nils-herrmann/clean_scirepeval huggingface dataset:
On-Line Portfolio Selection: A Survey

Online portfolio selection is a fundamental problem in computational finance, which has been extensively studied across several research communities, including finance, statistics, artificial intelligence, machine learning, and data mining. This article aims to provide a comprehensive survey and a structural understanding of online portfolio selection techniques published in the literature. From an online machine learning perspective, we first formulate online portfolio selection as a sequential decision problem, and then we survey a variety of state-of-the-art approaches, which are grouped into several major categories, including benchmarks, Follow-the-Winner approaches, Follow-the-Loser approaches, Pattern-Matching--based approaches, and Meta-Learning Algorithms. In addition to the problem formulation and related algorithms, we also discuss the relationship of these algorithms with the capital growth theory so as to better understand the similarities and differences of their underlying trading ideas. This article aims to provide a timely and comprehensive survey for both machine learning and data mining researchers in academia and quantitative portfolio managers in the financial industry to help them understand the state of the art and facilitate their research and practical applications. We also discuss some open issues and evaluate some emerging new trends for future research.
SENTIMENT ANALYSIS OF RESTAURANT REVIEWS USING HYBRID CLASSIFICATION METHOD

The area of sentiment mining (also called sentiment extraction, opinion mining, opinion extraction, sentiment analysis, etc.) has seen a large increase in academic interest in the last few years. Researchers in the areas of natural language processing, data mining, machine learning, and others have tested a variety of methods of automating the sentiment analysis process. In this research work, new hybrid classification method is proposed based on coupling classification methods using arcing classifier and their performances are analyzed in terms of accuracy. A Classifier ensemble was designed using Naïve Bayes (NB), Support Vector Machine (SVM) and Genetic Algorithm (GA). In the proposed work, a comparative study of the effectiveness of ensemble technique is made for sentiment classification. The feasibility and the benefits of the proposed approaches are demonstrated by means of restaurant review that is widely used in the field of sentiment classification. A wide range of comparative experiments are conducted and finally, some in-depth discussion is presented and conclusions are drawn about the effectiveness of ensemble technique for sentiment classification.  Accuracy, Arcing classifier, Genetic Algorithm (GA). Naïve Bayes (NB), Sentiment Mining, Support Vector Machine (SVM)
Trace-driven simulation of multithreaded applications

Over the past few years, computer architecture research has moved towards execution-driven simulation, due to the inability of traces to capture timing-dependent thread execution interleaving. However, trace-driven simulation has many advantages over execution-driven that are being missed in multithreaded application simulations. We present a methodology to properly simulate multithreaded applications using trace-driven environments. We distinguish the intrinsic application behavior from the computation for managing parallelism. Application traces capture the intrinsic behavior in the sections of code that are independent from the dynamic multithreaded nature, and the points where parallelism-management computation occurs. The simulation framework is composed of a trace-driven simulation engine and a dynamic-behavior component that implements the parallelism-management operations for the application. Then, at simulation time, these operations are reproduced by invoking their implementation in the dynamic-behavior component. The decisions made by these operations are based on the simulated architecture, allowing to dynamically reschedule sections of code taken from the trace to the target simulated components. As the captured sections of code are independent from the parallel state of the application, they can be simulated on the trace-driven engine, while the parallelism-management operations, that require to be re-executed, are carried out by the execution-driven component, thus achieving the best of both trace- and execution-driven worlds. This simulation methodology creates several new research opportunities, including research on scheduling and other parallelism-management techniques for future architectures, and hardware support for programming models.
A Data-Driven Sparse GLM for fMRI Analysis Using Sparse Dictionary Learning With MDL Criterion

We propose a novel statistical analysis method for functional magnetic resonance imaging (fMRI) to overcome the drawbacks of conventional data-driven methods such as the independent component analysis (ICA). Although ICA has been broadly applied to fMRI due to its capacity to separate spatially or temporally independent components, the assumption of independence has been challenged by recent studies showing that ICA does not guarantee independence of simultaneously occurring distinct activity patterns in the brain. Instead, sparsity of the signal has been shown to be more promising. This coincides with biological findings such as sparse coding in V1 simple cells, electrophysiological experiment results in the human medial temporal lobe, etc. The main contribution of this paper is, therefore, a new data driven fMRI analysis that is derived solely based upon the sparsity of the signals. A compressed sensing based data-driven sparse generalized linear model is proposed that enables estimation of spatially adaptive design matrix as well as sparse signal components that represent synchronous, functionally organized and integrated neural hemodynamics. Furthermore, a minimum description length (MDL)-based model order selection rule is shown to be essential in selecting unknown sparsity level for sparse dictionary learning. Using simulation and real fMRI experiments, we show that the proposed method can adapt individual variation better compared to the conventional ICA methods.
Building comprehensible customer churn prediction models with advanced rule induction techniques

