nils-herrmann/clean_scirepeval huggingface dataset:
Efficient 3 D Pose Estimation and 3 D Model Retrieval

Retrieving 3D models for objects in 2D images is an increasingly important problem, driven by the recent emergence of large databases of 3D models such as ShapeNet . However, this task is challenging for two main reasons: (1) 2D images and 3D models have considerably different representations and characteristics, making it hard to compare them. (2) The appearance of objects can significantly vary with the pose, but it is in general unknown and, thus, multiple poses have to be considered, which is very inefficient. To overcome these problems, in  we proposed first to predict the object’s pose and then to use the estimated pose as a prior to retrieve 3D models from a database. In the following, we give a short summary of the approach in Sec. II and a sketch of results in Sec. III. For more details, we refer to .
Representation Learning of Knowledge Graphs with Hierarchical Types

Representation learning of knowledge graphs aims to encode both entities and relations into a continuous low-dimensional vector space. Most existing methods only concentrate on learning representations with structured information located in triples, regardless of the rich information located in hierarchical types of entities, which could be collected in most knowledge graphs. In this paper, we propose a novel method named Type-embodied Knowledge Representation Learning (TKRL) to take advantages of hierarchical entity types. We suggest that entities should have multiple representations in different types. More specifically, we consider hierarchical types as projection matrices for entities, with two type encoders designed to model hierarchical structures. Meanwhile, type information is also utilized as relation-specific type constraints. We evaluate our models on two tasks including knowledge graph completion and triple classification, and further explore the performances on long-tail dataset. Experimental results show that our models significantly outperform all baselines on both tasks, especially with long-tail distribution. It indicates that our models are capable of capturing hierarchical type information which is significant when constructing representations of knowledge graphs. The source code of this paper can be obtained from https://github.com/thunlp/TKRL.
Reliable detection of LSB steganography in color and grayscale images

A large number of commercial steganographic programs use the Least Significant Bit embedding (LSB) as the method of choice for message hiding in 24-bit, 8-bit color images, and grayscale images. It is commonly believed that changes to the LSBs of colors cannot be detected due to noise that is always present in digital images. In this paper, we describe a new very accurate and reliable method that can detect LSB embedding in randomly scattered pixels in both 24-bit color images and 8-bit grayscale or color images. It is based on our previous work on lossless data embedding . By inspecting the differences in the number of regular and singular groups for the LSB and the "shifted LSB plane", we can reliably detect messages as short as 0.03bpp.
Bone augmentation procedures in implant dentistry.

This review evaluated (1) the success of different surgical techniques for the reconstruction of edentulous deficient alveolar ridges and (2) the survival/success rates of implants placed in the augmented areas.



Clinical investigations published in English involving more than 10 consecutively treated patients and mean follow-up of at least 12 months after commencement of prosthetic loading were included. The following procedures were considered: onlay bone grafts, sinus floor elevation via a lateral approach, Le Fort I osteotomy with interpositional grafts, split ridge/ridge expansion techniques, and alveolar distraction osteogenesis. Full-text articles were identified using computerized and hand searches by key words. Success and related morbidity of augmentation procedures and survival/success rates of implants placed in the augmented sites were analyzed.



A wide range of surgical procedures were identified. However, it was difficult to demonstrate that one surgical procedure offered better outcomes than another. Moreover, it is not yet known if some surgical procedures, eg, reconstruction of atrophic edentulous mandibles with onlay autogenous bone grafts or maxillary sinus grafting procedures in case of limited/moderate sinus pneumatization, improve long-term implant survival. Every surgical procedure presents advantages and disadvantages. Priority should be given to those procedures which are simpler and less invasive, involve less risk of complications, and reach their goals within the shortest time frame. The main limit encountered in this literature review was the overall poor methodological quality of the published articles. Larger well-designed long-term trials are needed.
Mining software engineering data

Software engineering data (such as code bases, execution traces, historical code changes, mailing lists, and bug databases) contains a wealth of information about a project's status, progress, and evolution. Using well-established data mining techniques, practitioners and researchers have started exploring the potential of this valuable data in order to better manage their projects and to produce higher quality software systems that are delivered on time and within budget.
 This tutorial presents the latest research in mining software engineering data, discusses challenges associated with mining software engineering data, highlights success stories of mining software engineering data, and outlines future research directions. Attendees will acquire the knowledge and skills needed to integrate the mining of software engineering data in their own research or practice. This tutorial builds on several successful offerings at ICSE since 2007.
Building high-level features using large scale unsupervised learning

