nils-herrmann/clean_scirepeval huggingface dataset:
Deep filter banks for texture recognition and segmentation

Research in texture recognition often concentrates on the problem of material recognition in uncluttered conditions, an assumption rarely met by applications. In this work we conduct a first study of material and describable texture attributes recognition in clutter, using a new dataset derived from the OpenSurface texture repository. Motivated by the challenge posed by this problem, we propose a new texture descriptor, FV-CNN, obtained by Fisher Vector pooling of a Convolutional Neural Network (CNN) filter bank. FV-CNN substantially improves the state-of-the-art in texture, material and scene recognition. Our approach achieves 79.8% accuracy on Flickr material dataset and 81% accuracy on MIT indoor scenes, providing absolute gains of more than 10% over existing approaches. FV-CNN easily transfers across domains without requiring feature adaptation as for methods that build on the fully-connected layers of CNNs. Furthermore, FV-CNN can seamlessly incorporate multi-scale information and describe regions of arbitrary shapes and sizes. Our approach is particularly suited at localizing “stuff” categories and obtains state-of-the-art results on MSRC segmentation dataset, as well as promising results on recognizing materials and surface attributes in clutter on the OpenSurfaces dataset.
Time-series data mining

In almost every scientific field, measurements are performed over time. These observations lead to a collection of organized data called time series. The purpose of time-series data mining is to try to extract all meaningful knowledge from the shape of data. Even if humans have a natural capacity to perform these tasks, it remains a complex problem for computers. In this article we intend to provide a survey of the techniques applied for time-series data mining. The first part is devoted to an overview of the tasks that have captured most of the interest of researchers. Considering that in most cases, time-series task relies on the same components for implementation, we divide the literature depending on these common aspects, namely representation techniques, distance measures, and indexing methods. The study of the relevant literature has been categorized for each individual aspects. Four types of robustness could then be formalized and any kind of distance could then be classified. Finally, the study submits various research trends and avenues that can be explored in the near future. We hope that this article can provide a broad and deep understanding of the time-series data mining research field.
Maitland: Lighter-Weight VM Introspection to Support Cyber-security in the Cloud

Despite defensive advances, malicious software (malware) remains an ever present cyber-security threat. Cloud environments are far from malware immune, in that: i) they innately support the execution of remotely supplied code, and ii) escaping their virtual machine (VM) confines has proven relatively easy to achieve in practice. The growing interest in clouds by industries and governments is also creating a core need to be able to formally address cloud security and privacy issues. VM introspection provides one of the core cyber-security tools for analyzing the run-time behaviors of code. Traditionally, introspection approaches have required close integration with the underlying hypervisors and substantial re-engineering when OS updates and patches are applied. Such heavy-weight introspection techniques, therefore, are too invasive to fit well within modern commercial clouds. Instead, lighter-weight introspection techniques are required that provide the same levels of within-VM observability but without the tight hypervisor and OS patch-level integration. This work introduces Maitland as a prototype proof-of-concept implementation a lighter-weight introspection tool, which exploits paravirtualization to meet these end-goals. The work assesses Maitland's performance, highlights its use to perform packer-independent malware detection, and assesses whether, with further optimizations, Maitland could provide a viable approach for introspection in commercial clouds.
Taylor–Fourier Analysis of Blood Pressure Oscillometric Waveforms

Blood pressure oscillometric waveforms behave as amplitude modulated nonlinear signals with frequency fluctuations. Their oscillating nature can be better analyzed by the digital Taylor-Fourier transform (DTFT), recently proposed for phasor estimation in oscillating power systems. Based on a relaxed signal model that includes Taylor components greater than zero, the DTFT is able to estimate not only the oscillation itself, as does the digital Fourier transform (DFT), but also its derivatives included in the signal model. In this paper, an oscillometric waveform is analyzed with the DTFT, and its zeroth and first oscillating harmonics are illustrated. The results show that the breathing activity can be separated from the cardiac one through the critical points of the first component, determined by the zero crossings of the amplitude derivatives estimated from the third Taylor order model. On the other hand, phase derivative estimates provide the fluctuations of the cardiac frequency and its derivative, new parameters that could improve the precision of the systolic and diastolic blood pressure assignment. The DTFT envelope estimates uniformly converge from K=3, substantially improving the harmonic separation of the DFT.
Inside YAGO2s: a transparent information extraction architecture

