nils-herrmann/clean_scirepeval huggingface dataset:
Identification of QR codes based on pattern recognition

To avoid the limitation of present QR code algorithms which are only available on the paper presswork, this paper proposed an algorithm based on Pattern Recognition to realize the identification of the QR codes printed on various materials with different models. The QR codes from the sample images were manually marked first, and then the input images were divided into blocks. For every single block, its MRH and LBP features were calculated and such texture patterns were trained to obtain a QR code identifier using the Spatial Boost algorithm. For real-time identification, this identifier could divide the real-time input images, and output the classified results of the blocks, considering the texture features (MRH, LBP) of the blocks as input vectors. The blocks confirmed to be 2D barcode were combined into QR codes. The QR codes identifier obtained by the last training reached a 100% rate of detection in the test database of the experiment.
A new primary power regulation method for contactless power transfer

This paper presents a new power regulation method for contactless power transfer systems that combines current magnitude control of a series tuned resonant converter with energy injection concepts suitable for constant load or single pick-up applications. In the latter, the control of the primary converter operates with asymmetrical control, to regulate the magnitude of the high frequency primary track current according to the load requirement of the secondary power pickup. The feedback control algorithm is developed by monitoring the output voltage of the secondary power pickup. Switching losses and EMI are reduced due to the ZCS (Zero Current Switching) operation of the converter. Simulation and experimental results show that the primary converter has a fast and smooth start-up transient response, and the magnitudes of the primary current and the output voltage of a secondary power pickup are both fully controllable against load variations.
Text Detection and Recognition in Imagery: A Survey

This paper analyzes, compares, and contrasts technical challenges, methods, and the performance of text detection and recognition research in color imagery. It summarizes the fundamental problems and enumerates factors that should be considered when addressing these problems. Existing techniques are categorized as either stepwise or integrated and sub-problems are highlighted including text localization, verification, segmentation and recognition. Special issues associated with the enhancement of degraded text and the processing of video text, multi-oriented, perspectively distorted and multilingual text are also addressed. The categories and sub-categories of text are illustrated, benchmark datasets are enumerated, and the performance of the most representative approaches is compared. This review provides a fundamental comparison and analysis of the remaining problems in the field.
A novel spring mechanism to reduce energy consumption of robotic arms

Most conventional robotic arms use motors to accelerate the manipulator. This leads to an unnecessary high energy consumption when performing repetitive tasks. This paper presents an approach to reduce energy consumption in robotic arms by performing its repetitive tasks with the help of a parallel spring mechanism. A special non-linear spring characteristic has been achieved by attaching a spring to two connected pulleys. This parallel spring mechanism provides for the accelerations of the manipulator without compromising its ability to vary the task parameters (the time per stroke, the displacement per stroke the grasping time and the payload). The energy consumption of the arm with the spring mechanism is compared to that of the same arm without the spring mechanism. Optimal control studies show that the robotic arm uses 22% less energy due to the spring mechanism. On the 2 DOF prototype, we achieved an energy reduction of 20%. The difference was due to model simplifications. With a spring mechanism, there is an extra energetic cost, because potential energy has to be stored into the spring during startup. This cost is equal to the total energy savings of the 2 DOF arm during 8 strokes. Next, there could have been an energetic cost to position the manipulator outside the equilibrium position. We have designed the spring mechanism in such a way that this holding cost is negligible for a range of start- and end positions. The performed experiments showed that the implementation of the proposed spring mechanism results in a reduction of the energy consumption while the arm is still able to handle varying task parameters.
Depth driven people counting using deep region proposal network

People counting is a crucial subject in video surveillance application. Factors such as severe occlusions, scene perspective distortions in real application scenario make this task challenging. In this paper, we carefully designed a deep detection framework based on depth information for people counting in crowded environments. Our system performs head detection on depth images collected by an overhead vertical Kinect sensor. To the best of our knowledge, this is the first attempt to use the deep convolutional neural networks on depth images for people counting. We explored the impact of the number and quality of RPN positive anchors on the performance of Faster R-CNN and proposed a solution. Our method is very simple but effective, not only showing promising results but also efficiency as it runs in real-time at a frame rate of about 110 frames per second on a GPU.
Self-eating and self-killing: crosstalk between autophagy and apoptosis

The functional relationship between apoptosis ('self-killing') and autophagy ('self-eating') is complex in the sense that, under certain circumstances, autophagy constitutes a stress adaptation that avoids cell death (and suppresses apoptosis), whereas in other cellular settings, it constitutes an alternative cell-death pathway. Autophagy and apoptosis may be triggered by common upstream signals, and sometimes this results in combined autophagy and apoptosis; in other instances, the cell switches between the two responses in a mutually exclusive manner. On a molecular level, this means that the apoptotic and autophagic response machineries share common pathways that either link or polarize the cellular responses.
Ecological Resilience, Biodiversity, and Scale

