nils-herrmann/clean_scirepeval huggingface dataset:
Learning Whom to Trust with MACE

Non-expert annotation services like Amazon’s Mechanical Turk (AMT) are cheap and fast ways to evaluate systems and provide categorical annotations for training data. Unfortunately, some annotators choose bad labels in order to maximize their pay. Manual identification is tedious, so we experiment with an item-response model. It learns in an unsupervised fashion to a) identify which annotators are trustworthy and b) predict the correct underlying labels. We match performance of more complex state-of-the-art systems and perform well even under adversarial conditions. We show considerable improvements over standard baselines, both for predicted label accuracy and trustworthiness estimates. The latter can be further improved by introducing a prior on model parameters and using Variational Bayes inference. Additionally, we can achieve even higher accuracy by focusing on the instances our model is most confident in (trading in some recall), and by incorporating annotated control instances. Our system, MACE (Multi-Annotator Competence Estimation), is available for download1.
Image recognition for visually impaired people by sound

The aim of this paper is to represent a method where a blind person can get information about the shape of an image through speech signal. In this paper we proposed an algorithm for image recognition by speech sound. Blind people face a number of challenges when interacting with their environments because so much information is encoded visually. The proposed method enables the visually impaired people to see with the help of ears. The novelty of this paper is to convert the image to sound using the methodology of edge detection.
A collaborative filtering approach to ad recommendation using the query-ad click graph

Search engine logs contain a large amount of click-through data that can be leveraged as soft indicators of relevance. In this paper we address the sponsored search retrieval problem which is to find and rank relevant ads to a search query. We propose a new technique to determine the relevance of an ad document for a search query using click-through data. The method builds on a collaborative filtering approach to discover new ads related to a query using a click graph. It is implemented on a graph with several million edges and scales to larger sizes easily. The proposed method is compared to three different baselines that are state-of-the-art for a commercial search engine. Evaluations on editorial data indicate that the model discovers many new ads not retrieved by the baseline methods. The ads from the new approach are on average of better quality than the baselines.
Using Pre-Trained Models for Fine-Grained Image Classification in Fashion Field

Fashion image classification offers online stores a fast and effective way to manage the volume of their products. It could be used in product categorization by their brands, types, styles, etc. It could be also used to make fashion ensembles of clothes and even to create product recommendations. The results of apparel image classification are also useful for such purposes as product filtering and search. A wide range of different models have been successfully applied to coarse level image classification, but only a few of them could be applied to fashion images due to the nature of the data: such images could belong to the one high-level topic such as 't-shirt', 'skirt' or 'sport shoes', but at the lower level be fine-grained (e.g. 'high heels', 'medium heels'). Each apparel shop can have its own fine-grained hierarchy leading to difficulties to acquire a big dataset. This work shows necessity of pre-trained model usage to achieve a good predictive quality. In addition, the article contains a description and an experimental study of an approach to stack features from pretrained model and features learned directly from an image pixel representation. This approach significantly outperforms baseline methods in fine-grained image classification [1, 2, 3] and a classification based only on a pre-trained model.
A study into the believability of animated characters in the context of bullying intervention

The VICTEC (Virtual ICT with Empathic Characters) project provides an opportunity to explore the use of animated characters in a virtual environment for educational issues such as bullying behaviour. Our research aim was to evaluate whether an early prototype of the VICTEC demonstrator could provide useful information about character and story believability, physical aspects of the characters and story comprehensibility. Results from an evaluation with 76 participants revealed high levels of bullying story believability and character conversation was rated as convincing and interesting. In contrast, character movement was poorly rated. Overall the results imply that poor physical aspects of characters do not have detrimental effects on story believability and interest levels with the demonstrator. It is concluded that even at this early design phase, the demonstrator provides a suitable means to explore how virtual environments in terms of character and storyline believability may assist in the development of systems to deal with the educational issue of bullying.
NutriNet: A Deep Learning Food and Drink Image Recognition System for Dietary Assessment

