nils-herrmann/clean_scirepeval huggingface dataset:
Learning RoI Transformer for Detecting Oriented Objects in Aerial Images

Object detection in aerial images is an active yet challenging task in computer vision because of the birdview perspective, the highly complex backgrounds, and the variant appearances of objects. Especially when detecting densely packed objects in aerial images, methods relying on horizontal proposals for common object detection often introduce mismatches between the Region of Interests (RoIs) and objects. This leads to the common misalignment between the final object classification confidence and localization accuracy. Although rotated anchors have been used to tackle this problem, the design of them always multiplies the number of anchors and dramatically increases the computational complexity. In this paper, we propose a RoI Transformer to address these problems. More precisely, to improve the quality of region proposals, we first designed a Rotated RoI (RRoI) learner to transform a Horizontal Region of Interest (HRoI) into a Rotated Region of Interest (RRoI). Based on the RRoIs, we then proposed a Rotated Position Sensitive RoI Align (RPS-RoI-Align) module to extract rotation-invariant features from them for boosting subsequent classification and regression. Our RoI Transformer is with light weight and can be easily embedded into detectors for oriented object detection. A simple implementation of the RoI Transformer has achieved state-of-the-art performances on two common and challenging aerial datasets, i.e., DOTA and HRSC2016, with a neglectable reduction to detection speed. Our RoI Transformer exceeds the deformable Position Sensitive RoI pooling when oriented bounding-box annotations are available. Extensive experiments have also validated the flexibility and effectiveness of our RoI Transformer. The results demonstrate that it can be easily integrated with other detector architectures and significantly improve the performances.
A Transfer-Learnable Natural Language Interface for Databases

Relational database management systems (RDBMSs) are powerful because they are able to optimize and answer queries against any relational database. A natural language interface (NLI) for a database, on the other hand, is tailored to support that specific database. In this work, we introduce a general purpose transfer-learnable NLI with the goal of learning one model that can be used as NLI for any relational database. We adopt the data management principle of separating data and its schema, but with the additional support for the idiosyncrasy and complexity of natural languages. Specifically, we introduce an automatic annotation mechanism that separates the schema and the data, where the schema also covers knowledge about natural language. Furthermore, we propose a customized sequence model that translates annotated natural language queries to SQL statements. We show in experiments that our approach outperforms previous NLI methods on the WikiSQL dataset and the model we learned can be applied to another benchmark dataset OVERNIGHT without retraining.
Opportunistic Wireless Energy Harvesting in Cognitive Radio Networks

Wireless networks can be self-sustaining by harvesting energy from ambient radio-frequency (RF) signals. Recently, researchers have made progress on designing efficient circuits and devices for RF energy harvesting suitable for low-power wireless applications. Motivated by this and building upon the classic cognitive radio (CR) network model, this paper proposes a novel method for wireless networks coexisting where low-power mobiles in a secondary network, called secondary transmitters (STs), harvest ambient RF energy from transmissions by nearby active transmitters in a primary network, called primary transmitters (PTs), while opportunistically accessing the spectrum licensed to the primary network. We consider a stochastic-geometry model in which PTs and STs are distributed as independent homogeneous Poisson point processes (HPPPs) and communicate with their intended receivers at fixed distances. Each PT is associated with a guard zone to protect its intended receiver from ST's interference, and at the same time delivers RF energy to STs located in its harvesting zone. Based on the proposed model, we analyze the transmission probability of STs and the resulting spatial throughput of the secondary network. The optimal transmission power and density of STs are derived for maximizing the secondary network throughput under the given outage-probability constraints in the two coexisting networks, which reveal key insights to the optimal network design. Finally, we show that our analytical result can be generally applied to a non-CR setup, where distributed wireless power chargers are deployed to power coexisting wireless transmitters in a sensor network.
Nonequilibrium Thermodynamics Modeling of Coupled Biochemical Cycles in Living Cells

