nils-herrmann/clean_scirepeval huggingface dataset:
Automatically Designing CNN Architectures Using Genetic Algorithm for Image Classification

Convolutional Neural Networks (CNNs) have gained a remarkable success on many real-world problems in recent years. However, the performance of CNNs is highly relied on their architectures. For some state-of-the-art CNNs, their architectures are hand-crafted with expertise in both CNNs and the investigated problems. To this end, it is difficult for researchers, who have no extended expertise in CNNs, to explore CNNs for their own problems of interest. In this paper, we propose an automatic architecture design method for CNNs by using genetic algorithms, which is capable of discovering a promising architecture of a CNN on handling image classification tasks. The proposed algorithm does not need any pre-processing before it works, nor any post-processing on the discovered CNN, which means it is completely automatic. The proposed algorithm is validated on widely used benchmark datasets, by comparing to the state-of-the-art peer competitors covering eight manually designed CNNs, four semi-automatically designed CNNs and additional four automatically designed CNNs. The experimental results indicate that the proposed algorithm achieves the best classification accuracy consistently among manually and automatically designed CNNs. Furthermore, the proposed algorithm also shows the competitive classification accuracy to the semi-automatic peer competitors, while reducing 10 times of the parameters. In addition, on the average the proposed algorithm takes only one percentage of computational resource compared to that of all the other architecture discovering algorithms. Experimental codes and the discovered architectures along with the trained weights are made public to the interested readers.
Detection of Political Manipulation in Online Communities through Measures of Effort and Collaboration

Online social media allow users to interact with one another by sharing opinions, and these opinions have a critical impact on the way readers think and behave. Accordingly, an increasing number of <i>manipulators</i> deliberately spread messages to influence the public, often in an organized manner. In particular, political manipulation—manipulation of opponents to win political advantage—can result in serious consequences: antigovernment riots can break out, leading to candidates’ defeat in an election. A few approaches have been proposed to detect such manipulation based on the level of social interaction (i.e., manipulators actively post opinions but infrequently befriend and reply to other users). However, several studies have shown that the interactions can be forged at a low cost and thus may not be effective measures of manipulation.
 To go one step further, we collect a dataset for real, large-scale political manipulation, which consists of opinions found on Internet forums. These opinions are divided into manipulators and nonmanipulators. Using this collection, we demonstrate that manipulators inevitably work hard, in teams, to quickly influence a large audience. With this in mind, it could be said that a high level of collaborative efforts strongly indicates manipulation. For example, a group of manipulators may jointly post numerous opinions with a consistent theme and selectively recommend the same, well-organized opinion to promote its rank. We show that the effort measures, when combined with a supervised learning algorithm, successfully identify greater than 95% of the manipulators. We believe that the proposed method will help system administrators to accurately detect manipulators in disguise, significantly decreasing the intensity of manipulation.
Deep RNNs Encode Soft Hierarchical Syntax

We present a set of experiments to demonstrate that deep recurrent neural networks (RNNs) learn internal representations that capture soft hierarchical notions of syntax from highly varied supervision. We consider four syntax tasks at different depths of the parse tree; for each word, we predict its part of speech as well as the first (parent), second (grandparent) and third level (great-grandparent) constituent labels that appear above it. These predictions are made from representations produced at different depths in networks that are pretrained with one of four objectives: dependency parsing, semantic role labeling, machine translation, or language modeling. In every case, we find a correspondence between network depth and syntactic depth, suggesting that a soft syntactic hierarchy emerges. This effect is robust across all conditions, indicating that the models encode significant amounts of syntax even in the absence of an explicit syntactic training supervision.
A Game-Theoretic Approach to Word Sense Disambiguation

This article presents a new model for word sense disambiguation formulated in terms of evolutionary game theory, where each word to be disambiguated is represented as a node on a graph whose edges represent word relations and senses are represented as classes. The words simultaneously update their class membership preferences according to the senses that neighboring words are likely to choose. We use distributional information to weigh the influence that each word has on the decisions of the others and semantic similarity information to measure the strength of compatibility among the choices. With this information we can formulate the word sense disambiguation problem as a constraint satisfaction problem and solve it using tools derived from game theory, maintaining the textual coherence. The model is based on two ideas: Similar words should be assigned to similar classes and the meaning of a word does not depend on all the words in a text but just on some of them. The article provides an in-depth motivation of the idea of modeling the word sense disambiguation problem in terms of game theory, which is illustrated by an example. The conclusion presents an extensive analysis on the combination of similarity measures to use in the framework and a comparison with state-of-the-art systems. The results show that our model outperforms state-of-the-art algorithms and can be applied to different tasks and in different scenarios.
Linguistic Dimensions of Subtitling . Perspectives from Taiwan

