nils-herrmann/clean_scirepeval huggingface dataset:
Natural Gradient Works Efficiently in Learning

When a parameter space has a certain underlying structure, the ordinary gradient of a function does not represent its steepest direction, but the natural gradient does. Information geometry is used for calculating the natural gradients in the parameter space of perceptrons, the space of matrices (for blind source separation), and the space of linear dynamical systems (for blind source deconvolution). The dynamical behavior of natural gradient online learning is analyzed and is proved to be Fisher efficient, implying that it has asymptotically the same performance as the optimal batch estimation of parameters. This suggests that the plateau phenomenon, which appears in the backpropagation learning algorithm of multilayer perceptrons, might disappear or might not be so serious when the natural gradient is used. An adaptive method of updating the learning rate is proposed and analyzed.
Social recommendation: a review

Recommender systems play an important role in helping online users find relevant information by suggesting information of potential interest to them. Due to the potential value of social relations in recommender systems, social recommendation has attracted increasing attention in recent years. In this paper, we present a review of existing recommender systems and discuss some research directions. We begin by giving formal definitions of social recommendation and discuss the unique property of social recommendation and its implications compared with those of traditional recommender systems. Then, we classify existing social recommender systems into memory-based social recommender systems and model-based social recommender systems, according to the basic models adopted to build the systems, and review representative systems for each category. We also present some key findings from both positive and negative experiences in building social recommender systems, and research directions to improve social recommendation capabilities.
Adaptive dimension reduction using discriminant analysis and K-means clustering

We combine linear discriminant analysis (LDA) and K-means clustering into a coherent framework to adaptively select the most discriminative subspace. We use K-means clustering to generate class labels and use LDA to do subspace selection. The clustering process is thus integrated with the subspace selection process and the data are then simultaneously clustered while the feature subspaces are selected. We show the rich structure of the general LDA-Km framework by examining its variants and their relationships to earlier approaches. Relations among PCA, LDA, K-means are clarified. Extensive experimental results on real-world datasets show the effectiveness of our approach.
Multimodal Gesture Recognition Using Multi-stream Recurrent Neural Network

In this paper, we present a novel method for multimodal gesture recognition based on neural networks. Our multi-stream recurrent neural network (MRNN) is a completely data-driven model that can be trained from end to end without domain-specific hand engineering. The MRNN extends recurrent neural networks with Long Short-Term Memory cells (LSTM-RNNs) that facilitate the handling of variable-length gestures. We propose a recurrent approach for fusing multiple temporal modalities using multiple streams of LSTM-RNNs. In addition, we propose alternative fusion architectures and empirically evaluate the performance and robustness of these fusion strategies. Experimental results demonstrate that the proposed MRNN outperforms other state-of-theart methods in the Sheffield Kinect Gesture (SKIG) dataset, and has significantly high robustness to noisy inputs.
Automatic Labeling of Topic Models Using Text Summaries

Labeling topics learned by topic models is a challenging problem. Previous studies have used words, phrases and images to label topics. In this paper, we propose to use text summaries for topic labeling. Several sentences are extracted from the most related documents to form the summary for each topic. In order to obtain summaries with both high relevance, coverage and discrimination for all the topics, we propose an algorithm based on submodular optimization. Both automatic and manual analysis have been conducted on two real document collections, and we find 1) the summaries extracted by our proposed algorithm are superior over the summaries extracted by existing popular summarization methods; 2) the use of summaries as labels has obvious advantages over the use of words and phrases.
Learning about objects through action -initial steps towards artificial cognition

Within the field of Neuro Robotics we are driven primarily by the desire to understand how humans and animals live and grow and solve every day’s problems. To this aim we adopted a “learn by doing” approach by building artificial systems, e.g. robots that not only look like human beings but also represent a model of some brain process. They should, ideally, behave and interact like human beings (being situated). The main emphasis in robotics has been on systems that act as a reaction to an external stimulus (e.g. tracking, reaching), rather than as a result of an internal drive to explore or “understand” the environment. We think it is now appropriate to try to move from acting, in the sense explained above, to “understanding”. As a starting point we addressed the problem of learning about the effects and consequences of self-generated actions. How does the robot learn how to pull an object toward itself or to push it away? How does the robot learn that spherical objects roll while a cube only slides if pushed? Interacting with objects is important because it implicitly explores object representation, event understanding, and can provide definition of objecthood that could not be grasped with a mere passive observation of the world. Further, learning to understand what one’s own body can do is an essential step toward learning by imitation. In this view two actions are similar not only if their kinematics and dynamics are similar but rather if the effects on the external world are the same. Along this line of research we discuss some recent experiments performed at the AILab at MIT and at the LIRA-Lab at the University of Genova on COG and Babybot respectively. We show how the humanoid robots can learn how to poke and prod objects to obtain a consistently repeatable effect (e.g. sliding in a given direction), to help visual segmentation, and to interpret a poking action performed by a human manipulator.
Deep Q-learning From Demonstrations