Living cells represent open, nonequilibrium, self organizing, and dissipative systems maintained with the continuous supply of outside and inside material, energy, and information flows. The energy in the form of adenosine triphosphate is utilized in biochemical cycles, transport processes, protein synthesis, reproduction, and performing other biological work. The processes in molecular and cellular biological systems are stochastic in nature with varying spatial and time scales, and bounded with conservation laws, kinetic laws, and thermodynamic constraints, which should be taken into account by any approach for modeling biological systems. In component biology, this review focuses on the modeling of enzyme kinetics and fluctuation of single biomolecules acting as molecular motors, while in systems biology it focuses on modeling biochemical cycles and networks in which all the components of a biological system interact functionally over time and space. Biochemical cycles emerge from collective and functional efforts to devise a cyclic flow of optimal energy degradation rate, which can only be described by nonequilibrium thermodynamics. Therefore, this review emphasizes the role of nonequilibrium thermodynamics through the formulations of thermodynamically coupled biochemical cycles, entropy production, fluctuation theorems, bioenergetics, and reaction-diffusion systems. Fluctuation theorems relate the forward and backward dynamical randomness of the trajectories or paths, bridge the microscopic and macroscopic domains, and link the time-reversible and irreversible descriptions of biological systems. However, many of these approaches are in their early stages of their development and no single computational or experimental technique is able to span all the relevant and necessary spatial and temporal scales. Wide range of experimental and novel computational techniques with high accuracy, precision, coverage, and efficiency are necessary for understanding biochemical cycles.
Predicting healthcare trajectories from medical records: A deep learning approach

Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, stored in electronic medical records are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes. At the data level, DeepCare represents care episodes as vectors and models patient health state trajectories by the memory of historical records. Built on Long Short-Term Memory (LSTM), DeepCare introduces methods to handle irregularly timed events by moderating the forgetting and consolidation of memory. DeepCare also explicitly models medical interventions that change the course of illness and shape future medical risk. Moving up to the health state level, historical and present health states are then aggregated through multiscale temporal pooling, before passing through a neural network that estimates future outcomes. We demonstrate the efficacy of DeepCare for disease progression modeling, intervention recommendation, and future risk prediction. On two important cohorts with heavy social and economic burden - diabetes and mental health - the results show improved prediction accuracy.
GenAttack: Practical Black-box Attacks with Gradient-Free Optimization

Deep neural networks (DNNs) are vulnerable to adversarial examples, even in the black-box case, where the attacker is limited to solely query access. Existing black-box approaches to generating adversarial examples typically require a significant number of queries, either for training a substitute network or estimating gradients from the output scores. We introduce GenAttack, a gradient-free optimization technique which uses genetic algorithms for synthesizing adversarial examples in the black-box setting. Our experiments on the MNIST, CIFAR-10, and ImageNet datasets show that GenAttack can successfully generate visually imperceptible adversarial examples against state-of-the-art image recognition models with orders of magnitude fewer queries than existing approaches. For example, in our CIFAR-10 experiments, GenAttack required roughly 2,568 times less queries than the current state-of-the-art black-box attack. Furthermore, we show that GenAttack can successfully attack both the state-of-the-art ImageNet defense, ensemble adversarial training, and non-differentiable, randomized input transformation defenses. GenAttack’s success against ensemble adversarial training demonstrates that its query efficiency enables it to exploit the defense’s weakness to direct black-box attacks. GenAttack’s success against non-differentiable input transformations indicates that its gradient-free nature enables it to be applicable against defenses which perform gradient masking/obfuscation to confuse the attacker. Our results suggest that evolutionary algorithms open up a promising area of research into effective gradient-free black-box attacks.
The Internet of Things - A survey of topics and trends

The Internet of Things is a paradigm where everyday objects can be equipped with identifying, sensing, networking and processing capabilities that will allow them to communicate with one another and with other devices and services over the Internet to accomplish some objective. Ultimately, IoT devices will be ubiquitous, context-aware and will enable ambient intelligence. This article reports on the current state of research on the Internet of Things by examining the literature, identifying current trends, describing challenges that threaten IoT diffusion, presenting open research questions and future directions and compiling a comprehensive reference list to assist researchers.
First Comes Love, Then Comes Google: An Investigation of Uncertainty Reduction Strategies and Self-Disclosure in Online Dating

This study investigates relationships between privacy concerns, uncertainty reduction behaviors, and self-disclosure among online dating participants, drawing on uncertainty reduction theory and the warranting principle. The authors propose a conceptual model integrating privacy concerns, self-efficacy, and Internet experience with uncertainty reduction strategies and amount of self-disclosure and then test this model on a nationwide sample of online dating participants (N = 562). The study findings confirm that the frequency of use of uncertainty reduction strategies is predicted by three sets of online dating concerns—personal security, misrepresentation, and recognition—as well as selfefficacy in online dating. Furthermore, the frequency of uncertainty reduction strategies mediates the relationship between these variables and amount of self-disclosure with potential online dating partners. The authors explore the theoretical implications of these findings for our understanding of uncertainty reduction, warranting, and self-disclosure processes in online contexts.
Multi-view Sentence Representation Learning