This study investigates the linguistic dimensions of the subtitling of English movies into Chinese. The data derive from literature analysis, interviews with experienced subtitlers and administrators of translation agencies, my self-analysis as an experienced subtitler, and an analysis of four English movies rendered into Chinese subtitles. Grounded research methods are used to analyze the data and complete the report. The results indicate that several factors affect the quality of subtitling, including movie pirating, uncontrollable outsourced projects, economic factors, and linguistic factors. In the Chinese subtitles, swearwords are toned down, some punctuation marks and inessential information are omitted, and despite the source-language register, literary Chinese is used for brevity.
Strategic Brand Management of the Luxury Brand GUCCI by

In recent years, on the one hand, Japanese apparel companies are suffering badly from decreases in profits under the variable markets, but on the other hand, many luxury brand companies are advancing into Japan or overseas markets and achieving market share with high profit ratios. This paper reveals which strategy and brand management is effective for luxury branding through the case of the luxury brand GUCCI. It shows that the luxury brand companies have to manage and control the quality of products, prices, distribution channels and promotions to establish and maintain their position as a luxury brand. That is to say, they take “Luxury strategy”. It also shows that sustaining innovation and disruptive innovation are necessary to establish and maintain their position not only in the HDD industry, but also in luxury brand industry.
Towards Unified Object Detection and Semantic Segmentation

Object detection and semantic segmentation are two strongly correlated tasks, yet typically solved separately or sequentially with substantially different techniques. Motivated by the complementary effect observed from the typical failure cases of the two tasks, we propose a unified framework for joint object detection and semantic segmentation. By enforcing the consistency between final detection and segmentation results, our unified framework can effectively leverage the advantages of leading techniques for these two tasks. Furthermore, both local and global context information are integrated into the framework to better distinguish the ambiguous samples. By jointly optimizing the model parameters for all the components, the relative importance of different component is automatically learned for each category to guarantee the overall performance. Extensive experiments on the PASCAL VOC 2010 and 2012 datasets demonstrate encouraging performance of the proposed unified framework for both object detection and semantic segmentation tasks.
Natural Language Processing in PROLOG

Natural language processing in Prolog started with Pro-log itself, when Colmerauer in the early 1970's developed a Prolog-based natural language system called metamorphosis grammar (Colmerauer,1978). The ideas developed here have since been applied in various logic grammar formalisms and systems, e.g. by Dahl (1977), who made a natural language system for Spanish. The most influential formalism, Definite Clause Grammar , was developed by Warren and Pereira and applied to make a knowledge based system CHAT-80 (Pereira,1980) for geography with a natural language interface. CHAT-80 can answer the following question in extensio: " Which country bordering the Mediterranean borders a country that is bordered by a country whose population exceeds the population of India " The solution, Turkey, was found within 405 milliseconds (1980).
Grasp Quality Evaluation with Whole Arm Kinematic Noise Propagation

In this paper we propose a new approach to evaluate grasps that accounts for both the kinematic structure of the robot and the noise at its joints. Our starting observation is that with a redundant robot the same grasp can be implemented with different arm configurations, and these may display significant differences in terms of robustness to disturbances. Consequently, the grasp quality metric is seen as a random variable depending on the arm configuration. Starting from a first order approximation for the error, we introduce the high probability force closure region as a tool to evaluate the local robustness of an arm configuration, and we then introduce a new metric $Q_{arm}$ to rank different configurations according to the robustness to noise. By combining this method in an offline/online framework, we demonstrate through large scale simulations that this approach successfully captures aspects that were neglected in former literature regarding grasp evaluation, and can successfully be integrated into future grasp planners.
A Distributional Perspective on Reinforcement Learning

This talk will present an overview of our recent research on distributional reinforcement learning. Our starting point is our recent ICML paper, in which we argued for the fundamental importance of the value distribution: the distribution of random returns received by a reinforcement learning agent. This is in contrast to the common approach, which models the expectation of this return, or value. Back then, we were able to design a new algorithm that learns the value distribution through a TD-like bootstrap process and achieved state-of-the-art performance on games from the Arcade Learning Environment (ALE). However, this left open the question as to why the distributional approach should perform better at all. We’ve since delved deeper into what makes distributional RL work: first by improving the original using quantile regression, which directly minimizes the Wasserstein metric; and second by unearthing surprising connections between the original C51 algorithm and the distant cousin of the Wasserstein metric, the Cramer distance.
7 February 1999 KNOWLEDGE MANAGEMENT SYSTEMS : ISSUES , CHALLENGES , AND BENEFITS

