nils-herrmann/clean_scirepeval huggingface dataset:
Adaptive Knowledge Transfer for Multiple Instance Learning in Image Classification

Multiple Instance Learning (MIL) is a popular learning technique in various vision tasks including image classification. However, most existing MIL methods do not consider the problem of insufficient examples in the given target category. In this case, it is difficult for traditional MIL methods to build an accurate classifier due to the lack of training examples. Motivated by the empirical success of transfer learning, this paper proposes a novel approach of Adaptive Knowledge Transfer for Multiple Instance Learning (AKT-MIL) in image classification. The new method transfers cross-category knowledge from source categories under multiple instance setting for boosting the learning process. A unified learning framework with a data-dependent mixture model is designed to adaptively combine the transferred knowledge from sources with a weak classifier built in the target domain. Based on this framework, an iterative coordinate descent method with Constraint Concave-Convex Programming (CCCP) is proposed as the optimization procedure. An extensive set of experimental results demonstrate that the proposed AKT-MIL approach substantially outperforms several state-of-the-art algorithms on two benchmark datasets, especially in the scenario when very few training examples are available in the target domain.
Brain MRI super-resolution using deep 3D convolutional networks

Example-based single image super-resolution (SR) has recently shown outcomes with high reconstruction performance. Several methods based on neural networks have successfully introduced techniques into SR problem. In this paper, we propose a three-dimensional (3D) convolutional neural network to generate high-resolution (HR) brain image from its input low-resolution (LR) with the help of patches of other HR brain images. Our work demonstrates the need of fitting data and network parameters for 3D brain MRI.
A Ciphertext-Policy Attribute-Based Proxy Re-encryption with Chosen-Ciphertext Security

Cipher text-Policy Attribute-Based Proxy Re-Encryption (CP-ABPRE) extends the traditional Proxy Re-Encryption (PRE) by allowing a semi-trusted proxy to transform a cipher text under an access policy to the one with the same plaintext under another access policy (i.e. attribute-based re-encryption). The proxy, however, learns nothing about the underlying plaintext. CP-ABPRE has many real world applications, such as fine-grained access control in cloud storage systems and medical records sharing among different hospitals. Previous CP-ABPRE schemes leave how to be secure against Chosen-Cipher text Attacks (CCA) as an open problem. This paper, for the first time, proposes a new CP-ABPRE to tackle the problem. The new scheme supports attribute-based re-encryption with any monotonic access structures. Despite our scheme is constructed in the random oracle model, it can be proved CCA secure under the decisional q-parallel bilinear Diffie-Hellman exponent assumption.
Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities

The anatomical location of imaging features is of crucial importance for accurate diagnosis in many medical tasks. Convolutional neural networks (CNN) have had huge successes in computer vision, but they lack the natural ability to incorporate the anatomical location in their decision making process, hindering success in some medical image analysis tasks. In this paper, to integrate the anatomical location information into the network, we propose several deep CNN architectures that consider multi-scale patches or take explicit location features while training. We apply and compare the proposed architectures for segmentation of white matter hyperintensities in brain MR images on a large dataset. As a result, we observe that the CNNs that incorporate location information substantially outperform a conventional segmentation method with handcrafted features as well as CNNs that do not integrate location information. On a test set of 50 scans, the best configuration of our networks obtained a Dice score of 0.792, compared to 0.805 for an independent human observer. Performance levels of the machine and the independent human observer were not statistically significantly different (p-value = 0.06).
Shoulder actuation mechanisms for arm rehabilitation exoskeletons

Rehabilitation robots start to become an important tool in stroke rehabilitation. Compared to manual arm training, robot-supported training can be more intensive, of longer duration, repetitive and task-oriented. Therefore, these devices have the potential to improve the rehabilitation process in stroke patients. While in the past, most groups have been working with endeffector-based robots, exoskeleton robots become more and more important, mainly because they offer a better guidance of the single human joints, especially during movements with large ranges. Regarding the upper extremities, the shoulder is the most complex human joint and its actuation is, therefore, challenging. This paper deals with shoulder actuation principles for exoskeleton robots. First, a quantitative analysis of the human shoulder movement is presented. Based on that analysis two shoulder actuation principles that provide motion of the center of the glenohumeral joint are presented and evaluated.
Forecasting Volatility in Financial Markets : A Review

