nils-herrmann/clean_scirepeval huggingface dataset:
Steady-state analysis of an interleaved boost converter with coupled inductors

Boost converters are widely used as power-factorcorrected preregulators. In high-power applications, interleaved operation of two or more boost converters has been proposed to increase the output power and to reduce the output ripple. A major design criterion then is to ensure equal current sharing among the parallel converters. In this paper, a converter consisting of two interleaved and intercoupled boost converter cells is proposed and investigated. The boost converter cells have very good current sharing characteristics even in the presence of relatively large duty cycle mismatch. In addition, it can be designed to have small input current ripple and zero boost-rectifier reverse-recovery loss. The operating principle, steady-state analysis, and comparison with the conventional boost converter are presented. Simulation and experimental results are also given.
Real-time People and Vehicle Detection from UAV Imagery

A generic and robust approach for the real-time detection of people and vehicles from an Unmanned Aerial Vehicle (UAV) is an important goal within the framework of fully autonomous UAV deployment for aerial reconnaissance and surveillance. Here we present an approach for the automatic detection of vehicles based on using multiple trained cascaded Haar classifiers with secondary confirmation in thermal imagery. Additionally we present a related approach for people detection in thermal imagery based on a similar cascaded classification technique combining additional multivariate Gaussian shape matching. The results presented show the successful detection of vehicle and people under varying conditions in both isolated rural and cluttered urban environments with minimal false positive detection. Performance of the detector is optimized to reduce the overall false positive rate by aiming at the detection of each object of interest (vehicle/person) at least once in the environment (i.e. per search patter flight path) rather than every object in each image frame. Currently the detection rate for people is ~70% and cars ~80% although the overall episodic object detection rate for each flight pattern exceeds 90%.
Using Virtual Machine Allocation Policies to Defend against Co-Resident Attacks in Cloud Computing

Cloud computing enables users to consume various IT resources in an on-demand manner, and with low management overhead. However, customers can face new security risks when they use cloud computing platforms. In this paper, we focus on one such threat—the co-resident attack, where malicious users build side channels and extract private information from virtual machines co-located on the same server. Previous works mainly attempt to address the problem by eliminating side channels. However, most of these methods are not suitable for immediate deployment due to the required modifications to current cloud platforms. We choose to solve the problem from a different perspective, by studying how to improve the virtual machine allocation policy, so that it is difficult for attackers to co-locate with their targets. Specifically, we (1) define security metrics for assessing the attack; (2) model these metrics, and compare the difficulty of achieving co-residence under three commonly used policies; (3) design a new policy that not only mitigates the threat of attack, but also satisfies the requirements for workload balance and low power consumption; and (4) implement, test, and prove the effectiveness of the policy on the popular open-source platform OpenStack.
Intra color-shape classification for traffic sign recognition

This paper presents a novel traffic sign recognition system comprising of: (i) Color/shape classification, (ii) Pictogram extraction, (iii) Features selection and, (iv) Lyapunov Theory-based Radial Basis Function neural network (RBFNN). In the proposed system, traffic signs are first segmented and classified with regard to its unique color and shape in order to partition a large set of data into smaller subclasses. Within these subclasses, all redundant information except the pictogram is discarded for feature selection since the pictogram contains critical information for road users. Principle Component Analysis (PCA) is applied to extract salient points for traffic sign dimensionality reduction. This is followed by the Fisher's Linear Discriminant (FLD) to further obtain the most discriminant features. These features are fed into RBFNN for training with a proposed weight updating scheme based on Lyapunov stability theory. The performance of the proposed system is evaluated with Malaysian road signs with promising recognition rate.
Factorization Bandits for Interactive Recommendation

(3) because ‖(xa,va)‖2 ≤ L and ηt only has a finite variance. For the first term on the right-hand side in Eq (2), if the regularization parameter λ1 is sufficiently large, the Hessian matrix of the loss function specified in the paper is positive definite at the optimizer based on the property of alternating least square (Uschmajew 2012). The estimation of Θ and va is thus locally q-linearly convergent to the optimizer. This indicates that for every 1 > 0, we have, ‖v̂a,t+1 − v a‖2 ≤ (q1 + 1)‖v̂a,t − v a‖2 (4) where 0 < q1 < 1. As a conclusion, we have for any δ > 0, with probability at least 1− δ,
Mitigating Catastrophic Forgetting in Temporal Difference Learning with Function Approximation

