nils-herrmann/clean_scirepeval huggingface dataset:
Assessing semantic annotation activities with formal concept analysis

This paper describes an approach to assessing semantic annotation activities based on formal concept analysis (FCA). In this approach, annotators use taxonomical ontologies created by domain experts to annotate digital resources. Then, using FCA, domain experts are provided with concept lattices that graphically display how their ontologies were used during the semantic annotation process. In consequence, they can advise annotators on how to better use the ontologies, as well as how to refine these ontologies to better suit the needs of the semantic annotators. To illustrate the approach, we describe its implementation in @note, a Rich Internet Application (RIA) for the collaborative annotation of digitized literary texts, we exemplify its use with a case study, and we provide some evaluation results using the method. The enormous efforts to digitize physical resources (documents, books, museum exhibits, etc.), along with recent advances in information and communication technologies, have democratized access to a cultural, scientific and academic heritage previously available to only a few. Likewise, the current trend is to produce new resources in a digital format (e.g., in the context of social networks), which entails an in-depth paradigm shift in almost all the humanistic, social, scientific and technological fields. In particular, the field of the humanities is one which is going through a significant transformation as a result of these digitalization efforts and the paradigm shift associated with the digital age. Indeed, we are witnessing the emergence of a whole host of disciplines, those of Digital Humanities (Berry, 2012), which are closely dependent on the production and proper organization of digital collections. As a result of the undoubted importance of digital collections in modern society, the search for effective and efficient methods to carry out the production, preservation and enhancement of such digital collections has become a key challenge in modern society (Calhoun, 2013). In particular, the annotation of resources with metadata that enables their proper cataloging, search, retrieval and use in different application scenarios is one of the key elements to ensuring the profitability of these collections of digital objects. While the cataloging and retrieval of resources (whether digital or non-digital) have been the object of study in library sciences for decades (Calhoun, 2013), modern applications require annotating resources in semantically richer and more flexible ways, in many cases allowing multiple alternative annotations in the same collection. In consequence, the tendency is to introduce the use of ontology-based semantic technologies, in …
Are two spaces better than one? The effect of spacing following periods and commas during reading.

The most recent edition of the American Psychological Association (APA) Manual states that two spaces should follow the punctuation at the end of a sentence. This is in contrast to the one-space requirement from previous editions. However, to date, there has been no empirical support for either convention. In the current study, participants performed (1) a typing task to assess spacing usage and (2) an eye-tracking experiment to assess the effect that punctuation spacing has on reading performance. Although comprehension was not affected by punctuation spacing, the eye movement record suggested that initial processing of the text was facilitated when periods were followed by two spaces, supporting the change made to the APA Manual. Individuals' typing usage also influenced these effects such that those who use two spaces following a period showed the greatest overall facilitation from reading with two spaces.
Facial Expression Recognition using Convolutional Neural Networks: State of the Art

The ability to recognize facial expressions automatically enables novel applications in human-computer interaction and other areas. Consequently, there has been active research in this field, with several recent works utilizing Convolutional Neural Networks (CNNs) for feature extraction and inference. These works differ significantly in terms of CNN architectures and other factors. Based on the reported results alone, the performance impact of these factors is unclear. In this paper, we review the state of the art in image-based facial expression recognition using CNNs and highlight algorithmic differences and their performance impact. On this basis, we identify existing bottlenecks and consequently directions for advancing this research field. Furthermore, we demonstrate that overcoming one of these bottlenecks – the comparatively basic architectures of the CNNs utilized in this field – leads to a substantial performance increase. By forming an ensemble of modern deep CNNs, we obtain a FER2013 test accuracy of 75.2%, outperforming previous works without requiring auxiliary training data or face registration.
Deep reinforcement learning for de novo drug design

We have devised and implemented a novel computational strategy for de novo design of molecules with desired properties termed ReLeaSE (Reinforcement Learning for Structural Evolution). On the basis of deep and reinforcement learning (RL) approaches, ReLeaSE integrates two deep neural networks—generative and predictive—that are trained separately but are used jointly to generate novel targeted chemical libraries. ReLeaSE uses simple representation of molecules by their simplified molecular-input line-entry system (SMILES) strings only. Generative models are trained with a stack-augmented memory network to produce chemically feasible SMILES strings, and predictive models are derived to forecast the desired properties of the de novo–generated compounds. In the first phase of the method, generative and predictive models are trained separately with a supervised learning algorithm. In the second phase, both models are trained jointly with the RL approach to bias the generation of new chemical structures toward those with the desired physical and/or biological properties. In the proof-of-concept study, we have used the ReLeaSE method to design chemical libraries with a bias toward structural complexity or toward compounds with maximal, minimal, or specific range of physical properties, such as melting point or hydrophobicity, or toward compounds with inhibitory activity against Janus protein kinase 2. The approach proposed herein can find a general use for generating targeted chemical libraries of novel compounds optimized for either a single desired property or multiple properties.
A Study on Earthquake Detection Using Machine Learning