VOLATILITY FORECASTING IS AN important task in financial markets, and it has held the attention of academics and practitioners over the last two decades. At the time of writing, there are at least 93 published and working papers that study forecasting performance of various volatility models, and several times that number have been written on the subject of volatility modelling without the forecasting aspect. This extensive research reflects the importance of volatility in investment, security valuation, risk management, and monetary policy making.
Computational Thinking in Education: Where does it Fit? A systematic literary review

Computational Thinking (CT) has been described as an essential skill which everyone should learn and can therefore include in their skill set. Seymour Papert  is credited as concretising Computational Thinking in 1980 but since Wing  popularised the term in 2006 and brought it to the international community’s attention, more and more research has been conducted on CT in education. The aim of this systematic literary review is to give educators and education researchers an overview of what work has been carried out in the domain, as well as potential gaps and opportunities that still exist. Overall it was found in this review that, although there is a lot of work currently being done around the world in many different educational contexts, the work relating to CT is still in its infancy. Along with the need to create an agreed-upon definition of CT lots of countries are still in the process of, or have not yet started, introducing CT into curriculums in all levels of education. It was also found that Computer Science/Computing, which could be the most obvious place to teach CT, has yet to become a mainstream subject in some countries, although this is improving. Of encouragement to educators is the wealth of tools and resources being developed to help teach CT as well as more and more work relating to curriculum development. For those teachers looking to incorporate CT into their schools or classes then there are bountiful options which include programming, hands-on exercises and more. The need for more detailed lesson plans and curriculum structure however, is something that could be of benefit to teachers. 1 Corresponding author 2 Table of
Reflection nebula visualization

Stars form in dense clouds of interstellar gas and dust. The residual dust surrounding a young star scatters and diffuses its light, making the star's "cocoon" of dust observable from Earth. The resulting structures, called reflection nebulae, are commonly very colorful in appearance due to wavelength-dependent effects in the scattering and extinction of light. The intricate interplay of scattering and extinction cause the color hues, brightness distributions, and the apparent shapes of such nebulae to vary greatly with viewpoint. We describe an interactive visualization tool for realistically rendering the appearance of arbitrary 3D dust distributions surrounding one or more illuminating stars. Our rendering algorithm is based on the physical models used in astrophysics research. The tool can be used to create virtual fly-throughs of reflection nebulae for interactive desktop visualizations, or to produce scientifically accurate animations for educational purposes, e.g., in planetarium shows. The algorithm is also applicable to investigate on-the-fly the visual effects of physical parameter variations, exploiting visualization technology to help gain a deeper and more intuitive understanding of the complex interaction of light and dust in real astrophysical settings.
No Noun Phrase Left Behind: Detecting and Typing Unlinkable Entities

Entity linking systems link noun-phrase mentions in text to their corresponding Wikipedia articles. However, NLP applications would gain from the ability to detect and type all entities mentioned in text, including the long tail of entities not prominent enough to have their own Wikipedia articles. In this paper we show that once the Wikipedia entities mentioned in a corpus of textual assertions are linked, this can further enable the detection and fine-grained typing of the unlinkable entities. Our proposed method for detecting unlinkable entities achieves 24% greater accuracy than a Named Entity Recognition baseline, and our method for fine-grained typing is able to propagate over 1,000 types from linked Wikipedia entities to unlinkable entities. Detection and typing of unlinkable entities can increase yield for NLP applications such as typed question answering.
Video Summarization via Semantic Attended Networks

The goal of video summarization is to distill a raw video into a more compact form without losing much semantic information. However, previous methods mainly consider the diversity and representation interestingness of the obtained summary, and they seldom pay sufficient attention to semantic information of resulting frame set, especially the long temporal range semantics. To explicitly address this issue, we propose a novel technique which is able to extract the most semantically relevant video segments (i.e., valid for a long term temporal duration) and assemble them into an informative summary. To this end, we develop a semantic attended video summarization network (SASUM) which consists of a frame selector and video descriptor to select an appropriate number of video shots by minimizing the distance between the generated description sentence of the summarized video and the human annotated text of the original video. Extensive experiments show that our method achieves a superior performance gain over previous methods on two benchmark datasets.
Handwritten Digit Recognition Using Multi-Layer Feedforward Neural Networks with Periodic and Monotonic Activation Functions

