nils-herrmann/clean_scirepeval huggingface dataset:
Permanent-magnet, direct-drive, starter/alternator machine with weakened flux linkage for constant-power operation over extremely wide speed range

As consequence of the considerable increase of the electrical power demand in vehicles, the adoption of a combined direct-drive starter/alternator system is being seriously pursued and a new generation of vehicle alternators delivering power up to 6 kW over the entire range of the engine speed is soon expected for use with connection to a 42 V bus. The surface permanent magnet (SPM) machines offer many of the features sought for such future automotive power generation systems, and thereby a substantial improvement in the control of their output voltage would allow the full exploitation of their attractive characteristics in the direct-drive starter/alternator application without significant penalties otherwise resulting on the machine-fed power converter. Concerning that, this paper reports on the original solution adopted in a proof-of-concept axial-flux permanent magnet machine (AFPM) prototype to provide weakening of the flux linkage with speed and thereby achieve constant-power operation over a wide speed range. The principle being utilized is introduced and described, including design dimensions and experimental data taken from the proof-of-concept machine prototype.
Image Processing and Machine Learning for Automated Fruit Grading System: A Technical Review

In India, demand for various fruits and vegetables are increasing as population grows. Automation in agriculture plays a vital role in increasing the productivity and economical growth of the Country, therefore there is a need for automated system for accurate, fast and quality fruits determination. Researchers have developed numerous algorithms for quality grading and sorting of fruit. Color is most striking feature for identifying disease and maturity of the fruit. In this paper; efficient algorithms for color feature extraction are reviewed. Then after, various classification techniques are compared based on their merits and demerits. The objective of the paper is to provide introduction to machine learning and color based grading algorithms, its components and current work reported on an automatic fruit grading system.
Opinion dynamics and bounded confidence: models, analysis and simulation

When does opinion formation within an interacting group lead to consensus, polarization or fragmentation? The article investigates various models for the dynamics of continuous opinions by analytical methods as well as by computer simulations. Section 2 develops within a unified framework the classical model of consensus formation, the variant of this model due to Friedkin and Johnsen, a time-dependent version and a nonlinear version with bounded confidence of the agents. Section 3 presents for all these models major analytical results. Section 4 gives an extensive exploration of the nonlinear model with bounded confidence by a series of computer simulations. An appendix supplies needed mathematical definitions, tools, and theorems.
Alerts Analysis and Visualization in Network-based Intrusion Detection Systems

The alerts produced by network-based intrusion detection systems, e.g. Snort, can be difficult for network administrators to efficiently review and respond to due to the enormous number of alerts generated in a short time frame. This work describes how the visualization of raw IDS alert data assists network administrators in understanding the current state of a network and quickens the process of reviewing and responding to intrusion attempts. The project presented in this work consists of three primary components. The first component provides a visual mapping of the network topology that allows the end-user to easily browse clustered alerts. The second component is based on the flocking behavior of birds such that birds tend to follow other birds with similar behaviors. This component allows the end-user to see the clustering process and provides an efficient means for reviewing alert data. The third component discovers and visualizes patterns of multistage attacks by profiling the attacker’s behaviors.
Multimodal Emotion Recognition for AVEC 2016 Challenge

This paper describes a systems for emotion recognition and its application on the dataset from the AV+EC 2016 Emotion Recognition Challenge. The realized system was produced and submitted to the AV+EC 2016 evaluation, making use of all three modalities (audio, video, and physiological data). Our work primarily focused on features derived from audio. The original audio features were complement with bottleneck features and also text-based emotion recognition which is based on transcribing audio by an automatic speech recognition system and applying resources such as word embedding models and sentiment lexicons. Our multimodal fusion reached CCC=0.855 on dev set for arousal and 0.713 for valence. CCC on test set is 0.719 and 0.596 for arousal and valence respectively.
Exploring Ambient and Artistic Visualization for Residential Energy Use Feedback

Providing effective feedback on resource consumption in the home is a key challenge of environmental conservation efforts. One promising approach for providing feedback about residential energy consumption is the use of ambient and artistic visualizations. Pervasive computing technologies enable the integration of such feedback into the home in the form of distributed point-of-consumption feedback devices to support decision-making in everyday activities. However, introducing these devices into the home requires sensitivity to the domestic context. In this paper we describe three abstract visualizations and suggest four design requirements that this type of device must meet to be effective: pragmatic, aesthetic, ambient, and ecological. We report on the findings from a mixed methods user study that explores the viability of using ambient and artistic feedback in the home based on these requirements. Our findings suggest that this approach is a viable way to provide resource use feedback and that both the aesthetics of the representation and the context of use are important elements that must be considered in this design space.
Learning to Predict Denotational Probabilities For Modeling Entailment

