nils-herrmann/clean_scirepeval huggingface dataset:
Relay-by-smartphone: realizing multihop device-to-device communications

The Great East Japan Earthquake and Tsunami drastically changed Japanese society, and the requirements for ICT was completely redefined. After the disaster, it was impossible for disaster victims to utilize their communication devices, such as cellular phones, tablet computers, or laptop computers, to notify their families and friends of their safety and confirm the safety of their loved ones since the communication infrastructures were physically damaged or lacked the energy necessary to operate. Due to this drastic event, we have come to realize the importance of device-to-device communications. With the recent increase in popularity of D2D communications, many research works are focusing their attention on a centralized network operated by network operators and neglect the importance of decentralized infrastructureless multihop communication, which is essential for disaster relief applications. In this article, we propose the concept of multihop D2D communication network systems that are applicable to many different wireless technologies, and clarify requirements along with introducing open issues in such systems. The first generation prototype of relay by smartphone can deliver messages using only users' mobile devices, allowing us to send out emergency messages from disconnected areas as well as information sharing among people gathered in evacuation centers. The success of field experiments demonstrates steady advancement toward realizing user-driven networking powered by communication devices independent of operator networks.
Automatic feature selection in neuroevolution

Feature selection is the process of finding the set of inputs to a machine learning algorithm that will yield the best performance. Developing a way to solve this problem automatically would make current machine learning methods much more useful. Previous efforts to automate feature selection rely on expensive meta-learning or are applicable only when labeled training data is available. This paper presents a novel method called FS-NEAT which extends the NEAT neuroevolution method to automatically determine an appropriate set of inputs for the networks it evolves. By learning the network's inputs, topology, and weights simultaneously, FS-NEAT addresses the feature selection problem without relying on meta-learning or labeled data. Initial experiments in an autonomous car racing simulation demonstrate that FS-NEAT can learn better and faster than regular NEAT. In addition, the networks it evolves are smaller and require fewer inputs. Furthermore, FS-NEAT's performance remains robust even as the feature selection task it faces is made increasingly difficult.
A Hybrid ARIMA and Neural Network Model to Forecast Particulate Matter Concentration in Changsha , China

Autoregressive integrated moving average (ARIMA) is a popular linear models in time series forecasting during the past years. Recent research activities with artificial neural networks (ANNs) suggest that ANNs could be a good selection when the predictor and predictand were not the simple linear relationship. Due to the complex linear and non-linear patterns, there were no ideal methods only using linear or non-linear regression to forecast the particulate matter concentration. In view of the situation, a hybrid methodology that combines both ARIMA and ANN models was developed to improve the forecast accuracy in this paper. The impact of wind direction and the traffic vehicle to the particulate matter concentration was introduction to model by defining wind-weighted-traffic road length density. The paper used road length density, which was obtained using geographic information system (GIS), as a proxy due to the absence of the traffic vehicle date. To demonstrate the utility of the technique, daily average PM10 concentration monitored at a site in Changsha in 2008 was utilized. First we use ARIMA model to model the linear component and then a neural network model was developed to model the residuals from the ARIMA model using wind-weighted-traffic road information around the monitor station. The results indicated that hybrid model can be an effective way to improve the PM10 forecasting accuracy comparing with the single ARIMA model. The approach demonstrates the potential to be applied to other areas of the word.
Voting for Voting in Online Point Cloud Object Detection

This paper proposes an efficient and effective scheme to applying the sliding window approach popular in computer vision to 3D data. Specifically, the sparse nature of the problem is exploited via a voting scheme to enable a search through all putative object locations at any orientation. We prove that this voting scheme is mathematically equivalent to a convolution on a sparse feature grid and thus enables the processing, in full 3D, of any point cloud irrespective of the number of vantage points required to construct it. As such it is versatile enough to operate on data from popular 3D laser scanners such as a Velodyne as well as on 3D data obtained from increasingly popular push-broom configurations. Our approach is “embarrassingly parallelisable” and capable of processing a point cloud containing over 100K points at eight orientations in less than 0.5s. For the object classes car, pedestrian and bicyclist the resulting detector achieves best-in-class detection and timing performance relative to prior art on the KITTI dataset as well as compared to another existing 3D object detection approach.
SmartGridLab: A Laboratory-Based Smart Grid Testbed

The evolution of traditional electricity grid into a state-of-the-art Smart Grid will need innovation in a number of dimensions: seamless integration of renewable energy sources, management of intermittent power supplies, realtime demand response, energy pricing strategy etc. The grid configuration will change from the central broadcasting network into a more distributed and dynamic network with two-way energy transmission. Information network is another necessary component that will be built on the power grid, which will measure the status of the whole power grid and control the energy flow. In this perspective of unsolved problems, we have designed SmartGridLab, an efficient Smart Grid testbed to help the research community analyze their designs and protocols in lab environment. This will foster the Smart Grid researchers to develop, analyze and compare different designs conveniently and efficiently. Our designed testbed consists of following major components: Intelligent Power Switch, power supply (main supply and renewable energy supply), energy demander (e.g. appliance), and an information network containing Power Meter. We have validated the usage of our designed testbed for greater research problems in Smart Grid.
Industrial districts ’ evolution and technological regimes : Italy and Taiwan

