nils-herrmann/clean_scirepeval huggingface dataset:
Language Teaching Approaches : An Overview

The field of second (or foreign) language teaching has undergone many fluctuations and dramatic shifts over the years. As opposed to physics or chemistry, where progress is more or less steady until a major discovery causes a radical theoretical revision (Kuhn, 1970), language teaching is a field where fads and heroes have come and gone in a manner fairly consistent with the kinds of changes that occur in youth culture. I believe that one reason for the frequent changes that have been taking place until recently is the fact that very few language teachers have even the vaguest sense of history about their profession and are unclear concerning the historical bases of the many methodological options they currently have at their disposal. It is hoped that this brief and necessarily oversimplified survey will encourage many language teachers to learn more about the origins of their profession. Such knowledge will give some healthy perspective in evaluating the socalled innovations or new approaches to methodology that will continue to emerge over time.
End-to-End Speech-Driven Facial Animation with Temporal GANs

Speech-driven facial animation is the process which uses speech signals to automatically synthesize a talking character. The majority of work in this domain creates a mapping from audio features to visual features. This often requires post-processing using computer graphics techniques to produce realistic albeit subject dependent results. We present a system for generating videos of a talking head, using a still image of a person and an audio clip containing speech, that doesn’t rely on any handcrafted intermediate features. To the best of our knowledge, this is the first method capable of generating subject independent realistic videos directly from raw audio. Our method can generate videos which have (a) lip movements that are in sync with the audio and (b) natural facial expressions such as blinks and eyebrow movements 1. We achieve this by using a temporal GAN with 2 discriminators, which are capable of capturing different aspects of the video. The effect of each component in our system is quantified through an ablation study. The generated videos are evaluated based on their sharpness, reconstruction quality, and lip-reading accuracy. Finally, a user study is conducted, confirming that temporal GANs lead to more natural sequences than a static GAN-based approach.
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning

Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and deep convolutional neural networks (CNNs). CNNs enable learning data-driven, highly representative, hierarchical image features from sufficient training data. However, obtaining datasets as comprehensively annotated as ImageNet in the medical imaging domain remains a challenge. There are currently three major techniques that successfully employ CNNs to medical image classification: training the CNN from scratch, using off-the-shelf pre-trained CNN features, and conducting unsupervised CNN pre-training with supervised fine-tuning. Another effective method is transfer learning, i.e., fine-tuning CNN models pre-trained from natural image dataset to medical image tasks. In this paper, we exploit three important, but previously understudied factors of employing deep convolutional neural networks to computer-aided detection problems. We first explore and evaluate different CNN architectures. The studied models contain 5 thousand to 160 million parameters, and vary in numbers of layers. We then evaluate the influence of dataset scale and spatial image context on performance. Finally, we examine when and why transfer learning from pre-trained ImageNet (via fine-tuning) can be useful. We study two specific computer-aided detection (CADe) problems, namely thoraco-abdominal lymph node (LN) detection and interstitial lung disease (ILD) classification. We achieve the state-of-the-art performance on the mediastinal LN detection, and report the first five-fold cross-validation classification results on predicting axial CT slices with ILD categories. Our extensive empirical evaluation, CNN model analysis and valuable insights can be extended to the design of high performance CAD systems for other medical imaging tasks.
An optimization model for collaborative recommendation using a covariance-based regularizer

This paper suggests a convex regularized optimization model to produce recommendations, which is adaptable, fast, and scalable—while remaining very competitive to state-of-the-art methods in terms of accuracy. We introduce a regularizer based on the covariance matrix such that the model minimizes two measures ensuring that the recommendations provided to a user are guided by both the preferences of the other users in the system and the known preferences of the user being processed. It is adaptable since (1) it can be viewed from both user and item perspectives (allowing to choose, depending on the task, the formulation with fewer decision variables) and (2) multiple constraints depending on the context (and not only based on the accuracy, but also on the utility of personalized recommendations) can easily be added, as shown in this paper through two examples. Since our regularizer is based on the covariance matrix, this paper also describes how to improve computational and space complexities by using matrix factorization techniques in the optimization model, leading to a fast and scalable model. To illustrate all these concepts, experiments were conducted on four real datasets of different sizes (i.e., FilmTrust, Ciao, MovieLens, and Netflix) and comparisons with state-of-the-art methods are provided, showing that our context-sensitive approach is very competitive in terms of accuracy.
Biomass pretreatment: fundamentals toward application.