Automatic food image recognition systems are alleviating the process of food-intake estimation and dietary assessment. However, due to the nature of food images, their recognition is a particularly challenging task, which is why traditional approaches in the field have achieved a low classification accuracy. Deep neural networks have outperformed such solutions, and we present a novel approach to the problem of food and drink image detection and recognition that uses a newly-defined deep convolutional neural network architecture, called NutriNet. This architecture was tuned on a recognition dataset containing 225,953 512 × 512 pixel images of 520 different food and drink items from a broad spectrum of food groups, on which we achieved a classification accuracy of 86 . 72 % , along with an accuracy of 94 . 47 % on a detection dataset containing 130 , 517 images. We also performed a real-world test on a dataset of self-acquired images, combined with images from Parkinson's disease patients, all taken using a smartphone camera, achieving a top-five accuracy of 55 % , which is an encouraging result for real-world images. Additionally, we tested NutriNet on the University of Milano-Bicocca 2016 (UNIMIB2016) food image dataset, on which we improved upon the provided baseline recognition result. An online training component was implemented to continually fine-tune the food and drink recognition model on new images. The model is being used in practice as part of a mobile app for the dietary assessment of Parkinson's disease patients.
Analyzing and retrieving illicit drug-related posts from social media

Illicit drug use is a serious problem around the world. Social media has increasingly become an important tool for analyzing drug use patterns and monitoring emerging drug abuse trends. Accurately retrieving illicit drug-related social media posts is an important step in this research. Frequently, hashtags are used to identify and retrieve posts on a specific topic. However hashtags are highly ambiguous. Posts with the same hashtags are not always on the same topic. Moreover, hashtags are evolving, especially those related to illicit drugs. New street names are introduced constantly to avoid detection. In this paper, we employ topic modeling to disambiguate hashtags and track the changes of hashtags using semantic word embedding. Our preliminary evaluation shows the promise of these methods.
Facial Skin Surface Temperature Changes During a “Concealed Information” Test

When individuals who commit a crime are questioned, they often show involuntary physiological responses to remembered details of that crime. This phenomenon is the basis for the concealed information test, in which rarely occurring crime-related details are embedded in a series of more frequently occurring crime-irrelevant items while respiratory, cardiovascular, and electrodermal responses are recorded. Two experiments were completed to investigate the feasibility of using facial skin surface temperature (SST) measures recorded using high definition thermographic images as the physiological measure during a concealed information test. Participants were randomly assigned to nondeceptive or deceptive groups. Deceptive participants completed a mock-crime paradigm. A focal plane array thermal imaging radiometer was used to monitor SST while crime-relevant and crime-irrelevant items were verbally presented to each participant. During both experiments, there were significant facial SST differences between deceptive and nondeceptive participants early in the analysis interval. In the second experiment, hemifacial (i.e., “half-face” divided along the longitudinal axis) effects were combined with the bilateral responses to correctly classify 91.7% of participants. These results suggest that thermal image analysis can be effective in discriminating deceptive and nondeceptive individuals during a concealed information test.
Tachyon: Reliable, Memory Speed Storage for Cluster Computing Frameworks

Tachyon is a distributed file system enabling reliable data sharing at memory speed across cluster computing frameworks. While caching today improves read workloads, writes are either network or disk bound, as replication is used for fault-tolerance. Tachyon eliminates this bottleneck by pushing lineage, a well-known technique, into the storage layer. The key challenge in making a long-running lineage-based storage system is timely data recovery in case of failures. Tachyon addresses this issue by introducing a checkpointing algorithm that guarantees bounded recovery cost and resource allocation strategies for recomputation under commonly used resource schedulers. Our evaluation shows that Tachyon outperforms in-memory HDFS by 110x for writes. It also improves the end-to-end latency of a realistic workflow by 4x. Tachyon is open source and is deployed at multiple companies.
Ensemble Methods for Multi-label Classification

Ensemble methods have been shown to be an effectiv e tool for solving multi-label classification tasks. In the RAndom k-labELsets (RAKEL) algorithm, each member of the en semble is associated with a small randomly-selected subset of k labels. Then, a single label classifier is trained according to each combination of elements in the subset. In this paper we adopt a similar approach, however, instead of rando mly choosing subsets, we select the minimum required su bsets of k labels that cover all labels and meet additional constraints such as coverage of inter-label correla tions. Construction of the cover is achieved by form ulating the subset selection as a minimum set covering prob lem (SCP) and solving it by using approximation algo rithms. Every cover needs only to be prepared once by offline algorithms. Once prepared, a cover may b e applied to the classification of any given multi-la bel dataset whose properties conform with those of the cover. The contribution of this paper is two-fold. Fir st, we introduce SCP as a general framework for con structing label covers while allowing the user to incorpo rate cover construction constraints. We demonstrate he effectiveness of this framework by proposing two co nstruction constraints whose enforcement produces c overs that improve the prediction performance of ran dom selection. Second, we provide theoretical bound s that quantify the probabilities of random selection t produce covers that meet the proposed construct ion riteria. The experimental results indicate that the p roposed methods improve multi-label classification accuracy and stability compared with the RAKEL algorithm a nd to other state-of-the-art algorithms.
On the derivation of coseismic displacement fields using differential radar interferometry: The Landers earthquake

