nils-herrmann/clean_scirepeval huggingface dataset:
Hashedcubes: Simple, Low Memory, Real-Time Visual Exploration of Big Data

We propose Hashedcubes, a data structure that enables real-time visual exploration of large datasets that improves the state of the art by virtue of its low memory requirements, low query latencies, and implementation simplicity. In some instances, Hashedcubes notably requires two orders of magnitude less space than recent data cube visualization proposals. In this paper, we describe the algorithms to build and query Hashedcubes, and how it can drive well-known interactive visualizations such as binned scatterplots, linked histograms and heatmaps. We report memory usage, build time and query latencies for a variety of synthetic and real-world datasets, and find that although sometimes Hashedcubes offers slightly slower querying times to the state of the art, the typical query is answered fast enough to easily sustain a interaction. In datasets with hundreds of millions of elements, only about 2% of the queries take longer than 40ms. Finally, we discuss the limitations of data structure, potential spacetime tradeoffs, and future research directions.
Multileave Gradient Descent for Fast Online Learning to Rank

Modern search systems are based on dozens or even hundreds of ranking features. The dueling bandit gradient descent (DBGD) algorithm has been shown to effectively learn combinations of these features solely from user interactions. DBGD explores the search space by comparing a possibly improved ranker to the current production ranker. To this end, it uses interleaved comparison methods, which can infer with high sensitivity a preference between two rankings based only on interaction data. A limiting factor is that it can compare only to a single exploratory ranker. We propose an online learning to rank algorithm called multileave gradient descent (MGD) that extends DBGD to learn from so-called multileaved comparison methods that can compare a set of rankings instead of merely a pair. We show experimentally that MGD allows for better selection of candidates than DBGD without the need for more comparisons involving users. An important implication of our results is that orders of magnitude less user interaction data is required to find good rankers when multileaved comparisons are used within online learning to rank. Hence, fewer users need to be exposed to possibly inferior rankers and our method allows search engines to adapt more quickly to changes in user preferences.
Distributed community detection in delay tolerant networks

Community is an important attribute of Pocket Switched Networks (PSN), because mobile devices are carried by people who tend to belong to communities. We analysed community structure from mobility traces and used for forwarding algorithms , which shows significant impact of community. Here, we propose and evaluate three novel distributed community detection approaches with great potential to detect both static and temporal communities. We find that with suitable configuration of the threshold values, the distributed community detection can approximate their corresponding centralised methods up to 90% accuracy.
A class of parallel tiled linear algebra algorithms for multicore architectures

As multicore systems continue to gain ground in the High Performance Computing world, linear algebra algorithms have to be reformulated or new algorithms have to be developed in order to take advantage of the architectural features on these new processors. Fine grain parallelism becomes a major requirement and introduces the necessity of loose synchronization in the parallel execution of an operation. This paper presents an algorithm for the Cholesky, LU and QR factorization where the operations can be represented as a sequence of small tasks that operate on square blocks of data. These tasks can be dynamically scheduled for execution based on the dependencies among them and on the availability of computational resources. This may result in an out of order execution of the tasks which will completely hide the presence of intrinsically sequential tasks in the factorization. Performance comparisons are presented with the LAPACK algorithms where parallelism can only be exploited at the level of the BLAS operations and vendor implementations.
Face Detection in the Near-IR Spectrum

* To whom all correspondence should be addressed.  Face detection is an important prerequisite step for successful face recognition. The performance of previous face detection methods reported in the literature is far from perfect and deteriorates ungracefully where lighting conditions cannot be controlled. We propose a method that outperforms state-of-the-art face detection methods in environments with stable lighting. In addition, our method can potentially perform well in environments with variable lighting conditions. The approach capitalizes upon our near-IR skin detection method reported elsewhere [13][14]. It ascertains the existence of a face within the skin region by finding the eyes and eyebrows. The eyeeyebrow pairs are determined by extracting appropriate features from multiple near-IR bands. Very successful feature extraction is achieved by simple algorithmic means like integral projections and template matching. This is because processing is constrained in the skin region and aided by the near-IR phenomenology. The effectiveness of our method is substantiated by comparative experimental results with the Identix face detector .
Spiking Neuron Models: Single Neurons, Populations, Plasticity

mathematical framework of the population equations. It will turn out that the results are – of course – consistent with those derived from the population equation. We study a homogeneous network of N identical neurons which are mutually coupled with strength wij = J0/N where J0 > 0 is a positive constant. In other words, the (excitatory) interaction is scaled with one over N so that the total input to a neuron i is of order one even if the number of neurons is large (N →∞). Since we are interested in synchrony we suppose that all neurons have fired simultaneously at t̂ = 0. When will the neurons fire again? Since all neurons are identical we expect that the next firing time will also be synchronous. Let us calculate the period T between one synchronous pulse and the next. We start from the firing condition of SRM0 neurons θ = ui(t) = η(t− t̂i) + ∑
Towards a smart city based on cloud of things

