nils-herrmann/clean_scirepeval huggingface dataset:
Optimization Techniques for Semi-Supervised Support Vector Machines

Due to its wide applicability, the problem of semi-supervised classification is attracting increasing attention in machine learning. Semi-Supervised Support Vector Machines (S3VMs) are based on applying the margin maximization principle to both labeled and unlabeled examples. Unlike SVMs, their formulation leads to a non-convex optimization problem. A suite of algorithms have recently been proposed for solving S3VMs. This paper reviews key ideas in this literature. The performance and behavior of various S3VM algorithms is studied together, under a common experimental setting.
Moving average reversion strategy for on-line portfolio selection

On-line portfolio selection, a fundamental problem in comp utational finance, has attracted increasing interests from artificial intelligence and machine learning communities i n recent years. Empirical evidence shows that stock’s high and low prices are temporary and stock price relatives are li kely to follow the mean reversion phenomenon. While existing mean reversion strategies are shown to achieve goo d empirical performance on many real datasets, they often make thesingle-period mean reversion assumption, which is not always satisfied, leading to poor pe rformance in certain real datasets. To overcome this limitation, this ar ticle proposes amultiple-period mean reversion , or so-called “Moving Average Reversion” (MAR), and a new on-line portfol io selection strategy named “On-Line Moving Average Reversion” (OLMAR), which exploits MAR via efficient and sca lable online machine learning techniques. From our empirical results on real markets, we found that OLMAR can ov ercome the drawbacks of existing mean reversion algorithms and achieve significantly better results, espec ially on the datasets where existing mean reversion algorit hms failed. In addition to its superior empirical performance, OLMAR also runs extremely fast, further supporting its practical applicability to a wide range of applications. Fi nally, to ensure our work is re-producible, we have made all the data sets and source codes of this work publicly availabl e thttp://olps.stevenhoi.org/OLMAR/ .
Time Windows Based Dynamic Routing in Multi-AGV Systems

This paper presents a dynamic routing method for supervisory control of multiple automated guided vehicles (AGVs) that are traveling within a layout of a given warehouse. In dynamic routing a calculated path particularly depends on the number of currently active AGVs' missions and their priorities. In order to solve the shortest path problem dynamically, the proposed routing method uses time windows in a vector form. For each mission requested by the supervisor, predefined candidate paths are checked if they are feasible. The feasibility of a particular path is evaluated by insertion of appropriate time windows and by performing the windows overlapping tests. The use of time windows makes the algorithm apt for other scheduling and routing problems. Presented simulation results demonstrate efficiency of the proposed dynamic routing. The proposed method has been successfully implemented in the industrial environment in a form of a multiple AGV control system.
Facebook and privacy: it's complicated

We measure users' attitudes toward interpersonal privacy concerns on Facebook and measure users' strategies for reconciling their concerns with their desire to share content online. To do this, we recruited 260 Facebook users to install a Facebook application that surveyed their privacy concerns, their friend network compositions, the sensitivity of posted content, and their privacy-preserving strategies. By asking participants targeted questions about people randomly selected from their friend network and posts shared on their profiles, we were able to quantify the extent to which users trust their "friends" and the likelihood that their content was being viewed by unintended audiences. We found that while strangers are the most concerning audience, almost 95% of our participants had taken steps to mitigate those concerns. At the same time, we observed that 16.5% of participants had at least one post that they were uncomfortable sharing with a specific friend---someone who likely already had the ability to view it---and that 37% raised more general concerns with sharing their content with friends. We conclude that the current privacy controls allow users to effectively manage the outsider threat, but that they are unsuitable for mitigating concerns over the insider threat---members of the friend network who dynamically become inappropriate audiences based on the context of a post.
IDUTC: an intelligent decision-making system for urban traffic-control applications

The design of systems for intelligent control of urban traffic is important in providing a safe environment for pedestrians and motorists. Artificial neural networks (ANNs) (learning systems) and expert systems (knowledge-based systems) have been extensively explored as approaches for decision making. While the ANNs compute decisions by learning from successfully solved examples, the expert systems rely on a knowledge base developed by human reasoning for decision making. It is possible to integrate the learning abilities of an ANN and the knowledge-based decision-making ability of the expert system. This paper presents a real-time intelligent decision making system, IDUTC, for urban traffic control applications. The system integrates a backpropagation-based ANN that can learn and adapt to the dynamically changing environment and a fuzzy expert system for decision making. The performance of the proposed intelligent decision-making system is evaluated by mapping the the adaptable traffic light control problem. The application is implemented using the ANN approach, the FES approach, and the proposed integrated system approach. The results of extensive simulations using the three approaches indicate that the integrated system provides better performance and leads to a more efficient implementation than the other two approaches.
To Discount or Not to Discount in Reinforcement Learning: A Case Study Comparing R Learning and Q Learning