We describe existing models of the relationship between species diversity and ecological function, and propose a conceptual model that relates species richness, ecological resilience, and scale. We suggest that species interact with scale-dependent sets of ecological structures and processes that determine functional opportunities. We propose that ecological resilience is generated by diverse, but overlapping, function within a scale and by apparently redundant species that operate at different scales, thereby reinforcing function across scales. The distribution of functional diversity within and across scales enables regeneration and renewal to occur following ecological disruption over a wide range of scales.
A probabilistic theory of clustering

Data clustering is typically considered a subjective process, which makes it problematic. For instance, how does one make statistical inferences based on clustering? The matter is di0erent with pattern classi1cation, for which two fundamental characteristics can be stated: (1) the error of a classi1er can be estimated using “test data,” and (2) a classi1er can be learned using “training data.” This paper presents a probabilistic theory of clustering, including both learning (training) and error estimation (testing). The theory is based on operators on random labeled point processes. It includes an error criterion in the context of random point sets and representation of the Bayes (optimal) cluster operator for a given random labeled point process. Training is illustrated using a nearest-neighbor approach, and trained cluster operators are compared to several classical clustering algorithms. ? 2003 Pattern Recognition Society.
Why and How Investors Use ESG Information : Evidence from a Global Survey

Using survey data from a sample of senior investmen professionals from mainstream (i.e. not SRI funds ) investment organizations we provide insights into w hy and how investors use reported environmental, social and governance (ESG) information. The primar y reason survey respondents consider ESG information in investment decisions is because they consider it financially material to investment performance. ESG information is perceived to provid e information primarily about risk rather than a company’s competitive positioning. There is no one siz fits all, with the financial materiality of di fferent ESG issues varying across sectors. Lack of comparab ility due to the lack of reporting standards is the primary impediment to the use of ESG information. M ost frequently, the information is used to screen companies with the most often used method being neg ative screening. However, negative screening is perceived as the least investment beneficial while ful integration into stock valuation and positive screening considered more beneficial. Respondents expect nega tiv screening to be used less in the future, while positive screening and active ownership to be used more.
Black-Box Test Data Generation for GUI Testing

Effective system testing of applications with a Graphical User Interface (GUI) front-end demands careful generation of event sequences as well as providing relevant test data for parameterized widgets, i.e., widgets that accept input values such as textboxes and textareas. Current GUI testing techniques either manipulate the source code of the application under test (AUT) to generate the test data, or blindly use a set of random string values. In this paper, we propose a third novel way to generate relevant test data for GUI testing. We exploit the information provided in the GUI structure to extract a set of key identifiers for each parameterized widget. These identifiers are used to compose appropriate search phrases and collect relevant test data from the Internet. The results of an empirical study on five GUI-based applications show that the proposed approach is applicable and can get some hard-to-cover branches in the subject programs to execute. The proposed technique works from the black-box perspective and is entirely independent from GUI modeling and event sequence generation, thus it does not need access to the source code of AUT and provides an opportunity to be integrated with the existing GUI testing frameworks.
Classifying Purchase Decision Based on User Clickstream in E-Commerce Using Web Usage Mining

The advance of internet usage in Indonesia gives positive impact on the development of e-commerce in Indonesia where 63.5% of internet users have made online transactions. Along with e-commerce B2C growth in Indonesia, firm needs for an effective promotional strategy to understand the preferences and potential purchases for each consumer to increase return on investment (ROI). This empirical study investigated purchase decision of e-commerce users using Web Usage Mining framework. The high combination of purchasing product categories by users of e-commerce website required a multi-label classification technique that could classify those pair of purchase decision. Label Powerset method with Support Vector Machine (SVM) algorithm was applied to classify e-commerce users purchase decisions using general and detailed information features. Feature selection using Information retained 60 from 90 features. The proposed feature selection with Information Gain and parameter selection using Grid Search proved that they had an ability to enhance performance to classify purchase decision of e-commerce user. Radial Basis Function (RBF) as the most effective kernel presented an accuracy of 75.6%, with slightly difference of 2.2% with classification without using feature selection.
Measuring Machine Intelligence Through Visual Question Answering