Neural networks have had many great successes in recent years, particularly with the advent of deep learning and many novel training techniques. One issue that has prevented reinforcement learning from taking full advantage of scalable neural networks is that of catastrophic forgetting. The latter affects supervised learning systems when highly correlated input samples are presented, as well as when input patterns are non-stationary. However, most real-world problems are non-stationary in nature, resulting in prolonged periods of time separating inputs drawn from different regions of the input space. Unfortunately, reinforcement learning presents a worst-case scenario when it comes to precipitating catastrophic forgetting in neural networks. Meaningful training examples are acquired as the agent explores different regions of its state/action space. When the agent is in one such region, only highly correlated samples from that region are typically acquired. Moreover, the regions that the agent is likely to visit will depend on its current policy, suggesting that an agent that has a good policy may avoid exploring particular regions. The confluence of these factors means that without some mitigation techniques, supervised neural networks as function approximation in temporal-difference learning will only be applicable to the simplest test cases. In this work, we develop a feed forward neural network architecture that mitigates catastrophic forgetting by partitioning the input space in a manner that selectively activates a different subset of hidden neurons for each region of the input space. We demonstrate the effectiveness of the proposed framework on a cart-pole balancing problem for which other neural network architectures exhibit training instability likely due to catastrophic forgetting. We demonstrate that our technique produces better results, particularly with respect to a performance-stability measure.
Improved Garbled Circuit: Free XOR Gates and Applications

We present a new garbled circuit construction for two-party secure function evaluation (SFE). In our one-round protocol, XOR gates are evaluated “for free”, which results in the corresponding improvement over the best garbled circuit implementations (e.g. Fairplay [19]). We build permutation networks [26] and Universal Circuits (UC)  almost exclusively of XOR gates; this results in a factor of up to 4 improvement (in both computation and communication) of their SFE. We also improve integer addition and equality testing by factor of up to 2. We rely on the Random Oracle (RO) assumption. Our constructions are proven secure in the semi-honest model.
Knowledge Management Metrics via a Balanced Scorecard Methodology

IT professionals are finding that more of their IT investments are being measured against a knowledge management (KM) metric. Those who want to deploy foundation technologies such as groupware, CRM or decision support tools, but fail to justify them on the basis of their contribution to KM, may find it difficult to get funding unless they can frame them within the KM context. Determining KM's pervasiveness and impact is analogous to measuring the contribution of marketing, employee development, or any other management or organizational competency. This paper addresses the problem of developing measurement models for KM metrics and discusses what current KM metrics are in use, and examine their sustainability and soundness in assessing knowledge utilization and retention of generating revenue. The paper will then discuss the use of a Balanced Scorecard approach to determine a business-oriented relationship between strategic KM usage and IT strategy and implementation.
Millimeter Wave Communications: From Point-to-Point Links to Agile Network Connections

Millimeter wave (mmWave) technologies promise to revolutionize wireless networks by enabling multi-gigabit data rates. However, they suffer from high attenuation, and hence have to use highly directional antennas to focus their power on the receiver. Existing radios have to scan the space to find the best alignment between the transmitter’s and receiver’s beams, a process that takes up to a few seconds. This delay is problematic in a network setting where the base station needs to quickly switch between users and accommodate mobile clients.
 We present Agile-Link, the first mmWave beam steering system that is demonstrated to find the correct beam alignment without scanning the space. Instead of scanning, Agile- Link hashes the beam directions using a few carefully chosen hash functions. It then identifies the correct alignment by tracking how the energy changes across different hash functions. Our results show that Agile-Link reduces beam steering delay by orders of magnitude.
Hypothesis generation guided by co-word clustering

Co-word analysis was applied to keywords assigned to MEDLINE documents contained in sets of complementary but disjoint literatures. In strategical diagrams of disjoint literatures, based on internal density and external centrality of keyword-containing clusters, intermediate terms (linking the disjoint partners) were found in regions of below-median centrality and density. Terms representing the disjoint literature themes were found in close vicinity in strategical diagrams of intermediate literatures. Based on centrality-density ratios, characteristic values were found which allow a rapid identification of clusters containing possible intermediate and disjoint partner terms. Applied to the already investigated disjoint pairs Raynaud"s Disease - Fish Oil, Migraine - Magnesium, the method readily detected known and unknown (but relevant) intermediate and disjoint partner terms. Application of the method to the literature on Prions led to Manganese as possible disjoint partner term. It is concluded that co-word clustering is a powerful method for literature-based hypothesis generation and knowledge discovery.
Optimal Design of a 4-DOF Parallel Manipulator: From Academia to Industry