We present a map of the coseismic displacement field resulting from the Landers, California, June 28, 1992, earthquake derived using data acquired from an orbiting high-resolution radar system. We achieve results more accurate than previous space studies and similar in accuracy to those obtained by conventional field survey techniques. Data from the ERS 1 synthetic aperture radar instrument acquired in April, July, and August 1992 are used to generate a high-resolution, wide area map of the displacements. The data represent the motion in the direction of the radar line of sight to centimeter level precision of each 30-m resolution element in a 113 km by 90 km image. Our coseismic displacement contour map gives a lobed pattern consistent with theoretical models of the displacement field from the earthquake. Fine structure observed as displacement tiling in regions several kilometers from the fault appears to be the result of local surface fracturing. Comparison of these data with Global Positioning System and electronic distance measurement survey data yield a correlation of 0.96; thus the radar measurements are a means to extend the point measurements acquired by traditional techniques to an area map format. The technique we use is (1) more automatic, (2) more precise, and (3) better validated than previous similar applications of differential radar interferometry. Since we require only remotely sensed satellite data with no additional requirements for ancillary information, the technique is well suited for global seismic monitoring and analysis.
Path Optimization in Stream-Based Overlay Networks

The emergence of sensor networks and distributed applications that generate data streams has created a need for Internet overlays designed for streaming data. Such stream-based overlay network (SBONs) consist of a set of Internet hosts that collect, process, and deliver stream-based data to multiple applications. A key challenge in the design and implementation of SBONs is efficient path optimization when mapping logical query streams to physical network hosts and paths. Suboptimal placements can induce poor utilization of network resources, leading to severe performance penalties, link saturation, and network hotspots. Our goal is to realize efficient stream placement that takes the physical topology of the Internet into account, thereby minimizing overall network utilization. In this paper, we describe a novel, network-aware path optimization algorithm for stream-based overlay networks. Our approach is based on a spring relaxation model that operates in a metric space defined by the pairwise node latency within the SBON. This relaxation placement algorithm utilizes the underlying network resources efficiently, is capable of performing inter-stream optimization, and can be implemented in a scalable and decentralized way. To evaluate its performance, we define a set of evaluation metrics for path optimization in terms of network utilization, application delay, and resource contention. Our simulation experiments with a realistic network topology show that relaxation placement approaches optimal network utilization without introducing an undue delay penalty or resource contention. Compared to an optimal placement strategy, relaxation placement causes only 15% more network traffic on average, while adding a24% delay penalty for the application. We validate our simulation results with actual measurements involving 70 PlanetLab nodes.
Exploring personality characteristics of Chinese adolescents with internet-related addictive behaviors: trait differences for gaming addiction and social networking addiction.

This study investigated the associations between personality traits, based on the Big Five model, and addictive behaviors to different online activities among adolescents. A sample of 920 participants was recruited from four secondary schools in different districts using random cluster sampling. A structured questionnaire, including demographic information, internet usage pattern, the Internet Addiction Test, the Game Addiction Scale, the Bergen Facebook Addiction Scale - Revised, and the Big Five Inventory, was administered to each participant. The results demonstrated a significant difference in personality traits for addictive behaviors related to different online activities. Specifically, higher neuroticism (β=0.15, p<0.001) and less conscientiousness (β=0.12, p<0.001) displayed significant associations with internet addiction in general; less conscientiousness (β=0.09, p<0.01) and low openness (β=0.06, p<0.05) were significantly associated with gaming addiction; and neuroticism (β=0.15, p<0.001) and extraversion (β=0.10, p<0.01) were significantly associated with social networking addiction. Our findings may provide a better understanding of the etiopathology of internet-related addictive behaviors and have implications for psychoeducation and psychotherapy programs.
Diffusion maps for edge-aware image editing

Edge-aware operations, such as edge-preserving smoothing and edge-aware interpolation, require assessing the degree of similarity between pairs of pixels, typically defined as a simple monotonic function of the Euclidean distance between pixel values in some feature space. In this work we introduce the idea of replacing these Euclidean distances with diffusion distances, which better account for the global distribution of pixels in their feature space. These distances are approximated using diffusion maps: a set of the dominant eigenvectors of a large affinity matrix, which may be computed efficiently by sampling a small number of matrix columns (the Nyström method). We demonstrate the benefits of using diffusion distances in a variety of image editing contexts, and explore the use of diffusion maps as a tool for facilitating the creation of complex selection masks. Finally, we present a new analysis that establishes a connection between the spatial interaction range between two pixels, and the number of samples necessary for accurate Nyström approximations.
Autonomous railway crack detector robot for bangladesh: SCANOBOT