YAGO [9, 6] is one of the largest public ontologies constructed by information extraction. In a recent refactoring called YAGO2s, the system has been given a modular and completely transparent architecture. In this demo, users can see how more than 30 individual modules of YAGO work in parallel to extract facts, to check facts for their correctness, to deduce facts, and to merge facts from different sources. A GUI allows users to play with different input files, to trace the provenance of individual facts to their sources, to change deduction rules, and to run individual extractors. Users can see step by step how the extractors work together to combine the individual facts to the coherent whole of the YAGO ontology.
Critical Success Factors to Improve the Game Development Process from a Developer’s Perspective

The growth of the software game development industry is enormous and is gaining importance day by day. This growth imposes severe pressure and a number of issues and challenges on the game development community. Game development is a complex process, and one important game development choice is to consider the developer’s perspective to produce good-quality software games by improving the game development process. The objective of this study is to provide a better understanding of the developer’s dimension as a factor in software game success. It focuses mainly on an empirical investigation of the effect of key developer’s factors on the software game development process and eventually on the quality of the resulting game. A quantitative survey was developed and conducted to identify key developer’s factors for an enhanced game development process. For this study, the developed survey was used to test the research model and hypotheses. The results provide evidence that game development organizations must deal with multiple key factors to remain competitive and to handle high pressure in the software game industry. The main contribution of this paper is to investigate empirically the influence of key developer’s factors on the game development process.
A Generalized Dual-Band Wilkinson Power Divider With Parallel $L, C,$ and $R$ Components

A generalized model of a two-way dual-band Wilkinson power divider (WPD) with a parallel LC circuit at midpoints of two-segment transformers is proposed and compared with that of a conventional two-way dual-band WPD with a parallel LC circuit at the ends of two-segment transformers. The sum of power reflected at an output port and power transmitted to an isolation port from another isolation port in the proposed divider is smaller than that in the conventional divider. Therefore, wide bandwidths for S22, S33, and S32 can be expected for proposed dividers. In the case of equal power division, frequency characteristics of return loss at output ports and isolation of the proposed divider are wider than those of the convention one. The resonant frequencies of LC circuits in the proposed divider and a conventional divider are equal; however, the inductance L used in the proposed divider is always smaller than that in the conventional divider. Design charts and calculated bandwidths as a function of frequency ratio from 1 to 7 are presented. In experiments, two symmetrical and two asymmetrical circuits were fabricated. The experimental results showed good agreement with theoretical results.
Minimum jitter adaptive decision feedback equalizer for 4PAM serial links

This paper presents a minimum jitter-based adaptive decision feedback equalizer (DFE) for 4PAM serial links. For each signal level, a dedicated sign-sign least-mean-square (SS-LMS) algorithm is employed to adjust corresponding DFE tap coefficient as per data rate and the characteristics of channels. The proposed adaptive DFE is embedded in a 2 Gbps serial link over a 12-inch FR4 channel implemented in an IBM 130 nm 1.2V CMOS technology. The link is analyzed using Spectre from Cadence Design Systems with BSIM4 device models. Simulation results demonstrate that the proposed adaptive DFE is capable of opening completely closed data eyes.
PAC Bounds for Multi-armed Bandit and Markov Decision Processes

The bandit problem is revisited and considered under the PAC model. Our main contribution in this part is to show that given n arms, it suffices to pull the arms O( n 2 log 1 δ ) times to find an -optimal arm with probability of at least 1 − δ. This is in contrast to the naive bound of O( n 2 log n δ ). We derive another algorithm whose complexity depends on the specific setting of the rewards, rather than the worst case setting. We also provide a matching lower bound. We show how given an algorithm for the PAC model Multi-Armed Bandit problem, one can derive a batch learning algorithm for Markov Decision Processes. This is done essentially by simulating Value Iteration, and in each iteration invoking the multi-armed bandit algorithm. Using our PAC algorithm for the multi-armed bandit problem we improve the dependence on the number of actions.
MUSIC: Mutation-based SQL Injection Vulnerability Checking

SQL injection is one of the most prominent vulnerabilities for web-based applications. Exploitation of SQL injection vulnerabilities (SQLIV) through successful attacks might result in severe consequences such as authentication bypassing, leaking of private information etc. Therefore, testing an application for SQLIV is an important step for ensuring its quality. However, it is challenging as the sources of SQLIV vary widely, which include the lack of effective input filters in applications, insecure coding by programmers, inappropriate usage of APIs for manipulating databases etc. Moreover, existing testing approaches do not address the issue of generating adequate test data sets that can detect SQLIV. In this work, we present a mutation-based testing approach for SQLIV testing. We propose nine mutation operators that inject SQLIV in application source code. The operators result in mutants, which can be killed only with test data containing SQL injection attacks. By this approach, we force the generation of an adequate test data set containing effective test cases capable of revealing SQLIV. We implement a MUtation-based SQL Injection vulnerabilities Checking (testing) tool (MUSIC) that automatically generates mutants for the applications written in Java Server Pages (JSP) and performs mutation analysis. We validate the proposed operators with five open source web-based applications written in JSP. We show that the proposed operators are effective for testing SQLIV.
Using Linguistic Cues for the Automatic Recognition of Personality in Conversation and Text