We consider the problem of building high-level, class-specific feature detectors from only unlabeled data. For example, is it possible to learn a face detector using only unlabeled images? To answer this, we train a deep sparse autoencoder on a large dataset of images (the model has 1 billion connections, the dataset has 10 million 200×200 pixel images downloaded from the Internet). We train this network using model parallelism and asynchronous SGD on a cluster with 1,000 machines (16,000 cores) for three days. Contrary to what appears to be a widely-held intuition, our experimental results reveal that it is possible to train a face detector without having to label images as containing a face or not. Control experiments show that this feature detector is robust not only to translation but also to scaling and out-of-plane rotation. We also find that the same network is sensitive to other high-level concepts such as cat faces and human bodies. Starting from these learned features, we trained our network to recognize 22,000 object categories from ImageNet and achieve a leap of 70% relative improvement over the previous state-of-the-art.
Gaussian Process Dynamical Models for Human Motion

We introduce Gaussian process dynamical models (GPDMs) for nonlinear time series analysis, with applications to learning models of human pose and motion from high-dimensional motion capture data. A GPDM is a latent variable model. It comprises a low-dimensional latent space with associated dynamics, as well as a map from the latent space to an observation space. We marginalize out the model parameters in closed form by using Gaussian process priors for both the dynamical and the observation mappings. This results in a nonparametric model for dynamical systems that accounts for uncertainty in the model. We demonstrate the approach and compare four learning algorithms on human motion capture data, in which each pose is 50-dimensional. Despite the use of small data sets, the GPDM learns an effective representation of the nonlinear dynamics in these spaces.
Syntax Helps ELMo Understand Semantics: Is Syntax Still Relevant in a Deep Neural Architecture for SRL?

Do unsupervised methods for learning rich, contextualized token representations obviate the need for explicit modeling of linguistic structure in neural network models for semantic role labeling (SRL)? We address this question by incorporating the massively successful ELMo embeddings (Peters et al., 2018) into LISA (Strubell et al., 2018), a strong, linguisticallyinformed neural network architecture for SRL. In experiments on the CoNLL-2005 shared task we find that though ELMo outperforms typical word embeddings, beginning to close the gap in F1 between LISA with predicted and gold syntactic parses, syntactically-informed models still outperform syntax-free models when both use ELMo, especially on out-of-domain data. Our results suggest that linguistic structures are indeed still relevant in this golden age of deep learning for NLP.
Out-Of-Core Algorithms for Scientific Visualization and Computer Graphics

This course will focus on describing techniques for handling datasets larger than main memory in scientific visualization and computer graphics. Recently, several external memory techniques have been developed for a wide variety of graphics and visualization problems, including surface simplification, volume rendering, isosurface generation, ray tracing, surface reconstruction, and so on. This work has had significant impact given that in recent years there has been a rapid increase in the raw size of datasets. Several technological trends are contributing to this, such as the development of high-resolution 3D scanners, and the need to visualize ASCI-size (Accelerated Strategic Computing Initiative) datasets. Another important push for this kind of technology is the growing speed gap between main memory and caches, such a gap penalizes algorithms which do not optimize for coherence of access. Because of these reasons, much research in computer graphics focuses on developing out-of-core (and often cache-friendly) techniques. This course reviews fundamental issues, current problems, and unresolved solutions, and presents an in-depth study of external memory algorithms developed in recent years. Its goal is to provide students and graphics researchers and professionals with an effective knowledge of current techniques, as well as the foundation to develop novel techniques on their own. Schedule (tentative) 5 min Introduction to the course Silva 45 min Overview of external memory algorithms Chiang 40 min Out-of-core scientific visualization Silva
ECGLens: Interactive Visual Exploration of Large Scale ECG Data for Arrhythmia Detection

The Electrocardiogram (ECG) is commonly used to detect arrhythmias. Traditionally, a single ECG observation is used for diagnosis, making it difficult to detect irregular arrhythmias. Recent technology developments, however, have made it cost-effective to collect large amounts of raw ECG data over time. This promises to improve diagnosis accuracy, but the large data volume presents new challenges for cardiologists. This paper introduces ECGLens, an interactive system for arrhythmia detection and analysis using large-scale ECG data. Our system integrates an automatic heartbeat classification algorithm based on convolutional neural network, an outlier detection algorithm, and a set of rich interaction techniques. We also introduce A-glyph, a novel glyph designed to improve the readability and comparison of ECG signals. We report results from a comprehensive user study showing that A-glyph improves the efficiency in arrhythmia detection, and demonstrate the effectiveness of ECGLens in arrhythmia detection through two expert interviews.
A Generative Model for Non-Intrusive Load Monitoring in Commercial Buildings