Deep reinforcement learning (RL) has achieved several high profile successes in difficult decision-making problems. However, these algorithms typically require a huge amount of data before they reach reasonable performance. In fact, their performance during learning can be extremely poor. This may be acceptable for a simulator, but it severely limits the applicability of deep RL to many real-world tasks, where the agent must learn in the real environment. In this paper we study a setting where the agent may access data from previous control of the system. We present an algorithm, Deep Q-learning from Demonstrations (DQfD), that leverages small sets of demonstration data to massively accelerate the learning process even from relatively small amounts of demonstration data and is able to automatically assess the necessary ratio of demonstration data while learning thanks to a prioritized replay mechanism. DQfD works by combining temporal difference updates with supervised classification of the demonstrator’s actions. We show that DQfD has better initial performance than Prioritized Dueling Double Deep Q-Networks (PDD DQN) as it starts with better scores on the first million steps on 41 of 42 games and on average it takes PDD DQN 83 million steps to catch up to DQfD’s performance. DQfD learns to out-perform the best demonstration given in 14 of 42 games. In addition, DQfD leverages human demonstrations to achieve state-of-the-art results for 11 games. Finally, we show that DQfD performs better than three related algorithms for incorporating demonstration data into DQN.
Horton Tables: Fast Hash Tables for In-Memory Data-Intensive Computing

Hash tables are important data structures that lie at the heart of important applications such as key-value stores and relational databases. Typically bucketized cuckoo hash tables (BCHTs) are used because they provide highthroughput lookups and load factors that exceed 95%. Unfortunately, this performance comes at the cost of reduced memory access efficiency. Positive lookups (key is in the table) and negative lookups (where it is not) on average access 1.5 and 2.0 buckets, respectively, which results in 50 to 100% more table-containing cache lines to be accessed than should be minimally necessary. To reduce these surplus accesses, this paper presents the Horton table, a revamped BCHT that reduces the expected cost of positive and negative lookups to fewer than 1.18 and 1.06 buckets, respectively, while still achieving load factors of 95%. The key innovation is remap entries, small in-bucket records that allow (1) more elements to be hashed using a single, primary hash function, (2) items that overflow buckets to be tracked and rehashed with one of many alternate functions while maintaining a worst-case lookup cost of 2 buckets, and (3) shortening the vast majority of negative searches to 1 bucket access. With these advancements, Horton tables outperform BCHTs by 17% to 89%.
Real-Time Trajectory Planning for Autonomous Urban Driving: Framework, Algorithms, and Verifications

This paper focuses on the real-time trajectory planning problem for autonomous vehicles driving in realistic urban environments. To solve the complex navigation problem, we adopt a hierarchical motion planning framework. First, a rough reference path is extracted from the digital map using commands from the high-level behavioral planner. The conjugate gradient nonlinear optimization algorithm and the cubic B-spline curve are employed to smoothen and interpolate the reference path sequentially. To follow the refined reference path as well as handle both static and moving objects, the trajectory planning task is decoupled into lateral and longitudinal planning problems within the curvilinear coordinate framework. A rich set of kinematically feasible path candidates are generated to deal with the dynamic traffic both deliberatively and reactively. In the meanwhile, the velocity profile generation is performed to improve driving safety and comfort. After that, the generated trajectories are carefully evaluated by an objective function, which combines behavioral decisions by reasoning about the traffic situations. The optimal collision-free, smooth, and dynamically feasible trajectory is selected and transformed into commands executed by the low-level lateral and longitudinal controllers. Field experiments have been carried out with our test autonomous vehicle on the realistic inner-city roads. The experimental results demonstrated capabilities and effectiveness of the proposed trajectory planning framework and algorithms to safely handle a variety of typical driving scenarios, such as static and moving objects avoidance, lane keeping, and vehicle following, while respecting the traffic rules.
SCPSE: Security-Oriented Cyber-Physical State Estimation for Power Grid Critical Infrastructures

