nils-herrmann/clean_scirepeval huggingface dataset:
Concepts and Meaning

It certainly appears that there should be a relationship between concepts and meaning, but it is not entirely clear what this relation is. We shall assume that concepts are people's psychological representations of categories (e.g., apple, chair); whereas meanings are people's understandings of words and other linguistic expressions (e.g., "apple", "large chair"). 1 Currently, many cognitive scientists, especially psychologists, believe that concepts and meanings are at least roughly equivalent, with the meaning of an expression being its conceptual representation in human knowledge. From identifying the content of a concept, the meaning of the associated expression follows. Malt (1991) and Murphy (1991) review this position and various alternatives. In our paper, we shall argue that concepts and meanings differ substantially. Although they are related in important ways, the relationship is one of complementarity, not equivalence. To reach our conclusion, we shall review standard assumptions about concepts and meaning, challenge these assumptions, and present alternatives. The assumptions that we challenge, and that organize the paper, are: (1) propositional expressions represent concepts; (2) concepts are prototypes of exemplars; (3) concepts are decontextualized and universal in scope; (4) the meanings of words are concepts. We shall argue instead that perceptual symbols represent concepts; concepts are models for types of individuals in world models; concepts are contextualized and local in scope to situations; word meanings use concepts but are not concepts. Our tack in exploring these issues is to develop a theory of concepts and memory in the first three sections of the paper. In the spirit of cognitive linguistics, this theory utilizes perceptual representations and situational knowledge extensively. The final section compares concepts, as defined in our theory, with relatively well-accepted notions about meaning. We shall then assess whether concepts are equivalent to meaning, or whether they exhibit some other relationship. Please note that the bulk of this 'working paper' represents a theory of concepts and memory in the early stages of development. We are the first to acknowledge that the majority of our claims lack strong empirical support and that increased precision is necessary at the theoretical level. Although evidence exists for some aspects of our theory, many aspects rest on more of a rationalist analysis of how a cognitive system might compute concepts and meaning. This paper outlines our theory in its current form so that we can begin to examine its claims empirically and implement it computationally. A variety of experimental …
Recurrent Neural Networks with Attention for Genre Classification

Using the GTZAN Genre Collection , we start with a set of 1000 30 second song excerpts subdivided into 10 pre-classified genres: Blues, Classical, Country, Disco, Hip-Hop, Jazz, Metal, Pop, Reggae, and Rock. We downsampled to 4000 Hz, and further split each excerpt into 5-second clips For each clip, we compute a spectrogram using Fast Fourier Transforms, giving us 22 timestep vectors of dimensionality 513 for each clip. Spectrograms separate out component audio signals at different frequencies from a raw audio signal, and provide us with a tractable, loosely structured feature set for any given audio clip that is well-suited for deep learning techniques.
Modeling Li-ion Battery Capacity Depletion in a Particle Filtering Framework

This paper presents an empirical model to describe battery behavior during individual discharge cycles as well as over its cycle life. The basis for the form of the model has been linked to the internal processes of the battery and validated using experimental data. Subsequently, the model has been used in a Particle Filtering framework to make predictions of remaining useful life for individual discharge cycles as well as for cycle life. The prediction performance was found to be satisfactory as measured by performance metrics customized for prognostics. The work presented here provides initial steps towards a comprehensive health management solution for energy storage devices.
Photo-based question answering

Photo-based question answering is a useful way of finding information about physical objects. Current question answering (QA) systems are text-based and can be difficult to use when a question involves an object with distinct visual features. A photo-based QA system allows direct use of a photo to refer to the object. We develop a three-layer system architecture for photo-based QA that brings together recent technical achievements in question answering and image matching. The first, template-based QA layer matches a query photo to online images and extracts structured data from multimedia databases to answer questions about the photo. To simplify image matching, it exploits the question text to filter images based on categories and keywords. The second, information retrieval QA layer searches an internal repository of resolved photo-based questions to retrieve relevant answers. The third, human-computation QA layer leverages community experts to handle the most difficult cases. A series of experiments performed on a pilot dataset of 30,000 images of books, movie DVD covers, grocery items, and landmarks demonstrate the technical feasibility of this architecture. We present three prototypes to show how photo-based QA can be built into an online album, a text-based QA, and a mobile application.
On the effect of data set size on bias and variance in classification learning

