nils-herrmann/clean_scirepeval huggingface dataset:
E-TOURISM: AN INNOVATIVE APPROACH FOR THE SMALL AND MEDIUM-SIZED TOURISM ENTERPRISES (SMTES) IN KOREA

This paper deals with e-tourism, innovation and growth. The Internet is revolutionising the distribution of tourism information and sales. The Korean small and medium-sized tourism enterprises (SMTEs) with well-developed and innovative Web sites can now have “equal Internet access” to international tourism markets. This paper examines problems and solutions related to electronic commerce in the tourism industry and suggests recommendations for successful e-commerce strategies in tourism to be applied by the industry and the government in Korea.  The definitions of tourism innovation (e.g. product, service and technological innovations) remains unclear, with the exception maybe of the Internet. New technologies can produce an essential contribution to tourism development. For tourism businesses, the Internet offers the potential to make information and booking facilities available to large numbers of tourists at relatively low costs. It also provides a tool for communication between tourism suppliers, intermediaries, as well as end-consumers.  (2000) revealed that the advent of Internet-based electronic commerce offers considerable opportunities for firms to expand their customer base, enter new product markets and rationalise their business. WTO (2001) also indicated that electronic business offers SMEs the opportunity to undertake their business in new and more cost-effective ways. According to WTO, the Internet is revolutionising the distribution of tourism information and sales. An increasing proportion of Internet users are buying on–line and tourism will gain a larger and larger share of the online commerce market. Obviously, the Internet is having a major impact as a source of information for tourism. However, the SMTEs are facing more stringent impediments to the adoption of new information technology, in particular, e-business. Part of the problem relates to the scale and affordability of information technology, as well as the facility of implementation within rapidly growing and changing organisations. In addition, new solutions configured for large, stable, and internationally-oriented firms do not fit well for small, dynamic, and locally-based tourism firms. Despite these challenges, SMTEs with well-developed and innovative Web sites can now have “equal Internet access” to international tourism markets. This implies equal access to telecom infrastructure, as well as to marketing management and education. According to a UN report (2001), “it is not the cost of being there, on the on-line market place, which must be reckoned with, but the cost of not being there.” It is certain that embracing digital communication and information technology is no longer an option, but a necessity. Thus, one of the most important characteristics of
Deep classification in large-scale text hierarchies

Most classification algorithms are best at categorizing the Web documents into a few categories, such as the top two levels in the Open Directory Project. Such a classification method does not give very detailed topic-related class information for the user because the first two levels are often too coarse. However, classification on a large-scale hierarchy is known to be intractable for many target categories with cross-link relationships among them. In this paper, we propose a novel deep-classification approach to categorize Web documents into categories in a large-scale taxonomy. The approach consists of two stages: a search stage and a classification stage. In the first stage, a category-search algorithm is used to acquire the category candidates for a given document. Based on the category candidates, we prune the large-scale hierarchy to focus our classification effort on a small subset of the original hierarchy. As a result, the classification model is trained on the small subset before being applied to assign the category for a new document. Since the category candidates are sufficiently close to each other in the hierarchy, a statistical-language-model based classifier using n-gram features is exploited. Furthermore, the structure of the taxonomy can be utilized in this stage to improve the performance of classification. We demonstrate the performance of our proposed algorithms on the Open Directory Project with over 130,000 categories. Experimental results show that our proposed approach can reach 51.8% on the measure of Mi-F1 at the 5th level, which is 77.7% improvement over top-down based SVM classification algorithms.
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision

After , , , , , , minimum cut/maximum flow algorithms on graphs emerged as an increasingly useful tool for exact or approximate energy minimization in low-level vision. The combinatorial optimization literature provides many min-cut/max-flow algorithms with different polynomial time complexity. Their practical efficiency, however, has to date been studied mainly outside the scope of computer vision. The goal of this paper is to provide an experimental comparison of the efficiency of min-cut/max flow algorithms for applications in vision. We compare the running times of several standard algorithms, as well as a new algorithm that we have recently developed. The algorithms we study include both Goldberg-Tarjan style "push-relabel" methods and algorithms based on Ford-Fulkerson style "augmenting paths." We benchmark these algorithms on a number of typical graphs in the contexts of image restoration, stereo, and segmentation. In many cases, our new algorithm works several times faster than any of the other methods, making near real-time performance possible. An implementation of our max-flow/min-cut algorithm is available upon request for research purposes.
Null hypothesis significance tests. A mix-up of two different theories: the basis for widespread confusion and numerous misinterpretations