We propose a framework that captures the denotational probabilities of words and phrases by embedding them in a vector space, and present a method to induce such an embedding from a dataset of denotational probabilities. We show that our model successfully predicts denotational probabilities for unseen phrases, and that its predictions are useful for textual entailment datasets such as SICK and SNLI.
Review of the BCI Competition IV

The BCI competition IV stands in the tradition of prior BCI competitions that aim to provide high quality neuroscientific data for open access to the scientific community. As experienced already in prior competitions not only scientists from the narrow field of BCI compete, but scholars with a broad variety of backgrounds and nationalities. They include high specialists as well as students. The goals of all BCI competitions have always been to challenge with respect to novel paradigms and complex data. We report on the following challenges: (1) asynchronous data, (2) synthetic, (3) multi-class continuous data, (4) session-to-session transfer, (5) directionally modulated MEG, (6) finger movements recorded by ECoG. As after past competitions, our hope is that winning entries may enhance the analysis methods of future BCIs.
On a Connection between Importance Sampling and the Likelihood Ratio Policy Gradient

Likelihood ratio policy gradient methods have been some of the most successful reinforcement learning algorithms, especially for learning on physical systems. We describe how the likelihood ratio policy gradient can be derived from an importance sampling perspective. This derivation highlights how likelihood ratio methods under-use past experience by (i) using the past experience to estimate only the gradient of the expected return U(θ) at the current policy parameterization θ, rather than to obtain a more complete estimate of U(θ), and (ii) using past experience under the current policy only rather than using all past experience to improve the estimates. We present a new policy search method, which leverages both of these observations as well as generalized baselines—a new technique which generalizes commonly used baseline techniques for policy gradient methods. Our algorithm outperforms standard likelihood ratio policy gradient algorithms on several testbeds.
Spatial Databases - Accomplishments and Research Needs

Spatial databases, addressing the growing data management and analysis needs of spatial applications such as Geographic Information Systems, have been an active area of research for more than two decades. This research has produced a taxonomy of models for space, spatial data types and operators, spatial query languages and processing strategies, as well as spatial indexes and clustering techniques. However, more research is needed to improve support for network and field data, as well as query processing (e.g., cost models, bulk load). Another important need is to apply spatial data management accomplishments to newer applications, such as data warehouses and multimedia information systems. The objective of this paper is to identify recent accomplishments and associated research needs of the near term.
The Internet of Battle Things

On the battlefields of the future, multitudes of intelligent things will be communicating, acting, and collaborating with one another and with human warfighters. This will demand major advances in science and technology. With the growth of the Internet of Things (IoT), it's clear that industry and military IoT applications need to operate at a very large scale. Here we learn about the Internet of Battle Things (IoBT) and the unique set of challenges the military faces while under threat from adversaries.
Describing Visual Scenes using Transformed Dirichlet Processes

Motivated by the problem of learning to detect and recognize objects with minimal supervision, we develop a hierarchical probabilistic model for the spatial structure of visual scenes. In contrast with most existing models, our approach explicitly captures uncertainty in the numberof object instances depicted in a given image. Our scene model is based on the transformed Dirichlet process (TDP), a novel extension of the hierarchical DP in which a set of stochastically transformed mixture components are shared between multiple groups of data. For visual scenes, mixture components describe the spatial structure of visual features in an object–centered coordinate frame, while transformations model the object positions in a particular image. Learning and inference in the TDP, which has many potential applications beyond computer vision, is based on an empirically effective Gibbs sampler. Applied to a dataset of partially labeled street scenes, we show that the TDP’s inclusion of spatial structure improves detection performance, flexibly exploiting partially labeled training images.
Controlled and Automatic Human Information Processing: 1. Detection, Search, and Attention.