In the recent years, there has been an increasing academic and industrial interest for analyzing the electrical consumption of commercial buildings. Whilst having similarities with the Non Intrusive Load Monitoring (NILM) tasks for residential buildings, the nature of the signals that are collected from large commercial buildings introduces additional difficulties to the NILM research causing existing NILM approaches to fail. On the other hand, the amount of publicly available datasets collected from commercial buildings is very limited, which makes the NILM research even more challenging for this type of large buildings. In this study, we aim at addressing these issues. We first present an extensive statistical analysis of both commercial and residential measurements from public and private datasets and show important differences. Secondly, we develop an algorithm for generating synthetic current waveforms. We then demonstrate using real measurement and quantitative metrics that both our device model and our simulations are realistic and can be used to evaluate NILM algorithms. Finally, to encourage research on commercial buildings we release a synthesized dataset.  to Energy and Building July 30, 2018 ar X iv :1 80 3. 00 51 5v 1 [ cs .O H ] 2 6 Fe b 20 18
Anomaly Detection in Data Mining : A Review

Anomaly detection is the new research topic to this new generation researcher in present time. Anomaly detection is a domain i.e., the key for the upcoming data mining. The term ‘data mining’ is referred for methods and algorithms that allow extracting and analyzing data so that find rules and patterns describing the characteristic properties of the information. Techniques of data mining can be applied to any type of data to learn more about hidden structures and connections. In the present world, vast amounts of data are kept and transported from one location to another. The data when transported or kept is informed exposed to attack. Though many techniques or applications are available to secure data, ambiguities exist. As a result to analyze data and to determine different type of attack data mining techniques have occurred to make it less open to attack. Anomaly detection is used the techniques of data mining to detect the surprising or unexpected behaviour hidden within data growing the chances of being intruded or attacked. This paper work focuses on Anomaly Detection in Data mining. The main goal is to detect the anomaly in time series data using machine learning techniques.  Anomaly Detection; Data Mining; Time Series Data; Machine Learning Techniques.
A Survey of Deep Learning Techniques for Mobile Robot Applications

Advancements in deep learning over the years have attracted research into how deep artificial neural networks can be used in robotic systems. It is on this basis that the following research survey will present a discussion of the applications, gains, and obstacles to deep learning in comparison to physical robotic systems while using modern research as examples. The research survey will present a summarization of the current research with specific focus on the gains and obstacles in comparison to robotics. This will be followed by a primer on discussing how notable deep learning structures can be used in robotics with relevant examples. The next section will show the practical considerations robotics researchers desire to use in regard to deep learning neural networks. Finally, the research survey will show the shortcomings and solutions to mitigate them in addition to discussion of the future trends. The intention of this research is to show how recent advancements in the broader robotics field can inspire additional research in applying deep learning in robotics.
Commonsense mining as knowledge base completion? A study on the impact of novelty

Commonsense knowledge bases such as ConceptNet represent knowledge in the form of relational triples. Inspired by recent work by (Li et al., 2016), we analyse if knowledge base completion models can be used to mine commonsense knowledge from raw text. We propose novelty of predicted triples with respect to the training set as an important factor in interpreting results. We critically analyse the difficulty of mining novel commonsense knowledge, and show that a simple baseline method outperforms the previous state of the art on predicting more novel triples.
Rules and Representations : Chomsky and Representational Realism

Speaking as someone who has personally felt the influence of the “Chomskian Turn”, I believe that one of Chomsky’s most significant contributions to Psychology, or as it is now called, Cognitive Science was to bring back scientific realism. This may strike you as a very odd claim, for one does not usually think of science as needing to be talked into scientific realism. Science is, after all, the study of reality by the most precise instruments of measurement and analysis that humans have developed.
Deep Learning for Stress Field Prediction Using Convolutional Neural Networks

This research presents a deep learning based approach to predict stress fields in the solid material elastic deformation using convolutional neural networks (CNN). Two different architectures are proposed to solve the problem. One is Feature Representation embedded Convolutional Neural Network (FR-CNN) with a single input channel, and the other is Squeeze-and-Excitation Residual network modules embedded Fully Convolutional Neural network (SE-Res-FCN) with multiple input channels. Both the tow architectures are stable and converged reliably in training and testing on GPUs. Accuracy analysis shows that SE-Res-FCN has a significantly smaller mean squared error (MSE) and mean absolute error (MAE) than FR-CNN. Mean relative error (MRE) of the SE-Res-FCN model is about 0.25% with respect to the average ground truth. The validation results indicate that the SE-Res-FCN model can accurately predict the stress field. For stress field prediction, the hierarchical architecture becomes deeper within certain limits, and then its prediction becomes more accurate. Fully trained deep learning models have higher computational efficiency over conventional FEM models, so they have great foreground and potential in structural design and topology optimization.
Aeromonas hydrophila from marketed mullet (Mugil cephalus) in Egypt: PCR characterization of β-lactam resistance and virulence genes.