This paper presents an optimal design of a parallel manipulator aiming to perform pick-and-place operations at high speed and high acceleration. After reviewing existing architectures of high-speed and high-acceleration parallel manipulators, a new design of a 4-DOF parallel manipulator is presented, with an articulated traveling plate, which is free of internal singularities and is able to achieve high performances. The kinematic and simplified, but realistic, dynamic models are derived and validated on a manipulator prototype. Experimental tests show that this design is able to perform beyond the high targets, i.e., it reaches a speed of 5.5 m/s and an acceleration of 165 m/s2. The experimental prototype was further optimized on the basis of kinematic and dynamic criteria. Once the motors, gear ratio, and several link lengths are determined, a modified design of the articulated traveling plate is proposed in order to reach a better dynamic equilibrium among the four legs of the manipulator. The obtained design is the basis of a commercial product offering the shortest cycle times among all robots available in today's market.
Spice: Socially-driven learning-based mobile media prefetching

Mobile online social networks (OSNs) are emerging as the popular mainstream platform for information and content sharing among people. In order to provide Quality of Experience (QoE) support for mobile OSN services, in this paper we propose a socially-driven learning-based framework, namely Spice, for media content prefetching to reduce the access delay and enhance mobile user's satisfaction. Through a large-scale data-driven analysis over real-life mobile Twitter traces from over 17,000 users during a period of five months, we reveal that the social friendship has a great impact on user's media content click behavior. To capture this effect, we conduct social friendship clustering over the set of user's friends, and then develop a cluster-based Latent Bias Model for socially-driven learning-based prefetching prediction. We then propose a usage-adaptive prefetching scheduling scheme by taking into account that different users may possess heterogeneous patterns in the mobile OSN app usage. We comprehensively evaluate the performance of Spice framework using trace-driven emulations on smartphones. Evaluation results corroborate that the Spice can achieve superior performance, with an average 67.2% access delay reduction at the low cost of cellular data and energy consumption. Furthermore, by enabling users to offload their machine learning procedures to a cloud server, our design can achieve speed-up of a factor of 1000 over the local data training execution on smartphones.
On-line Random Forests

Random Forests (RFs) are frequently used in many computer vision and machine learning applications. Their popularity is mainly driven by their high computational efficiency during both training and evaluation while achieving state-of-the-art results. However, in most applications RFs are used off-line. This limits their usability for many practical problems, for instance, when training data arrives sequentially or the underlying distribution is continuously changing. In this paper, we propose a novel on-line random forest algorithm. We combine ideas from on-line bagging, extremely randomized forests and propose an on-line decision tree growing procedure. Additionally, we add a temporal weighting scheme for adaptively discarding some trees based on their out-of-bag-error in given time intervals and consequently growing of new trees. The experiments on common machine learning data sets show that our algorithm converges to the performance of the off-line RF. Additionally, we conduct experiments for visual tracking, where we demonstrate real-time state-of-the-art performance on well-known scenarios and show good performance in case of occlusions and appearance changes where we outperform trackers based on on-line boosting. Finally, we demonstrate the usability of on-line RFs on the task of interactive real-time segmentation.
The design and development of automatic fish feeder system using PIC microcontroller

This research devoted to reduce the labor cost as well as develop better pellet dispense system. Subsequently, this research was proposed to design an automatic fish feeder system using PIC microcontroller application. The device developed combines mechanical and electrical system in controlling fish feeding activity. This device, basically consists of pellet storage, former, stand, DC motor and microcontroller. The pellets controlled by DC motor which located under the pellet storage. A control system was then attached to this device allowing the fish to be fed at the right cycle time as required or predefined by user. Timer was employed in this device to control the motor rotation attached to sphere former, which dispense the pellets into the water. The pellets dispensed into the marking area of the pond based solely on the rotation speed of the motor itself. The controller came with a keypad giving user more option in determining the suitable speed for the motor depends on their cattle. In short, the pellets in the automatic fish feeder system will be controlled by the rotation speed of DC motor.
Finding the most unusual time series subsequence: algorithms and applications

In this work we introduce the new problem of finding time seriesdiscords. Time series discords are subsequences of longer time series that are maximally different to all the rest of the time series subsequences. They thus capture the sense of the most unusual subsequence within a time series. While discords have many uses for data mining, they are particularly attractive as anomaly detectors because they only require one intuitive parameter (the length of the subsequence) unlike most anomaly detection algorithms that typically require many parameters. While the brute force algorithm to discover time series discords is quadratic in the length of the time series, we show a simple algorithm that is three to four orders of magnitude faster than brute force, while guaranteed to produce identical results. We evaluate our work with a comprehensive set of experiments on diverse data sources including electrocardiograms, space telemetry, respiration physiology, anthropological and video datasets.
Natural problems for stereoscopic depth perception in virtual environments