Development of sustainable energy systems based on renewable biomass feedstocks is now a global effort. Lignocellulosic biomass contains polymers of cellulose, hemicellulose, and lignin, bound together in a complex structure. Liquid biofuels, such as ethanol, can be made from biomass via fermentation of sugars derived from the cellulose and hemicellulose within lignocellulosic materials, but the biomass must be subjected to pretreatment processes to liberate the sugars needed for fermentation. Production of value-added co-products along-side biofuels through integrated biorefinery processes creates the need for selectivity during pretreatment. This paper presents a survey of biomass pretreatment technologies with emphasis on concepts, mechanism of action and practicability. The advantages and disadvantages, and the potential for industrial applications of different pretreatment technologies are the highlights of this paper.
Melatonin as a chronobiotic.

Melatonin, hormone of the pineal gland, is concerned with biological timing. It is secreted at night in all species and in ourselves is thereby associated with sleep, lowered core body temperature, and other night time events. The period of melatonin secretion has been described as 'biological night'. Its main function in mammals is to 'transduce' information about the length of the night, for the organisation of daylength dependent changes, such as reproductive competence. Exogenous melatonin has acute sleepiness-inducing and temperature-lowering effects during 'biological daytime', and when suitably timed (it is most effective around dusk and dawn) it will shift the phase of the human circadian clock (sleep, endogenous melatonin, core body temperature, cortisol) to earlier (advance phase shift) or later (delay phase shift) times. The shifts induced are sufficient to synchronise to 24 h most blind subjects suffering from non-24 h sleep-wake disorder, with consequent benefits for sleep. Successful use of melatonin's chronobiotic properties has been reported in other sleep disorders associated with abnormal timing of the circadian system: jetlag, shiftwork, delayed sleep phase syndrome, some sleep problems of the elderly. No long-term safety data exist, and the optimum dose and formulation for any application remains to be clarified.
Dynamic trees as search trees via Euler tours, applied to the network simplex algorithm

The dynamic tree is an abstract data type that allows the maintenance of a collection of trees subject to joining by adding edges (linking) and splitting by deleting edges (cutting), while at the same time allowing reporting of certain combinations of vertex or edge values. For many applications of dynamic trees, values must be combined along paths. For other applications, values must be combined over entire trees. For the latter situation, we show that an idea used originally in parallel graph algorithms, to represent trees by Euler tours, leads to a simple implementation with a time of O(log n) per tree operation, where n is the number of tree vertices. We apply this representation to the implementation of two versions of the network simplex algorithm, resulting in a time of O(log n) per pivot, where n is the number of vertices in the problem network.
REDISCOVERY OF TRADITIONAL ECOLOGICAL KNOWLEDGE AS ADAPTIVE MANAGEMENT

Indigenous groups offer alternative knowledge and perspectives based on their own locally developed practices of resource use. We surveyed the international literature to focus on the role of Traditional Ecological Knowledge in monitoring, responding to, and managing ecosystem processes and functions, with special attention to ecological resilience. Case studies revealed that there exists a diversity of local or traditional practices for ecosystem management. These include multiple species management, resource rotation, succession management, landscape patchiness management, and other ways of responding to and managing pulses and ecological surprises. Social mechanisms behind these traditional practices include a number of adaptations for the generation, accumulation, and transmission of knowledge; the use of local institutions to provide leaders/stewards and rules for social regulation; mechanisms for cultural internalization of traditional practices; and the development of appropriate world views and cultural values. Some traditional knowledge and management systems were characterized by the use of local ecological knowledge to interpret and respond to feedbacks from the environment to guide the direction of resource management. These traditional systems had certain similarities to adaptive management with its emphasis on feedback learning, and its treatment of uncertainty and unpredictability intrinsic to all ecosystems.
Learning a Discriminative Feature Network for Semantic Segmentation

Most existing methods of semantic segmentation still suffer from two aspects of challenges: intra-class inconsistency and inter-class indistinction. To tackle these two problems, we propose a Discriminative Feature Network (DFN), which contains two sub-networks: Smooth Network and Border Network. Specifically, to handle the intra-class inconsistency problem, we specially design a Smooth Network with Channel Attention Block and global average pooling to select the more discriminative features. Furthermore, we propose a Border Network to make the bilateral features of boundary distinguishable with deep semantic boundary supervision. Based on our proposed DFN, we achieve state-of-the-art performance 86.2% mean IOU on PASCAL VOC 2012 and 80.3% mean IOU on Cityscapes dataset.
A conceptual analysis of information security education, information security training and information security awareness definitions

The importance of information security education, information security training, and information security awareness in organisations cannot be overemphasised. This paper presents working definitions for information security education, information security training and information security awareness. An investigation to determine if any differences exist between information security education, information security training and information security awareness was conducted. This was done to help institutions understand when they need to train or educate employees and when to introduce information security awareness programmes. A conceptual analysis based on the existing literature was used for proposing working definitions, which can be used as a reference point for future information security researchers. Three important attributes (namely focus, purpose and method) were identified as the distinguishing characteristics of information security education, information security training and information security awareness. It was found that these information security concepts are different in terms of their focus, purpose and methods of delivery.
The Study of Routing Protocols for UAVs using Paparazzi Mobility Model with Different Altitudes

