nils-herrmann/clean_scirepeval huggingface dataset:
Temporally-aware algorithms for document classification

Automatic Document Classification (ADC) is still one of the major information retrieval problems. It usually employs a supervised learning strategy, where we first build a classification model using pre-classified documents and then use this model to classify unseen documents. The majority of supervised algorithms consider that all documents provide equally important information. However, in practice, a document may be considered more or less important to build the classification model according to several factors, such as its timeliness, the venue where it was published in, its authors, among others. In this paper, we are particularly concerned with the impact that temporal effects may have on ADC and how to minimize such impact. In order to deal with these effects, we introduce a temporal weighting function (TWF) and propose a methodology to determine it for document collections. We applied the proposed methodology to ACM-DL and Medline and found that the TWF of both follows a lognormal. We then extend three ADC algorithms (namely kNN, Rocchio and Naïve Bayes) to incorporate the TWF. Experiments showed that the temporally-aware classifiers achieved significant gains, outperforming (or at least matching) state-of-the-art algorithms.
Breaking Visual CAPTCHAs with Naive Pattern Recognition Algorithms

Visual CAPTCHAs have been widely used across the Internet to defend against undesirable or malicious bot programs. In this paper, we document how we have broken most such visual schemes provided at Captchaservice.org, a publicly available web service for CAPTCHA generation. These schemes were effectively resistant to attacks conducted using a high-quality Optical Character Recognition program, but were broken with a near 100% success rate by our novel attacks. In contrast to early work that relied on sophisticated computer vision or machine learning algorithms, we used simple pattern recognition algorithms but exploited fatal design errors that we discovered in each scheme. Surprisingly, our simple attacks can also break many other schemes deployed on the Internet at the time of writing: their design had similar errors. We also discuss defence against our attacks and new insights on the design of visual CAPTCHA schemes.
Double-Input Bidirectional DC/DC Converter Using Cell-Voltage Equalizer With Flyback Transformer

In this paper, a double-input bidirectional dc/dc converter that uses a rechargeable battery and an ultracapacitor (UC) is proposed. This converter is connected to a cell-voltage equalizer between the battery and UC. The cell-voltage equalizer enables cell-voltage equalization and energy transfer between the battery and UC. This converter has six operational modes. These modes are investigated by reduced-power-scale circuit experiment. In addition, the circuit operation under the combination of the six modes is verified using a PSIM simulator in a large power scale.
Evaluation of information technology investment: a data envelopment analysis approach

The increasing use of information technology (IT) has resulted in a need for evaluating the productivity impacts of IT. The contemporary IT evaluation approach has focused on return on investment and return on management. IT investment has impacts on different stages of business operations. For example, in the banking industry, IT plays a key role in effectively generating (i) funds from the customer in the forms of deposits and then (ii) profits by using deposits as investment funds. Existing approaches based upon data envelopment analysis (DEA) only measure the IT efficiency or impact on one specific stage when a multi-stage business process is present. A detailed model is needed to characterize the impact of IT on each stage of the business operation. The current paper develops a DEA non-linear programming model to evaluate the impact of IT on multiple stages along with information on how to distribute the IT-related resources so that the efficiency is maximized. It is shown that this non-linear program can be treated as a parametric linear program. It is also shown that if there is only one intermediate measure, then the non-linear DEA model becomes a linear program. Our approach is illustrated with an example taken from previous studies.
A surgical parallel continuum manipulator with a cable-driven grasper

In this paper, we present the design, construction, and control of a six-degree-of-freedom (DOF), 12 mm diameter, parallel continuum manipulator with a 2-DOF, cable-driven grasper. This work is a proof-of-concept first step towards miniaturization of this type of manipulator design to provide increased dexterity and stability in confined-space surgical applications, particularly for endoscopic procedures. Our robotic system consists of six superelastic NiTi (Nitinol) tubes in a standard Stewart-Gough configuration and an end effector with 180 degree motion of its two jaws. Two Kevlar cables pass through the centers of the tube legs to actuate the end effector. A computationally efficient inverse kinematics model provides low-level control inputs to ten independent linear actuators, which drive the Stewart-Gough platform and end-effector actuation cables. We demonstrate the performance and feasibility of this design by conducting open-loop range-of-motion tests for our system.
Joint shot boundary detection and key frame extraction

Representing a video by a set of key frames is useful for efficient video browsing and retrieving. But key frame extraction keeps a challenge in the computer vision field. In this paper, we propose a joint framework to integrate both shot boundary detection and key frame extraction, wherein three probabilistic components are taken into account, i.e. the prior of the key frames, the conditional probability of shot boundaries and the conditional probability of each video frame. Thus the key frame extraction is treated as a Maximum A Posteriori which can be solved by adopting alternate strategy. Experimental results show that the proposed method preserves the scene level structure and extracts key frames that are representative and discriminative.
Voltage Doubler Rectified Boost-Integrated Half Bridge (VDRBHB) Converter for Digital Car Audio Amplifiers