The knowledge-based theory of the firm suggests that knowledge is the organizational asset that enables sustainable competitive advantage in hypercompetitive environments. The emphasis on knowledge in today’s organizations is based on the assumption that barriers to the transfer and replication of knowledge endow it with strategic importance. Many organizations are developing information systems designed specifically to facilitate the sharing and integration of knowledge. Such systems are referred to as Knowledge Management System (KMS). Because KMS are just beginning to appear in organizations, little research and field data exists to guide the development and implementation of such systems or to guide expectations of the potential benefits of such systems. This study provides an analysis of current practices and outcomes of KMS and the nature of KMS as they are evolving in fifty organizations. The findings suggest that interest in KMS across a variety of industries is very high, the technological foundations are varied, and the major
Network Structure Inference, A Survey: Motivations, Methods, and Applications

Networks represent relationships between entities in many complex systems, spanning from online social interactions to biological cell development and brain connectivity. In many cases, relationships between entities are unambiguously known: are two users “friends” in a social network? Do two researchers collaborate on a published article? Do two road segments in a transportation system intersect? These are directly observable in the system in question. In most cases, relationships between nodes are not directly observable and must be inferred: Does one gene regulate the expression of another? Do two animals who physically co-locate have a social bond? Who infected whom in a disease outbreak in a population?
 Existing approaches for inferring networks from data are found across many application domains and use specialized knowledge to infer and measure the quality of inferred network for a specific task or hypothesis. However, current research lacks a rigorous methodology that employs standard statistical validation on inferred models. In this survey, we examine (1) how network representations are constructed from underlying data, (2) the variety of questions and tasks on these representations over several domains, and (3) validation strategies for measuring the inferred network’s capability of answering questions on the system of interest.
The phonological unit of Japanese Kanji compounds: A masked priming investigation.

Using the masked priming paradigm, we examined which phonological unit is used when naming Kanji compounds. Although the phonological unit in the Japanese language has been suggested to be the mora, Experiment 1 found no priming for mora-related Kanji prime-target pairs. In Experiment 2, significant priming was only found when Kanji pairs shared the whole sound of their initial Kanji characters. Nevertheless, when the same Kanji pairs used in Experiment 2 were transcribed into Kana, significant mora priming was observed in Experiment 3. In Experiment 4, matching the syllable structure and pitch-accent of the initial Kanji characters did not lead to mora priming, ruling out potential alternative explanations for the earlier absence of the effect. A significant mora priming effect was observed, however, when the shared initial mora constituted the whole sound of their initial Kanji characters in Experiments 5. Lastly, these results were replicated in Experiment 6. Overall, these results indicate that the phonological unit involved when naming Kanji compounds is not the mora but the whole sound of each Kanji character. We discuss how different phonological units may be involved when processing Kanji and Kana words as well as the implications for theories dealing with language production processes.
Evaluating an NLG System using Post-Editing

Computer-generated texts, whether from Natural Language Generation (NLG) or Machine Translation (MT) systems, are often post-edited by humans before being released to users. The frequency and type of post-edits is a measure of how well the system works, and can be used for evaluation. We describe how we have used post-edit data to evaluate SUMTIME-MOUSAM, an NLG system that produces weather forecasts.
Multi-armed Bandit Algorithms and Empirical Evaluation

The multi-armed bandit problem for a gambler is to decide which arm of a K-slot machine to pull to maximize his total reward in a series of trials. Many real-world learning and optimization problems can be modeled in this way. Several strategies or algorithms have been proposed as a solution to this problem in the last two decades, but, to our knowledge, there has been no common evaluation of these algorithms. This paper provides a preliminary empirical evaluation of several multiarmed bandit algorithms. It also describes and analyzes a new algorithm, Poker (Price Of Knowledge and Estimated Reward) whose performance compares favorably to that of other existing algorithms in several experiments. One remarkable outcome of our experiments is that the most naive approach, the -greedy strategy, proves to be often hard to beat.
End-to-End Speech Recognition from the Raw Waveform