Customer churn prediction models aim to detect customers with a high propensity to attrite. Predictive accuracy, comprehensibility, and justifiability are three key aspects of a churn prediction model. An accurate model permits to correctly target future churners in a retention marketing campaign, while a comprehensible and intuitive rule-set allows to identify the main drivers for customers to churn, and to develop an effective retention strategy in accordance with domain knowledge. This paper provides an extended overview of the literature on the use of data mining in customer churn prediction modeling. It is shown that only limited attention has been paid to the comprehensibility and the intuitiveness of churn prediction models. Therefore, two novel data mining techniques are applied to churn prediction modeling, and benchmarked to traditional rule induction techniques such as C4.5 and RIPPER. Both AntMiner+ and ALBA are shown to induce accurate as well as comprehensible classification rule-sets. AntMiner+ is a high performing data mining technique based on the principles of Ant Colony Optimization that allows to include domain knowledge by imposing monotonicity constraints on the final rule-set. ALBA on the other hand combines the high predictive accuracy of a non-linear support vector machine model with the comprehensibility of the rule-set format. The results of the benchmarking experiments show that ALBA improves learning of classification techniques, resulting in comprehensible models with increased performance. AntMiner+ results in accurate, comprehensible, but most importantly justifiable models, unlike the other modeling techniques included in this study.
A Privacy-Preserving Framework for Integrating Person-Specific Databases

Many organizations capture personal information, but the quantity of records needed to detect statistically significant patterns is often beyond the grasp of a single data collector. In the biomedical realm, this problem has pressed regulatory agencies to require funded investigators to share research-derived data to public repositories. The challenge; however, is that shared records must not reveal the identity of the subjects. In this paper, we extend a secure framework in which data holders contribute and query encrypted person-specific data stored on a third party’s server. Specifically, we develop protocols that enable data holders to merge personal records, thus creating larger profiles and diminishing duplication. The repository administrator can merge records via encrypted identifiers without decrypting or inferring the contents of the joined records. Our model is more practical than prior secure join methods because each data holder needs only a single interaction with the central repository. We further present an extension to the protocol that permits the revelation of k-anonymous demographics, such that the administrator can perform joins more efficiently with the guarantee that each record can be linked to no less than k individuals in the population. We prove the privacy preserving features of our protocols and experimentally evaluate their efficiency in a real world Census dataset.
Examining the Impact of Ranking on Consumer Behavior and Search Engine Revenue

In this paper, we study the effects of three different kinds of search engine rankings on consumer behavior and search engine revenues: direct ranking effect, interaction effect between ranking and product ratings, and personalized ranking effect. We combine a hierarchical Bayesian model estimated on approximately one million online sessions from Travelocity, together with randomized experiments using a real-world hotel search engine application. Our archival data analysis and randomized experiments are consistent in demonstrating the following: (1) a consumer utility-based ranking mechanism can lead to a significant increase in overall search engine revenue. (2) Significant interplay occurs between search engine ranking and product ratings. An inferior position on the search engine affects “higher-class” hotels more adversely. On the other hand, hotels with a lower customer rating are more likely to benefit from being placed on the top of the screen. These findings illustrate that product search engines could benefit from directly incorporating signals from social media into their ranking algorithms. (3) Our randomized experiments also reveal that an “active” (wherein users can interact with and customize the ranking algorithm) personalized ranking system leads to higher clicks but lower purchase propensities and lower search engine revenue compared to a “passive” (wherein users cannot interact with the ranking algorithm) personalized ranking system. This result suggests that providing more information during the decision-making process may lead to fewer consumer purchases because of information overload. Therefore, product search engines should not adopt personalized ranking systems by default. Overall, our study unravels the economic impact of ranking and its interaction with social media on product search engines.
Bridging the Gap Between Value and Policy Based Reinforcement Learning