A new voltage doubler rectified boost-integrated half bridge converter for digital car audio amplifiers is proposed. The proposed converter shows low conduction loss due to low voltage stress of the secondary diodes, no dc magnetizing current for the transformer, and a lack of stored energy in the transformer. Moreover, since the primary MOSFETs are turned-on under zero voltage switching conditions and the secondary diodes are turned-off under zero current switching conditions, the proposed converter has minimized switching losses. In addition, the input filter can be minimized due to a continuous input current, and an output filter does not need an inductor in the proposed converter. Therefore, the proposed converter has the desired features, high efficiency and low profile, for a viable power supplies for digital car audio amplifiers. A 60-W industrial sample of the proposed converter has been implemented for digital car audio amplifiers with a measured efficiency of 88.3% at nominal input voltage.
Actinomycetes from the South China Sea sponges: isolation, diversity, and potential for aromatic polyketides discovery

Marine sponges often harbor dense and diverse microbial communities including actinobacteria. To date no comprehensive investigation has been performed on the culturable diversity of the actinomycetes associated with South China Sea sponges. Structurally novel aromatic polyketides were recently discovered from marine sponge-derived Streptomyces and Saccharopolyspora strains, suggesting that sponge-associated actinomycetes can serve as a new source of aromatic polyketides. In this study, a total of 77 actinomycete strains were isolated from 15 South China Sea sponge species. Phylogenetic characterization of the isolates based on 16S rRNA gene sequencing supported their assignment to 12 families and 20 genera, among which three rare genera (Marihabitans, Polymorphospora, and Streptomonospora) were isolated from marine sponges for the first time. Subsequently, β-ketoacyl synthase (KSα) gene was used as marker for evaluating the potential of the actinomycete strains to produce aromatic polyketides. As a result, KSα gene was detected in 35 isolates related to seven genera (Kocuria, Micromonospora, Nocardia, Nocardiopsis, Saccharopolyspora, Salinispora, and Streptomyces). Finally, 10 strains were selected for small-scale fermentation, and one angucycline compound was detected from the culture extract of Streptomyces anulatus strain S71. This study advanced our knowledge of the sponge-associated actinomycetes regarding their diversity and potential in producing aromatic polyketides.
Distributed Antennas for Indoor Radio Communications

The idea of implementing an indoor radio communications system serving an entire building from a single central antenna appears to be an attractive proposition. However, based on various indoor propagation measurements of the signal attenuation and the multipath delay spread, such a centralized approach appears to be limited to small buildings and to narrow-band FDMA-type systems with limited reliability and flexibility. In this paper, we present the results of indoor radio propagation measurements of two signal distribution approaches that improve the picture dramatically. In the first, the building is divided into many small cells, each served from an antenna located in its own center, and with adjacent cells operating in different frequency bands. In the second approach, the building is divided into one or more large cells, each served from a distributed antenna system or a “leaky feeder” that winds its way through the hallways. This approach eliminates the frequency cell handoff problem that is bound to exist in the first approach, while still preserving the dramatic reductions in multipath delay spread and signal attenuation compared to a centralized system. For example, the measurements show that, with either approach, the signal attenuation can be reduced by as much as a few tens of decibels and the rms delay spread becomes limited to 20 to 50 ns, even in large buildings. This can make possible the implementation of sophisticated broad-band TDMA-type systems that are flexible, robust, and virtually building-independent.
Multi-scale Recognition with DAG-CNNs

We explore multi-scale convolutional neural nets (CNNs) for image classification. Contemporary approaches extract features from a single output layer. By extracting features from multiple layers, one can simultaneously reason about high, mid, and low-level features during classification. The resulting multi-scale architecture can itself be seen as a feed-forward model that is structured as a directed acyclic graph (DAG-CNNs). We use DAG-CNNs to learn a set of multi-scale features that can be effectively shared between coarse and fine-grained classification tasks. While fine-tuning such models helps performance, we show that even "off-the-self" multi-scale features perform quite well. We present extensive analysis and demonstrate state-of-the-art classification performance on three standard scene benchmarks (SUN397, MIT67, and Scene15). In terms of the heavily benchmarked MIT67 and Scene15 datasets, our results reduce the lowest previously-reported error by 23.9% and 9.5%, respectively.
Integral imaging using color multiplexed holographic optical element