It is well known that utterances convey a great deal of information about the speaker in addition to their semantic content. One such type of information consists of cues to the speaker’s personality traits, the most fundamental dimension of variation between humans. Recent work explores the automatic detection of other types of pragmatic variation in text and conversation, such as emotion, deception, speaker charisma, dominance, point of view, subjectivity, opinion and sentiment. Personality affects these other aspects of linguistic production, and thus personality recognition may be useful for these tasks, in addition to many other potential applications. However, to date, there is little work on the automatic recognition of personality traits. This article reports experimental results for recognition of all Big Five personality traits, in both conversation and text, utilising both self and observer ratings of personality. While other work reports classification results, we experiment with classification, regression and ranking models. For each model, we analyse the effect of different feature sets on accuracy. Results show that for some traits, any type of statistical model performs significantly better than the baseline, but ranking models perform best overall. We also present an experiment suggesting that ranking models are more accurate than multi-class classifiers for modelling personality. In addition, recognition models trained on observed personality perform better than models trained using selfreports, and the optimal feature set depends on the personality trait. A qualitative analysis of the learned models confirms previous findings linking language and personality, while revealing many new linguistic markers.
K-SVD : DESIGN OF DICTIONARIES FOR SPARSE REPRESENTATION

In recent years there is a growing interest in the study of sparse representation for signals. Using an overcomplete dictionary that contains prototype signal-atoms, signals are described by sparse linear combinations of these atoms. Recent activity in this field concentrated mainly on the study of pursuit algorithms that decompose signals with respect to a given dictionary. In this paper we propose a novel algorithm – the K-SVD algorithm – generalizing the K-Means clustering process, for adapting dictionaries in order to achieve sparse signal representations. We analyze this algorithm and demonstrate its results on both synthetic tests and in applications on real data.
Direct self-control (DSC) of inverter fed induktion machine

The new "Direct Self-Control" (DSC) is a simple method of signal processing, which gives converter fed three-phase machines an excellent dynamic performance. To control the torque e.g. of an induction motor it is sufficient to process the measured signals of the stator currents and the total flux linkages only. Optimal performance of drive systems is accomplished in steady state as well as under transient conditions by combination of several two limits controls. The expenses are less than in the case of proposed predictive control systems or FAM, if the converters switching frequency has to be kept minimal.
Text mining approaches for stock market prediction

Stock market prediction is an attractive research problem to be investigated. News contents are one of the most important factors that have influence on market. Considering the news impact in analyzing the stock market behavior, leads to more precise predictions and as a result more profitable trades. So far various prototypes have been developed which consider the impact of news in stock market prediction. In this paper, the main components of such forecasting systems have been introduced. In addition, different developed prototypes have been introduced and the way whereby the main components are implemented compared. Based on studied attempts, the potential future research activities have been suggested.
Mapping CMMI-DEV maturity levels to ISO/IEC 15504 capability profiles

CMMI and ISO/IEC 15504 are two main models for software process assessment and improvement. CMMI staged representation provides the standard way to process improvement and the attractive simple measure for organization’s software process maturity. ISO/IEC 15504 ensures the possibility to assess the capability of each process, to get the detailed organization’s processes capability profile and to define an individual improvement path. This paper investigates relationship between CMMI-DEV maturity levels and ISO/IEC 15504 processes capability. The mapping approach and ISO/IEC 15504 processes capability profiles ensured by all CMMI maturity levels are presented.  Software process assessment and improvement, organization’s maturity, processes capability profile, CMMI, ISO/IEC 15504.
Improved vision-based lane tracker performance using vehicle localization

In this paper, we present improved lane tracking using vehicle localization. Lane markers are detected using a bank of steerable filters, and lanes are tracked using Kalman filtering. On-road vehicle detection has been achieved using an active learning approach, and vehicles are tracked using a Condensation particle filter. While most state-of-the art lane tracking systems are not capable of performing in high-density traffic scenes, the proposed framework exploits robust vehicle tracking to allow for improved lane tracking in high density traffic. Experimental results demonstrate that lane tracking performance, robustness, and temporal response are significantly improved in the proposed framework, while also tracking vehicles, with minimal additional hardware requirements.
Compound polar codes