Most work in reinforcement learning (RL) is based on discounted techniques, such as Q learning, where long-term rewards are geometrically attenuated based on the delay in their occurence. Schwartz recently proposed an undiscounted RL technique called R learning that optimizes average reward, and argued that it was a better metric than the discounted one optimized by Q learning. In this paper we compare R learning with Q learning on a simulated robot box-pushing task. We compare these two techniques across three diierent exploration strategies: two of them undirected, Boltz-mann and semi-uniform, and one recency-based directed strategy. Our results show that Q learning performs better than R learning , even when both are evaluated using the same undiscounted performance measure. Furthermore, R learning appears to be very sensitive to choice of exploration strategy. In particular, a surprising result is that R learn-ing's performance noticeably deteriorates under Boltzmann exploration. We identify precisely a limit cycle situation that causes R learning's performance to deteriorate when combined with Boltzmann exploration, and show where such limit cycles arise in our robot task. However, R learning performs much better (although not as well as Q learning) when combined with semi-uniform and recency-based exploration. In this paper, we also argue for using medians over means as a better distribution-free estimator of average performance, and describe a simple non-parametric signiicance test for comparing learning data from two RL techniques.
Power Management of an Ultracapacitor/Battery Hybrid Energy Storage System in an HEV

To overcome the power delivery limitations of batteries and energy storage limitations of ultracapacitors, hybrid energy storage systems, which combine the two energy sources, have been proposed. A comprehensive review of the state of the art is presented. In addition, a method of optimizing the operation of a battery/ultracapacitor hybrid energy storage system (HESS) is presented. The goal is to set the state of charge of the ultracapacitor and the battery in a way which ensures that the available power and energy is sufficient to supply the drivetrain. By utilizing an algorithm where the states of charge of both systems are tightly controlled, we allow for the overall system size to reduce since more power is available from a smaller energy storage system
Gain-Scheduled Missile Autopilot Design Using Linear Parameter Varying Transformations

This paper presents a gain-scheduled design for a missile longitudinal autopilot. The gain-scheduled design is novel in that it does not involve linearizations about trim conditions of the missile dynamics. Rather, the missile dynamics are brought to a quasilinear parameter varying (LPV) form via a state transformation. An LPV system is defined as a linear system whose dynamics depend on an exogenous variable whose values are unknown a priori but can be measured upon system operation. In this case, the variable is the angle of attack. This is actually an endogenous variable, hence the expression "quasi-LPV." Once in a quasi-LPV form, a robust controller using H synthesis is designed to achieve angle-of-attack control via fin deflections. The final design is an inner/outerloop structure, with angle-of-attack control being the inner loop and normal acceleration control being the outer loop.
Affordances, development and imitation

We present a developmental perspective of robot learning that uses affordances as the link between sensory-motor coordination and imitation. The key concept is a general model for affordances able to learn the statistical relations between actions, object properties and the effects of actions on objects. Based on the learned affordances, it is possible to perform simple imitation games providing both task interpretation and planning capabilities. To evaluate the approach, we provide results of affordance learning with a real robot and simple imitation games with people.
LCL-filter design for grid-connected PCS using total harmonic distortion and ripple attenuation factor

This paper deals with a design method of LCL filter for grid-connected three-phase PWM voltage source inverters (VSI). By analyzing the total harmonic distortion of the current (THDi) in the inverter-side inductor and the ripple attenuation factor of the current (RAF) injected to the grid through the LCL network, the parameter of LCL can be clearly designed. The described LCL filter design method is verified by showing a good agreement between the target current THD and the actual one through simulation and experiment.
On the Performance of IEEE 802.11 under Jamming

In this paper, we study the performance of the IEEE 802.11 MAC protocol under a range of jammers that covers both channel-oblivious and channel-aware jamming. We study two channel-oblivious jammers: a periodic jammer that jams deterministically at a specified rate, and a memoryless jammer whose signals arrive according to a Poisson process. We also develop new models for channel-aware jamming, including a reactive jammer that only jams non-colliding transmissions and an omniscient jammer that optimally adjusts its strategy according to current states of the participating nodes. Our study comprises of a theoretical analysis of the saturation throughput of 802.11 under jamming, an extensive simulation study, and a testbed to conduct real world experimentation of jamming IEEE 802.11 using GNU Radio and USRP platform. In our theoretical analysis, we use a discrete-time Markov chain analysis to derive formulae for the saturation throughput of IEEE 802.11 under memoryless, reactive and omniscient jamming. One of our key results is a characterization of optimal omniscient jamming that establishes a lower bound on the saturation throughput of 802.11 under arbitrary jammer attacks. We validate the theoretical analysis by means of Qualnet simulations. Finally, we measure the real-world performance of periodic and memoryless jammers using our GNU radio jammer prototype.
Morph-fitting: Fine-Tuning Word Vector Spaces with Simple Language-Specific Rules