We propose a novel integral imaging system using holographic optical element (HOE) which can provide multi-color 3-dimensional (3D) images. The optical properties of a lens array are recorded on a photopolymer using color multiplexing method with two different wavelength lasers (473 nm and 532 nm). The lens array HOE recorded on the photopolymer functions as 3D imaging HOE for integral imaging instead of the conventional lens array or concave mirror array. Recording scheme for color multiplexed HOE is presented and the proposed system is experimentally verified.
Clustering-based selection for evolutionary multi-objective optimization

In this study, a novel clustering-based selection strategy of nondominated individuals for evolutionary multi-objective optimization is proposed. The new strategy partitions the nondominated individuals in current Pareto front adaptively into desired clusters. Then one representative individual will be selected in each cluster for pruning nondominated individuals. In order to evaluate the validity of the new strategy, we apply it into one state of the art multi-objective evolutionary algorithm. The experimental results based on thirteen benchmark problems show that the new strategy improves the performance obviously in terms of breadth and uniformity of nondominated solutions.
EPOpt: Learning Robust Neural Network Policies Using Model Ensembles

Sample complexity and safety are major challenges when learning policies with reinforcement learning for real-world tasks – especially when the policies are represented using rich function approximators like deep neural networks. Modelbased methods where the real-world target domain is approximated using a simulated source domain provide an avenue to tackle the above challenges by augmenting real data with simulated data. However, discrepancies between the simulated source domain and the target domain pose a challenge for simulated training. We introduce the EPOpt algorithm, which uses an ensemble of simulated source domains and a form of adversarial training to learn policies that are robust and generalize to a broad range of possible target domains, including to unmodeled effects. Further, the probability distribution over source domains in the ensemble can be adapted using data from target domain and approximate Bayesian methods, to progressively make it a better approximation. Thus, learning on a model ensemble, along with source domain adaptation, provides the benefit of both robustness and learning/adaptation.
Role of self-care in management of diabetes mellitus

Diabetes mellitus (DM) is a chronic progressive metabolic disorder characterized by hyperglycemia mainly due to absolute (Type 1 DM) or relative (Type 2 DM) deficiency of insulin hormone. World Health Organization estimates that more than 346 million people worldwide have DM. This number is likely to more than double by 2030 without any intervention. The needs of diabetic patients are not only limited to adequate glycemic control but also correspond with preventing complications; disability limitation and rehabilitation. There are seven essential self-care behaviors in people with diabetes which predict good outcomes namely healthy eating, being physically active, monitoring of blood sugar, compliant with medications, good problem-solving skills, healthy coping skills and risk-reduction behaviors. All these seven behaviors have been found to be positively correlated with good glycemic control, reduction of complications and improvement in quality of life. Individuals with diabetes have been shown to make a dramatic impact on the progression and development of their disease by participating in their own care. Despite this fact, compliance or adherence to these activities has been found to be low, especially when looking at long-term changes. Though multiple demographic, socio-economic and social support factors can be considered as positive contributors in facilitating self-care activities in diabetic patients, role of clinicians in promoting self-care is vital and has to be emphasized. Realizing the multi-faceted nature of the problem, a systematic, multi-pronged and an integrated approach is required for promoting self-care practices among diabetic patients to avert any long-term complications.
A Unified Approach to Word Sense Representation and Disambiguation

The lexical ambiguity of words has been successfully clarified by representing words at a sense level instead of a word level. This is known as word sense representation (WSR). However, WSR models are typically trained in an unsupervised fashion without any guidance from sense inventories. Therefore, the number of sense vectors assigned to a word varies from model to model. This implies that some senses are missed or unnecessarily added. Moreover, to utilize their sense vectors in natural language processing tasks, we must determine which sense of a word to choose. In this paper, we introduce a unified neural model that incorporates WSR into word sense disambiguation (WSD), thereby leveraging the sense inventory. We use bidirectional long short-term memory networks to capture the sequential information of contexts effectively. To overcome the limitation of size with the labeled dataset, we train our model in a semi-supervised fashion to scale up the size of the dataset by leveraging a large-scale unlabeled dataset. We evaluate our proposed model on both WSR and WSD tasks. The experimental results demonstrate that our model outperforms state-of-the-art on WSR task by 0.27%, while, on WSD task, by 1.4% in terms of Spearman's correlation and F'l-score, respectively.
Sentiment analysis using Latent Dirichlet Allocation and topic polarity wordcloud visualization