A capacity-achieving scheme based on polar codes is proposed for reliable communication over multi-channels which can be directly applied to bit-interleaved coded modulation schemes. We start by reviewing the ground-breaking work of polar codes and then discuss our proposed scheme. Instead of encoding separately across the individual underlying channels, which requires multiple encoders and decoders, we take advantage of the recursive structure of polar codes to construct a unified scheme with a single encoder and decoder that can be used over the multi-channels. We prove that the scheme achieves the capacity over this multi-channel. Numerical analysis and simulation results for BICM channels at finite block lengths shows a considerable improvement in the probability of error comparing to a conventional separated scheme.
Smart power management system for home appliances and wellness based on wireless sensors network and mobile technology

This work is focused on the development of a new management system for building and home automation that provides a fully real time monitor of household appliances and home environmental parameters. The developed system consists of a smart sensing unit, wireless sensors and actuators and a Web-based interface for remote and mobile applications. The main advantages of the proposed solution rely on the reliability of the developed algorithmics, on modularity and open-system characteristics, on low power consumption and system cost efficiency.
Wideband RCS Reduction of a Slot Array Antenna Using Polarization Conversion Metasurfaces

A new approach to reducing the monostatic radar cross section (RCS) and preserving the radiation characteristics of a slot array antenna by employing polarization conversion metasurfaces (PCMs) is presented in this communication. The PCM is arranged in a chessboard configuration consisting of fishbone-shaped element. It is placed on the surface of the slot array antenna. The characteristics and mechanism of the RCS reduction are analyzed. Simulated and experimental results show that the monostatic RCS reduction band of the antenna with PCM ranges between 6.0 and 18.0 GHz for normally impinging both x- and y-polarized waves. The radiation characteristics of the antenna are well preserved simultaneously in terms of the impedance bandwidth, radiation patterns, and realized boresight gains.
Pattern separation, completion, and categorisation in the hippocampus and neocortex

The mechanisms for pattern completion and pattern separation are described in the context of a theory of hippocampal function in which the hippocampal CA3 system operates as a single attractor or autoassociation network to enable rapid, one-trial, associations between any spatial location (place in rodents, or spatial view in primates) and an object or reward, and to provide for completion of the whole memory during recall from any part. The factors important in the pattern completion in CA3 and also a large number of independent memories stored in CA3 include: a sparse distributed representation, representations that are independent due to the randomizing effect of the mossy fibres, heterosynaptic long-term depression as well as long-term potentiation in the recurrent collateral synapses, and diluted connectivity to minimize the number of multiple synapses between any pair of CA3 neurons which otherwise distort the basins of attraction. Recall of information from CA3 is implemented by the entorhinal cortex perforant path synapses to CA3 cells, which in acting as a pattern associator allow some pattern generalization. Pattern separation is performed in the dentate granule cells using competitive learning to convert grid-like entorhinal cortex firing to place-like fields, and in the dentate to CA3 connections that have diluted connectivity. Recall to the neocortex is achieved by a reverse hierarchical series of pattern association networks implemented by the hippocampo-cortical backprojections, each one of which performs some pattern generalization, to retrieve a complete pattern of cortical firing in higher-order cortical areas. New results on competitive networks show which factors contribute to their ability to perform pattern separation, pattern clustering, and pattern categorisation, and how these apply in different hippocampal and neocortical systems.
Remote Data Auditing in Cloud Computing Environments: A Survey, Taxonomy, and Open Issues

Cloud computing has emerged as a long-dreamt vision of the utility computing paradigm that provides reliable and resilient infrastructure for users to remotely store data and use on-demand applications and services. Currently, many individuals and organizations mitigate the burden of local data storage and reduce the maintenance cost by outsourcing data to the cloud. However, the outsourced data is not always trustworthy due to the loss of physical control and possession over the data. As a result, many scholars have concentrated on relieving the security threats of the outsourced data by designing the Remote Data Auditing (RDA) technique as a new concept to enable public auditability for the stored data in the cloud. The RDA is a useful technique to check the reliability and integrity of data outsourced to a single or distributed servers. This is because all of the RDA techniques for single cloud servers are unable to support data recovery; such techniques are complemented with redundant storage mechanisms. The article also reviews techniques of remote data auditing more comprehensively in the domain of the distributed clouds in conjunction with the presentation of classifying ongoing developments within this specified area. The thematic taxonomy of the distributed storage auditing is presented based on significant parameters, such as scheme nature, security pattern, objective functions, auditing mode, update mode, cryptography model, and dynamic data structure. The more recent remote auditing approaches, which have not gained considerable attention in distributed cloud environments, are also critically analyzed and further categorized into three different classes, namely, replication based, erasure coding based, and network coding based, to present a taxonomy. This survey also aims to investigate similarities and differences of such a framework on the basis of the thematic taxonomy to diagnose significant and explore major outstanding issues.