nils-herrmann/clean_scirepeval huggingface dataset:
On social networks and collaborative recommendation

Social network systems, like last.fm, play a significant role in Web 2.0, containing large amounts of multimedia-enriched data that are enhanced both by explicit user-provided annotations and implicit aggregated feedback describing the personal preferences of each user. It is also a common tendency for these systems to encourage the creation of virtual networks among their users by allowing them to establish bonds of friendship and thus provide a novel and direct medium for the exchange of data.
 We investigate the role of these additional relationships in developing a track recommendation system. Taking into account both the social annotation and friendships inherent in the social graph established among users, items and tags, we created a collaborative recommendation system that effectively adapts to the personal information needs of each user. We adopt the generic framework of Random Walk with Restarts in order to provide with a more natural and efficient way to represent social networks.
 In this work we collected a representative enough portion of the music social network last.fm, capturing explicitly expressed bonds of friendship of the user as well as social tags. We performed a series of comparison experiments between the Random Walk with Restarts model and a user-based collaborative filtering method using the Pearson Correlation similarity. The results show that the graph model system benefits from the additional information embedded in social knowledge. In addition, the graph model outperforms the standard collaborative filtering method.
Boredom in the Workplace: A New Look at an Old Problem

We review historical and more recent efforts in boredom research and related fields. A framework is presented that organizes the various facets of boredom, particularly in supervisory control settings, and research gaps and future potential areas for study are highlighted.



Given the ubiquity of boredom across a wide spectrum of work environments--exacerbated by increasingly automated systems that remove humans from direct, physical system interaction and possibly increasing tedium in the workplace--there is a need not only to better understand the multiple facets of boredom in work environments but to develop targeted mitigation strategies.



To better understand the relationships between the various influences and outcomes of boredom, a systems-based framework, called the Boredom Influence Diagram, is proposed that describes various elements of boredom and their interrelationships.



Boredom is closely related to vigilance, attention management, and task performance. This review highlights the need to develop more naturalistic experiments that reflect the characteristics of a boring work environment.



With the increase in automation, boredom in the workplace will likely become a more prevalent issue for motivation and retention. In addition, developing continuous measures of boredom based on physiological signals is critical.



Personnel selection and improvements in system and task design can potentially mitigate boredom. However, more work is needed to develop and evaluate other potential interventions.
Natural language based financial forecasting: a survey

Natural language processing (NLP), or the pragmatic research perspective of computational linguistics, has become increasingly powerful due to data availability and various techniques developed in the past decade. This increasing capability makes it possible to capture sentiments more accurately and semantics in a more nuanced way. Naturally, many applications are starting to seek improvements by adopting cutting-edge NLP techniques. Financial forecasting is no exception. As a result, articles that leverage NLP techniques to predict financial markets are fast accumulating, gradually establishing the research field of natural language based financial forecasting (NLFF), or from the application perspective, stock market prediction. This review article clarifies the scope of NLFF research by ordering and structuring techniques and applications from related work. The survey also aims to increase the understanding of progress and hotspots in NLFF, and bring about discussions across many different disciplines.
Automatic Music Genres Classification using Machine Learning

Classification of music genre has been an inspiring job in the area of music information retrieval (MIR). Classification of genre can be valuable to explain some actual interesting problems such as creating song references, finding related songs, finding societies who will like that specific song. The purpose of our research is to find best machine learning algorithm that predict the genre of songs using k-nearest neighbor (k-NN) and Support Vector Machine (SVM). This paper also presents comparative analysis between k-nearest neighbor (k-NN) and Support Vector Machine (SVM) with dimensionality return and then without dimensionality reduction via principal component analysis (PCA). The Mel Frequency Cepstral Coefficients (MFCC) is used to extract information for the data set. In addition, the MFCC features are used for individual tracks. From results we found that without the dimensionality reduction both k-nearest neighbor and Support Vector Machine (SVM) gave more accurate results compare to the results with dimensionality reduction. Overall the Support Vector Machine (SVM) is much more effective classifier for classification of music genre. It gave an overall accuracy of 77%. K-nearest neighbor (k-NN); Support Vector Machine (SVM); music; genre; classification; features; Mel Frequency Cepstral Coefficients (MFCC); principal component analysis (PCA)
EVALUATION OF MODAL PUSHOVER ANALYSIS USING VERTICALLY IRREGULAR FRAMES