Flying Ad hoc NETwork (FANET) can be classified as a new family member of ad hoc networks, e.g. MANET (Mobile Ad hoc NETwork), and VANET (Vehicle Ad hoc NETwork). It is formed by Unmanned Aerial Vehicles (UAVs). In literatures, most researchers study the effects of routing protocols in the same altitude for FANET. The interesting question is whether the routing protocols can perform better in different altitude or not.
 In this paper, the performances of three routing protocols, i.e. DSDV, DSR and AODV, are studied. The paparazzi mobility model is selected because it is similar to the reality movement of UAVs and there are five different movement patterns. In this work, different altitude scenarios at 1 and 2 meters apart are studied and compared with the same altitude scenario. Four performance metrics, i.e. packet delivery ratio (PDR), end-to-end delay, throughput, and routing overhead are compared. The best results at 1 meter different show that the performance in the different altitude scenarios outperforms the same altitude scenario in AODV. AODV showed the improvement on end-to-end delay which decreased 75.86% and routing overhead which decreased 20.53%. In addition, PDR and throughput are both increased 3.46%. For DSR and DSDV, the performances are similar in all scenarios. However, the performances at 2-meter apart scenario are not as good as at 1-meter apart scenario. It is interesting to analyze and investigate more on the different altitude scenarios.
Deep Learning Based Chatbot Models

A conversational agent (chatbot) is a piece of software that is able to communicate with humans using natural language. Modeling conversation is an important task in natural language processing and artificial intelligence (AI). Indeed, ever since the birth of AI, creating a good chatbot remains one of the field’s hardest challenges. While chatbots can be used for various tasks, in general they have to understand users’ utterances and provide responses that are relevant to the problem at hand. In the past, methods for constructing chatbot architectures have relied on hand-written rules and templates or simple statistical methods. With the rise of deep learning these models were quickly replaced by end-to-end trainable neural networks around 2015. More specifically, the recurrent encoder-decoder model [Cho et al., 2014] dominates the task of conversational modeling. This architecture was adapted from the neural machine translation domain, where it performs extremely well. Since then a multitude of variations [Serban et al., 2016] and features were presented that augment the quality of the conversation that chatbots are capable of. In my work, I conduct an in-depth survey of recent literature, examining over 70 publications related to chatbots published in the last 3 years. Then I proceed to make the argument that the very nature of the general conversation domain demands approaches that are different from current state-of-the-art architectures. Based on several examples from the literature I show why current chatbot models fail to take into account enough priors when generating responses and how this affects the quality of the conversation. In the case of chatbots these priors can be outside sources of information that the conversation is conditioned on like the persona [Li et al., 2016a] or mood of the conversers. In addition to presenting the reasons behind this problem, I propose several ideas on how it could be remedied. The next section of my paper focuses on adapting the very recent Tranformer [Vaswani et al., 2017] model to the chatbot domain, which is currently the state-of-the-art in neural machine translation. I first present my experiments with the vanilla model, using conversations extracted from the Cornell Movie-Dialog Corpus [Danescu-Niculescu-Mizil and Lee, 2011]. Secondly, I augment the model with some of my ideas regarding the issues of encoder-decoder architectures. More specifically, I feed additional features into the model like mood or persona together with the raw conversation data. Finally, I conduct a detailed analysis of how the vanilla model performs on conversational data by comparing it to previous chatbot models and how the additional features, affect the quality of the generated responses.
The effects of perceived relevance of travel blogs' content on the behavioral intention to visit a tourist destination