The problem of handwritten digit recognition is tackled by multi-layer feedforward neural networks with different types of neuronal activation functions. Three types of activation functions are adopted in the network, namely, the traditional sigmoid function, the sinusoidal function and a periodic function that can be considered as a combination of the first two functions. To speed up the learning, as well as to reduce the network size, the Extended Kalman Filter (EKF) algorithm conjunct with a pruning method is used to train the network. Simulation results show that periodic activation functions perform better than monotonic ones in solving multi-cluster classification problems such as handwritten digit recognition.
Modeling of V2 Current-Mode Control

Recently, the V2 type of constant on-time control has been widely used to improve light-load efficiency. In V2 implementation, the nonlinear PWM modulator is much more complicated than usual, since not only is the inductor current information fed back to the modulator, but the capacitor voltage ripple information is also fed back to the modulator. Generally speaking, there is no sub-harmonic oscillation in constant on-time control. However, the delay due to the capacitor ripple results in sub-harmonic oscillation in V2 constant on-time control. So far, there has been no accurate model to predict instability issue due to the capacitor ripple. This paper presents a new modeling approach for V2 constant on-time control. The power stage, the switches and the PWM modulator are treated as a single entity and modeled based the describing function method. The model for the V2 constant on-time control achieved by the new approach can accurately predict sub-harmonic oscillation. Two solutions are discussed to solve the instability issue. The extension of the model to other types of V2 current-mode control is also shown in the paper. Simulation and experimental results are used to verify the proposed model.
Driver drowsiness detection using face expression recognition

An important application of machine vision and image processing could be driver drowsiness detection system due to its high importance. In recent years there have been many research projects reported in the literature in this field. In this paper, unlike conventional drowsiness detection methods, which are based on the eye states alone, we used facial expressions to detect drowsiness. There are many challenges involving drowsiness detection systems. Among the important aspects are: change of intensity due to lighting conditions, the presence of glasses and beard on the face of the person. In this project, we propose and implement a hardware system which is based on infrared light and can be used in resolving these problems. In the proposed method, following the face detection step, the facial components that are more important and considered as the most effective for drowsiness, are extracted and tracked in video sequence frames. The system has been tested and implemented in a real environment.
The Commonsense Algorithm as a Basis for Comfuter Models of Human Memory, Inference, Belief and Contextual Language Comprehension

The notion of a commonsense algorithm is presented as a basic data structure for modeling human cognition. This data structure unifies many current ideas about human memory and information processing. The structure is defined by specifying a set of proposed cognitive primitive links which, when used to build up large structures of actions, states, statechanges and tendencies, provide an adequate formalism for expressing human plans and activities, as well as general mechanisms and computer algorithms. The commonsense algorithm is a type of framework (as Minsky has defined the term) for representing algorithmic processes, hopefully the way humans do.
Sustainable Making? Balancing Optimism and Criticism in HCI Discourse

We examine the recent move from a rhetoric of “users” toward one of “makers,” “crafters,” and “hackers” within HCI discourse. Through our analysis, we make several contributions. First, we provide a general overview of the structure and common framings within research on makers. We discuss how these statements reconfigure themes of empowerment and progress that have been central to HCI rhetoric since the field's inception. In the latter part of the article, we discuss the consequences of these shifts for contemporary research problems. In particular, we explore the problem of designed obsolescence, a core issue for Sustainable Interaction Design (SID) research. We show how the framing of the maker, as an empowered subject, presents certain opportunities and limitations for this research discourse. Finally, we offer alternative framings of empowerment that can expand maker discourse and its use in contemporary research problems such as SID.
Load-balanced clustering of wireless sensor networks

Wireless sensor networks have potential to monitor environments for both military and civil applications. Due to inhospitable conditions these sensors are not always deployed uniformly in the area of interest. Since sensors are generally constrained in on-board energy supply, efficient management of the network is crucial to extend the life of the sensors. Sensors energy cannot support long haul communication to reach a remote command site and thus requires many levels of hops or a gateway to forward the data on behalf of the sensor. In this paper we propose an algorithm to network these sensors in to well define clusters with less-energy-constrained gateway nodes acting as clusterheads, and balance load among these gateways. Simulation results show how our approach can balance the load and improve the lifetime of the system.
Omnidirectional 3D reconstruction in augmented Manhattan worlds