Null hypothesis statistical significance tests (NHST) are widely used in quantitative research in the empirical sciences including scientometrics. Nevertheless, since their introduction nearly a century ago significance tests have been controversial. Many researchers are not aware of the numerous criticisms raised against NHST. As practiced, NHST has been characterized as a ‘null ritual’ that is overused and too often misapplied and misinterpreted. NHST is in fact a patchwork of two fundamentally different classical statistical testing models, often blended with some wishful quasi-Bayesian interpretations. This is undoubtedly a major reason why NHST is very often misunderstood. But NHST also has intrinsic logical problems and the epistemic range of the information provided by such tests is much more limited than most researchers recognize. In this article we introduce to the scientometric community the theoretical origins of NHST, which is mostly absent from standard statistical textbooks, and we discuss some of the most prevalent problems relating to the practice of NHST and trace these problems back to the mix-up of the two different theoretical origins. Finally, we illustrate some of the misunderstandings with examples from the scientometric literature and bring forward some modest recommendations for a more sound practice in quantitative data analysis.
10 Networking Papers: Recommended Reading

Christophe Diot, the Editor-in-Chief of this ACM Computer Communication Review, and I were recently discussing our impression that we often seem to be drowning in a sea of papers – there are more interesting papers being published than one could read in a lifetime. We discussed the idea of a series of occasional contributions to CCR by members of our technical community on networking papers that they would recommend to others. Certainly, I’d like to hear what my colleagues would recommend as interesting, important, or thought-provoking papers in our field! Christophe asked me to kickoff this series, and I have taken him up on his offer.
Chameleon: A Versatile Emulator for Contactless Smartcards

We develop a new, custom-built hardware for emulating contactless smartcards compliant to ISO 14443. The device is based on a modern low-cost microcontroller and can support basically all relevant (cryptographic) protocols used by contactless smartcards today, e.g., those based on AES or Triple-DES. As a proof of concept, we present a full emulation of Mifare Classic cards on the basis of our highly optimized implementation of the stream cipher Crypto1. The implementation enables the creation of exact clones of such cards, including the UID. We furthermore reverse-engineered the protocol of DESFire EV1 and realize the first emulation of DESFire and DESFire EV1 cards in the literature. We practically demonstrate the capabilities of our emulator by spoofing several real-world systems, e.g., creating a contactless payment card which allows an attacker to set the stored credit balance as desired and hence make an infinite amount of payments.
Speech Synthesis for Mixed-Language Navigation Instructions

Text-to-Speech (TTS) systems that can read navigation instructions are one of the most widely used speech interfaces today. Text in the navigation domain may contain named entities such as location names that are not in the language that the TTS database is recorded in. Moreover, named entities can be compound words where individual lexical items belong to different languages. These named entities may be transliterated into the script that the TTS system is trained on. This may result in incorrect pronunciation rules being used for such words. We describe experiments to extend our previous work in generating code-mixed speech to synthesize navigation instructions, with a mixed-lingual TTS system. We conduct subjective listening tests with two sets of users, one being students who are native speakers of an Indian language and very proficient in English, and the other being drivers with low English literacy, but familiarity with location names. We find that in both sets of users, there is a significant preference for our proposed system over a baseline system that synthesizes instructions in English.
Ontologies in biology: design, applications and future challenges

Biological knowledge is inherently complex and so cannot readily be integrated into existing databases of molecular (for example, sequence) data. An ontology is a formal way of representing knowledge in which concepts are described both by their meaning and their relationship to each other. Unique identifiers that are associated with each concept in biological ontologies (bio-ontologies) can be used for linking to and querying molecular databases. This article reviews the principal bio-ontologies and the current issues in their design and development: these include the ability to query across databases and the problems of constructing ontologies that describe complex knowledge, such as phenotypes.
Automatic labeling hierarchical topics

Recently, statistical topic modeling has been widely applied in text mining and knowledge management due to its powerful ability. A topic, as a probability distribution over words, is usually difficult to be understood. A common, major challenge in applying such topic models to other knowledge management problem is to accurately interpret the meaning of each topic. Topic labeling, as a major interpreting method, has attracted significant attention recently. However, previous works simply treat topics individually without considering the hierarchical relation among topics, and less attention has been paid to creating a good hierarchical topic descriptors for a hierarchy of topics. In this paper, we propose two effective algorithms that automatically assign concise labels to each topic in a hierarchy by exploiting sibling and parent-child relations among topics. The experimental results show that the inter-topic relation is effective in boosting topic labeling accuracy and the proposed algorithms can generate meaningful topic labels that are useful for interpreting the hierarchical topics.
A Direct AC–AC Converter for Inductive Power-Transfer Systems