We establish a new connection between value and policy based reinforcement learning (RL) based on a relationship between softmax temporal value consistency and policy optimality under entropy regularization. Specifically, we show that softmax consistent action values correspond to optimal entropy regularized policy probabilities along any action sequence, regardless of provenance. From this observation, we develop a new RL algorithm, Path Consistency Learning (PCL), that minimizes a notion of soft consistency error along multi-step action sequences extracted from both onand off-policy traces. We examine the behavior of PCL in different scenarios and show that PCL can be interpreted as generalizing both actor-critic and Q-learning algorithms. We subsequently deepen the relationship by showing how a single model can be used to represent both a policy and the corresponding softmax state values, eliminating the need for a separate critic. The experimental evaluation demonstrates that PCL significantly outperforms strong actor-critic and Q-learning baselines across several
Understanding Emergence and Outcomes of Information Privacy Concerns: a Case of Facebook

Drawing on content analysis of user responses to the revisions in the Facebook Privacy Policy, this study develops a process model to explain emergence and outcome processes of users’ information privacy concerns in an online social networking context. The first phase of the model proposes three broad categories of informational practices – collection and storage; processing and use; and dissemination of personal data—associated with users’ information privacy concerns. This phase also identifies the conditions under which proposed practices are attributed as privacy issues. The second phase of the model describes outcomes of perceived privacy issues by proposing users’ affective and behavioral responses. The findings provide evidence for, (1) the important role of trigger conditions in emergence of users’ information privacy concerns, (2) the gap between privacy issues that are perceived by users and identified by domain experts, (3) the uniqueness of online social networking context in providing distinct privacy challenges.
Control of a visual keyboard using an electrocorticographic brain-computer interface.

Brain-computer interfaces (BCIs) are devices that enable severely disabled people to communicate and interact with their environments using their brain waves. Most studies investigating BCI in humans have used scalp EEG as the source of electrical signals and focused on motor control of prostheses or computer cursors on a screen. The authors hypothesize that the use of brain signals obtained directly from the cortical surface will more effectively control a communication/spelling task compared to scalp EEG.



A total of 6 patients with medically intractable epilepsy were tested for the ability to control a visual keyboard using electrocorticographic (ECOG) signals. ECOG data collected during a P300 visual task paradigm were preprocessed and used to train a linear classifier to subsequently predict the intended target letters.



The classifier was able to predict the intended target character at or near 100% accuracy using fewer than 15 stimulation sequences in 5 of the 6 people tested. ECOG data from electrodes outside the language cortex contributed to the classifier and enabled participants to write words on a visual keyboard.



This is a novel finding because previous invasive BCI research in humans used signals exclusively from the motor cortex to control a computer cursor or prosthetic device. These results demonstrate that ECOG signals from electrodes both overlying and outside the language cortex can reliably control a visual keyboard to generate language output without voice or limb movements.
Narrative abstraction model for story-oriented video

TV program review services, especially drama review services, are one of the most popular video on demand services on the Web. In this paper, we propose a novel video abstraction model for a review service of story-oriented video such as dramas. In a drama review service, viewers want to understand the story in a short time and service providers want to provide video abstracts at minimum cost. The proposed model enables the automatic creation of a video abstract that still allows viewers to understand the overall story of the source video. Also, the model has a flexible structure so that the duration of an abstract can be adjusted depending on the requirements given by viewers. We get clues for human understanding of a story from scenario writing rules and editorial techniques which are popularly used in the process of video producing. We have implemented the proposed model and successfully applied it to several TV dramas.
Textons, Contours and Regions: Cue Integration in Image Segmentation

This paper makes two contributions. It provides (1) an operational definition of textons, the putative elementary units of texture perception, and (2) an algorithm for partitioning the image into disjoint regions of coherent brigh tness and texture, where boundaries of regions are defined by peaks in contour orientation energy and differences in texton densities across the contour. Julesz introduced the term texton, analogous to a phoneme in speech recognition, but did not provide an operational definition for gray-level images. Here we re-invent textons as frequently co-occurring combinations of orient ed linear filter outputs. These can be learned using a K-means approach. By mapping each pixel to its nearest texton, the image can be analyzed into texton channels, each of which is a point set where discrete techniques such as Voronoi diagrams become applicable. Local histograms of texton frequencies can be used with a 2 test for significant differences to find texture boundaries. Natural images contain both textured and untextured regions, so we combine this cue with that of the presence of peaks of contour energy derived from outputs of oddand even-symmetric oriented Gaussian derivative filters. Each of these cues has a domain of applicability, so to facilitate cue combination we introduce a gating operator based on a statistical test for isotropy of Delaunay neighbors. Havin g obtained a local measure of how likely two nearby pixels are to belong to the same region, we use the spectral graph theoretic framework of normalized cuts to find partitions of the image into regions of coherent texture and brightness. Experimental results on a wide range of images are shown.
Using Crowdsourcing to Improve Profanity Detection