Seismology is the scientific study of earthquakes & the propagation of seismic waves through the earth. The large improvement has been seen in seismology from around hundreds of years. The seismic data plays important role in the seismic data acquisition. This data can be used for analysis which helps to locate the correct location of the earthquake. The more efficient systems are used now a day to locate the earthquakes as large improvements has been done in this field. In older days analog systems are used for data acquisition. The analog systems record seismic signals in a permanent way. These systems are large in size, costly and are incompatible with computer. Due to these drawbacks these analog systems are replaced by digital systems so that data can be recorded digitally. Using different sensor to indentify the natural disaster, MEMS, VIBRATION sensor is used to monitor the earth condition , the different values of the different sensor is given to the ADC to convert the values in digital format, if any changes occurs or in abnormality condition BUZZER will ring.
The neurodevelopment of empathy in humans.

Empathy, which implies a shared interpersonal experience, is implicated in many aspects of social cognition, notably prosocial behavior, morality and the regulation of aggression. The purpose of this paper is to critically examine the current knowledge in developmental and affective neuroscience with an emphasis on the perception of pain in others. It will be argued that human empathy involves several components: affective arousal, emotion understanding and emotion regulation, each with different developmental trajectories. These components are implemented by a complex network of distributed, often recursively connected, interacting neural regions including the superior temporal sulcus, insula, medial and orbitofrontal cortices, amygdala and anterior cingulate cortex, as well as autonomic and neuroendocrine processes implicated in social behaviors and emotional states. Decomposing the construct of empathy into subcomponents that operate in conjunction in the healthy brain and examining their developmental trajectory provides added value to our current approaches to understanding human development. It can also benefit our understanding of both typical and atypical development.
Irregular surface area measuring instrument

The objective here is to develop a flat surface area measuring system which is used to calculate the surface area of any irregular sheet. The irregular leather sheet is used in this work. The system is self protected by user name and password set through software for security purpose. Only authorize user can enter into the system by entering the valid pin code. After entering into the system, the user can measure the area of any irregular sheet, monitor and control the system. The heart of the system is Programmable Logic Controller (Master K80S) which controls the complete working of the system. The controlling instructions for the system are given through the designed Human to Machine Interface (HMI). For communication purpose the GSM modem is also interfaced with the Programmable Logic Controller (PLC). The remote user can also monitor the current status of the devices by sending SMS message to the GSM modem.
Ultrasensitive, rapid and inexpensive detection of DNA using paper based lateral flow assay

Sensitive, specific, rapid, inexpensive and easy-to-use nucleic acid tests for use at the point-of-need are critical for the emerging field of personalised medicine for which companion diagnostics are essential, as well as for application in low resource settings. Here we report on the development of a point-of-care nucleic acid lateral flow test for the direct detection of isothermally amplified DNA. The recombinase polymerase amplification method is modified slightly to use tailed primers, resulting in an amplicon with a duplex flanked by two single stranded DNA tails. This tailed amplicon facilitates detection via hybridisation to a surface immobilised oligonucleotide capture probe and a gold nanoparticle labelled reporter probe. A detection limit of 1 × 10-11 M (190 amol), equivalent to 8.67 × 105 copies of DNA was achieved, with the entire assay, both amplification and detection, being completed in less than 15 minutes at a constant temperature of 37 °C. The use of the tailed primers obviates the need for hapten labelling and consequent use of capture and reporter antibodies, whilst also avoiding the need for any post-amplification processing for the generation of single stranded DNA, thus presenting an assay that can facilely find application at the point of need.
Apparel silhouette attributes recognition

Computer vision and machine learning have great potential to aid in aesthetic judgments and exploration, particularly in the understanding of shapes. This paper presents our work in a well-defined but largely unexplored problem in this field: the automated recognition of apparel silhouette attributes for real-world products. Silhouette attributes, such as v-neck for dresses and open toe for shoes, are very important attributes for understanding the appearance of apparel but difficult to recognize automatically. We propose methods employing multi-modal features and supervised learning to automatically recognize silhouette attributes based on product images and the associated text. These algorithms are extensively tested on a large dataset of dresses, tops, and shoes provided by online retailers. The proposed silhouette recognition approach achieves high recognition accuracy on the nine silhouette categories. Our approach and experiments are also expected to stimulate future research on this topic.
On-Line Mobile Robot Model Identification Using Integrated Perturbative Dynamics

We present an approach to the problem of real-time identification of vehicle motion models based on fitting, on a continuous basis, parametrized slip models to observed behavior. Our approach is unique in that we generate parametric models capturing the dynamics of systematic error (i.e. slip) and then predict trajectories for arbitrary inputs on arbitrary terrain. The integrated error dynamics are linearized with respect to the unknown parameters to produce an observer relating errors in predicted slip to errors in the parameters.
Considerations in planning vegan diets: infants.