An automatic railway track crack detector system for Bangladesh Railway has been proposed here which aims in building a robot that can detect and analyze any kind of crack on the railway line and send the coordinates of that faulty line to the concerned authority. This robot includes two ultrasonic sensors, GPS, GSM modules, and Arduino Mega based crack detection assembly which is cost effective and robust to facilitate better safety standards in railways. As soon as the robot passed through a crack that might cause the derailment of a train, the ultrasonic sensors sense that and generate a signal. Then this signal is fed into the Arduino Mega. At that point, with the assistance of GSM and GPS modules, an alert SMS consist of the geographic coordinate of that damaged track is sent to the nearby railway authority who can easily take necessary steps to resolve the problem before any major accident occurs. This will save several trains in Bangladesh from an unwanted discontinuity from the rail track.
Community detection and visualization in social networks: Integrating structural and semantic information

Due to the explosion of social networking and the information sharing among their users, the interest in analyzing social networks has increased over the recent years. Two general interests in this kind of studies are community detection and visualization. In the first case, most of the classic algorithms for community detection use only the structural information to identify groups, that is, how clusters are formed according to the topology of the relationships. However, these methods do not take into account any semantic information which could guide the clustering process, and which may add elements to conduct further analyses. In the second case most of the layout algorithms for clustered graphs have been designed to differentiate the groups within the graph, but they are not designed to analyze the interactions between such groups. Identifying these interactions gives an insight into the way different communities exchange messages or information, and allows the social network researcher to identify key actors, roles, and paths from one community to another.
 This article presents a novel model to use, in a conjoint way, the semantic information from the social network and its structural information to, first, find structurally and semantically related groups of nodes, and second, a layout algorithm for clustered graphs which divides the nodes into two types, one for nodes with edges connecting other communities and another with nodes connecting nodes only within their own community. With this division the visualization tool focuses on the connections between groups facilitating deep studies of augmented social networks.
Steganalysis of QIM Steganography in Low-Bit-Rate Speech Signals

Steganalysis of the quantization index modulation QIM steganography in a low-bit-rate encoded speech stream is conducted in this research. According to the speech generation theory and the phoneme distribution properties in language, we first point out that the correlation characteristics of split vector quantization VQ codewords of linear predictive coding filter coefficients are changed after the QIM steganography. Based on this observation, we construct a model called the Quantization codeword correlation network QCCN based on split VQ codeword from adjacent speech frames. Furthermore, the QCCN model is pruned to yield a stronger correlation network. After quantifying the correlation characteristics of vertices in the pruned correlation network, we obtain feature vectors that are sensitive to steganalysis. Finally, we build a high-performance detector using the support vector machine SVM classifier. It is shown by experimental results that the proposed QCCN steganalysis method can effectively detect the QIM steganography in encoded speech stream when it is applied to low-bit-rate speech codec such as G.723.1 and G.729.
Meta-path-based link prediction in schema-rich heterogeneous information network

Recent years have witnessed the boom of heterogeneous information network (HIN), which contains different types of nodes and relations. Complex structure and rich semantics are unique features of HIN. Meta-path, the sequence of object types and relations connecting them, has been widely used to mine this semantic information in HIN. Link prediction is an important data mining task to predict the potential links among nodes, which are required in many applications, e.g., filling missing links. The contemporary link prediction is usually based on simple HIN whose schema is bipartite or star schema. In these works, the meta-paths should be predefined or enumerated. However, in many real networked data, it is hard to describe their network structures with simple schema. For example, the RDF-formatted Knowledge Graph which includes tens of thousands types of objects and links is a kind of schema-rich HIN. In this kind of schema-rich HIN, it is impossible to enumerate meta-paths so that the contemporary work is invalid. In this paper, we study link prediction in schema-rich HIN and propose a novel method named Link Prediction with automatic meta Path (LiPaP). The LiPaP designs an algorithm called automatic meta-path generation to automatically extract meta-paths from schema-rich HIN in the approximate order of relevance and adopt a supervised method with likelihood function to learn the weights of extracted meta-paths. Extensive experiments on real knowledge database, Yago, demonstrate that LiPaP is an effective, steady and efficient approach.