Smart City represents one of the most promising and prominent Internet of Things (IoT) applications. In the last few years, smart city concept has played an important role in academic and industry fields, with the development and deployment of various middleware platforms. However, this expansion has followed distinct approaches creating a fragmented scenario, in which different IoT ecosystems are not able to communicate between them. To fill this gap, there is a need to revisit the smart city IoT semantic and offer a global common approach. To this purpose, this paper browses the semantic annotation of the sensors in the cloud, and innovative services can be implemented and considered by bridging Clouds and IoT. Things-like semantic will be considered to perform the aggregation of heterogeneous resources by defining the Clouds of Things (CoT) paradigm. We survey the smart city vision, providing information on the main requirements and highlighting the benefits of integrating different IoT ecosystems within the cloud under this new CoT vision and discuss relevant challenges in this research area.
Clustering the Mixed Numerical and Categorical Dataset using Similarity Weight and Filter Method

Clustering is a challenging task in data mining technique. The aim of clustering is to group the similar data into number of clusters. Various clustering algorithms have been developed to group data into clusters. However, these clustering algorithms work effectively either on pure numeric data or on pure categorical data, most of them perform poorly on mixed categorical and numerical data types in previous k-means algorithm was used but it is not accurate for large datasets. In this paper we cluster the mixed numeric and categorical data set in efficient manner. In this paper we present a clustering algorithm based on similarity weight and filter method paradigm that works well for data with mixed numeric and categorical features. We propose a modified description of cluster center to overcome the numeric data only limitation and provide a better characterization of clusters. The performance of this algorithm has been studied on benchmark data sets.
Deep convolutional neural networks for sign language recognition

Extraction of complex head and hand movements along with their constantly changing shapes for recognition of sign language is considered a difficult problem in computer vision. This paper proposes the recognition of Indian sign language gestures using a powerful artificial intelligence tool, convolutional neural networks (CNN). Selfie mode continuous sign language video is the capture method used in this work, where a hearing-impaired person can operate the SLR mobile application independently. Due to non-availability of datasets on mobile selfie sign language, we initiated to create the dataset with five different subjects performing 200 signs in 5 different viewing angles under various background environments. Each sign occupied for 60 frames or images in a video. CNN training is performed with 3 different sample sizes, each consisting of multiple sets of subjects and viewing angles. The remaining 2 samples are used for testing the trained CNN. Different CNN architectures were designed and tested with our selfie sign language data to obtain better accuracy in recognition. We achieved 92.88% recognition rate compared to other classifier models reported on the same dataset.
Cognitive Strategies for the Visual Search of Hierarchical Computer Displays

This article investigates the cognitive strategies that people use to search computer displays. Several different visual layouts are examined: unlabeled layouts that contain multiple groups of items but no group headings, labeled layouts in which items are grouped and each group has a useful heading, and a target-only layout that contains just one item. A number of plausible strategies were proposed for each layout. Each strategy was programmed into the EPIC cognitive architecture, producing models that simulate the human visual-perceptual, oculomotor, and cognitive processing required for the task. The models generate search time predictions. For unlabeled layouts, the mean layout search times are predicted by a purely random search strategy, and the more detailed positional search times are predicted by a noisy systematic strategy. The labeled layout search times are predicted by a hierarchical strategy in which first the group labels are systematically searched, and then the contents of the target group. The target-only layout search times are predicted by a strategy in which the eyes move directly to the sudden appearance of the target. The models demonstrate that human visual search performance can be explained largely in terms of the cognitive strategy HUMAN–COMPUTER INTERACTION, 2004, Volume 19, pp. 183–223  The models provide insights pertaining to the visual-perceptual and oculomotor processes involved in visual search and contribute to the science base needed for predictive interface analysis.
Induction cooking systems with single switch inverter using new driving techniques

In this paper, some novel and modified driving techniques for a single switch zero voltage switching (ZVS) topology are introduced. These medium/high frequency and digitally synthesized driving techniques can be applied to decrease the dangers of peak currents that may damage the switching circuit when switching in out of nominal conditions. The technique is fully described and evaluated experimentally in a 2500W prototype intended for a domestic induction cooking application.
Social Engineering Attack Strategies and Defence Approaches