This paper proposes a direct ac-ac converter to generate a high-frequency current for inductive power-transfer (IPT) systems. Unlike traditional dc-ac converters for IPT systems, the proposed converter can achieve a high-frequency current generation directly from an ac power source without a dc link. The power converter can maintain high-frequency resonance by natural circuit oscillation and discrete energy injection control. A variable-frequency control strategy is developed to vary the switching frequency to keep the high-frequency current operating with zero-current switching. Thus, the switching stress, power losses, and electromagnetic interference of the converter are reduced. Theoretical analysis, simulation, and experimental results show that the output track current is fully controllable with good waveforms for contactless power-transfer applications.
Accurate Synchronization of Speech and EGG Signal Using Phase Information

Synchronization of speech and corresponding Electroglottographic (EGG) signal is very helpful for speech processing research and development. During simultaneous recording of speech and EGG signals, the speech signal will be delayed by the duration corresponding to the speech wave propagation from the glottis to the microphone relative to the EGG signal. Even in same session of recording, the delay between the speech and the EGG signals is varying due to the natural movement of speaker’s head and movement of microphone in case MIC is held by hand. To study and model the information within glottal cycles, precise synchronization of speech and EGG signals is of utmost necessity. In this work, we propose a method for synchronization of speech and EGG signals based on the glottal activity information present in the signals. The performance of the proposed method is demonstrated by estimation of delay between the two signals (speech signals and corresponding EGG signals) and synchronizing these signals by compensating the estimated delay. The CMU-Arctic database consist of simultaneous recording of the speech and the EGG signals is used for the evaluation of the proposed method.
A low cost smart irrigation system using MQTT protocol

In India agriculture plays a very important role. The Indian economy is also greatly affected by agricultural, as about 50 percent of total population is directly or indirectly depend on the agricultural related activities. A farmer has to go to the farm to check the water level in the field and to turn on and turn off the water pump, sometimes even in the middle of the night. This problem can be overcome by improving old methods of farming. A new system can be developed or designed which transform the old traditional farming into the smart farming. This paper tries to design a simple water pump controller by using a soil moisture sensor and Esp8266 NodeMCU-12E. A Message Queue Telemetry Transport protocol is used for transmitting and receiving sensor information. Depending on a status of soil moisture content NodeMCU-12E controls a water pump action and displays the soil moisture sensor data and water pump status on a web page or mobile application. In this way, a secure, flexible, trust-able and economical system is developed to solve above mentioned agricultural irrigation problem.
Mental illness in film.

JVLental illness has been portrayed on film in a number of ways, many of which have been negative and incorrect. With such a powerful medium, these false depictions have infiltrated the populace and have added stigma to an already misunderstood disease. The danger is greater than one might thinl<. For people with no connection to mental illness, neither their own nor that of loved ones, cinema may be the only exposure they have. As a person living with schizophrenia, these inaccuracies can actually be painful to watch, causing both anger and sadness. The film industry has on the whole done a great disservice to society's comprehension of mental illness, and it is only recently that it has started to depict a more accurate and sympathetic portrayal.
Adaptive Routing Protocol with Energy Efficiency and Event Clustering for Wireless Sensor Networks

Wireless sensor network (WSN) is a promising approach for a variety of applications. Routing protocol for WSNs is very challenging because it should be simple, scalable, energy-efficient, and robust to deal with a very large number of nodes, and also self-configurable to node failures and changes of the network topology dynamically. Recently, many researchers have focused on developing hierarchical protocols for WSNs. However, most protocols in the literatures cannot scale well to large sensor networks and difficult to apply in the real applications. In this paper, we propose a novel adaptive routing protocol for WSNs called ARPEES. The main design features of the proposed method are: energy efficiency, dynamic event clustering, and multi-hop relay considering the trade-off relationship between the residual energy available of relay nodes and distance from the relay node to the base station. With a distributed and light overhead traffic approach, we spread energy consumption required for aggregating data and relaying them to different sensor nodes to prolong the lifetime of the whole network. In this method, we consider energy and distance as the parameters in the proposed function to select relay nodes and finally select the optimal path among cluster heads, relay nodes and the base station. The simulation results show that our routing protocol achieves better performance than other previous routing protocols. key words: energy efficiency, event clustering, routing protocol, wireless sensor networks
Perceived racism and mental health among Black American adults: a meta-analytic review.