This paper investigates some plausible models of evolution of industrial districts (IDs) and clusters in light of the peculiar current features of technology and technological change. An insightful explanation of the variety of possible evolution of industrial clusters is provided focusing on the concept of ‘ technological regime s’. Within this interpretative framework, the authors carried out original field studies and survey questionnaires in Italy and Taiwan to gather microeconomic evidence on the restructuring efforts and sources of competitiveness of selected smalland medium-sized enterprises (SMEs). The shift in the technological paradigm, that applies to all sectors, requires a substantial industrial reorganisation. Firms traditionally operating within industrial districts need to reorganise their knowledge linkages from a cluster-based approach to a global and broader approach. A key explanation of the success of SMEs competing in globalized high-tech industries, supported by our survey evidence, is the co-evolution of domestic and international knowledge linkages. Inter-firm and inter-institution linkages need to be built to provide local SMEs with the necessary externalities to cope with the dual challenge of knowledge creation and internationalisation. In Taiwan, this took the form of global production networks.
Vision-based state estimation for autonomous rotorcraft MAVs in complex environments

In this paper, we consider the development of a rotorcraft micro aerial vehicle (MAV) system capable of vision-based state estimation in complex environments. We pursue a systems solution for the hardware and software to enable autonomous flight with a small rotorcraft in complex indoor and outdoor environments using only onboard vision and inertial sensors. As rotorcrafts frequently operate in hover or nearhover conditions, we propose a vision-based state estimation approach that does not drift when the vehicle remains stationary. The vision-based estimation approach combines the advantages of monocular vision (range, faster processing) with that of stereo vision (availability of scale and depth information), while overcoming several disadvantages of both. Specifically, our system relies on fisheye camera images at 25 Hz and imagery from a second camera at a much lower frequency for metric scale initialization and failure recovery. This estimate is fused with IMU information to yield state estimates at 100 Hz for feedback control. We show indoor experimental results with performance benchmarking and illustrate the autonomous operation of the system in challenging indoor and outdoor environments.
Probabilistic segmentation of white matter lesions in MR imaging

A new method has been developed for fully automated segmentation of white matter lesions (WMLs) in cranial MR imaging. The algorithm uses information from T1-weighted (T1-w), inversion recovery (IR), proton density-weighted (PD), T2-weighted (T2-w) and fluid attenuation inversion recovery (FLAIR) scans. It is based on the K-Nearest Neighbor (KNN) classification technique that builds a feature space from voxel intensities and spatial information. The technique generates images representing the probability per voxel being part of a WML. By application of thresholds on these probability maps, binary segmentations can be obtained. ROC curves show that the segmentations achieve both high sensitivity and specificity. A similarity index (SI), overlap fraction (OF) and extra fraction (EF) are calculated for additional quantitative analysis of the result. The SI is also used for determination of the optimal probability threshold for generation of the binary segmentation. Using probabilistic equivalents of the SI, OF and EF, the probability maps can be evaluated directly, providing a powerful tool for comparison of different classification results. This method for automated WML segmentation reaches an accuracy that is comparable to methods for multiple sclerosis (MS) lesion segmentation and is suitable for detection of WMLs in large and longitudinal population studies.
The Link Prediction Problem in Bipartite Networks

We define and study the link prediction problem in bipartite networks, specializing general link prediction algorithms to the bipartite case. In a graph, a link prediction function of two vertices denotes the similarity or proximity of the vertices. Common link prediction functions for general graphs are defined using paths of length two between two nodes. Since in a bipartite graph adjacency vertices can only be connected by paths of odd lengths, these functions do not apply to bipartite graphs. Instead, a certain class of graph kernels (spectral transformation kernels) can be generalized to bipartite graphs when the positivesemidefinite kernel constraint is relaxed. This generalization is realized by the odd component of the underlying spectral transformation. This construction leads to several new link prediction pseudokernels such as the matrix hyperbolic sine, which we examine for rating graphs, authorship graphs, folksonomies, document–feature networks and other types of bipartite networks.
Neurophysiological predictor of SMR-based BCI performance