With the advent of data mining, machine learning has come of age and is now a critical technology in many businesses. However, machine learning evolved in a different research context to that in which it now finds itself employed. A particularly important problem in the data mining world is working effectively with large data sets. However, most machine learning research has been conducted in the context of learning from very small data sets. To date most approaches to scaling up machine learning to large data sets have attempted to modify existing algorithms to deal with large data sets in a more computationally efficient and effective manner. But is this necessarily the best method? This paper explores the possibility of designing algorithms specifically for large data sets. Specifically, the paper looks at how increasing data set size affects bias and variance error decompositions for classification algorithms. Preliminary results of experiments to determine these effects are presented, showing that, as hypothesised variance can be expected to decrease as training set size increases. No clear effect of training set size on bias was observed. These results have profound implications for data mining from large data sets, indicating that developing effective learning algorithms for large data sets is not simply a matter of finding computationally efficient variants of existing learning algorithms.
Optimizing NoSQL DB on Flash: A Case Study of RocksDB

A solid-state drive (SSD) gains fast I/O speed and is becoming an ideal replacement for traditional rotating storage. However, its speed and responsiveness heavily depend on internal fragmentation. With a high degree of fragmentation, an SSD may experience sharp performance degradation. Hence, minimizing fragmentation in the SSD is an effective way to sustain its high performance. In this paper, we propose an innovative file data placement strategy for Rocks DB, a widely used embedded NoSQL database. The proposed strategy steers data to a write unit exposed by an SSD according to predicted data lifetime. By placing data with similar lifetime in the same write unit, fragmentation in the SSD is controlled at the time of data write. We evaluate our proposed strategy using the Yahoo! Cloud Serving Benchmark. Our experimental results demonstrate that the proposed strategy improves the Rocks DB performance significantly: the throughput can be increased by up to 41%, 99.99%ile latency reduced by 59%, and SSD lifetime extended by up to 18%.
Modeling and understanding students' off-task behavior in intelligent tutoring systems

We present a machine-learned model that can automatically detect when a student using an intelligent tutoring system is off-task, i.e., engaged in behavior which does not involve the system or a learning task. This model was developed using only log files of system usage (i.e. no screen capture or audio/video data). We show that this model can both accurately identify each student's prevalence of off-task behavior and can distinguish off-task behavior from when the student is talking to the teacher or another student about the subject matter. We use this model in combination with motivational and attitudinal instruments, developing a profile of the attitudes and motivations associated with off-task behavior, and compare this profile to the attitudes and motivations associated with other behaviors in intelligent tutoring systems. We discuss how the model of off-task behavior can be used within interactive learning environments which respond to when students are off-task.
Learning to Write with Cooperative Discriminators

Despite their local fluency, long-form text generated from RNNs is often generic, repetitive, and even self-contradictory. We propose a unified learning framework that collectively addresses all the above issues by composing a committee of discriminators that can guide a base RNN generator towards more globally coherent generations. More concretely, discriminators each specialize in a different principle of communication, such as Grice’s maxims, and are collectively combined with the base RNN generator through a composite decoding objective. Human evaluation demonstrates that text generated by our model is preferred over that of baselines by a large margin, significantly enhancing the overall coherence, style, and information of the generations.
Contextual deep CNN based hyperspectral classification

In this paper, we describe a novel deep convolutional neural networks (CNN) based approach called contextual deep CNN that can jointly exploit spatial and spectral features for hyperspectral image classification. The contextual deep CNN first concurrently applies multiple 3-dimensional local convolutional filters with different sizes jointly exploiting spatial and spectral features of a hyperspectral image. The initial spatial and spectral feature maps obtained from applying the variable size convolutional filters are then combined together to form a joint spatio-spectral feature map. The joint feature map representing rich spectral and spatial properties of the hyperspectral image is then fed through fully convolutional layers that eventually predict the corresponding label of each pixel vector. The proposed approach is tested on two benchmark datasets: the Indian Pines dataset and the Pavia University scene dataset. Performance comparison shows enhanced classification performance of the proposed approach over the current state of the art on both datasets.
Exponentially Weighted Moving Average Charts for Detecting Concept Drift

0167-8655/$ see front matter 2011  2075940990; E-mail addresses: gordon.ross03@ic.ac.uk, gr203@i ic.ac.uk (N.M. Adams), d.tasoulis@ic.ac.uk (D.K. Tas Hand). Classifying streaming data requires the development of methods which are computationally efficient and able to cope with changes in the underlying distribution of the stream, a phenomenon known in the literature as concept drift. We propose a new method for detecting concept drift which uses an exponentially weighted moving average (EWMA) chart to monitor the misclassification rate of an streaming classifier. Our approach is modular and can hence be run in parallel with any underlying classifier to provide an additional layer of concept drift detection. Moreover our method is computationally efficient with overhead O(1) and works in a fully online manner with no need to store data points in memory. Unlike many existing approaches to concept drift detection, our method allows the rate of false positive detections to be controlled and kept constant over time.
Natural Image Denoising with Convolutional Networks