This paper examines the role and value of information security awareness efforts in defending against social engineering attacks. It categories the different social engineering threats and tactics used in targeting employees and the approaches to defend against such attacks. While we review these techniques, we attempt to develop a thorough understanding of human security threats, with a suitable balance between structured improvements to defend human weaknesses, and efficiently focused security training and awareness building. Finally, the paper shows that a multi-layered shield can mitigate various security risks and minimize the damage to systems and data.
Circulant Binary Embedding

Binary embedding of high-dimensional data requires long codes to preserve the discriminative power of the input space. Traditional binary coding methods often suffer from very high computation and storage costs in such a scenario. To address this problem, we propose Circulant Binary Embedding (CBE) which generates binary codes by projecting the data with a circulant matrix. The circulant structure enables the use of Fast Fourier Transformation to speed up the computation. Compared to methods that use unstructured matrices, the proposed method improves the time complexity from O(d) to O(d log d), and the space complexity from O(d) to O(d) where d is the input dimensionality. We also propose a novel time-frequency alternating optimization to learn data-dependent circulant projections, which alternatively minimizes the objective in original and Fourier domains. We show by extensive experiments that the proposed approach gives much better performance than the state-of-the-art approaches for fixed time, and provides much faster computation with no performance degradation for fixed number of bits.
Development of Power-Assist Robot Arm using pneumatic rubbermuscles with a balloon sensor

The present paper describes a power-assist robot arm that uses pneumatic artificial rubber muscles (PARMs) covered with exoskeleton suits to mimic the motion of biarticular muscles. One robot arm uses a single PARM, which assists the wrist and elbow joint of the user. This robot arm is linked to a waist belt through a suspender-like structure of three PARMs. PI control (Proportional Integral control), which is based on the pressure value from a balloon sensor, is used to achieve accurate power-assist motion, and the present study focuses on the motions associated with lifting an object. In order to evaluate the validity of the proposed power-assist robot arm, skin surface electromyography (EMG) signals of forearm muscles are measured during a lifting task and a follow-up questionnaire survey is conducted.
Performance Evaluation of NoSQL Databases: A Case Study

The choice of a particular NoSQL database imposes a specific distributed software architecture and data model, and is a major determinant of the overall system throughput. NoSQL database performance is in turn strongly influenced by how well the data model and query capabilities fit the application use cases, and so system-specific testing and characterization is required. This paper presents a method and the results of a study that selected among three NoSQL databases for a large, distributed healthcare organization. While the method and study considered consistency, availability, and partition tolerance (CAP) tradeoffs, and other quality attributes that influence the selection decision, this paper reports on the performance evaluation method and results. In our testing, a typical workload and configuration produced throughput that varied from 225 to 3200 operations per second between database products, while read operation latency varied by a factor of 5 and write latency by a factor of 4 (with the highest throughput product delivering the highest latency). We also found that achieving strong consistency reduced throughput by 10-25% compared to eventual consistency.
Architectural Complexity Measures of Recurrent Neural Networks

In this paper, we systematically analyse the connecting architectures of recurrent neural networks (RNNs). Our main contribution is twofold: first, we present a rigorous graphtheoretic framework describing the connecting architectures of RNNs in general. Second, we propose three architecture complexity measures of RNNs: (a) the recurrent depth, which captures the RNN’s over-time nonlinear complexity, (b) the feedforward depth, which captures the local input-output nonlinearity (similar to the “depth” in feedforward neural networks (FNNs)), and (c) the recurrent skip coefficient which captures how rapidly the information propagates over time. Our experimental results show that RNNs might benefit from larger recurrent depth and feedforward depth. We further demonstrate that increasing recurrent skip coefficient offers performance boosts on long term dependency problems, as we improve the state-of-the-art for sequential MNIST dataset.
Time Series Classification under More Realistic Assumptions

Most literature on time series classification assumes that the beginning and ending points of the pattern of interest can be correctly identified, both during the training phase and later deployment. In this work, we argue that this assumption is unjustified, and this has in many cases led to unwarranted optimism about the performance of the proposed algorithms. As we shall show, the task of correctly extracting individual gait cycles, heartbeats, gestures, behaviors, etc., is generally much more difficult than the task of actually classifying those patterns. We propose to mitigate this problem by introducing an alignment-free time series classification framework. The framework requires only very weakly annotated data, such as “in this ten minutes of data, we see mostly normal heartbeats...,” and by generalizing the classic machine learning idea of data editing to streaming/continuous data, allows us to build robust, fast and accurate classifiers. We demonstrate on several diverse real-world problems that beyond removing unwarranted assumptions and requiring essentially no human intervention, our framework is both significantly faster and significantly more accurate than current state-of-the-art approaches.
Predicting Exploitation of Disclosed Software Vulnerabilities Using Open-source Data