A two-process theory of human information processing is proposed and applied to detection, search, and attention phenomena. Automatic processing is activation of a learned sequence of elements in long-term memory that is initiated by appropriate inputs and then proceeds automatically—without subject control, without stressing the capacity limitations of the system, and without necessarily demanding attention. Controlled processing is a temporary activation of a sequence of elements that can be set up quickly and easily but requires attention, is capacity-limited (usually serial in nature), and is controlled by the subject. A series of studies using both reaction time and accuracy measures is presented, which traces these concepts in the form of automatic detection and controlled, search through the areas of detection, search, and attention. Results in these areas are shown to arise from common mechanisms. Automatic detection is shown to develop following consistent mapping of stimuli to responses over trials. Controlled search is utilized in varied-mapping paradigms, and in our studies, it takes the form of serial, terminating search. The approach resolves a number of apparent conflicts in the literature.
Provably secure session key distribution: the three party case

We study session key distribution in the three-party setting of Needham and Schroeder. (This is the trust model assumed by the popular Kerberos authentication system.) Such protocols are basic building blocks for contemporary distributed systems|yet the underlying problem has, up until now, lacked a de nition or provably-good solution. One consequence is that incorrect protocols have proliferated. This paper provides the rst treatment of this problem in the complexitytheoretic framework of modern cryptography. We present a de nition, protocol, and a proof that the protocol satis es the de nition, assuming the (minimal) assumption of a pseudorandom function. When this assumption is appropriately instantiated, our protocols are simple and e cient.  May 1995.
A Single-Stage Single-Phase Transformer-Less Doubly Grounded Grid-Connected PV Interface

A transformer provides galvanic isolation and grounding of the photovoltaic (PV) array in a PV-fed grid-connected inverter. Inclusion of the transformer, however, may increase the cost and/or bulk of the system. To overcome this drawback, a single-phase, single-stage [no extra converter for voltage boost or maximum power point tracking (MPPT)], doubly grounded, transformer-less PV interface, based on the buck-boost principle, is presented. The configuration is compact and uses lesser components. Only one (undivided) PV source and one buck-boost inductor are used and shared between the two half cycles, which prevents asymmetrical operation and parameter mismatch problems. Total harmonic distortion and DC component of the current supplied to the grid is low, compared to existing topologies and conform to standards like IEEE 1547. A brief review of the existing, transformer-less, grid-connected inverter topologies is also included. It is demonstrated that, as compared to the split PV source topology, the proposed configuration is more effective in MPPT and array utilization. Design and analysis of the inverter in discontinuous conduction mode is carried out. Simulation and experimental results are presented.
A parameter-free hedging algorithm

We study the problem of decision-theoretic online learning (DTOL). Motivated by practical applications, we focus on DTOL when the number o f actions is very large. Previous algorithms for learning in this framework h ave a tunable learning rate parameter, and a barrier to using online-learning in pr actical applications is that it is not understood how to set this parameter optimally , particularly when the number of actions is large. In this paper, we offer a clean solution by proposing a novel a nd completely parameter-free algorithm for DTOL. We introduce a new notio n of regret, which is more natural for applications with a large number of actio ns. We show that our algorithm achieves good performance with respect to this ne w notion of regret; in addition, it also achieves performance close to that of the b est ounds achieved by previous algorithms with optimally-tuned parameters, a ccording to previous notions of regret.
A Survey of Artificial Intelligence for Prognostics

Integrated Systems Health Management includes as key elements fault detection, fault diagnostics, and failure prognostics. Whereas fault detection and diagnostics have been the subject of considerable emphasis in the Artificial Intelligence (AI) community in the past, prognostics has not enjoyed the same attention. The reason for this lack of attention is in part because prognostics as a discipline has only recently been recognized as a game-changing technology that can push the boundary of systems health management. This paper provides a survey of AI techniques applied to prognostics. The paper is an update to our previously published survey of data-driven prognostics.
Stanford's Multi-Pass Sieve Coreference Resolution System at the CoNLL-2011 Shared Task

This paper details the coreference resolution system submitted by Stanford at the CoNLL2011 shared task. Our system is a collection of deterministic coreference resolution models that incorporate lexical, syntactic, semantic, and discourse information. All these models use global document-level information by sharing mention attributes, such as gender and number, across mentions in the same cluster. We participated in both the open and closed tracks and submitted results using both predicted and gold mentions. Our system was ranked first in both tracks, with a score of 57.8 in the closed track and 58.3 in the open track.
Non-orthogonal Multiple Access (NOMA) with Successive Interference Cancellation for Future Radio Access