Sentiment analysis is a field of study that analyzes sentiment. One method for doing sentiment analysis is Latent Dirichlet Allocation (LDA) that extracts the topic of documents where the topic is represented as the appearance of the words with different topic probability. Therefore, we need data representation in visual form that is easier to understand than text and tables. One form of data visualization is wordcloud that provides a visual representation of words frequency. This research will perform sentiment analysis from the students' comments toward a university, in this case the Universitas Diponegoro, using LDA and topic polarity wordcloud visualization. The purpose of this study is to generate the topic polarity wordcloud of the students' comments by using the best combination of parameters. The best combination is the parameter with the value of alpha 0.1, value of beta 0.1, number of topics 9, threshold 10−7, and perplexity values 8.07. Such parameter combination produces 3 topics as positive sentiment and 6 topics as negative sentiment. In addition, we also compare the proposed method to several algorithms such as Naïve Bayes and Logistic Regression. The final result shows that the proposed method outperforms the Naïve Bayes and Logistic Regression in terms of F-Measure by 61%, 54%, and 56%, respectively.
A Reconfigurable Implementation of the New Secure Hash Algorithm

The main applications of the hash functions are met in the fields of communication's integrity and signature authentication. Many hash algorithms have been investigated and developed in the last years. This work is related to hash functions FPGA implementation. Field programmable gate arrays (FPGAs) being reconfigurable, flexible and physically secure are a natural choice for implementation of hash functions in a broad range of applications with different area-performance requirements. We propose a configurable secure hash algorithm (SHA) processor for extended signature authentication. This paper investigates different optimizations algorithms of recent techniques that have been proposed in the literature. In our implementation based on Xilinx Virtex FPGAs, the throughput of SHA processor is equal to 1296 Mbit/s. Speed/area results from these processors are analyzed and shown to compare favorably with other FPGA-based implementations. A fastest data throughput is achieved by our optimized algorithm
On the Reuse of Past Optimal Queries

Vijay V. Raghavan Hayri Sever The Center for Advanced Computer Studies The Department of Computer Science University of Southwestern Louisiana University of Southwestern Louisiana Lafayette, LA 70504, USA Lafayette, LA 70504, USA e-mail: raghavan@cacs.usl. edu Information Retrieval (IR) systems exploit user feedback by generating an optimal query with respect to a particular information need. Since obtaining an optimal query is an expensive process, the need for mechanisms to save and reuse past optimal queries for future queries is obvions. In this article, we propose the use of a query base, a set of persistent past optimal queries, and investigate similarity measures between queries. The query base can be used either to answer user queries or to formulate optimal queries. We justify the former case analytically and the latter case by experiment.
Mersenne Twister A Pseudo-Random Number Generator

Pseudo random number generators have been widely used in numbers of applications, particularly simulation and cryptography. Among them is a Mersenne Twister. Introduced in 1998 by Makoto Matsumoto and Takuji Nishimura, it has been a highly preferred generator since it provides long period, high order of dimensional equidistribution, speed and reliability. In this paper, we will focus mainly on describing the theories and algorithm of the Mersenne Twister. We also briefly introduce its variants and discuss its limitations and improvements.
Bad to the bone: facial structure predicts unethical behaviour.

Researchers spanning many scientific domains, including primatology, evolutionary biology and psychology, have sought to establish an evolutionary basis for morality. While researchers have identified social and cognitive adaptations that support ethical behaviour, a consensus has emerged that genetically determined physical traits are not reliable signals of unethical intentions or actions. Challenging this view, we show that genetically determined physical traits can serve as reliable predictors of unethical behaviour if they are also associated with positive signals in intersex and intrasex selection. Specifically, we identify a key physical attribute, the facial width-to-height ratio, which predicts unethical behaviour in men. Across two studies, we demonstrate that men with wider faces (relative to facial height) are more likely to explicitly deceive their counterparts in a negotiation, and are more willing to cheat in order to increase their financial gain. Importantly, we provide evidence that the link between facial metrics and unethical behaviour is mediated by a psychological sense of power. Our results demonstrate that static physical attributes can indeed serve as reliable cues of immoral action, and provide additional support for the view that evolutionary forces shape ethical judgement and behaviour.
Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks

Face detection and alignment in unconstrained environment are challenging due to various poses, illuminations, and occlusions. Recent studies show that deep learning approaches can achieve impressive performance on these two tasks. In this letter, we propose a deep cascaded multitask framework that exploits the inherent correlation between detection and alignment to boost up their performance. In particular, our framework leverages a cascaded architecture with three stages of carefully designed deep convolutional networks to predict face and landmark location in a coarse-to-fine manner. In addition, we propose a new online hard sample mining strategy that further improves the performance in practice. Our method achieves superior accuracy over the state-of-the-art techniques on the challenging face detection dataset and benchmark and WIDER FACE benchmarks for face detection, and annotated facial landmarks in the wild benchmark for face alignment, while keeps real-time performance.