The tourism industry is characterized by ever-increasing competition, causing destinations to seek new methods to attract tourists. Traditionally, a decision to visit a destination is interpreted, in part, as a rational calculation of the costs/benefits of a set of alternative destinations, which were derived from external information sources, including e-WOM (word-of-mouth) or travelers' blogs. There are numerous travel blogs available for people to share and learn about travel experiences. Evidence shows, however, that not every blog exerts the same degree of influence on tourists. Therefore, which characteristics of these travel blogs attract tourists' attention and influence their decisions, becomes an interesting research question. Based on the concept of information relevance, a model is proposed for interrelating various attributes specific to blog's content and perceived enjoyment, an intrinsic motivation of information systems usage, to mitigate the above-mentioned gap. Results show that novelty, understandability, and interest of blogs' content affect behavioral intention through blog usage enjoyment. Finally, theoretical and practical implications are proposed. Tourism is a popular activity in modern life and has contributed significantly to economic development for decades. However, competition in almost every sector of this industry has intensified during recent years & Pan, 2008); tourism service providers are now finding it difficult to acquire and keep customers (Echtner & Ritchie, 1991; Ho, 2007). Therefore, methods of attracting tourists to a destination are receiving greater attention from researchers, policy makers, and marketers. Before choosing a destination, tourists may search for information to support their decision-making By understanding the relationships between various information sources' characteristics and destination choice, tourism managers can improve their marketing efforts. Recently, personal blogs have become an important source for acquiring travel information With personal blogs, many tourists can share their travel experiences with others and potential tourists can search for and respond to others' experiences. Therefore, a blog can be seen as an asynchronous and many-to-many channel for conveying travel-related electronic word-of-mouth (e-WOM). By using these forms of inter-personal influence media, companies in this industry can create a competitive advantage (Litvin et al., 2008; Singh et al., 2008). Weblogs are now widely available; therefore, it is not surprising that the quantity of available e-WOM has increased (Xiang & Gret-zel, 2010) to an extent where information overload has become a Empirical evidence , however, indicates that people may not consult numerous blogs for advice; the degree of inter-personal influence varies from blog to blog (Zafiropoulos, 2012). Determining …
A Literature Review of Word of Mouth and Electronic Word of Mouth: Implications for Consumer Behavior

The rise and spread of the Internet has led to the emergence of a new form of word of mouth (WOM): electronic word of mouth (eWOM), considered one of the most influential informal media among consumers, businesses, and the population at large. Drawing on these ideas, this paper reviews the relevant literature, analyzing the impact of traditional WOM and eWOM in the field of consumer behavior and highlighting the main differences between the two types of recommendations, with a view to contributing to a better understanding of the potential of both.
Linguistic Meaning , Communicated Meaning and Cognitive Pragmatics

Within the philosophy of language, pragmatics has tended to be seen as an adjunct to, and a means of solving problems in, semantics. A cognitive-scientific conception of pragmatics as a mental processing system responsible for interpreting ostensive communicative stimuli (specifically, verbal utterances) has effected a transformation in the pragmatic issues pursued and the kinds of explanation offered. Taking this latter perspective, I compare two distinct proposals on the kinds of processes, and the architecture of the system(s), responsible for the recovery of speaker meaning (both explicitly and implicitly communicated meaning). 1. Pragmatics as a Cognitive System 1.1 From Philosophy of Language to Cognitive Science Broadly speaking, there are two perspectives on pragmatics: the ‘philosophical’ and the ‘cognitive’. From the philosophical perspective, an interest in pragmatics has been largely motivated by problems and issues in semantics. A familiar instance of this was Grice’s concern to maintain a close semantic parallel between logical operators and their natural language counterparts, such as ‘not’, ‘and’, ‘or’, ‘if’, ‘every’, ‘a/some’, and ‘the’, in the face of what look like quite major divergences in the meaning of the linguistic elements (see Grice 1975, 1981). The explanation he provided was pragmatic, i.e. in terms of what occurs when the logical semantics of these terms is put to rational communicative use. Consider the case of ‘and’: (1) a. Mary went to a movie and Sam read a novel. b. She gave him her key and he opened the door. c. She insulted him and he left the room. While (a) seems to reflect the straightforward truth-functional symmetrical connection, (b) and (c) communicate a stronger asymmetric relation: temporal Many thanks to Richard Breheny, Sam Guttenplan, Corinne Iten, Deirdre Wilson and Vladimir Zegarac for helpful comments and support during the writing of this paper. Address for correspondence: Department of Phonetics & Linguistics, University College London, Gower Street, London WC1E 6BT, UK.  robyn linguistics.ucl.ac.uk Mind & Language,  Nos 1 and 2 February/April 2002, pp. 127–148.  2002, 108 Cowley Road, Oxford, OX4 1JF, UK and 350 Main Street, Malden, MA 02148, USA.
Integrated Marketing Communication : A Review Paper Corresponding author :

Integrated marketing communication (IMC) is being practicedworldwide at very large scale. The purpose of this review paper is to enhance the understanding of what is IMC, its impact and the difference betweenIMC and traditional marketing communication. Integrated marketing communication is consumer oriented approach rather than organizational oriented that focuses on organizational needs. IMC is performed in a manner of synergy rather than in isolation.IMC is associated with some positive results like brand awareness, customer satisfaction, brand loyalty, positive brand image, unique brand association,greater profitability, increased sales and cost savings. IMC has major impact on organizational performance and brand equity.There are some barriers to successful implementation of IMC program.