This paper proposes a method for high-quality omnidirectional 3D reconstruction of augmented Manhattan worlds from catadioptric stereo video sequences. In contrast to existing works we do not rely on constructing virtual perspective views, but instead propose to optimize depth jointly in a unified omnidirectional space. Furthermore, we show that plane-based prior models can be applied even though planes in 3D do not project to planes in the omnidirectional domain. Towards this goal, we propose an omnidirectional slanted-plane Markov random field model which relies on plane hypotheses extracted using a novel voting scheme for 3D planes in omnidirectional space. To quantitatively evaluate our method we introduce a dataset which we have captured using our autonomous driving platform AnnieWAY which we equipped with two horizontally aligned catadioptric cameras and a Velodyne HDL-64E laser scanner for precise ground truth depth measurements. As evidenced by our experiments, the proposed method clearly benefits from the unified view and significantly outperforms existing stereo matching techniques both quantitatively and qualitatively. Furthermore, our method is able to reduce noise and the obtained depth maps can be represented very compactly by a small number of image segments and plane parameters.
Comparative Analysis of LPCC, MFCC and BFCC for the Recognition of Hindi Words using Artificial Neural Networks

Most important way of communication among humans is language and primary medium used for the said is speech. The speech recognizers make use of a parametric form of a signal to obtain the most important distinguishable features of speech signal for recognition purpose. In this paper, Linear Prediction Cepstral Coefficient (LPCC), Mel Frequency Cepstral Coefficient (MFCC) and Bark frequency Cepstral coefficient (BFCC) feature extraction techniques for recognition of Hindi Isolated, Paired and Hybrid words have been studied and the corresponding recognition rates are compared. Artifical Neural Network is used as back end processor. The experimental results show that the better recognition rate is obtained for MFCC as compared to LPCC and BFCC for all the three types of words.
Tackling high dimensional nonseparable optimization problems by cooperatively coevolving particle swarms

This paper attempts to address the question of scaling up Particle Swarm Optimization (PSO) algorithms to high dimensional optimization problems. We present a cooperative coevolving PSO (CCPSO) algorithm incorporating random grouping and adaptive weighting, two techniques that have been shown to be effective for handling high dimensional nonseparable problems. The proposed CCPSO algorithms out-performed a previously developed coevolving PSO algorithm on nonseparable functions of 30 dimensions. Furthermore, the scalability of the proposed algorithm to high dimensional nonseparable problems (of up to 1000 dimensions) is examined and compared with two existing coevolving Differential Evolution (DE) algorithms, and new insights are obtained. Our experimental results show the proposed CCPSO algorithms can perform reasonably well with only a small number of evaluations. The results also suggest that both the random grouping and adaptive weighting schemes are viable approaches that can be generalized to other evolutionary optimization methods.
The discrete fractional Fourier transform

We propose and consolidate a definition of the discrete fractional Fourier transform that generalizes the discrete Fourier transform (DFT) in the same sense that the continuous fractional Fourier transform generalizes the continuous ordinary Fourier transform. This definition is based on a particular set of eigenvectors of the DFT matrix, which constitutes the discrete counterpart of the set of Hermite–Gaussian functions. The definition is exactlyunitary, index additive, and reduces to the DFT for unit order. The fact that this definition satisfies all the desirable properties expected of the discrete fractional Fourier transform supports our confidence that it will be accepted as the definitive definition of this transform.
Load balanced routing for low power and lossy networks

The RPL routing protocol published in RFC 6550 was designed for efficient and reliable data collection in low-power and lossy networks. Specifically, it constructs a Destination Oriented Directed Acyclic Graph (DODAG) for data forwarding. However, due to the uneven deployment of sensor nodes in large areas, and the heterogeneous traffic patterns in the network, some sensor nodes may have much heavier workload in terms of packets forwarded than others. Such unbalanced workload distribution will result in these sensor nodes quickly exhausting their energy, and therefore shorten the overall network lifetime. In this paper, we propose a load balanced routing protocol based on the RPL protocol, named LB-RPL, to achieve balanced workload distribution in the network. Targeted at the low-power and lossy network environments, LB-RPL detects workload imbalance in a distributed and non-intrusive fashion. In addition, it optimizes the data forwarding path by jointly considering both workload distribution and link-layer communication qualities. We demonstrate the performance superiority of our LB-RPL protocol over original RPL through extensive simulations.