and given importance. Thus, Kahoot, game-based learning platform has been used to stimulate students’ from a rural area in southern Malaysia to learn English as their second language more effectively, actively and interestingly. The research design for this study is action research. Nine students were chosen as the target group for this study. The Kahoot game was conducted for three different topics in English after they completed the lesson each day. The data was collected using Attitude and Motivation Test Battery questionnaires consisting of 10 items, interview session with 5 semi-structured questions for three selected students and results of each Kahoot game played by the students. The data was analysed using descriptive analysis. The results of the questionnaires, interview session and results of the game were shown in figures and tables. Findings of the study show that all the nine students were able to engage actively in the game and they were able to master the target language effectively. They enjoy learning English using games and wish to have more games in future.
Modeling Attention Flow on Graphs

Real-world scenarios demand reasoning about process, more than final outcome prediction, to discover latent causal chains and better understand complex systems. It requires the learning algorithms to offer both accurate predictions and clear interpretations. We design a set of trajectory reasoning tasks on graphs with only the source and the destination observed. We present the attention flow mechanism to explicitly model the reasoning process, leveraging the relational inductive biases by basing our models on graph networks. We study the way attention flow can effectively act on the underlying information flow implemented by message passing. Experiments demonstrate that the attention flow driven by and interacting with graph networks can provide higher accuracy in prediction and better interpretation for trajectory reasoning.
Constrained Hierarchical Clustering for News Events

Knowledge discovery from web news events has received great attention in recent years. In practice, this knowledge is a digital representation (virtual world) of various phenomena that occur in our physical world. Hierarchical clustering algorithms are used to organize related events into groups and subgroups according to some similarity measure. The main motivation for this organization is based on the hypothesis that if the user is interested in a specific event of a certain cluster, then the user may also be interested in other related events of this same cluster. However, existing event clustering methods do not effectively use the different types of information about events, such as temporal information, geographical data, name of people and organizations. In this paper, we propose the COH-KMeans algorithm (Constrained Hierarchical K-Means) that obtains a hierarchical clustering structure considering certain conditions imposed by the users, for example, events of similar content that occurred in nearby geographic locations or that occurred within a predefined time window. A statistical analysis of the experimental results reveals that the incorporation of constraints performed by COH-KMeans allows to obtain higher quality clusters when compared to a state-of-the-art unsupervised hierarchical clustering method. Moreover, we present our tool for exploratory analysis of events and we discuss how event clustering can be used to support the decision-making process from the perspective of a Data Analytics System.
Interest flooding attack and countermeasures in Named Data Networking

Distributed Denial of Service (DDoS) attacks are an ongoing problem in today's Internet, where packets from a large number of compromised hosts thwart the paths to the victim site and/or overload the victim machines. In a newly proposed future Internet architecture, Named Data Networking (NDN), end users request desired data by sending Interest packets, and the network delivers Data packets upon request only, effectively eliminating many existing DDoS attacks. However, an NDN network can be subject to a new type of DDoS attack, namely Interest packet flooding. In this paper we investigate effective solutions to mitigate Interest flooding. We show that NDN's inherent properties of storing per packet state on each router and maintaining flow balance (i.e., one Interest packet retrieves at most one Data packet) provides the basis for effective DDoS mitigation algorithms. Our evaluation through simulations shows that the solution can quickly and effectively respond and mitigate Interest flooding.
PivotPaths: Strolling through Faceted Information Spaces

We present PivotPaths, an interactive visualization for exploring faceted information resources. During both work and leisure, we increasingly interact with information spaces that contain multiple facets and relations, such as authors, keywords, and citations of academic publications, or actors and genres of movies. To navigate these interlinked resources today, one typically selects items from facet lists resulting in abrupt changes from one subset of data to another. While filtering is useful to retrieve results matching specific criteria, it can be difficult to see how facets and items relate and to comprehend the effect of filter operations. In contrast, the PivotPaths interface exposes faceted relations as visual paths in arrangements that invite the viewer to `take a stroll' through an information space. PivotPaths supports pivot operations as lightweight interaction techniques that trigger gradual transitions between views. We designed the interface to allow for casual traversal of large collections in an aesthetically pleasing manner that encourages exploration and serendipitous discoveries. This paper shares the findings from our iterative design-and-evaluation process that included semi-structured interviews and a two-week deployment of PivotPaths applied to a large database of academic publications.
Wordnet improves Text Document Clustering