We present an approach to low-level vision that combines two main ideas: the use of convolutional networks as an image processing architecture and an unsupervised learning procedure that synthesizes training samples from specific noise models. We demonstrate this approach on the challenging problem of natural image denoising. Using a test set with a hundred natural images, we find that convolutional networks provide comparable and in some cases superior performance to state of the art wavelet and Markov random field (MRF) methods. Moreover, we find that a convolutional network offers similar performance in the blind denoising setting as compared to other techniques in the non-blind setting. We also show how convolutional networks are mathematically related to MRF approaches by presenting a mean field theory for an MRF specially designed for image denoising. Although these approaches are related, convolutional networks avoid computational difficulties in MRF approaches that arise from probabilistic learning and inference. This makes it possible to learn image processing architectures that have a high degree of representational power (we train models with over 15,000 parameters), but whose computational expense is significantly less than that associated with inference in MRF approaches with even hundreds of parameters.
Causal inference and the data-fusion problem.

We review concepts, principles, and tools that unify current approaches to causal analysis and attend to new challenges presented by big data. In particular, we address the problem of data fusion-piecing together multiple datasets collected under heterogeneous conditions (i.e., different populations, regimes, and sampling methods) to obtain valid answers to queries of interest. The availability of multiple heterogeneous datasets presents new opportunities to big data analysts, because the knowledge that can be acquired from combined data would not be possible from any individual source alone. However, the biases that emerge in heterogeneous environments require new analytical tools. Some of these biases, including confounding, sampling selection, and cross-population biases, have been addressed in isolation, largely in restricted parametric models. We here present a general, nonparametric framework for handling these biases and, ultimately, a theoretical solution to the problem of data fusion in causal inference tasks.
Investigating enterprise systems adoption: uncertainty avoidance, intrinsic motivation, and the technology acceptance model

24 November 2004  6 March 2005 2nd Revision: 22  5 May 2005  Enterprise systems are gaining interest from both practitioners and researchers because of their potential linkages to organizational and individual user’s productivity. Information systems (IS) researchers have been investigating the implementation and adoption issues of enterprise systems based on the organizational IS management perspectives. However, there are few papers that investigate enterprise systems management and implementation issues based on the informal control mechanisms, although the enterprise systems are control tools in the organization. Specifically, this paper applies Enterprise Resource Planning (ERP) adoption and implementation to the informal controls, such as cultural control and self-control, which can be viewed as a tacit perspective in knowledge management. Uncertainty avoidance and perceived enjoyment are used as informal controls in the ERP implementation in this paper, and are linked to the technology acceptance variables to investigate the relationships among them. Sociotechnical design, organizational control mechanism, knowledge management, and individual motivation are reviewed to support this potential linkage in the model. Field data via the online survey of ERP systems user group (n1⁄4 101) are analyzed with partial least squares and supported our hypotheses. Uncertainty avoidance cultural control and intrinsic motivation as self-control are the important antecedents of ERP systems adoption. Furthermore, the result helps the systems manager understand that informal controls should be applied to the ERP systems implementation to enhance tacit and social aspects of IS management.  (2005) 14, 150–161. doi:10.1057/palgrave.ejis.3000532
Personalised Query Suggestion for Intranet Search with Temporal User Profiling

Recent research has shown the usefulness of using collective user interaction data (e.g., query logs) to recommend query modification suggestions for Intranet search. However, most of the query suggestion approaches for Intranet search follow an ``one size fits all'' strategy, whereby different users who submit an identical query would get the same query suggestion list. This is problematic, as even with the same query, different users may have different topics of interest, which may change over time in response to the user's interaction with the system.
 We address the problem by proposing a personalised query suggestion framework for Intranet search. For each search session, we construct two temporal user profiles: a click user profile using the user's clicked documents and a query user profile using the user's submitted queries. We then use the two profiles to re-rank the non-personalised query suggestion list returned by a state-of-the-art query suggestion method for Intranet search. Experimental results on a large-scale query logs collection show that our personalised framework significantly improves the quality of suggested queries.
Methods for evaluating games: how to measure usability and user experience in games?