Brain-computer interfaces (BCIs) allow a user to control a computer application by brain activity as measured, e.g., by electroencephalography (EEG). After about 30years of BCI research, the success of control that is achieved by means of a BCI system still greatly varies between subjects. For about 20% of potential users the obtained accuracy does not reach the level criterion, meaning that BCI control is not accurate enough to control an application. The determination of factors that may serve to predict BCI performance, and the development of methods to quantify a predictor value from psychological and/or physiological data serve two purposes: a better understanding of the 'BCI-illiteracy phenomenon', and avoidance of a costly and eventually frustrating training procedure for participants who might not obtain BCI control. Furthermore, such predictors may lead to approaches to antagonize BCI illiteracy. Here, we propose a neurophysiological predictor of BCI performance which can be determined from a two minute recording of a 'relax with eyes open' condition using two Laplacian EEG channels. A correlation of r=0.53 between the proposed predictor and BCI feedback performance was obtained on a large data base with N=80 BCI-naive participants in their first session with the Berlin brain-computer interface (BBCI) system which operates on modulations of sensory motor rhythms (SMRs).
Demo : Fall Detection Using 3 D Head Trajectory Extracted From a Single Camera Video Sequence

In Western societies, the population grows old, and we must think about solutions to help them to stay at home in a secure environment. By providing a specific analysis of people behavior, computer vision offers a good solution for healthcare systems, and particularly for fall detection. This demo will show the results of a new method to detect falls using a monocular camera. The main characteristic of this method is the use of head 3D trajectories for fall detection.
Fine-Grained Visual Classification of Aircraft

This paper introduces FGVC-Aircraft, a new dataset containing 10,000 images of aircraft spanning 100 aircraft models, organised in a three-level hierarchy. At the finer level, differences between models are often subtle but always visually measurable, making visual recognition challenging but possible. A benchmark is obtained by defining corresponding classification tasks and evaluation protocols, and baseline results are presented. The construction of this dataset was made possible by the work of aircraft enthusiasts, a strategy that can extend to the study of number of other object classes. Compared to the domains usually considered in fine-grained visual classification (FGVC), for example animals, aircraft are rigid and hence less deformable. They, however, present other interesting modes of variation, including purpose, size, designation, structure, historical style, and branding.
A kernel decomposition architecture for binary-weight Convolutional Neural Networks

The binary-weight CNN is one of the most efficient solutions for mobile CNNs. However, a large number of operations are required to process each image. To reduce such a huge operation count, we propose an energy-efficient kernel decomposition architecture, based on the observation that a large number of operations are redundant. In this scheme, all kernels are decomposed into sub-kernels to expose the common parts. By skipping the redundant computations, the operation count for each image was consequently reduced by 47.7%. Furthermore, a low cost bit-width quantization technique was implemented by exploiting the relative scales of the feature data. Experimental results showed that the proposed architecture achieves a 22% energy reduction.
Protecting C++ Dynamic Dispatch Through VTable Interleaving

With new defenses against traditional control-flow attacks like stack buffer overflows, attackers are increasingly using more advanced mechanisms to take control of execution. One common such attack is vtable hijacking, in which the attacker exploits bugs in C++ programs to overwrite pointers to the virtual method tables (vtables) of objects. We present a novel defense against this attack. The key insight of our approach is a new way of laying out vtables in memory through careful ordering and interleaving. Although this layout is very different from a traditional layout, it is backwards compatible with the traditional way of performing dynamic dispatch. Most importantly, with this new layout, checking the validity of a vtable at runtime becomes an efficient range check, rather than a set membership test. Compared to prior approaches that provide similar guarantees, our approach does not use any profiling information, has lower performance overhead (about 1%) and has lower code bloat overhead (about 1.7%).
Local text reuse detection

Text reuse occurs in many different types of documents and for many different reasons. One form of reuse, duplicate or near-duplicate documents, has been a focus of researchers because of its importance in Web search. Local text reuse occurs when sentences, facts or passages, rather than whole documents, are reused and modified. Detecting this type of reuse can be the basis of new tools for text analysis. In this paper, we introduce a new approach to detecting local text reuse and compare it to other approaches. This comparison involves a study of the amount and type of reuse that occurs in real documents, including TREC newswire and blog collections.
Text Detection and Localization in Complex Scene Images using Constrained AdaBoost Algorithm

We have proposed a complete system for text detection and localization in gray scale scene images. A boosting framework integrating feature and weak classifier selection based on computational complexity is proposed to construct efficient text detectors. The proposed scheme uses a small set of heterogeneous features which are spatially combined to build a large set of features. A neural network based localizer learns necessary rules for localization. The evaluation is done on the challenging ICDAR 2003 robust reading and text locating database. The results are encouraging and our system can localize text of various font sizes and styles in complex background.
Mise en Place: Unsupervised Interpretation of Instructional Recipes