Text document clustering plays an important role in providing intuitive navigation and browsing mechanisms by organizing large amounts of information into a small number of meaningful clusters. The bag of words representation used for these clustering methods is often unsatisfactory as it ignores relationships between important terms that do not co-occur literally. In order to deal with the problem, we integrate background knowledge — in our application Wordnet — into the process of clustering text documents. We cluster the documents by a standard partitional algorithm. Our experimental evaluation on Reuters newsfeeds compares clustering results with pre-categorizations of news. In the experiments, improvements of results by background knowledge compared to the baseline can be shown for many interesting tasks.
Full-Duplex Cooperative Non-Orthogonal Multiple Access With Beamforming and Energy Harvesting

In this paper, a novel communication scheme that combines beamforming, energy harvesting, and cooperative non-orthogonal multiple access (NOMA) system is introduced. In the proposed scheme, NOMA is first combined with beamforming to serve more than one user in each beamforming vector. Later, simultaneous wireless information and power transfer (SWIPT) technique is exploited to encourage the strong user to relay the weak user’s information messages in full-duplex (FD) manner. However, one major drawback of FD communications is the self-interference (SI) signal due to signal leakage from terminal’s output to the input. Three different cases are adopted. In the first case, the SI signal is assumed to be fully cancelled at the strong user using SI cancellation techniques. In the second case, SI is only cancelled at the information decoding circuits, while being harvested in the energy harvester to provide extra energy. The third case investigates the impact of imperfect SI cancellation on system performance is investigated. It is proved that introducing SWIPT doesn’t only motivate users to collaborate, but also mitigates the impact of the SI signal. In addition, the problem of total sum rate maximisation is formulated and solved locally by means of two-step difference of convex functions (DC)-based procedure. Furthermore, the outage probability of both weak and strong user is derived and provided in closed-form expressions. Numerical simulations are presented to illustrate the sum rate gain of the proposed scheme if compared with existing schemes, and to verify the derived formulas.
A Successive Approximation Recursive Digital Low-Dropout Voltage Regulator With PD Compensation and Sub-LSB Duty Control

This paper presents a recursive digital low-dropout (RLDO) regulator that improves response time, quiescent power, and load regulation dynamic range over prior digital LDO designs by 1–2 orders of magnitude. The proposed RLDO enables a practical digital replacement to analog LDOs by using an SAR-like binary search algorithm in a coarse loop and a sub-LSB pulse width modulation duty control scheme in a fine loop. A proportional-derivative compensation scheme is employed to ensure stable operation independent of load current, the size of the output decoupling capacitor, and clock frequency. Implemented in 0.0023 mm2 in 65 nm CMOS, the 7-bit RLDO achieves, at a 0.5-V input, a response time of 15.1 ns with a figure of merit of 199.4 ps, along with stable operation across a 20 000 $\times $  dynamic load range.
LoOP: local outlier probabilities

Many outlier detection methods do not merely provide the decision for a single data object being or not being an outlier but give also an outlier score or "outlier factor" signaling "how much" the respective data object is an outlier. A major problem for any user not very acquainted with the outlier detection method in question is how to interpret this "factor" in order to decide for the numeric score again whether or not the data object indeed is an outlier. Here, we formulate a local density based outlier detection method providing an outlier "score" in the range of [0, 1] that is directly interpretable as a probability of a data object for being an outlier.
INTEL-TUT Dataset for Camera Invariant Color Constancy Research

corresponds to the robustness of an algorithm's performance when run on images of the same scene taken by different cameras. Accordingly, images in the database correspond to several lab and field scenes each of which are captured by three different cameras with minimal registration errors. The lab scenes are also captured under five different illuminations. The spectral responses of cameras and the spectral power distributions of the lab light sources are also provided, as they may prove beneficial for training future algorithms to achieve color constancy. For a fair evaluation of future methods, we provide guidelines for supervised methods with indicated training, validation and testing partitions. Accordingly, we evaluate a recently proposed convolutional neural network based color constancy algorithm as a baseline for future research. As a side contribution, this dataset also includes images tak en by a mobile c amer a with color shading corr ec ted and uncorrected results. This allows research on the effect of color shading as well.
FlexRadio : Fully Flexible Radios