This workshop addresses current needs in the games developers' community and games industry to evaluate the overall user experience of games. New forms of interaction techniques, like gestures, eye-tracking or even bio-physiological input and feedback present the limits of current evaluation methods for user experience, and even standard usability evaluation used during game development. This workshop intends to bring together practitioners and researchers sharing their experiences using methods from HCI to explore and measure usability and user experience in games. To this workshop we also invite contributions from other disciplines (especially from the games industry) showing new concepts for user experience evaluation.
Change-point monitoring for the detection of DoS attacks

This paper presents a simple and robust mechanism, called change-point monitoring (CPM), to detect denial of service (DoS) attacks. The core of CPM is based on the inherent network protocol behavior and is an instance of the sequential change point detection. To make the detection mechanism insensitive to sites and traffic patterns, a nonparametric cumulative sum (CUSUM) method is applied, thus making the detection mechanism robust, more generally applicable, and its deployment much easier. CPM does not require per-flow state information and only introduces a few variables to record the protocol behaviors. The statelessness and low computation overhead of CPM make itself immune to any flooding attacks. As a case study, the efficacy of CPM is evaluated by detecting a SYN flooding attack - the most common DoS attack. The evaluation results show that CPM has short detection latency and high detection accuracy
Interconnections in multi-core architectures: understanding mechanisms, overheads and scaling

This paper examines the area, power, performance, and design issues for the on-chip interconnects on a chip multiprocessor, attempting to present a comprehensive view of a class of interconnect architectures. It shows that the design choices for the interconnect have significant effect on the rest of the chip, potentially consuming a significant fraction of the real estate and power budget. This research shows that designs that treat interconnect as an entity that can be independently architected and optimized would not arrive at the best multi-core design. Several examples are presented showing the need for careful co-design. For instance, increasing interconnect bandwidth requires area that then constrains the number of cores or cache sizes, and does not necessarily increase performance. Also, shared level-2 caches become significantly less attractive when the overhead of the resulting crossbar is accounted for. A hierarchical bus structure is examined which negates some of the performance costs of the assumed base-line architecture.
Intelligent Search of Correlated Alarms from Database containing Noise Data

Alarm correlation plays an important role in improving the service and reliability in modern telecommunication networks. Most previous research of alarm correlation didnt consider the effects of noise data in the database. This paper focuses on the method of discovering alarm correlation rules from the database containing noise data. We firstly define two parameters Win_freq and Win_add as the measures of noise data and then present the Robust_search algorithm to solve the problem. At different size of Win_freq and Win_add, the experiments on alarm database containing noise data show that the Robust_search Algorithm can discover more rules with the bigger size of Win_add. We also compare two different interestingness measures of confidence and correlation by experiments.
Lithium-Ion Battery Aging Experiments at Subzero Temperatures and Model Development for Capacity Fade Estimation

Lithium-ion (Li-ion) batteries widely used in electric vehicles (EVs) and hybrid EVs (HEVs) are insufficient for vehicle use after they have degraded to 70% to 80% of their original capacity. Battery lifespan is a large consideration when designing battery packs for EVs/HEVs. Aging mechanisms, such as metal dissolution, growth of the passivated surface film layer on the electrodes, and loss of both recyclable lithium ions, affect the longevity of the Li-ion battery at high-temperature operations. Even vehicle maneuvers at low temperatures <inline-formula> <tex-math notation="LaTeX">$(T < \mbox{0}\ ^\circ\mbox{C})$</tex-math></inline-formula> contribute to battery lifetime degradation, owing to the anode electrode vulnerability to other degradation mechanisms such as lithium plating. Nowadays, only a few battery thermal management schemes have properly considered low-temperature degradation. This is due to the lack of studies on aging of Li-ion batteries at subzero temperature. This paper investigates how load cycle and calendar life properties affect the lifetime and aging processes of Li-ion cells at low temperatures. Accelerated aging tests were used to determine the effect of the ambient temperature on the performance of three 100-Ah LiFeMnP0<sub>4</sub> Li-ion cells. Two of them were aged through a normalized driving cycle at two temperature tests ( <inline-formula> <tex-math notation="LaTeX">$-\mbox{20}\ ^\circ\mbox{C}$</tex-math></inline-formula> and 25 °C). The calendar test was carried out on one single battery at –20 °C and mid-range of state of charge (50%). Their capacities were continuously measured every two or three days. An aging model is developed and added to a preliminary single-cell electrothermal model to establish, in future works, a thermal strategy capable of predicting how the cell ages. This aging model was then validated by comparing its predictions with the aging data obtained from a cycling test at 0 °C.