Preserving the availability and integrity of the power grid critical infrastructures in the face of fast-spreading intrusions requires advances in detection techniques specialized for such large-scale cyber-physical systems. In this paper, we present a security-oriented cyber-physical state estimation (SCPSE) system, which, at each time instant, identifies the compromised set of hosts in the cyber network and the maliciously modified set of measurements obtained from power system sensors. SCPSE fuses uncertain information from different types of distributed sensors, such as power system meters and cyber-side intrusion detectors, to detect the malicious activities within the cyber-physical system. We implemented a working prototype of SCPSE and evaluated it using the IEEE 24-bus benchmark system. The experimental results show that SCPSE significantly improves on the scalability of traditional intrusion detection techniques by using information from both cyber and power sensors. Furthermore, SCPSE was able to detect all the attacks against the control network in our experiments.
The Spatial Semantic Hierarchy

The Spatial Semantic Hierarchy is a model of knowledge of large-scale space consisting of multiple interacting representations, both qualitative and quantitative. The SSH is inspired by the properties of the human cognitive map, and is intended to serve both as a model of the human cognitive map and as a method for robot exploration and map-building. The multiple levels of the SSH express states of partial knowledge, and thus enable the human or robotic agent to deal robustly with uncertainty during both learning and problem-solving. The control level represents useful patterns of sensorimotor interaction with the world in the form of trajectory-following and hill-climbing control laws leading to locally distinctive states. Local geometric maps in local frames of reference can be constructed at the control level to serve as observers for control laws in particular neighborhoods. The causal level abstracts continuous behavior among distinctive states into a discrete model consisting of states linked by actions. The topological level introduces the external ontology of places, paths and regions by abduction to explain the observed pattern of states and actions at the causal level. Quantitative knowledge at the control, causal and topological levels supports a “patchwork map” of local geometric frames of reference linked by causal and topological connections. The patchwork map can be merged into a single global frame of reference at the metrical level when sufficient information and computational resources are available. We describe the assumptions and guarantees behind the generality of the SSH across environments and sensorimotor systems. Evidence is presented from several partial implementations of the SSH on simulated and physical robots.
Binarized Convolutional Landmark Localizers for Human Pose Estimation and Face Alignment with Limited Resources

Our goal is to design architectures that retain the groundbreaking performance of CNNs for landmark localization and at the same time are lightweight, compact and suitable for applications with limited computational resources. To this end, we make the following contributions: (a) we are the first to study the effect of neural network binarization on localization tasks, namely human pose estimation and face alignment. We exhaustively evaluate various design choices, identify performance bottlenecks, and more importantly propose multiple orthogonal ways to boost performance. (b) Based on our analysis, we propose a novel hierarchical, parallel and multi-scale residual architecture that yields large performance improvement over the standard bottleneck block while having the same number of parameters, thus bridging the gap between the original network and its binarized counterpart. (c) We perform a large number of ablation studies that shed light on the properties and the performance of the proposed block. (d) We present results for experiments on the most challenging datasets for human pose estimation and face alignment, reporting in many cases state-of-the-art performance. Code can be downloaded from https://www.adrianbulat.com/binary-cnn-landmarks
Conditional Graphical Lasso for Multi-label Image Classification

Multi-label image classification aims to predict multiple labels for a single image which contains diverse content. By utilizing label correlations, various techniques have been developed to improve classification performance. However, current existing methods either neglect image features when exploiting label correlations or lack the ability to learn image-dependent conditional label structures. In this paper, we develop conditional graphical Lasso (CGL) to handle these challenges. CGL provides a unified Bayesian framework for structure and parameter learning conditioned on image features. We formulate the multi-label prediction as CGL inference problem, which is solved by a mean field variational approach. Meanwhile, CGL learning is efficient due to a tailored proximal gradient procedure by applying the maximum a posterior (MAP) methodology. CGL performs competitively for multi-label image classification on benchmark datasets MULAN scene, PASCAL VOC 2007 and PASCAL VOC 2012, compared with the state-of-the-art multi-label classification algorithms.
New Recommendation Techniques for Multicriteria Rating Systems

Personalization technologies and recommender systems help online consumers avoid information overload by making suggestions regarding which information is most relevant to them. Most online shopping sites and many other applications now use recommender systems. Two new recommendation techniques leverage multicriteria ratings and improve recommendation accuracy as compared with single-rating recommendation approaches. Taking full advantage of multicriteria ratings in personalization applications requires new recommendation techniques. In this article, we propose several new techniques for extending recommendation technologies to incorporate and leverage multicriteria rating information.
Photorealistic Facial Texture Inference Using Deep Neural Networks