For a long time the capability of wireless devices to be configured either as a transmitter or a receiver at a time was the norm. The invention of full duplex shattered this norm. Theoretically, given N-RF chains with full-duplex capability, if each one can be configured as a receiver or a transmitter, we should be able to generate N transmission modes. Can a system capable of utilizing all possible transmission modes exist? Is this achieved flexibility critical to enhance the wirless communication system? We show that the answer to these questions is yes. This paper presents the design and implementation of FlexRadio, the first system enabling flexible RF resource allocation. This paper presents a novel RF cancellation technique that minimizes the number of canecalltion elements needed to realize FlexRadio. We implement FlexRadio on the NI PXIe 1082 platform using XCVR2450 radio front-ends. Our FlexRadio implementation shows a median gain of 2X over MIMO and fixed full-duplex radios having the same number of RF chains.
An Efficient End-to-End Neural Model for Handwritten Text Recognition

Offline handwritten text recognition from images is an important problem for enterprises attempting to digitize large volumes of handmarked scanned documents/reports. Deep recurrent models such as Multi-dimensional LSTMs  have been shown to yield superior performance over traditional Hidden Markov Model based approaches that suffer from the Markov assumption and therefore lack the representational power of RNNs. In this paper we introduce a novel approach that combines a deep convolutional network with a recurrent Encoder-Decoder network to map an image to a sequence of characters corresponding to the text present in the image. The entire model is trained end-to-end using Focal Loss [18], an improvement over the standard Cross-Entropy loss that addresses the class imbalance problem, inherent to text recognition. To enhance the decoding capacity of the model, Beam Search algorithm is employed which searches for the best sequence out of a set of hypotheses based on a joint distribution of individual characters. Our model takes as input a downsampled version of the original image thereby making it both computationally and memory efficient. The experimental results were benchmarked against two publicly available datasets, IAM and RIMES. We surpass the state-of-the-art word level accuracy on the evaluation set of both datasets by 3.5% & 1.1%, respectively.
Investment decisions in the renewable energy sector : An analysis of non-fi nancial drivers

Received in revised form 21 June 2012  Notwithstanding their many environmental, economic and social advantages, renewable energy technologies (RE) account for a small fraction of the world's primary energy supply. One possible cause for this limited diffusion is that private investments in the RE sector, although potentially appealing, remain insufficient. The lack of adequate financing is also a clear indication that our understanding of the process by which investors fund RE ventures is still incomplete. This paper aims to fill in this gap and to shed new light on RE investment decisions. Building upon behavioral finance and institutional theory, we posit that, in addition to a rational evaluation of the economics of the investment opportunities, various non-financial factors affect the decision to invest in renewables. We analyze the investment decisions of a large sample of investors, with the objective to identify the main determinants of their choices. Our results shed new light on the role of institutional and behavioral factors in determining the share of renewable energy technologies in energy portfolios, and have important implications for both investors and policy makers: they suggest that RE technologies still suffer from a series of biased perceptions and preconceptions that favor status quo energy production models over innovative alternatives.
Visual tree detection for autonomous navigation in forest environment

This paper describes a classification based tree detection method for autonomous navigation of forest vehicles in forest environment. Fusion of color, and texture cues has been used to segment the image into tree trunk and background objects. The segmentation of images into tree trunk and background objects is a challenging task due to high variations of illumination, effect of different color shades, non-homogeneous bark texture, shadows and foreshortening. To accomplish this, the approach has been to find the best combinations of color, and texture descriptors, and classification techniques. An additional task has been to estimate the distance between forest vehicle and the base of segmented trees using monocular vision. A simple heuristic distance measurement method is proposed that is based on pixel height and a reference width. The performance of various color and texture operators, and accuracy of classifiers has been evaluated using cross validation techniques.
FLEXIBLE MANUFACTURING SYSTEMS MODELLING AND PERFORMANCE EVALUATION USING AUTOMOD

In recent times flexible manufacturing systems emerged as a powerful technology to meet the continuous changing customer demands. Increase in the performance of flexible manufacturing systems is expected as a result of integration of the shop floor activities such as machine and vehicle scheduling. The authors made an attempt to integrate machine and vehicle scheduling with an objective to minimize the makespan using Automod. Automod is a discrete event simulation package used to model and simulate a wide variety of issues in automated manufacturing systems. The key issues related to the design and operation of automated guided vehicles such as flow path layout, number of vehicles and traffic control problems are considered in the study. The performance measures like throughput, machine and vehicle utilization are studied for different job dispatching and vehicle assignment rules in different flexible manufacturing system configurations.
Multiagent Bidirectionally-Coordinated Nets for Learning to Play StarCraft Combat Games

