nils-herrmann/clean_scirepeval huggingface dataset:
Zero is Not Enough : On The Lower Limit of Agent Intelligence For Continuous Double Auction Markets †

agent continuous double auction, trade Gode and Sunder's (1993) results from using "zero-intelligence" (ZI) traders, that act randomly within a structured market, appear to imply that convergence to the theoretical equilibrium price in continuous double-auction markets is determined more by market structure than by the intelligence of the traders in that market. However, it is demonstrated here that the average transaction prices of ZI traders can vary significantly from the theoretical equilibrium value when the market supply and demand are asymmetric, and that the degree of difference from equilibrium is predictable from a priori probabilistic analysis. In this sense, it is shown here that Gode and Sunder's results are artefacts of their experimental regime. Following this, 'zero-intelligence-plus' (ZIP) traders are introduced: like ZI traders, these simple agents make stochastic bids. Unlike ZI traders, they employ an elementary form of machine learning. Groups of ZIP traders interacting in experimental markets similar to those used by Smith (1962) and Gode and Sunder (1993) are demonstrated, and we show that the performance of ZIP traders is significantly closer to the human data than is the performance of Gode and Sunder's ZI traders. Because ZI traders are too simple, we offer ZIP traders as a slight upward revision of the intelligence required to achieve equilibrium in continuous double auction markets.
Global-and-local attention networks for visual recognition

State-of-the-art deep convolutional networks (DCNs) such as squeeze-andexcitation (SE) residual networks implement a form of attention, also known as contextual guidance, which is derived from global image features. Here, we explore a complementary form of attention, known as visual saliency, which is derived from local image features. We extend the SE module with a novel global-and-local attention (GALA) module which combines both forms of attention – resulting in state-of-the-art accuracy on ILSVRC. We further describe ClickMe.ai, a largescale online experiment designed for human participants to identify diagnostic image regions to co-train a GALA network. Adding humans-in-the-loop is shown to significantly improve network accuracy, while also yielding visual features that are more interpretable and more similar to those used by human observers.
An automatic bus route number recognition

This paper presents a bus route number recognition using image processing and machine learning techniques. The prototype is designed to extract bus route number from scene images and synthesize audio of bus number for people with low vision. The proposed system consists of two parts, text detection and digit recognition. Text detection based on MSERs is used to find and segment text area from input images. Digit recognizer based on Convolutional Neural Network is used to recognize digits on the detected text area. Finally, the recognized digit is converted to audio which can help the visually impaired people (low vision) to take the correct bus. The experimental results of the proposed method presented in precision, recall and F-measure in text detection and acquire 73.47% accuracy of a digit segment in self-collect Thai bus dataset.
Enhancing cluster labeling using wikipedia

This work investigates cluster labeling enhancement by utilizing Wikipedia, the free on-line encyclopedia. We describe a general framework for cluster labeling that extracts candidate labels from Wikipedia in addition to important terms that are extracted directly from the text. The "labeling quality" of each candidate is then evaluated by several independent judges and the top evaluated candidates are recommended for labeling.
 Our experimental results reveal that the Wikipedia labels agree with manual labels associated by humans to a cluster, much more than with significant terms that are extracted directly from the text. We show that in most cases even when human's associated label appears in the text, pure statistical methods have difficulty in identifying them as good descriptors. Furthermore, our experiments show that for more than 85% of the clusters in our test collection, the manual label (or an inflection, or a synonym of it) appears in the top five labels recommended by our system.
Exploring the physical layer frontiers of cellular uplink

Communication systems in practice are subject to many technical/technological constraints and restrictions. Multiple input, multiple output (MIMO) processing in current wireless communications, as an example, mostly employs codebook-based pre-coding to save computational complexity at the transmitters and receivers. In such cases, closed form expressions for capacity or bit-error probability are often unattainable; effects of realistic signal processing algorithms on the performance of practical communication systems rather have to be studied in simulation environments. The Vienna LTE-A Uplink Simulator is a 3GPP LTE-A standard compliant MATLAB-based link level simulator that is publicly available under an academic use license, facilitating reproducible evaluations of signal processing algorithms and transceiver designs in wireless communications. This paper reviews research results that have been obtained by means of the Vienna LTE-A Uplink Simulator, highlights the effects of single-carrier frequency-division multiplexing (as the distinguishing feature to LTE-A downlink), extends known link adaptation concepts to uplink transmission, shows the implications of the uplink pilot pattern for gathering channel state information at the receiver and completes with possible future research directions.
Ontology-Based Information Extraction for Knowledge Enrichment and Validation