We present a data-driven inference method that can synthesize a photorealistic texture map of a complete 3D face model given a partial 2D view of a person in the wild. After an initial estimation of shape and low-frequency albedo, we compute a high-frequency partial texture map, without the shading component, of the visible face area. To extract the fine appearance details from this incomplete input, we introduce a multi-scale detail analysis technique based on mid-layer feature correlations extracted from a deep convolutional neural network. We demonstrate that fitting a convex combination of feature correlations from a high-resolution face database can yield a semantically plausible facial detail description of the entire face. A complete and photorealistic texture map can then be synthesized by iteratively optimizing for the reconstructed feature correlations. Using these high-resolution textures and a commercial rendering framework, we can produce high-fidelity 3D renderings that are visually comparable to those obtained with state-of-the-art multi-view face capture systems. We demonstrate successful face reconstructions from a wide range of low resolution input images, including those of historical figures. In addition to extensive evaluations, we validate the realism of our results using a crowdsourced user study.
Fractional Order PID controller design for speed control of chopper fed DC Motor Drive using Artificial Bee Colony algorithm

This article deals with an interesting application of Fractional Order (FO) Proportional Integral Derivative (PID) Controller for speed regulation in a DC Motor Drive. The design of five interdependent Fractional Order controller parameters has been formulated as an optimization problem based on minimization of set point error and controller output. The task of optimization was carried out using Artificial Bee Colony (ABC) algorithm. A comparative study has also been made to highlight the advantage of using a Fractional order PID controller over conventional PID control scheme for speed regulation of application considered. Extensive simulation results are provided to validate the effectiveness of the proposed approach.
Multiword Expressions in NLP: General Survey and a Special Case of Verb-Noun Constructions

This chapter presents a survey of contemporary NLP research on Multiword Expressions (MWEs). MWEs pose a huge problem to precise language processing due to their idiosyncratic nature and diversity of their semantic, lexical, and syntactical properties. The chapter begins by considering MWEs definitions, describes some MWEs classes, indicates problems MWEs generate in language applications and their possible solutions, presents methods of MWE encoding in dictionaries and their automatic detection in corpora. The chapter goes into more detail on a particular MWE class called Verb-Noun Constructions (VNCs). Due to their frequency in corpus and unique characteristics, VNCs present a research problem in their own right. Having outlined several approaches to VNC representation in lexicons, the chapter explains the formalism of Lexical Function as a possible VNC representation. Such representation may serve as a tool for VNCs automatic detection in a corpus. The latter is illustrated on Spanish material applying some supervised learning methods commonly used for NLP tasks.
Strategic Hotel Development and Positioning

A study of more than nineteen hundred U.S. hotels for the years 2002 and 2003 found that a hotel’s net operating income percentage is most closely tied to its occupancy, although average daily rate (ADR) has a strong influence, as does market segment (also known as chain scale), the age of the property, and brand affiliation. A hotel’s size (that is, number of rooms) and location (e.g., urban or highway) also influence net operating income (NOI), but a hotel’s region does not significantly affect NOI percentage. The year 2002 data particularly show the importance of heads in beds. Hoteliers cut ADR heavily in that recession year, and those hotels that maintained strong occupancy were the ones that enjoyed strong NOI. While resorts and urban hotels generated the highest NOI in raw dollar volume, economy hotels had the highest NOI percentage and midscale hotels with food and beverage service (F&B) had the lowest NOI percentage.
Toward Standardization of Authenticated Caller ID Transmission

With the cost of telecommunication becoming as cheap as Internet data, the telephone network today is rife with telephone spam and scams. In recent years, the U.S. government has received record numbers of complaints on phone fraud and unwanted calls. Caller ID is at the heart of stopping telephone spam -- a variety of apps and services, including law enforcement, rely on caller ID information to defend against unwanted calls. However, spammers are using spoofed caller IDs to defeat call blockers, to evade identification, and to further a variety of scams. To provide a solution to this problem, this article proposes a standardized authentication scheme for caller ID that enables the possibility of a security indicator for telecommunication. The goal of this proposal is to help prevent users from falling victim to telephone spam and scams, as well as provide a foundation for future and existing defenses to stop unwanted telephone communication based on caller ID information.