Multi-view learning can provide self-supervision when different views are available of the same data. The distributional hypothesis provides another form of useful self-supervision from adjacent sentences which are plentiful in large unlabelled corpora. Motivated by the asymmetry in the two hemispheres of the human brain as well as the observation that different learning architectures tend to emphasise different aspects of sentence meaning, we create a unified multi-view sentence representation learning framework, in which, one view encodes the input sentence with a Recurrent Neural Network (RNN), and the other view encodes it with a simple linear model, and the training objective is to maximise the agreement specified by the adjacent context information between two views. We show that, after training, the vectors produced from our multi-view training provide improved representations over the single-view training, and the combination of different views gives further representational improvement and demonstrates solid transferability on standard downstream tasks.
Smart farm monitoring using Raspberry Pi and Arduino

This study aimed to investigate an establishment using an Intelligent System which employed an Embedded System and Smart Phone for chicken farming management and problem solving using Raspberry Pi and Arduino Uno. An experiment and comparative analysis of the intelligent system was applied in a sample chicken farm in this study. The findings of this study found that the system could monitor surrounding weather conditions including humidity, temperature, climate quality, and also the filter fan switch control in the chicken farm. The system was found to be comfortable for farmers to use as they could effectively control the farm anywhere at anytime, resulting in cost reduction, asset saving, and productive management in chicken farming.
LASSO: A feature selection technique in predictive modeling for machine learning

Feature selection is one of the techniques in machine learning for selecting a subset of relevant features namely variables for the construction of models. The feature selection technique aims at removing the redundant or irrelevant features or features which are strongly correlated in the data without much loss of information. It is broadly used for making the model much easier to interpret and increase generalization by reducing the variance. Regression analysis plays a vital role in statistical modeling and in turn for performing machine learning tasks. The traditional procedures such as Ordinary Least Squares (OLS) regression, Stepwise regression and partial least squares regression are very sensitive to random errors. Many alternatives have been established in the literature during the past few decades such as Ridge regression and LASSO and its variants. This paper explores the features of the popular regression methods, OLS regression, ridge regression and the LASSO regression. The performance of these procedures has been studied in terms of model fitting and prediction accuracy using real data and simulated environment with the help of R package.
Open Information Extraction with Tree Kernels

Traditional relation extraction seeks to identify pre-specified semantic relations within natural language text, while open Information Extraction (Open IE) takes a more general approach, and looks for a variety of relations without restriction to a fixed relation set. With this generalization comes the question, what is a relation? For example, should the more general task be restricted to relations mediated by verbs, nouns, or both? To help answer this question, we propose two levels of subtasks for Open IE. One task is to determine if a sentence potentially contains a relation between two entities? The other task looks to confirm explicit relation words for two entities. We propose multiple SVM models with dependency tree kernels for both tasks. For explicit relation extraction, our system can extract both noun and verb relations. Our results on three datasets show that our system is superior when compared to state-of-the-art systems like REVERB and OLLIE for both tasks. For example, in some experiments our system achieves 33% improvement on nominal relation extraction over OLLIE. In addition we propose an unsupervised rule-based approach which can serve as a strong baseline for Open IE systems.
Short text classification in twitter to improve information filtering

In microblogging services such as Twitter, the users may become overwhelmed by the raw data. One solution to this problem is the classification of short text messages. As short texts do not provide sufficient word occurrences, traditional classification methods such as "Bag-Of-Words" have limitations. To address this problem, we propose to use a small set of domain-specific features extracted from the author's profile and text. The proposed approach effectively classifies the text to a predefined set of generic classes such as News, Events, Opinions, Deals, and Private Messages.
A Fuzzy-Based Approach for Sensing, Coding and Transmission Configuration of Visual Sensors in Smart City Applications

The advance of technologies in several areas has allowed the development of smart city applications, which can improve the way of life in modern cities. When employing visual sensors in that scenario, still images and video streams may be retrieved from monitored areas, potentially providing valuable data for many applications. Actually, visual sensor networks may need to be highly dynamic, reflecting the changing of parameters in smart cities. In this context, characteristics of visual sensors and conditions of the monitored environment, as well as the status of other concurrent monitoring systems, may affect how visual sensors collect, encode and transmit information. This paper proposes a fuzzy-based approach to dynamically configure the way visual sensors will operate concerning sensing, coding and transmission patterns, exploiting different types of reference parameters. This innovative approach can be considered as the basis for multi-systems smart city applications based on visual monitoring, potentially bringing significant results for this research field.
Adversarial Text Generation via Feature-Mover's Distance