Recently, modal pushover analysis (MPA) has been developed to improve conventional pushover procedures by including higher-mode contributions to seismic demands. This study compares the seismic demands for vertically irregular frames determined by MPA procedure and the rigorous nonlinear response history analysis (RHA), due to an ensemble of 20 ground motions. Forty-eight irregular frames, all 12-story high with strong-columns and weak-beams, were designed with three types of irregularity— stiffness, strength, and combined stiffness and strength—introduced in eight different locations along the height using two modification factors. Next, the median and dispersion values of the ratio of story drift demands determined by modal pushover analysis (MPA) and nonlinear RHA were computed to measure the bias and dispersion of MPA estimates leading to the following results: (1) the bias in the MPA procedure does not increase, i.e., its accuracy does not deteriorate, in spite of irregularity in stiffness, strength, or stiffness and strength provided the irregularity is in the middle or upper story; (2) the MPA procedure is less accurate relative to the reference “regular” frame in estimating the seismic demands of frames with strong or stiff-and-strong first story; soft, weak, or soft-and-weak lower half; stiff, strong, or stiff-and-strong lower half; (3) in spite of the larger bias in estimating drift demands for some of the stories in particular cases, the MPA procedure identifies stories with largest drift demands and estimates them to a sufficient degree of accuracy, detecting critical stories in such frames; and (4) the bias in the MPA procedure for frames with soft, weak or soft-and-weak first story is about the same as for the “regular” frame.
ProxImaL: efficient image optimization using proximal algorithms

Computational photography systems are becoming increasingly diverse, while computational resources---for example on mobile platforms---are rapidly increasing. As diverse as these camera systems may be, slightly different variants of the underlying image processing tasks, such as demosaicking, deconvolution, denoising, inpainting, image fusion, and alignment, are shared between all of these systems. Formal optimization methods have recently been demonstrated to achieve state-of-the-art quality for many of these applications. Unfortunately, different combinations of natural image priors and optimization algorithms may be optimal for different problems, and implementing and testing each combination is currently a time-consuming and error-prone process. ProxImaL is a domain-specific language and compiler for image optimization problems that makes it easy to experiment with different problem formulations and algorithm choices. The language uses proximal operators as the fundamental building blocks of a variety of linear and nonlinear image formation models and cost functions, advanced image priors, and noise models. The compiler intelligently chooses the best way to translate a problem formulation and choice of optimization algorithm into an efficient solver implementation. In applications to the image processing pipeline, deconvolution in the presence of Poisson-distributed shot noise, and burst denoising, we show that a few lines of ProxImaL code can generate highly efficient solvers that achieve state-of-the-art results. We also show applications to the nonlinear and nonconvex problem of phase retrieval.
Entity-aware Image Caption Generation

Current image captioning approaches generate descriptions which lack specific information, such as named entities that are involved in the images. In this paper we propose a new task which aims to generate informative image captions, given images and hashtags as input. We propose a simple but effective approach to tackle this problem. We first train a convolutional neural networks long short term memory networks (CNN-LSTM) model to generate a template caption based on the input image. Then we use a knowledge graph based collective inference algorithm to fill in the template with specific named entities retrieved via the hashtags. Experiments on a new benchmark dataset collected from Flickr show that our model generates news-style image descriptions with much richer information. Our model outperforms unimodal baselines significantly with various evaluation metrics.
Making Deep Heatmaps Robust to Partial Occlusions for 3D Object Pose Estimation

We introduce a novel method for robust and accurate 3D object pose estimation from a single color image under large occlusions. Following recent approaches, we first predict the 2D projections of 3D points related to the target object and then compute the 3D pose from these correspondences using a geometric method. Unfortunately, as the results of our experiments show, predicting these 2D projections using a regular CNN or a Convolutional Pose Machine is highly sensitive to partial occlusions, even when these methods are trained with partially occluded examples. Our solution is to predict heatmaps from multiple small patches independently and to accumulate the results to obtain accurate and robust predictions. Training subsequently becomes challenging because patches with similar appearances but different positions on the object correspond to different heatmaps. However, we provide a simple yet effective solution to deal with such ambiguities. We show that our approach outperforms existing methods on two challenging datasets: The Occluded LineMOD dataset and the YCB-Video dataset, both exhibiting cluttered scenes with highly occluded objects.
Security Techniques in Distributed Systems

The security of information systems is the most important principle that can also be said that the most difficult, because security must be maintained throughout the system. At the beginning of this article we are going to introduce basic principles of security. The securities of distributed systems are divided into two parts: A portion of the communication between users and processes is concerned with examining issues such as authentication, message integrity and encryption will be discussed. In the next section, we will examine the guaranteed access permissions to resources in distributed systems. In addition to traditional access solutions, access control in mobile codes will be examined.
Critical period plasticity in local cortical circuits