Ontology is widely used as a mean to represent and share common concepts and knowledge from a particular domain or specialisation. As a knowledge representation, the knowledge within an ontology must be able to evolve along with the recent changes and updates within the community practice. In this paper, we propose a new Ontology-based Information Extraction (OBIE) system that extends existing systems in order to enrich and validate an ontology. Our model enables the ontology to find related recent knowledge in the domain from communities, by exploiting their underlying knowledge as keywords. The knowledge extraction process uses ontology-based and pattern-based information extraction technique. Not only the extracted knowledge enriches the ontology, it also validates contradictory instance-related statements within the ontology that is no longer relevant to recent practices. We determine a confidence value during the enrichment and validation process to ensure the stability of the enriched ontology. We implement the model and present a case study in herbal medicine domain. The result of the enrichment and validation process shows promising results. Moreover, we analyse how our proposed model contributes to the achievement of a richer and stable ontology.
Advanced Driver Assistance Systems and Animals

Advanced driver assistance systems are widely used. Some support and inform the driver. Others relieve him or her of certain tasks—and transform the human-guided system into a semi-autonomous one. For some years also fully autonomous systems have been on the roads, so-called self-driving cars, as prototypes of companies and within research projects. From the perspective of ethics—both of the special fields of ethics like animal ethics, information ethics and technology ethics and of machine ethics which can be understood as a counterpart to human ethics—advanced driver assistance systems raise various questions. The aim of this paper is to derive suggestions from animal ethics and other disciplines for the improvement and development of the systems. The basis are literature analysis and own classifications and considerations. The result is that there are many possibilities to expand existing systems and to develop new functions in the context with the aim to reduce the number of animal victims.
PWM regenerative rectifiers: state of the art

New regulations impose more stringent limits on current harmonics injected by power converters that are achieved with pulsewidth-modulated (PWM) rectifiers. In addition, several applications demand the capability of power regeneration to the power supply. This work presents the state of the art in the field of regenerative rectifiers with reduced input harmonics and improved power factor. Regenerative rectifiers are able to deliver energy back from the dc side to the ac power supply. Topologies for single- and three-phase power supplies are considered with their corresponding control strategies. Special attention is given to the application of voltage- and current-source PWM rectifiers in different processes with a power range from a few kilowatts up to several megawatts. This paper shows that PWM regenerative rectifiers are a highly developed and mature technology with a wide industrial acceptance.
The NASA automated requirements measurement tool: a reconstruction

In the late 1990s the National Aeronautics and Space Administration (NASA) Software Assurance Technology Center (SATC) developed a tool to automatically analyze a requirements document and produce a detailed quality report. The report was based on statistical analysis of word frequencies at various structural levels of the document. The Automated Requirements Measurement (ARM) tool was further enhanced to include additional functionality such as custom definitions of quality indicators inputs for document analysis. By 2011 work on the ARM tool was discontinued. This paper describes the reverse-engineering and reproduction of the functionality of ARM. Recreating the functionality of this tool yielded valuable insight into certain quality metrics and provides a benchmark tool for future research. In addition to recreating and working with the ARM tool, this paper explores both existing and potential definitions of quality metrics in requirements specifications. Automated requirements analysis is a convergence of various fields of research, including text mining, quality analysis, and natural language processing. Informed by tangential areas of research in document understanding and data mining, recommendations are made for future areas of research and development in automated requirements analysis.
Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation

Optical flow estimation is classically marked by the requirement of dense sampling in time. While coarse-to-fine warping schemes have somehow relaxed this constraint, there is an inherent dependency between the scale of structures and the velocity that can be estimated. This particularly renders the estimation of detailed human motion problematic, as small body parts can move very fast. In this paper, we present a way to approach this problem by integrating rich descriptors into the variational optical flow setting. This way we can estimate a dense optical flow field with almost the same high accuracy as known from variational optical flow, while reaching out to new domains of motion analysis where the requirement of dense sampling in time is no longer satisfied.
A benchmark of selected algorithmic differentiation tools on some problems in computer vision and machine learning