Morphologically rich languages accentuate two properties of distributional vector space models: 1) the difficulty of inducing accurate representations for lowfrequency word forms; and 2) insensitivity to distinct lexical relations that have similar distributional signatures. These effects are detrimental for language understanding systems, which may infer that inexpensive is a rephrasing for expensive or may not associate acquire with acquires. In this work, we propose a novel morph-fitting procedure which moves past the use of curated semantic lexicons for improving distributional vector spaces. Instead, our method injects morphological constraints generated using simple language-specific rules, pulling inflectional forms of the same word close together and pushing derivational antonyms far apart. In intrinsic evaluation over four languages, we show that our approach: 1) improves low-frequency word estimates; and 2) boosts the semantic quality of the entire word vector collection. Finally, we show that morph-fitted vectors yield large gains in the downstream task of dialogue state tracking, highlighting the importance of morphology for tackling long-tail phenomena in language understanding tasks.
3D Object Localisation from Multi-View Image Detections

In this work we present a novel approach to recover objects 3D position and occupancy in a generic scene using only 2D object detections from multiple view images. The method reformulates the problem as the estimation of a quadric (ellipsoid) in 3D given a set of 2D ellipses fitted to the object detection bounding boxes in multiple views. We show that a closed-form solution exists in the dual-space using a minimum of three views while a solution with two views is possible through the use of non-linear optimisation and object constraints on the size of the object shape. In order to make the solution robust toward inaccurate bounding boxes, a likely occurrence in object detection methods, we introduce a data preconditioning technique and a non-linear refinement of the closed form solution based on implicit subspace constraints. Results on synthetic tests and on different real datasets, involving challenging scenarios, demonstrate the applicability and potential of our method in several realistic scenarios.
Recognizing RGB Images by Learning from RGB-D Data

In this work, we propose a new framework for recognizing RGB images captured by the conventional cameras by leveraging a set of labeled RGB-D data, in which the depth features can be additionally extracted from the depth images. We formulate this task as a new unsupervised domain adaptation (UDA) problem, in which we aim to take advantage of the additional depth features in the source domain and also cope with the data distribution mismatch between the source and target domains. To effectively utilize the additional depth features, we seek two optimal projection matrices to map the samples from both domains into a common space by preserving as much as possible the correlations between the visual features and depth features. To effectively employ the training samples from the source domain for learning the target classifier, we reduce the data distribution mismatch by minimizing the Maximum Mean Discrepancy (MMD) criterion, which compares the data distributions for each type of feature in the common space. Based on the above two motivations, we propose a new SVM based objective function to simultaneously learn the two projection matrices and the optimal target classifier in order to well separate the source samples from different classes when using each type of feature in the common space. An efficient alternating optimization algorithm is developed to solve our new objective function. Comprehensive experiments for object recognition and gender recognition demonstrate the effectiveness of our proposed approach for recognizing RGB images by learning from RGB-D data.
DGA Botnet Detection Using Supervised Learning Methods

Modern botnets are based on Domain Generation Algorithms (DGAs) to build a resilient communication between bots and Command and Control (C&C) server. The basic aim is to avoid blacklisting and evade the Intrusion Protection Systems (IPS). Given the prevalence of this mechanism, numerous solutions have been developed in the literature. In particular, supervised learning has received an increased interest as it is able to operate on the raw domains and is amenable to real-time applications. Hidden Markov Model, C4.5 decision tree, Extreme Learning Machine, Long Short-Term Memory networks have become the state of the art in DGA botnet detection. There also exist several advanced supervised learning methods, namely Support Vector Machine (SVM), Recurrent SVM, CNN+LSTM and Bidirectional LSTM, which have not been suitably appropriated in such domain. This paper presents a first attempt to thoroughly investigate all the above methods, evaluate them on the real-world collected DGA dataset involving 38 classes with 168,900 samples, and should provide a valuable reference point for future research in this field.
Iterative Linear Quadratic Regulator Design for Nonlinear Biological Movement Systems

This paper presents an Iterative Linear Quadratic Regulator (ILQR) me thod for locally-optimal feedback control of nonlinear dynamical systems. The method is applied to a musculo-s ke etal arm model with 10 state dimensions and 6 controls, and is used to compute energy-optimal reach ing movements. Numerical comparisons with three existing methods demonstrate that the new method converge s substantially faster and finds slightly better solutions.
ADASYN: Adaptive synthetic sampling approach for imbalanced learning