State-of-the-art speech recognition systems rely on fixed, handcrafted features such as mel-filterbanks to preprocess the waveform before the training pipeline. In this paper, we study end-toend systems trained directly from the raw waveform, building on two alternatives for trainable replacements of mel-filterbanks that use a convolutional architecture. The first one is inspired by gammatone filterbanks (Hoshen et al., 2015; Sainath et al, 2015), and the second one by the scattering transform (Zeghidour et al., 2017). We propose two modifications to these architectures and systematically compare them to mel-filterbanks, on the Wall Street Journal dataset. The first modification is the addition of an instance normalization layer, which greatly improves on the gammatone-based trainable filterbanks and speeds up the training of the scattering-based filterbanks. The second one relates to the low-pass filter used in these approaches. These modifications consistently improve performances for both approaches, and remove the need for a careful initialization in scattering-based trainable filterbanks. In particular, we show a consistent improvement in word error rate of the trainable filterbanks relatively to comparable mel-filterbanks. It is the first time end-to-end models trained from the raw signal significantly outperform mel-filterbanks on a large vocabulary task under clean recording conditions.
The beat spectrum: a new approach to rhythm analysis

We introduce the beat spectrum, a new method of automatically characterizing the rhythm and tempo of music and audio. The beat spectrum is a measure of acoustic self-similarity as a function of time lag. Highly structured or repetitive music will have strong beat spectrum peaks at the repetition times. This reveals both tempo and the relative strength of particular beats, and therefore can distinguish between different kinds of rhythms at the same tempo. We also introduce the beat spectrogram which graphically illustrates rhythm variation over time. Unlike previous approaches to tempo analysis, the beat spectrum does not depend on particular attributes such as energy or frequency, and thus will work for any music or audio in any genre. We present tempo estimation results which are accurate to within 1% for a variety of musical genres. This approach has a variety of applications, including music retrieval by similarity and automatically generating music videos.
Generative Approaches to Ergativity

This article surveys the principal generative syntactic analyses that have been proposed for ergativity, found primarily in Inuit, Austronesian, Mayan, and Pama-Nyungan language families. The main puzzle for generative grammar is how to analyze the behavior of ergative and absolutive arguments in terms of the grammatical functions of subject and object. I show in this article that early approaches tend to treat the absolutive uniformly as a subject or an object, while later analyses move toward disassociating case from grammatical function. Descriptively speaking, this article identifies two types of morphological ergativity, differing in how absolutive case is assigned. Morphological ergativity is also distinguished from syntactic ergativity, which is characterized primarily by a restriction that only absolutives can undergo A’-movement. In other aspects of the grammar, ergativity is not strikingly different from accusativity.
A review of data mining applications in crime

Crime continues to remain a severe threat to all communities and nations across the globe alongside the sophistication in technology and processes that are being exploited to enable highly complex criminal activities. Data mining, the process of uncovering hidden information from Big Data, is now an important tool for investigating, curbing and preventing crime and is exploited by both private and government institutions around the world. The primary aim of this paper is to provide a concise review of the data mining applications in crime. To this end, the paper reviews over 100 applications of data mining in crime, covering a substantial quantity of research to date, presented in chronological order with an overview table of many important data mining applications in the crime domain as a reference directory. The data mining techniques themselves are briefly introduced to the reader and these include entity extraction, clustering, association rule mining, decision trees, support vector machines, naive Bayes rule, neural networks and social network analysis amongst others.
Ph.D. Research Proposal Information Extraction for Legal Documents

Information Extraction technique, which is a process of converting unstructured text into structured data, has been used in a variety range of applications such as traffic monitoring systems, biomedical systems and more general open knowledge bases for open language learning. However, the use of information extraction in legal area is still quite limited and suffers difficulties. For example, the existing system AustLII, which is developed and maintained by University of Technology Sydney and University of New South Wales, uses traditional information retrieval approach to organise all the legal documents and the system only supports keywords search. Another example, the LexML Web project in Brazil also applies similar approach. Although more sophisticated techniques such as case based reasoning, semantic network and ontology based approaches are introduced, not many of them are actually used in an integrated application. Hence, the aim of this study would be to come up with new design that applies information extraction techniques in order to construct a more accurate legal text management system. The new system is designed to manage laws as well as legal cases for both legal study and use. Comparing with AustLII, the new system will also focus on Australia laws but provide more accurate search results because of the use of information extraction methods. Australasian Legal Information Institute