We present an unsupervised hard EM approach to automatically mapping instructional recipes to action graphs, which define what actions should be performed on which objects and in what order. Recovering such structures can be challenging, due to unique properties of procedural language where, for example, verbal arguments are commonly elided when they can be inferred from context and disambiguation often requires world knowledge. Our probabilistic model incorporates aspects of procedural semantics and world knowledge, such as likely locations and selectional preferences for different actions. Experiments with cooking recipes demonstrate the ability to recover high quality action graphs, outperforming a strong sequential baseline by 8 points in F1, while also discovering general-purpose knowledge about cooking.
A review of 0.18-/spl mu/m full adder performances for tree structured arithmetic circuits

The general objective of our work is to investigate the area and power-delay performances of low-voltage full adder cells in different CMOS logic styles for the predominating tree structured arithmetic circuits. A new hybrid style full adder circuit is also presented. The sum and carry generation circuits of the proposed full adder are designed with hybrid logic styles. To operate at ultra-low supply voltage, the pass logic circuit that cogenerates the intermediate XOR and XNOR outputs has been improved to overcome the switching delay problem. As full adders are frequently employed in a tree structured configuration for high-performance arithmetic circuits, a cascaded simulation structure is introduced to evaluate the full adders in a realistic application environment. A systematic and elegant procedure to scale the transistor for minimal power-delay product is proposed. The circuits being studied are optimized for energy efficiency at 0.18-/spl mu/m CMOS process technology. With the proposed simulation environment, it is shown that some survival cells in stand alone operation at low voltage may fail when cascaded in a larger circuit, either due to the lack of drivability or unsatisfactory speed of operation. The proposed hybrid full adder exhibits not only the full swing logic and balanced outputs but also strong output drivability. The increase in the transistor count of its complementary CMOS output stage is compensated by its area efficient layout. Therefore, it remains one of the best contenders for designing large tree structured arithmetic circuits with reduced energy consumption while keeping the increase in area to a minimum.
Cyber Security and the Internet of Things : Vulnerabilities , Threats , Intruders and Attacks

Internet of Things (IoT) devices are rapidly becoming ubiquitous while IoT services are becoming pervasive. Their success has not gone unnoticed and the number of threats and attacks against IoT devices and services are on the increase as well. Cyber-attacks are not new to IoT, but as IoT will be deeply interwoven in our lives and societies, it is becoming necessary to step up and take cyber defense seriously. Hence, there is a real need to secure IoT, which has consequently resulted in a need to comprehensively understand the threats and attacks on IoT infrastructure. This paper is an attempt to classify threat types, besides analyze and characterize intruders and attacks facing IoT devices and services.
A 9-Bit 150-MS/s Subrange ADC Based on SAR Architecture in 90-nm CMOS

This paper presents a 9-bit subrange analog-to-digital converter (ADC) consisting of a 3.5-bit flash coarse ADC, a 6-bit successive-approximation-register (SAR) fine ADC, and a differential segmented capacitive digital-to-analog converter (DAC). The flash ADC controls the thermometer coarse capacitors of the DAC and the SAR ADC controls the binary fine ones. Both theoretical analysis and behavioral simulations show that the differential non-linearity (DNL) of a SAR ADC with a segmented DAC is better than that of a binary ADC. The merged switching of the coarse capacitors significantly enhances overall operation speed. At 150 MS/s, the ADC consumes 1.53 mW from a 1.2-V supply. The effective number of bits (ENOB) is 8.69 bits and the effective resolution bandwidth (ERBW) is 100 MHz. With a 1.3-V supply voltage, the sampling rate is 200 MS/s with 2.2-mW power consumption. The ENOB is 8.66 bits and the ERBW is 100 MHz. The FOMs at 1.3 V and 200 MS/s, 1.2 V and 150 MS/s and 1 V and 100 MS/s are 27.2, 24.7, and 17.7 fJ/conversion-step, respectively.
Learning a Mahalanobis Distance-Based Dynamic Time Warping Measure for Multivariate Time Series Classification

Multivariate time series (MTS) datasets broadly exist in numerous fields, including health care, multimedia, finance, and biometrics. How to classify MTS accurately has become a hot research topic since it is an important element in many computer vision and pattern recognition applications. In this paper, we propose a Mahalanobis distance-based dynamic time warping (DTW) measure for MTS classification. The Mahalanobis distance builds an accurate relationship between each variable and its corresponding category. It is utilized to calculate the local distance between vectors in MTS. Then we use DTW to align those MTS which are out of synchronization or with different lengths. After that, how to learn an accurate Mahalanobis distance function becomes another key problem. This paper establishes a LogDet divergence-based metric learning with triplet constraint model which can learn Mahalanobis matrix with high precision and robustness. Furthermore, the proposed method is applied on nine MTS datasets selected from the University of California, Irvine machine learning repository and Robert T. Olszewski's homepage, and the results demonstrate the improved performance of the proposed approach.