Algorithmic differentiation (AD) allows exact computation of derivatives given only an implementation of an objective function. Although many AD tools are available, a proper and efficient implementation of AD methods is not straightforward. The existing tools are often too different to allow for a general test suite. In this paper, we compare fifteen ways of computing derivatives including eleven automatic differentiation tools implementing various methods and written in various languages (C++, F#, MATLAB, Julia and Python), two symbolic differentiation tools, finite differences, and hand-derived computation. We look at three objective functions from computer vision and machine learning. These objectives are for the most part simple, in the sense that no iterative loops are involved, and conditional statements are encapsulated in functions such as abs or logsumexp. However, it is important for the success of algorithmic differentiation that such ‘simple’ objective functions are handled efficiently, as so many problems in computer vision and machine learning are of this form. Of course, our results depend on programmer skill, and familiarity with the tools. However, we contend that this paper presents an important datapoint: a skilled programmer devoting roughly a week to each tool produced the timings we present. We have made our implementations available as open source to allow the community to replicate and update these benchmarks.
Conditional Generative Adversarial Networks for Speech Enhancement and Noise-Robust Speaker Verification

Improving speech system performance in noisy environments remains a challenging task, and speech enhancement (SE) is one of the effective techniques to solve the problem. Motivated by the promising results of generative adversarial networks (GANs) in a variety of image processing tasks, we explore the potential of conditional GANs (cGANs) for SE, and in particular, we make use of the image processing framework proposed by Isola et al.  to learn a mapping from the spectrogram of noisy speech to an enhanced counterpart. The SE cGAN consists of two networks, trained in an adversarial manner: a generator that tries to enhance the input noisy spectrogram, and a discriminator that tries to distinguish between enhanced spectrograms provided by the generator and clean ones from the database using the noisy spectrogram as a condition. We evaluate the performance of the cGAN method in terms of perceptual evaluation of speech quality (PESQ), short-time objective intelligibility (STOI), and equal error rate (EER) of speaker verification (an example application). Experimental results show that the cGAN method overall outperforms the classical short-time spectral amplitude minimum mean square error (STSA-MMSE) SE algorithm, and is comparable to a deep neural network-based SE approach (DNN-SE).
Information security management needs more holistic approach: A literature review

Information technology has dramatically increased online business opportunities; however these opportunities have also created serious risks in relation to information security. Previously, information security issues were studied in a technological context, but growing security needs have extended researchers’ attention to explore the management role in information security management. Various studies have explored different management roles and activities, but none has given a comprehensive picture of these roles and activities to manage information security effectively. So it is necessary to accumulate knowledge about various managerial roles and activities from literature to enable managers to adopt these for a more holistic approach to information security management. In this paper, using a systematic literature review approach, we synthesised literature related to management’s roles in information security to explore specific managerial activities to enhance information security management. We found that numerous activities of management, particularly development and execution of information security policy, awareness, compliance training, development of effective enterprise information architecture, IT nformation architecture infrastructure management, business and IT alignment and human resources management, had a significant impact on the quality of management of information security. Thus, this research makes a novel contribution by arguing that a more holistic approach to information security is needed and we suggest the ways in which managers can play an effective role in information security. This research also opens up many new avenues for further research in this area.
The Traveling Salesman Problem for Cubic Graphs

We show how to find a Hamiltonian cycle in a graph of degree at most three with n vertices, in time O(2) ≈ 1.260 and linear space. Our algorithm can find the minimum weight Hamiltonian cycle (traveling salesman problem), in the same time bound. We can also count or list all Hamiltonian cycles in a degree three graph in time O(2) ≈ 1.297. We also solve the traveling salesman problem in graphs of degree at most four, by randomized and deterministic algorithms with runtime O((27/4)) ≈ 1.890 and O((27/4+ ǫ)) respectively. Our algorithms allow the input to specify a set of forced edges which must be part of any generated cycle. Our cycle listing algorithm shows that every degree three graph has O(2) Hamiltonian cycles; we also exhibit a family of graphs with 2 Hamiltonian cycles per graph. Article Type Communicated by Submitted Revised Regular paper J. S. B. Mitchell April 2004 Work supported in part by NSF grant CCR-9912338. A preliminary version of this paper appeared at the 8th Annual Workshop on Algorithms and Data Structures,
A Recent Survey on Colon Cancer Detection Techniques

Colon cancer causes deaths of about half a million people every year. Common method of its detection is histopathological tissue analysis, which, though leads to vital diagnosis, is significantly correlated to the tiredness, experience, and workload of the pathologist. Researchers have been working since decades to get rid of manual inspection, and to develop trustworthy systems for detecting colon cancer. Several techniques, based on spectral/spatial analysis of colon biopsy images, and serum and gene analysis of colon samples, have been proposed in this regard. Due to rapid evolution of colon cancer detection techniques, a latest review of recent research in this field is highly desirable. The aim of this paper is to discuss various colon cancer detection techniques. In this survey, we categorize the techniques on the basis of the adopted methodology and underlying data set, and provide detailed description of techniques in each category. Additionally, this study provides an extensive comparison of various colon cancer detection categories, and of multiple techniques within each category. Further, most of the techniques have been evaluated on similar data set to provide a fair performance comparison. Analysis reveals that neither of the techniques is perfect; however, research community is progressively inching toward the finest possible solution.
Single Image Super-Resolution via Multiple Mixture Prior Models