As machines have become more intelligent, there has been a renewed interest in methods for measuring their intelligence. A common approach is to propose tasks for which a human excels, but one which machines find difficult. However, an ideal task should also be easy to evaluate and not be easily gameable. We begin with a case study exploring the recently popular task of image captioning and its limitations as a task for measuring machine intelligence. An alternative and more promising task is Visual Question Answering that tests a machine’s ability to reason about language and vision. We describe a dataset unprecedented in size created for the task that contains over 760,000 human generated questions about images. Using around 10 million human generated answers, machines may be easily evaluated.
Quadcopter control system

The paper presents two different types of approach for mathematical modeling of quadcopter kinematics and dynamics. The first one is based on the equations of classical mechanics and the other one is derived from Denavit-Hartenberg formalism and Lagrangian mechanics. The obtained models were used to design the control of the quadcopter motion on one rotation axis. The paper also offers some details of the physical and software implementation of the system on which tests have been made.
Proposed hybrid RSA algorithm for cloud computing

Cloud computing is growing vastly by providing various functions such as global resource scheduling, fault tolerance, reliability and load balancing. cloud security is still a barrier to adoption of this service. Cloud computing has become a familiar term and most used technology around the world. It is a utility computing model which has huge potential and promising innovations that anyone cannot imagine. As more and more businesses are storing there data in cloud, concerns are growing about how safe this environment is. Cloud Security incorporates many security restrictions from the point of view of the customer and cloud providers. In the context of customers, who essentially concerned about that; where the data is stored? Who has the right to see the data? This paper is focused on security issues in cloud computing. In this we use cryptographic algorithm for securing encryption of data over our cloud. We also maintain the integrity of the data using hash codes.
Simple Smartphone-Based Guiding System for Visually Impaired People

Visually impaired people are often unaware of dangers in front of them, even in familiar environments. Furthermore, in unfamiliar environments, such people require guidance to reduce the risk of colliding with obstacles. This study proposes a simple smartphone-based guiding system for solving the navigation problems for visually impaired people and achieving obstacle avoidance to enable visually impaired people to travel smoothly from a beginning point to a destination with greater awareness of their surroundings. In this study, a computer image recognition system and smartphone application were integrated to form a simple assisted guiding system. Two operating modes, online mode and offline mode, can be chosen depending on network availability. When the system begins to operate, the smartphone captures the scene in front of the user and sends the captured images to the backend server to be processed. The backend server uses the faster region convolutional neural network algorithm or the you only look once algorithm to recognize multiple obstacles in every image, and it subsequently sends the results back to the smartphone. The results of obstacle recognition in this study reached 60%, which is sufficient for assisting visually impaired people in realizing the types and locations of obstacles around them.
WCL2R: A Benchmark Collection for Learning to Rank Research with Clickthrough Data

In this paper we present WCL2R, a benchmark collection for supporting research in learning to rank (L2R) algorithms which exploit clickthrough features. Differently from other L2R benchmark collections, such as LETOR and the recently released Yahoo!’s collection for a L2R competition, in WCL2R we focus on defining a significant (and new) set of features over clickthrough data extracted from the logs of a real-world search engine. In this paper, we describe the WCL2R collection by providing details about how the corpora, queries and relevance judgments were obtained, how the learning features were constructed and how the process of splitting the collection in folds for representative learning was performed. We also analyze the discriminative power of the WCL2R collection using traditional feature selection algorithms and show that the most discriminative features are, in fact, those based on clickthrough data. We then compare several L2R algorithms on WCL2R, showing that all of them obtain significant gains by exploiting clickthrough information over using traditional ranking approaches.
Evaluating deep learning architectures for Speech Emotion Recognition

Speech Emotion Recognition (SER) can be regarded as a static or dynamic classification problem, which makes SER an excellent test bed for investigating and comparing various deep learning architectures. We describe a frame-based formulation to SER that relies on minimal speech processing and end-to-end deep learning to model intra-utterance dynamics. We use the proposed SER system to empirically explore feed-forward and recurrent neural network architectures and their variants. Experiments conducted illuminate the advantages and limitations of these architectures in paralinguistic speech recognition and emotion recognition in particular. As a result of our exploration, we report state-of-the-art results on the IEMOCAP database for speaker-independent SER and present quantitative and qualitative assessments of the models' performances.
Exploring Hypergraph Representation on Face Anti-spoofing Beyond 2D Attacks