This paper presents our investigation of non-orthogonal multiple access (NOMA) as a novel and promising power-domain user multiplexing scheme for future radio access. Based on information theory, we can expect that NOMA with a successive interference canceller (SIC) applied to the receiver side will offer a better tradeoff between system efficiency and user fairness than orthogonal multiple access (OMA), which is widely used in 3.9 and 4G mobile communication systems. This improvement becomes especially significant when the channel conditions among the non-orthogonally multiplexed users are significantly different. Thus, NOMA can be expected to efficiently exploit the near-far effect experienced in cellular environments. In this paper, we describe the basic principle of NOMA in both the downlink and uplink and then present our proposed NOMA scheme for the scenario where the base station is equipped with multiple antennas. Simulation results show the potential system-level throughput gains of NOMA relative to OMA. key words: cellular system, non-orthogonal multiple access, superposition coding, successive interference cancellation
Online Social Networks: Threats and Solutions

Many online social network (OSN) users are unaware of the numerous security risks that exist in these networks, including privacy violations, identity theft, and sexual harassment, just to name a few. According to recent studies, OSN users readily expose personal and private details about themselves, such as relationship status, date of birth, school name, email address, phone number, and even home address. This information, if put into the wrong hands, can be used to harm users both in the virtual world and in the real world. These risks become even more severe when the users are children. In this paper, we present a thorough review of the different security and privacy risks, which threaten the well-being of OSN users in general, and children in particular. In addition, we present an overview of existing solutions that can provide better protection, security, and privacy for OSN users. We also offer simple-to-implement recommendations for OSN users, which can improve their security and privacy when using these platforms. Furthermore, we suggest future research directions.
A video game description language for model-based or interactive learning

We propose a powerful new tool for conducting research on computational intelligence and games. `PyVGDL' is a simple, high-level description language for 2D video games, and the accompanying software library permits parsing and instantly playing those games. The streamlined design of the language is based on defining locations and dynamics for simple building blocks, and the interaction effects when such objects collide, all of which are provided in a rich ontology. It can be used to quickly design games, without needing to deal with control structures, and the concise language is also accessible to generative approaches. We show how the dynamics of many classical games can be generated from a few lines of PyVGDL. The main objective of these generated games is to serve as diverse benchmark problems for learning and planning algorithms; so we provide a collection of interfaces for different types of learning agents, with visual or abstract observations, from a global or first-person viewpoint. To demonstrate the library's usefulness in a broad range of learning scenarios, we show how to learn competent behaviors when a model of the game dynamics is available or when it is not, when full state information is given to the agent or just subjective observations, when learning is interactive or in batch-mode, and for a number of different learning algorithms, including reinforcement learning and evolutionary search.
A survey on metrics for the evaluation of user simulations

User simulation is an important research area in the field of spoken dialogue systems (SDS) because collecting and annotating real human-machine interactions is often expensive and time consuming. However, such data are generally required for designing, training and assessing dialogue systems. User simulations are especially needed when using machine learning methods for optimising dialogue management strategies such as Reinforcement Learning, where the amount of data necessary for training is larger than existing corpora. The quality of the user simulation is therefore of crucial importance because it dramatically influences the results in terms of SDS performance analysis and the learnt strategy. Assessment of the quality of simulated dialogues and user simulation methods is an open issue and, although assessment metrics are required, there is no commonly adopted metric. In this paper, we give a survey of User Simulations Metrics in the literature, propose some extensions and discuss these metrics in terms of a list of desired features.
A broadband dual-polarized base station antenna element for European Digital Dividend, CDMA800 and GSM900 applications

In this paper, a novel broadband dual-polarized (slant ±45°) base station antenna element operating at 790–960 MHz is proposed. The antenna element consists of two pairs of symmetrical dipoles, four couples of baluns, a cricoid pedestal and two kinds of plastic fasteners. Specific shape metal reflector is also designed to achieve stable radiation pattern and high front-to-back ratio (FBR). All the simulated and measured results show that the proposed antenna element has wide impedance bandwidth (about 19.4%), low voltage standing wave ratio (VSWR < 1.4) and high port to port isolation (S21 < −25 dB) at the whole operating frequency band. Stable horizontal half-power beam width (HPBW) with 65°±4.83° and high gain (> 9.66 dBi) are also achieved. The proposed antenna element fabricated by integrated metal casting technology has great mechanical properties such as compact structure, low profile, good stability, light weight and easy to fabricate. Due to its good electrical and mechanical characteristics, the antenna element is suitable for European Digital Dividend, CDMA800 and GSM900 bands in base station antenna of modern mobile communication.