The literature indicates that perceived racism tends to be associated with adverse psychological and physiological outcomes; however, findings in this area are not yet conclusive. In this meta-analysis, we systematically reviewed 66 studies (total sample size of 18,140 across studies), published between January 1996 and April 2011, on the associations between racism and mental health among Black Americans. Using a random-effects model, we found a positive association between perceived racism and psychological distress (r = .20). We found a moderation effect for psychological outcomes, with anxiety, depression, and other psychiatric symptoms having a significantly stronger association than quality of life indicators. We did not detect moderation effects for type of racism scale, measurement precision, sample type, or type of publication. Implications for research and practice are discussed.
Modeling Discourse Segments in Lyrics Using Repeated Patterns

This study proposes a computational model of the discourse segments in lyrics to understand and to model the structure of lyrics. To test our hypothesis that discourse segmentations in lyrics strongly correlate with repeated patterns, we conduct the first large-scale corpus study on discourse segments in lyrics. Next, we propose the task to automatically identify segment boundaries in lyrics and train a logistic regression model for the task with the repeated pattern and textual features. The results of our empirical experiments illustrate the significance of capturing repeated patterns in predicting the boundaries of discourse segments in lyrics.
Exploring Factors Influencing the Adoption of Mobile Commerce

This study presents an extended technology acceptance model that integrates innovation diffusion theory to investigate what determine user mobile commerce acceptance. This paper models the factors relationships such as perceived usefulness, perceived ease of use, personal innovativeness, subjective norms, behavioral control and intention to adopt mobile commerce. The proposed model was empirically tested using data collected from a survey of mobile commerce consumers. Empirical data from regression analysis reflects users ease of use influence behavioral intention to adopt mobile commerce. The majority of positive relationships between perceived ease of use, subjective norms, behavioral control and intention to adopt are supported by empirical data. Results also reveal that behavioral control and subjective norms influence perceived ease of use which affects then their adoption intention. The paper concludes some important implications for the practitioners.
Robust object detection via soft cascade

We describe a method for training object detectors using a generalization of the cascade architecture, which results in a detection rate and speed comparable to that of the best published detectors while allowing for easier training and a detector with fewer features. In addition, the method allows for quickly calibrating the detector for a target detection rate, false positive rate or speed. One important advantage of our method is that it enables systematic exploration of the ROC surface, which characterizes the trade-off between accuracy and speed for a given classifier.
A back-illuminated 3D-stacked single-photon avalanche diode in 45nm CMOS technology

We report on the world's first back-illuminated 3D-stacked single-photon avalanche diode (SPAD) in 45nm CMOS technology. This SPAD achieves a dark count rate of 55.4cps/μm2, a maximum photon detection probability of 31.8% at 600nm, over 5% in the 420–920nm wavelength range, and timing jitter of 107.7ps at 2.5V excess bias voltage and room temperature. To the best of our knowledge, these are the best results ever reported for any back-illuminated 3D-stacked SPAD technology.
Modal analysis and transcription of strokes of the mridangam using non-negative matrix factorization

In this paper we use a Non-negative Matrix Factorization (NMF) based approach to analyze the strokes of the mridangam, a South Indian hand drum, in terms of the normal modes of the instrument. Using NMF, a dictionary of spectral basis vectors are first created for each of the modes of the mridangam. The composition of the strokes are then studied by projecting them along the direction of the modes using NMF. We then extend this knowledge of each stroke in terms of its basic modes to transcribe audio recordings. Hidden Markov Models are adopted to learn the modal activations for each of the strokes of the mridangam, yielding up to 88.40% accuracy during transcription.
Audio-Visual Speech Enhancement based on Multimodal Deep Convolutional Neural Network

Speech enhancement (SE) aims to reduce noise in speech signals. Most SE techniques focus on addressing audio information only. In this work, inspired by multimodal learning, which utilizes data from different modalities, and the recent success of convolutional neural networks (CNNs) in SE, we propose an audio-visual deep CNN (AVDCNN) SE model, which incorporates audio and visual streams into a unified network model. In the proposed AVDCNN SE model, audio and visual features are first processed using individual CNNs, and then, fused into a joint network to generate enhanced speech at an output layer. The AVDCNN model is trained in an end-toend manner, and parameters are jointly learned through backpropagation. We evaluate enhanced speech using five objective criteria. Results show that the AVDCNN yields notably better performance as compared to an audio-only CNN-based SE model, confirming the effectiveness of integrating visual information into the SE process.