Generative adversarial networks (GANs) have achieved significant success in generating realvalued data. However, the discrete nature of text hinders the application of GAN to textgeneration tasks. Instead of using the standard GAN objective, we propose to improve textgeneration GAN via a novel approach inspired by optimal transport. Specifically, we consider matching the latent feature distributions of real and synthetic sentences using a novel metric, termed the feature-mover’s distance (FMD). This formulation leads to a highly discriminative critic and easy-to-optimize objective, overcoming the mode-collapsing and brittle-training problems in existing methods.
In Defense of the Eight-Point Algorithm

The fundamental matrix is a basic tool in the analysis of scenes taken with two uncalibrated cameras, and the eight-point algorithm is a frequently cited method for computing the fundamental matrix from a set of eight or more point matches. It has the advantage of simplicity of implementation. The prevailing view is, however, that it is extremely susceptible to noise and hence virtually useless for most purposes. This paper challenges that view, by showing that by preceding the algorithm with a very simple normalization (translation and scaling) of the coordinates of the matched points, results are obtained comparable with the best iterative algorithms. This improved performance is justified by theory and verified by extensive experiments on real images. Index Terms —Fundamental matrix, eight-point algorithm, condition number, epipolar structure, stereo vision. —————————— ✦ ——————————
Single image depth estimation by dilated deep residual convolutional neural network and soft-weight-sum inference

This paper proposes a new residual convolutional neural network (CNN) architecture for single image depth estimation. Compared with existing deep CNN based methods, our method achieves much better results with fewer training examples and model parameters. The advantages of our method come from the usage of dilated convolution, skip connection architecture and soft-weight-sum inference. Experimental evaluation on the NYU Depth V2 dataset shows that our method outperforms other state-of-the-art methods by a margin.
Human Pose Estimation in Space and Time using 3D CNN

This paper explores the capabilities of convolutional neural networks to deal with a task that is easily manageable for humans: perceiving 3D pose of a human body from varying angles. However, in our approach, we are restricted to using a monocular vision system. For this purpose, we apply the convolutional neural networks approach on RGB videos and extend it to three dimensional convolutions. This is done via encoding the time dimension in videos as the 3rd dimension in convolutional space, and directly regressing to human body joint positions in 3D coordinate space. This research shows the ability of such a network to achieve state-of-theart performance on the selected Human3.6M dataset, thus demonstrating the possibility of successfully representing a temporal data with an additional dimension in the convolutional operation.
A blockchain-based Trust System for the Internet of Things

One of the biggest challenges for the Internet of Things (IoT) is to bridge the currently fragmented trust domains. The traditional PKI model relies on a common root of trust and does not fit well with the heterogeneous IoT ecosystem where constrained devices belong to independent administrative domains. In this work we describe a distributed trust model for the IoT that leverages the existing trust domains and bridges them to create end-to-end trust between IoT devices without relying on any common root of trust. Furthermore we define a new cryptographic primitive, denoted as obligation chain designed as a credit-based Blockchain with a built-in reputation mechanism. Its innovative design enables a wide range of use cases and business models that are simply not possible with current Blockchain-based solutions while not experiencing traditional blockchain delays. We provide a security analysis for both the obligation chain and the overall architecture and provide experimental tests that show its viability and quality.
Deep Variational Canonical Correlation Analysis

We present deep variational canonical correlation analysis (VCCA), a deep multi-view learning model that extends the latent variable model interpretation of linear CCA to nonlinear observation models parameterized by deep neural networks. We derive variational lower bounds of the data likelihood by parameterizing the posterior probability of the latent variables from the view that is available at test time. We also propose a variant of VCCA called VCCA-private that can, in addition to the “common variables” underlying both views, extract the “private variables” within each view, and disentangles the shared and private information for multi-view data without hard supervision. Experimental results on real-world datasets show that our methods are competitive across domains.
Web Crawler: A Review

Information Retrieval deals with searching and retrieving information within the documents and it also searches the online databases and internet. Web crawler is defined as a program or software which traverses the Web and downloads web documents in a methodical, automated manner. Based on the type of knowledge, web crawler is usually divided in three types of crawling techniques: General Purpose Crawling, Focused crawling and Distributed Crawling. In this paper, the applicability of Web Crawler in the field of web search and a review on Web Crawler to different problem domains in web search is discussed.