The use of virtual reality (VR) display systems has escalated over the last 5 yr and may have consequences for those working within vision research. This paper provides a brief review of the literature pertaining to the representation of depth in stereoscopic VR displays. Specific attention is paid to the response of the accommodation system with its cross-links to vergence eye movements, and to the spatial errors that arise when portraying three-dimensional space on a two-dimensional window. It is suggested that these factors prevent large depth intervals of three-dimensional visual space being rendered with integrity through dual two-dimensional arrays.
PALEO: A PERFORMANCE MODEL FOR DEEP NEURAL NETWORKS

Although various scalable deep learning software packages have been proposed, it remains unclear how to best leverage parallel and distributed computing infrastructure to accelerate their training and deployment. Moreover, the effectiveness of existing parallel and distributed systems varies widely based on the neural network architecture and dataset under consideration. In order to efficiently explore the space of scalable deep learning systems and quickly diagnose their effectiveness for a given problem instance, we introduce an analytical performance model called PALEO. Our key observation is that a neural network architecture carries with it a declarative specification of the computational requirements associated with its training and evaluation. By extracting these requirements from a given architecture and mapping them to a specific point within the design space of software, hardware and communication strategies, PALEO can efficiently and accurately model the expected scalability and performance of a putative deep learning system. We show that PALEO is robust to the choice of network architecture, hardware, software, communication schemes, and parallelization strategies. We further demonstrate its ability to accurately model various recently published scalability results for CNNs such as NiN, Inception and AlexNet.
World-Driven Access Control for Continuous Sensing

Modern applications increasingly rely on continuous monitoring of video, audio, or other sensor data to provide their functionality, particularly in platforms such as the Microsoft Kinect and Google Glass. Continuous sensing by untrusted applications poses significant privacy challenges for both device users and bystanders. Even honest users will struggle to manage application permissions using existing approaches.
 We propose a general, extensible framework for controlling access to sensor data on multi-application continuous sensing platforms. Our approach, world-driven access control, allows real-world objects to explicitly specify access policies. This approach relieves the user's permission management burden while mediating access at the granularity of objects rather than full sensor streams. A trusted policy module on the platform senses policies in the world and modifies applications' "views" accordingly. For example, world-driven access control allows the system to automatically stop recording in bathrooms or remove bystanders from video frames,without the user prompted to specify or activate such policies. To convey and authenticate policies, we introduce passports, a new kind of certificate that includes both a policy and optionally the code for recognizing a real-world object.
 We implement a prototype system and use it to study the feasibility of world-driven access control in practice. Our evaluation suggests that world-driven access control can effectively reduce the user's permission management burden in emerging continuous sensing systems. Our investigation also surfaces key challenges for future access control mechanisms for continuous sensing applications.
Simple Semi-Supervised POS Tagging

We tackle the question: how much supervision is needed to achieve state-of-the-art performance in part-of-speech (POS) tagging, if we leverage lexical representations given by the model of Brown et al. (1992)? It has become a standard practice to use automatically induced “Brown clusters” in place of POS tags. We claim that the underlying sequence model for these clusters is particularly well-suited for capturing POS tags. We empirically demonstrate this claim by drastically reducing supervision in POS tagging with these representations. Using either the bit-string form given by the algorithm of Brown et al. (1992) or the (less well-known) embedding form given by the canonical correlation analysis algorithm of Stratos et al. (2014), we can obtain 93% tagging accuracy with just 400 labeled words and achieve state-of-the-art accuracy (> 97%) with less than 1 percent of the original training data.
Unsupervised 3D object recognition and reconstruction in unordered datasets

This paper presents a system for fully automatic recognition and reconstruction of 3D objects in image databases. We pose the object recognition problem as one of finding consistent matches between all images, subject to the constraint that the images were taken from a perspective camera. We assume that the objects or scenes are rigid. For each image, we associate a camera matrix, which is parameterised by rotation, translation and focal length. We use invariant local features to find matches between all images, and the RANSAC algorithm to find those that are consistent with the fundamental matrix. Objects are recognised as subsets of matching images. We then solve for the structure and motion of each object, using a sparse bundle adjustment algorithm. Our results demonstrate that it is possible to recognise and reconstruct 3D objects from an unordered image database with no user input at all.