Neuronal circuits in the brain are shaped by experience during 'critical periods' in early postnatal life. In the primary visual cortex, this activity-dependent development is triggered by the functional maturation of local inhibitory connections and driven by a specific, late-developing subset of interneurons. Ultimately, the structural consolidation of competing sensory inputs is mediated by a proteolytic reorganization of the extracellular matrix that occurs only during the critical period. The reactivation of this process, and subsequent recovery of function in conditions such as amblyopia, can now be studied with realistic circuit models that might generalize across systems.
A Blockchain-Based Architecture for Collaborative DDoS Mitigation with Smart Contracts

The rapid growth in the number of insecure portable and stationary devices and the exponential increase of traffic volume makes Distributed Denial-of-Service (DDoS) attacks a top security threat to services provisioning. Existing defense mechanisms lack resources and flexibility to cope with attacks by themselves, and by utilizing other’s companies resources, the burden of the mitigation can be shared. Emerging technologies such as blockchain and smart contracts allows for the sharing of attack information in a fully distributed and automated fashion. In this paper, the design of a novel architecture is proposed by combining these technologies introducing new opportunities for flexible and efficient DDoS mitigation solutions across multiple domains. Main advantages are the deployment of an already existing public and distributed infrastructure to advertise white or blacklisted IP addresses, and the usage of such infrastructure as an additional security mechanism to existing DDoS defense systems, without the need to build specialized registries or other distribution mechanisms, which enables the enforcement of rules across multiple domains.
Question Answering by Reasoning Across Documents with Graph Convolutional Networks

Most research in reading comprehension has focused on answering questions based on individual documents or even single paragraphs. We introduce a method which integrates and reasons relying on information spread within documents and across multiple documents. We frame it as an inference problem on a graph. Mentions of entities are nodes of this graph where edges encode relations between different mentions (e.g., withinand cross-document co-references). Graph convolutional networks (GCNs) are applied to these graphs and trained to perform multi-step reasoning. Our Entity-GCN method is scalable and compact, and it achieves state-of-the-art results on the WIKIHOP dataset (Welbl et al., 2017).
MOTIVATION PARAMETERS FOR ENGINEERING MANAGERS USING MASLOW ’ S THEORY

This paper emphasizes total self-development of the individual for improved motivation and organization management. It builds on Maslow’s hierarchy of needs theory to examine motivational levels for four levels of engineering staff at a public construction agency. The researchers studied these engineering groups qualitatively through interviews and quantitatively using a questionnaire. Using a holistic approach, this study focused on 15 parameters from Maslow’s five essential needs—the physical, safety, social, esteem, and selfactualization levels. Considerable emphasis was placed on the development of Maslow’s principle of ‘‘self-actualization.’’ This difficult-to-grasp concept, as Maslow reported, is a prerequisite for enlightened management. The researchers analyzed engineers’ perceptions regarding the fulfillment of need parameters and measured their perception of the importance of those parameters. Among specific findings were that junior project engineers had higher scores on self-actualization than senior engineers. Findings showed that changes are desirable for satisfying meaningfulness of tasks, increasing self-sufficiency in doing the job, and improving individuality and sense of mission of employees. The application of the findings based on Maslow’s model directly benefits the agency studied and other organizations in development programs. It also helps in increasing morale.
Polymer-Nanoparticle Composites: From Synthesis to Modern Applications

The addition of inorganic spherical nanoparticles to polymers allows the modification of the polymers physical properties as well as the implementation of new features in the polymer matrix. This review article covers considerations on special features of inorganic nanoparticles, the most important synthesis methods for ceramic nanoparticles and nanocomposites, nanoparticle surface modification, and composite formation, including drawbacks. Classical nanocomposite properties, as thermomechanical, dielectric, conductive, magnetic, as well as optical properties, will be summarized. Finally, typical existing and potential applications will be shown with the focus on new and innovative applications, like in energy storage systems.
Best routes selection using Dijkstra and Floyd-Warshall algorithm

Road accidents are frequent and many cause casualties. Fast handling can minimize the number of deaths from traffic accidents. In addition to victims of traffic accidents, there are also patients who need emergency handling of the disease he suffered. One of the first help that can be given to the victim or patient is to use an ambulance equipped with medical personnel and equipment needed. The availability of ambulance and accurate information about victims and road conditions can help the first aid process for victims or patients. Supportive treatment can be done to deal with patients by determining the best route (nearest and fastest) to the nearest hospital. The best route can be known by utilizing the collaboration between the Dijkstra algorithm and the Floyd-warshall algorithm. This application applies Dijkstra's algorithm to determine the fastest travel time to the nearest hospital. The Floyd-warshall algorithm is implemented to determine the closest distance to the hospital. Data on some nearby hospitals will be collected by the system using Dijkstra's algorithm and then the system will calculate the fastest distance based on the last traffic condition using the Floyd-warshall algorithm to determine the best route to the nearest hospital recommended by the system. This application is built with the aim of providing support for the first handling process to the victim or the emergency patient by giving the ambulance calling report and determining the best route to the nearest hospital.
Using linked disambiguated distributional networks for word sense disambiguation