Aeromonas hydrophila has been isolated from various fish species in Egypt and is known to carry virulence and antimicrobial resistance genes, which pose a risk for public health. The aim of the present study is to report, for the first time, the infection of mullet (Mugil cephalus) with A. hydrophila and to clarify the potential association between antimicrobial resistance and virulence traits encoded in A. hydrophila.



In this study, the occurrence of A. hydrophila in marketed mullet and the antimicrobial resistance phenotypes of these isolates were determined. Aeromonas hydrophila isolates were screened for the presence of virulence and β-lactam resistance genes; the correlation between both gene groups was also investigated. The infection rate of examined mullet with A. hydrophila was 37% (50/135). The highest antimicrobial resistance was detected to cefoxitin (100%), followed by ampicillin (84%), ceftazidime (56%) and cefotaxime (40%). Only 4% of the isolates were resistant to erythromycin; 6% were resistant to both gentamicin and kanamycin with no resistance to ciprofloxacin. Variable frequencies of virulence and β-lactam resistance genes were evident from PCR, where aerA and blaTEM predominated. The study also indicated a general weak positive correlation (R = 0·3) between both virulence and β-lactam resistance genes. Some of the studied virulence genes (e.g. aerA:hlyA and hlyA:ast) were found to correlate positively.



The presence of virulence and resistance genes in A. hydrophila from food sources poses a serious threat to public health. To our knowledge, this is the first report describing the occurrence of A. hydrophila in mullet and highlighting the coexistence of virulence and β-lactam resistance genes encoded by these bacteria.



These data provide insights into the potential association of antimicrobial resistance and virulence genes in A. hydrophila from marketed mullet in Egypt, which could pose threats to humans even if a weak positive correlation exists between both genes.
Extended Co-occurrence HOG with Dense Trajectories for Fine-Grained Activity Recognition

In this paper we propose a novel feature descriptor Extended Co-occurrence HOG (ECoHOG) and integrate it with dense point trajectories demonstrating its usefulness in fine grained activity recognition. This feature is inspired by original Co-occurrence HOG (CoHOG) that is based on histograms of occurrences of pairs of image gradients in the image. Instead relying only on pure histograms we introduce a sum of gradient magnitudes of co-occurring pairs of image gradients in the image. This results in giving the importance to the object boundaries and straightening the difference between the moving foreground and static background. We also couple ECoHOG with dense point trajectories extracted using optical flow from video sequences and demonstrate that they are extremely well suited for fine grained activity recognition. Using our feature we outperform state of the art methods in this task and provide extensive quantitative evaluation.
"Smells Phishy?": An educational game about online phishing scams

We propose Smells Phishy?, a board game that contributes to raising users' awareness of online phishing scams. We designed and developed the board game and conducted user testing with 21 participants. The results showed that after playing the game, participants had better understanding of phishing scams and learnt how to better protect themselves. Participants enjoyed playing the game and said that it was a fun and exciting experience. The game increased knowledge and awareness, and encouraged discussion.
Julia: A Fresh Approach to Numerical Computing

Bridging cultures that have often been distant, Julia combines expertise from the diverse fields of computer science and computational science to create a new approach to numerical computing. Julia is designed to be easy and fast and questions notions generally held to be “laws of nature” by practitioners of numerical computing: 1. High-level dynamic programs have to be slow. 2. One must prototype in one language and then rewrite in another language for speed or deployment. 3. There are parts of a system appropriate for the programmer, and other parts that are best left untouched as they have been built by the experts. We introduce the Julia programming language and its design—a dance between specialization and abstraction. Specialization allows for custom treatment. Multiple dispatch, a technique from computer science, picks the right algorithm for the right circumstance. Abstraction, which is what good computation is really about, recognizes what remains the same after differences are stripped away. Abstractions in mathematics are captured as code through another technique from computer science, generic programming. Julia shows that one can achieve machine performance without sacrificing human convenience.