Profanity detection is often thought to be an easy task. However, past work has shown that current, list-based systems are performing poorly. They fail to adapt to evolving profane slang, identify profane terms that have been disguised or only partially censored (e.g., @ss, f$#%) or intentionally or unintentionally misspelled (e.g., biatch, shiiiit). For these reasons, they are easy to circumvent and have very poor recall. Secondly, they are a one-size fits all solution – making assumptions that the definition, use and perceptions of profane or inappropriate holds across all contexts. In this article, we present work that attempts to move beyond list-based profanity detection systems by identifying the context in which profanity occurs. The proposed system uses a set of comments from a social news site labeled by Amazon Mechanical Turk workers for the presence of profanity. This system far surpasses the performance of listbased profanity detection techniques. The use of crowdsourcing in this task suggests an opportunity to build profanity detection systems tailored to sites and communities.
Context Sensitive Entity Linking of Search Queries in Enterprise Knowledge Graphs

Fast and correct identification of named entities in queries is crucial for query understanding and to map the query to information in structured knowledge base. Most of the existing work have focused on utilizing search logs and manually curated knowledge bases for entity linking and often involve complex graph operations and are generally slow. We describe a simple, yet fast and accurate, probabilistic entity-linking algorithm used in enterprise settings where automatically constructed, domain specific Knowledge Graphs are used. In addition to the linked graph structure, textual evidence from the domain specific corpus is also utilized to improve the performance.
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models

Neural sequence models are widely used to model time-series data. Equally ubiquitous is the usage of beam search (BS) as an approximate inference algorithm to decode output sequences from these models. BS explores the search space in a greedy left-right fashion retaining only the top B candidates. This tends to result in sequences that differ only slightly from each other. Producing lists of nearly identical sequences is not only computationally wasteful but also typically fails to capture the inherent ambiguity of complex AI tasks. To overcome this problem, we propose Diverse Beam Search (DBS), an alternative to BS that decodes a list of diverse outputs by optimizing a diversity-augmented objective. We observe that our method not only improved diversity but also finds better top 1 solutions by controlling for the exploration and exploitation of the search space. Moreover, these gains are achieved with minimal computational or memory overhead compared to beam search. To demonstrate the broad applicability of our method, we present results on image captioning, machine translation, conversation and visual question generation using both standard quantitative metrics and qualitative human studies. We find that our method consistently outperforms BS and previously proposed techniques for diverse decoding from neural sequence models.
Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting

Inspired by how humans summarize long documents, we propose an accurate and fast summarization model that first selects salient sentences and then rewrites them abstractively (i.e., compresses and paraphrases) to generate a concise overall summary. We use a novel sentence-level policy gradient method to bridge the nondifferentiable computation between these two neural networks in a hierarchical way, while maintaining language fluency. Empirically, we achieve the new state-of-theart on all metrics (including human evaluation) on the CNN/Daily Mail dataset, as well as significantly higher abstractiveness scores. Moreover, by first operating at the sentence-level and then the word-level, we enable parallel decoding of our neural generative model that results in substantially faster (10-20x) inference speed as well as 4x faster training convergence than previous long-paragraph encoder-decoder models. We also demonstrate the generalization of our model on the test-only DUC2002 dataset, where we achieve higher scores than a state-of-the-art model.
A Stochastic Graph Evolution Framework for Robust Multi-target Tracking

Maintaining the stability of tracks on multiple targets in video over extended time periods remains a challenging problem. A few methods which have recently shown encouraging results in this direction rely on learning context models or the availability of training data. However, this may not be feasible in many application scenarios. Moreover, tracking methods should be able to work across different scenarios (e.g. multiple resolutions of the video) making such context models hard to obtain. In this paper, we consider the problem of long-term tracking in video in application domains where context information is not available a priori, nor can it be learned online. We build our solution on the hypothesis that most existing trackers can obtain reasonable short-term tracks (tracklets). By analyzing the statistical properties of these tracklets, we develop associations between them so as to come up with longer tracks. This is achieved through a stochastic graph evolution step that considers the statistical properties of individual tracklets, as well as the statistics of the targets along each proposed long-term track. On multiple real-life video sequences spanning low and high resolution data, we show the ability to accurately track over extended time periods (results are shown on many minutes of continuous video).