Each year, thousands of software vulnerabilities are discovered and reported to the public. Unpatched known vulnerabilities are a significant security risk. It is imperative that software vendors quickly provide patches once vulnerabilities are known and users quickly install those patches as soon as they are available. However, most vulnerabilities are never actually exploited. Since writing, testing, and installing software patches can involve considerable resources, it would be desirable to prioritize the remediation of vulnerabilities that are likely to be exploited. Several published research studies have reported moderate success in applying machine learning techniques to the task of predicting whether a vulnerability will be exploited. These approaches typically use features derived from vulnerability databases (such as the summary text describing the vulnerability) or social media posts that mention the vulnerability by name. However, these prior studies share multiple methodological shortcomings that inflate predictive power of these approaches. We replicate key portions of the prior work, compare their approaches, and show how selection of training and test data critically affect the estimated performance of predictive models. The results of this study point to important methodological considerations that should be taken into account so that results reflect real-world utility.
Probabilistic Label Relation Graphs with Ising Models

We consider classification problems in which the label space has structure. A common example is hierarchical label spaces, corresponding to the case where one label subsumes another (e.g., animal subsumes dog). But labels can also be mutually exclusive (e.g., dog vs cat) or unrelated (e.g., furry, carnivore). To jointly model hierarchy and exclusion relations, the notion of a HEX (hierarchy and exclusion) graph was introduced in . This combined a conditional random field (CRF) with a deep neural network (DNN), resulting in state of the art results when applied to visual object classification problems where the training labels were drawn from different levels of the ImageNet hierarchy (e.g., an image might be labeled with the basic level category "dog", rather than the more specific label "husky"). In this paper, we extend the HEX model to allow for soft or probabilistic relations between labels, which is useful when there is uncertainty about the relationship between two labels (e.g., an antelope is "sort of" furry, but not to the same degree as a grizzly bear). We call our new model pHEX, for probabilistic HEX. We show that the pHEX graph can be converted to an Ising model, which allows us to use existing off-the-shelf inference methods (in contrast to the HEX method, which needed specialized inference algorithms). Experimental results show significant improvements in a number of large-scale visual object classification tasks, outperforming the previous HEX model.
An Advanced Hybrid Peer-to-Peer Botnet

A “botnet” consists of a network of compromised computers controlled by an attacker (“botmaster”). Recently, botnets have become the root cause of many Internet attacks. To be well prepared for future attacks, it is not enough to study how to detect and defend against the botnets that have appeared in the past. More importantly, we should study advanced botnet designs that could be developed by botmasters in the near future. In this paper, we present the design of an advanced hybrid peer-to-peer botnet. Compared with current botnets, the proposed botnet is harder to be shut down, monitored, and hijacked. It provides robust network connectivity, individualized encryption and control traffic dispersion, limited botnet exposure by each bot, and easy monitoring and recovery by its botmaster. In the end, we suggest and analyze several possible defenses against this advanced botnet.
How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation

We investigate evaluation metrics for endto-end dialogue systems where supervised labels, such as task completion, are not available. Recent works in end-to-end dialogue systems have adopted metrics from machine translation and text summarization to compare a model’s generated response to a single target response. We show that these metrics correlate very weakly or not at all with human judgements of the response quality in both technical and non-technical domains. We provide quantitative and qualitative results highlighting specific weaknesses in existing metrics, and provide recommendations for future development of better automatic evaluation metrics for dialogue systems.
SuperAgent: A Customer Service Chatbot for E-commerce Websites

Conventional customer service chatbots are usually based on human dialogue, yet significant issues in terms of data scale and privacy. In this paper, we present SuperAgent, a customer service chatbot that leverages large-scale and publicly available ecommerce data. Distinct from existing counterparts, SuperAgent takes advantage of data from in-page product descriptions as well as user-generated content from ecommerce websites, which is more practical and cost-effective when answering repetitive questions, freeing up human support staff to answer much higher value questions. We demonstrate SuperAgent as an add-on extension to mainstream web browsers and show its usefulness to user’s online shopping experience.