We introduce a new method for unsupervised knowledge-based word sense disambiguation (WSD) based on a resource that links two types of sense-aware lexical networks: one is induced from a corpus using distributional semantics, the other is manually constructed. The combination of two networks reduces the sparsity of sense representations used for WSD. We evaluate these enriched representations within two lexical sample sense disambiguation benchmarks. Our results indicate that (1) features extracted from the corpus-based resource help to significantly outperform a model based solely on the lexical resource; (2) our method achieves results comparable or better to four state-of-the-art unsupervised knowledge-based WSD systems including three hybrid systems that also rely on text corpora. In contrast to these hybrid methods, our approach does not require access to web search engines, texts mapped to a sense inventory, or machine translation systems.
Twitter for Sentiment Analysis: When Language Resources are Not Available

Affective lexicons are a useful tool for emotion studies as well as for opinion mining and sentiment analysis. Such lexicons contain lists of words annotated with their emotional assessments. There exist a number of affective lexicons for English, Spanish, German and other languages. However, only a few of such resources are available for French. A lot of human efforts are needed to build and extend an affective lexicon. In our research, we propose to use Twitter, the most popular microblogging platform nowadays, to collect a dataset of emotional texts in French. Using the collected dataset, we estimated affective norms of words to construct an affective lexicon, which we use for polarity classification of video game reviews. Experimental results show that our method performs comparably to classic supervised learning methods.
A Reconfigurable SIW Cavity-Backed Slot Antenna With One Octave Tuning Range

A reconfigurable Substrate Integrated Waveguide Cavity-Backed Slot (SIW-CBS) antenna is presented with one octave tuning range and six different tuning states using a new tuning method. The method is based on loading the SIW cavity with shortening posts and manipulating the field distribution within the cavity. This tuning method shifts up the operating frequency of the antenna. Based on the number of the posts and their location, this frequency shift can be controlled. Benefiting from a two-layer structure and therefore being totally isolated from the biasing circuit, the CBS antenna can be easily tuned by switching the posts using p-i-n diodes. Using this technique a frequency tuning ratio (fTR = fup/flow) as high as an octave (1.1-2.2 GHz) is achieved. The impedance and radiation characteristics of the CBS antenna are presented and discussed. The antenna shows similar radiation patterns with low cross-polarization levels and high front to back ratio for all six states. To the authors' knowledge this is the first presentation of a SIW-CBS tunable antenna with one octave tuning and also the technique used for antenna tuning.
Neural Architecture Search : A Survey Neural Architecture Search : A Survey

Deep Learning has enabled remarkable progress over the last years on a variety of tasks, such as image recognition, speech recognition, and machine translation. One crucial aspect for this progress are novel neural architectures. Currently employed architectures have mostly been developed manually by human experts, which is a time-consuming and errorprone process. Because of this, there is growing interest in automated neural architecture search methods. We provide an overview of existing work in this field of research and categorize them according to three dimensions: search space, search strategy, and performance estimation strategy.
New literacy, new audiences: social media and cultural institutions

This paper reports on a major Australian research project which examines whether the evolution in digital content creation and social media can create a new audience of active cultural participants. The project draws together experts from major Australian museums, libraries and screen centres to examine the evolution in digital contentcreation and social media. It explores whether organizations can become active in content generation ('new literacy'), and thereby be linked into new modes of distribution, calling into being 'new audiences'. The paper presents interim findings of the project, describing the theories and methodologies developed to investigate the rise of social media and, more broadly, digital content creation, within cultural institutions.
Fraudulent Detection in Credit Card System Using SVM & Decision Tree

With growing advancement in the electronic commerce field, fraud is spreading all over the world, causing major financial losses. In current scenario, Major cause of financial losses is credit card fraud; it not only affects trades person but also individual clients. Decision tree, Genetic algorithm, Meta learning strategy, neural network, HMM are the presented methods use to detect credit card frauds. In contemplate system for fraudulent detection, artificial intelligence concept of Support Vector Machine (SVM) & decision tree is being used to solve the problem. Thus by implementation of this hybrid approach, financial losses can be reduced to greater extend. SVM; Decision Tree; Model File; Prediction File; Libsvmtools; LIBSVM; fraudster;