Real-world artificial intelligence (AI) applications often require multiple agents to work in a collaborative effort. Efficient learning for intra-agent communication and coordination is an indispensable step towards general AI. In this paper, we take StarCraft combat game as the test scenario, where the task is to coordinate multiple agents as a team to defeat their enemies. To maintain a scalable yet effective communication protocol, we introduce a multiagent bidirectionally-coordinated network (BiCNet [’bIknet]) with a vectorised extension of actor-critic formulation. We show that BiCNet can handle different types of combats under diverse terrains with arbitrary numbers of AI agents for both sides. Our analysis demonstrates that without any supervisions such as human demonstrations or labelled data, BiCNet could learn various types of coordination strategies that is similar to these of experienced game players. Moreover, BiCNet is easily adaptable to the tasks with heterogeneous agents. In our experiments, we evaluate our approach against multiple baselines under different scenarios; it shows state-of-the-art performance, and possesses potential values for large-scale real-world applications.
Functional cartography of complex metabolic networks

High-throughput techniques are leading to an explosive growth in the size of biological databases and creating the opportunity to revolutionize our understanding of life and disease. Interpretation of these data remains, however, a major scientific challenge. Here, we propose a methodology that enables us to extract and display information contained in complex networks. Specifically, we demonstrate that we can find functional modules in complex networks, and classify nodes into universal roles according to their pattern of intra- and inter-module connections. The method thus yields a ‘cartographic representation’ of complex networks. Metabolic networks are among the most challenging biological networks and, arguably, the ones with most potential for immediate applicability. We use our method to analyse the metabolic networks of twelve organisms from three different superkingdoms. We find that, typically, 80% of the nodes are only connected to other nodes within their respective modules, and that nodes with different roles are affected by different evolutionary constraints and pressures. Remarkably, we find that metabolites that participate in only a few reactions but that connect different modules are more conserved than hubs whose links are mostly within a single module.
C-Store: A Column-oriented DBMS

This paper presents the design of a read-optimized relational DBMS that contrasts sharply with most current systems, which are write-optimized. Among the many differences in its design are: storage of data by column rather than by row, careful coding and packing of objects into storage including main memory during query processing, storing an overlapping collection of columnoriented projections, rather than the current fare of tables and indexes, a non-traditional implementation of transactions which includes high availability and snapshot isolation for read-only transactions, and the extensive use of bitmap indexes to complement B-tree structures. We present preliminary performance data on a subset of TPC-H and show that the system we are building, C-Store, is substantially faster than popular commercial products. Hence, the architecture looks very encouraging.
MobiHide: A Mobilea Peer-to-Peer System for Anonymous Location-Based Queries

Modern mobile phones and PDAs are equipped with positioning capabilities (e.g., GPS). Users can access public location-based services (e.g., Google Maps) and ask spatial queries. Although communication is encrypted, privacy and confidentiality remain major concerns, since the queries may disclose the location and identity of the user. Commonly, spatial K-anonymity is employed to hide the query initiator among a group of K users. However, existing work either fails to guarantee privacy, or exhibits unacceptably long response time. In this paper we propose MobiHide, a Peer-to-Peer system for anonymous location-based queries, which addresses these problems. MobiHide employs the Hilbert space-filling curve to map the 2-D locations of mobile users to 1-D space. The transformed locations are indexed by a Chord-based distributed hash table, which is formed by the mobile devices. The resulting Peer-to-Peer system is used to anonymize a query by mapping it to a random group of K users that are consecutive in the 1-D space. Compared to existing state-of-the-art, MobiHide does not provide theoretical anonymity guarantees for skewed query distributions. Nevertheless, it achieves strong anonymity in practice, and it eliminates system hotspots. Our experimental evaluation shows that MobiHide has good load balancing and fault tolerance properties, and is applicable to real-life scenarios with numerous mobile users.
Cyber Security and Privacy Issues in Smart Grids

Smart grid is a promising power delivery infrastructure integrated with communication and information technologies. Its bi-directional communication and electricity flow enable both utilities and customers to monitor, predict, and manage energy usage. It also advances energy and environmental sustainability through the integration of vast distributed energy resources. Deploying such a green electric system has enormous and far-reaching economic and social benefits. Nevertheless, increased interconnection and integration also introduce cyber-vulnerabilities into the grid. Failure to address these problems will hinder the modernization of the existing power system. In order to build a reliable smart grid, an overview of relevant cyber security and privacy issues is presented. Based on current literatures, several potential research fields are discussed at the end of this paper.