This paper presents a novel adaptive synthetic (ADASYN) sampling approach for learning from imbalanced data sets. The essential idea of ADASYN is to use a weighted distribution for different minority class examples according to their level of difficulty in learning, where more synthetic data is generated for minority class examples that are harder to learn compared to those minority examples that are easier to learn. As a result, the ADASYN approach improves learning with respect to the data distributions in two ways: (1) reducing the bias introduced by the class imbalance, and (2) adaptively shifting the classification decision boundary toward the difficult examples. Simulation analyses on several machine learning data sets show the effectiveness of this method across five evaluation metrics.
Design of a High-Performance System for Secure Image Communication in the Internet of Things

Image or video exchange over the Internet of Things (IoT) is a requirement in diverse applications, including smart health care, smart structures, and smart transportations. This paper presents a modular and extensible quadrotor architecture and its specific prototyping for automatic tracking applications. The architecture is extensible and based on off-the-shelf components for easy system prototyping. A target tracking and acquisition application is presented in detail to demonstrate the power and flexibility of the proposed design. Complete design details of the platform are also presented. The designed module implements the basic proportional-integral-derivative control and a custom target acquisition algorithm. Details of the sliding-window-based algorithm are also presented. This algorithm performs $20\times $ faster than comparable approaches in OpenCV with equal accuracy. Additional modules can be integrated for more complex applications, such as search-and-rescue, automatic object tracking, and traffic congestion analysis. A hardware architecture for the newly introduced Better Portable Graphics (BPG) compression algorithm is also introduced in the framework of the extensible quadrotor architecture. Since its introduction in 1987, the Joint Photographic Experts Group (JPEG) graphics format has been the de facto choice for image compression. However, the new compression technique BPG outperforms the JPEG in terms of compression quality and size of the compressed file. The objective is to present a hardware architecture for enhanced real-time compression of the image. Finally, a prototyping platform of a hardware architecture for a secure digital camera (SDC) integrated with the secure BPG (SBPG) compression algorithm is presented. The proposed architecture is suitable for high-performance imaging in the IoT and is prototyped in Simulink. To the best of our knowledge, this is the first ever proposed hardware architecture for SBPG compression integrated with an SDC.
Entity disambiguation with hierarchical topic models

Disambiguating entity references by annotating them with unique ids from a catalog is a critical step in the enrichment of unstructured content. In this paper, we show that topic models, such as Latent Dirichlet Allocation (LDA) and its hierarchical variants, form a natural class of models for learning accurate entity disambiguation models from crowd-sourced knowledge bases such as Wikipedia. Our main contribution is a semi-supervised hierarchical model called Wikipedia-based Pachinko Allocation Model} (WPAM) that exploits: (1) All words in the Wikipedia corpus to learn word-entity associations (unlike existing approaches that only use words in a small fixed window around annotated entity references in Wikipedia pages), (2) Wikipedia annotations to appropriately bias the assignment of entity labels to annotated (and co-occurring unannotated) words during model learning, and (3) Wikipedia's category hierarchy to capture co-occurrence patterns among entities. We also propose a scheme for pruning spurious nodes from Wikipedia's crowd-sourced category hierarchy. In our experiments with multiple real-life datasets, we show that WPAM outperforms state-of-the-art baselines by as much as 16% in terms of disambiguation accuracy.
Application identification via network traffic classification

Recent developments in Internet technology have led to an increased importance of network traffic classification. In this study, we used machine-learning methods for the identification of applications using network traffic classification. Contrary to existing studies, which classify applications into categories like FTP, Instant Messaging, etc., we tried to identify popular end-user applications such as Facebook, Twitter, Skype and many more individually. We are motivated by the fact that individual identification of applications is of high importance for network security, QoS enforcement, and trend analysis. For our tests, we used UNB ISCX Network Traffic dataset and our internal dataset, consisting of 14 and 13 well-known applications respectively. In our experiments, we evaluated four classification algorithms, namely J48, Random Forest, k-NN, and Bayes Net. With the complete set of 111 features, k-NN gave the best result for the ISCX Dataset as 93.94% of accuracy using the value of k as 1, and Random Forest gave the best result for the internal dataset as 90.87% of accuracy. During the course of this study, the initial numbers of features were successfully reduced to two sets of 12 features specific to each dataset without a compromise to the success. Moreover, we observed a 2% increase in the success rate for the internal dataset. We believe that individual application identification by applying machine-learning methods is a viable solution and currently we are investigating a two-tier approach to make it more resilient to in category confusion.