Example learning-based single image super-resolution (SR) is a promising method for reconstructing a high-resolution (HR) image from a single-input low-resolution (LR) image. Lots of popular SR approaches are more likely either time-or space-intensive, which limit their practical applications. Hence, some research has focused on a subspace view and delivered state-of-the-art results. In this paper, we utilize an effective way with mixture prior models to transform the large nonlinear feature space of LR images into a group of linear subspaces in the training phase. In particular, we first partition image patches into several groups by a novel selective patch processing method based on difference curvature of LR patches, and then learning the mixture prior models in each group. Moreover, different prior distributions have various effectiveness in SR, and in this case, we find that student-t prior shows stronger performance than the well-known Gaussian prior. In the testing phase, we adopt the learned multiple mixture prior models to map the input LR features into the appropriate subspace, and finally reconstruct the corresponding HR image in a novel mixed matching way. Experimental results indicate that the proposed approach is both quantitatively and qualitatively superior to some state-of-the-art SR methods.
Clump splitting via bottleneck detection and shape classification

Under-segmentation of an image with multiple objects is a common problem in image segmentation algorithms. This paper presents a novel approach for splitting clumps formed by multiple objects due to under-segmentation. The proposed algorithm includes three steps: (1) decide whether to split a candidate connected component by application-specific shape classification; (2) find a pair of points for clump splitting and (3) join the pair of selected points. In the first step, a shape classifier is applied to determine whether a connected component should be split. In the second step, a pair of points for splitting is detected using a bottleneck rule, under the assumption that the desired objects have roughly a convex shape. In the third step, the selected splitting points from step two are joined by finding the optimal splitting line between them, based on minimizing an image energy. The shape classifier is built offline via various shape features and a support vector machine. Steps two and three are applicationindependent. The performance of this method is evaluated using images from various applications. Experimental results show that the proposed approach outperforms the state-of-the-art algorithms for the clump splitting problem.
A restart CMA evolution strategy with increasing population size

In this paper we introduce a restart-CMA-evolution strategy, where the population size is increased for each restart (IPOP). By increasing the population size the search characteristic becomes more global after each restart. The IPOP-CMA-ES is evaluated on the test suit of 25 functions designed for the special session on real-parameter optimization of CEC 2005. Its performance is compared to a local restart strategy with constant small population size. On unimodal functions the performance is similar. On multi-modal functions the local restart strategy significantly outperforms IPOP in 4 test cases whereas IPOP performs significantly better in 29 out of 60 tested cases.
SmartSLAM - An Efficient Smartphone Indoor Positioning System Exploiting Machine Learning and Opportunistic Sensing

The most promising solution to the ubiquitous positioning problem is the smartphone, and many smartphonebased indoor tracking methods exist today. To ensure consumer acceptance of the technologies, it is critical that these systems do not have a significant effect on the battery life of the device. Methods exploiting signal fingerprinting have been shown to provide good performance with low processing overhead but require prior surveying. Methods exploiting opportunistic sensing and machine learning techniques such as Simultaneous Localization and Mapping (SLAM) need no prior data but at the cost of high computational load. This paper describes a smartphone-based indoor positioning system that exploits a new intelligent filtering approach to reduce this computational load. SmartSLAM moves between different sensor fusion algorithms depending on the current level of certainty in the system, reducing the computational load of the tracking engine, maintaining good positioning performance, improving battery life and freeing CPU cycles for foreground processes.
Building Stronger Ties With Alumni Through Facebook to Increase Volunteerism and Charitable Giving

This research explores how strength of network ties, as fostered by the use of a social network site, Facebook, (a) influences alumni attitudes toward volunteering for and making charitable gifts to their alma mater, and (b) fortifies consistency between attitude and behavior. After exploratory interviews and participant observation, a survey of 3,085 alumni was conducted for hypothesis testing. Structural equation modeling analysis revealed: First, active participation in Facebook groups positively predicted strength of network ties along 2 dimensions: frequency of communication and emotional closeness. Second, both dimensions of tie strength influenced actual behavior, albeit via different routes. The paper also contributes to attitude change research in showing that strength of network ties can help ensure consistencies between attitude and behavior.