Face anti-spoofing plays a crucial role in protecting face recognition systems from various attacks. Previous modelbased and deep learning approaches achieve satisfactory performance for 2D face spoofs, but remain limited for more advanced 3D attacks such as vivid masks. In this paper, we address 3D face anti-spoofing via the proposed Hypergraph Convolutional Neural Networks (HGCNN). Firstly, we construct a computation-efficient and posture-invariant face representation with only a few key points on hypergraphs. The hypergraph representation is then fed into the designed HGCNN with hypergraph convolution for feature extraction, while the depth auxiliary is also exploited for 3D mask anti-spoofing. Further, we build a 3D face attack database with color, depth and infrared light information to overcome the deficiency of 3D face anti-spoofing data. Experiments show that our method achieves the state-of-theart performance over widely used 3D and 2D databases as well as the proposed one under various tests.
The nature of feelings: evolutionary and neurobiological origins

Feelings are mental experiences of body states. They signify physiological need (for example, hunger), tissue injury (for example, pain), optimal function (for example, well-being), threats to the organism (for example, fear or anger) or specific social interactions (for example, compassion, gratitude or love). Feelings constitute a crucial component of the mechanisms of life regulation, from simple to complex. Their neural substrates can be found at all levels of the nervous system, from individual neurons to subcortical nuclei and cortical regions.
Fusion of electrocardiogram with unobtrusive biometrics: An efficient individual authentication system

This paper explores the effectiveness of a novel multibiometric system that is resulted from the fusion of the electrocardiogram (ECG) with an unobtrusive biometric face and another biometric fingerprint which is known to be a least obtrusive for efficient individual authentication. The unimodal systems of the face and the fingerprint biometrics are neither secure nor they can achieve the optimum performance. Using the ECG signal as one of the biometrics offer advantage to a multibiometric system that ECG is inherited to an individual which is confidential, secured and difficult to be forged. It has an inherent feature of vitality signs that ensures a strong protection against spoof attacks to the system. Transformation based score fusion technique is used to measure the performance of the fused system. In particular, the weighted sum of score rule is used where weights are computed using equal error rate (EER) and match score distributions of the unimodal systems. The performance of the proposed multibiometric system is measured using EER and receiver operating characteristic (ROC) curve. The results show the optimum performance of the multibiometric system fusing the ECG signal with the face and fingerprint biometrics which is achieved to an EER of 0.22%, as compared to the unimodal systems that have the EER of 10.80%, 4.52% and 2.12%, respectively for the ECG signal, face and fingerprint biometrics.
Time Series Data Classification Using Recurrent Neural Network with Ensemble Learning

In statistics and signal processing, a time series is a sequence of data points, measured typically at successive times, spaced apart at uniform time intervals. Time series prediction is the use of a model to predict future events based on known past events; to predict future data points before they are measured. Solutions in such cases can be provided by non-parametric regression methods, of which each neural network based predictor is a class. As a learning method of time series data with neural network, Elman type Recurrent Neural Network has been known. In this paper, we propose the multi RNN. In order to verify the effectiveness of our proposed method, we experimented by the simple artificial data and the heart pulse wave data.
Compact microstrip feed networks for low-impedance quadrifillar helix antennas

Quadrifilar helix antennas (QHA) are widely used in various applications. QHA performance strongly depends on its feed network parameters. A proper network should equally divide power from input port between output ports and provide quadrature phases (0, 90, 180 and 270°) within antenna frequency band. Two microstrip feed networks are presented in the report. Both networks are based on Wilkinson power splitter and have similar shape — a 39 mm diameter circle. First one is designed for a small QHA with a low input impedance (12Ohm). It consists of planar two-port impedance-transforming Wilkinson splitters. Each splitter reduces output impedance by a factor of 2. Three splitters are cascaded to form a 4-port network. Proper phasing is achieved by means of microstrip delay lines. To equalize output signal amplitudes the splitters have been made non-symmetrical. Bandwidth of the network is much higher than required for a narrow-band (2–3%) QHA. Insertion loss is 1 dB higher than that of ideal network. Relatively high return loss (− 16 dB) results from interference with second stage dividers. Another drawback is high reflection from via holes which inevitably act as π-type filters. The second feed network is designed for a larger QHA with input impedance 28 Ohms. It is an impedance-transforming 4-port Wilkinson divider. Like in the first design, microstrip delay lines provide proper phasing. Network bandwidth and transmission losses are similar to the first one, but has the advantage of lower return losses due to absence of interference with second stage splitter. Simpler design allowed placing almost everything in the top plane of PCB except for splitter resistors. In a matched case, no current flows through these resistors, so the negative effects of via holes are negligible. Reducing complexity and demands for impedance transformation improved performance of this network compared to the first one. Return loss is lower than −25 dB within antenna operation range. Insertion loss is 0.7 dB higher than in case of ideal network and slightly lower than return loss of the first network.