Appropriately planned vegan diets can satisfy nutrient needs of infants. The American Dietetic Association and The American Academy of Pediatrics state that vegan diets can promote normal infant growth. It is important for parents to provide appropriate foods for vegan infants, using guidelines like those in this article. Key considerations when working with vegan families include composition of breast milk from vegan women, appropriate breast milk substitutes, supplements, type and amount of dietary fat, and solid food introduction. Growth of vegan infants appears adequate with post-weaning growth related to dietary adequacy. Breast milk composition is similar to that of non-vegetarians except for fat composition. For the first 4 to 6 months, breast milk should be the sole food with soy-based infant formula as an alternative. Commercial soymilk should not be the primary beverage until after age 1 year. Breastfed vegan infants may need supplements of vitamin B-12 if maternal diet is inadequate; older infants may need zinc supplements and reliable sources of iron and vitamins D and B-12. Timing of solid food introduction is similar to that recommended for non-vegetarians. Tofu, dried beans, and meat analogs are introduced as protein sources around 7-8 months. Vegan diets can be planned to be nutritionally adequate and support growth for infants.
Multiband Ring Fractal Monopole Antenna for Mobile Devices

A new form of a hybrid design of a microstrip-fed parasitic coupled ring fractal monopole antenna with semiellipse ground plane is proposed for modern mobile devices having a wireless local area network (WLAN) module along with a Worldwide Interoperability for Microwave Access (WiMAX) function. In comparison to the previous monopole structures, the miniaturized antenna dimension is only about 25 × 25 × 1 mm3 , which is 15 times smaller than the previous proposed design. By only increasing the fractal iterations, very good impedance characteristics are obtained. Throughout this letter, the improvement process of the impedance and radiation properties is completely presented and discussed.
Optimisation of 100V high side LDMOS using multiple simulation techniques

A combination of conventional cross sectional process and device simulations combined with top down and 3D device simulations have been used to design and optimise the integration of a 100V Lateral DMOS (LDMOS) device for high side bridge applications. This combined simulation approach can streamline the device design process and gain important information about end effects which are lost from 2D cross sectional simulations. Design solutions to negate detrimental end effects are proposed and optimised by top down and 3D simulations and subsequently proven on tested silicon.
Towards Computer Assisted International Sign Language Recognition System: A Systematic Survey

There is a number of automated sign language recognition systems proposed in the computer vision literature. The biggest drawback of all these systems is that every nation has their own culture oriented sign language. In other words, everyone needs to develop a specific sign language recognition system for their nation. Although the main building blocks of all signs are gestures and facial expressions in all sign languages, the nation specific requirements make it difficult to design a multinational recognition framework. In this paper, we focus on the advancements in computer assisted sign language recognition systems. More specifically, we discuss if the ongoing research may trigger the start of an international sign language design. We categorize and present a summary of the current sign language recognition systems. In addition, we present a list of publicly available databases that can be used for designing sign language recognition systems.
Impact of Flow and Brand Equity in 3D Virtual Worlds

This research is a partial test of Park et al.’s (2008) model to assess the impact of flow and brand equity in 3D virtual worlds. It draws on flow theory as its main theoretical foundation to understand and empirically assess the impact of flow on brand equity and behavioral intention in 3D virtual worlds. The findings suggest that the balance of skills and challenges in 3D virtual worlds influences users’ flow experience, which in turn influences brand equity. Brand equity then increases behavioral intention. The authors also found that the impact of flow on behavioral intention in 3D virtual worlds is indirect because the relationship between them is mediated by brand equity. This research highlights the importance of balancing the challenges posed by 3D virtual world branding sites with the users’ skills to maximize their flow experience and brand equity to increase the behavioral intention associated with the brand.
Optimization for dynamic ride-sharing: A review

Dynamic ride-share systems aim to bring together travelers with similar itineraries and time schedules on short-notice. These systems may provide significant societal and environmental benefits by reducing the number of cars used for personal travel and improving the utilization of available seat capacity. Effective and efficient optimization technology that matches drivers and riders in real-time is one of the necessary components for a successful dynamic ride-share system. We systematically outline the optimization challenges that arise when developing technology to support ride-sharing and survey the related operations research models in the academic literature. We hope that this paper will encourage more research by the transportation science and logistics community in this exciting, emerging area of public transportation.
Using UML 2 activity diagrams to design information flows and feedback-loops in self-organising emergent systems

Designing self-organising emergence requires explicit support to design system-wide information flows and feedback loops in those flows. The problem-solving power of emergence mainly resides in the coordination between agents which needs exchange of inherently decentralised information through the system. Self-organisation is only possible when there are ongoing feedback loops in those flows. To avoid that engineers are overwhelmed with the details of decentralised coordination mechanisms such as digital pheromones or gradient fields, the design should focus on the bigger coordination picture: information flows. “Information Flow” is proposed as design abstraction which allows to design a solution independently of the coordination mechanism details by explicitly identifying the essential information flows and designing feedback-loops. UML 2 activity diagrams are used as notation. A simple data clustering application together with a realistic automated guided vehicle transportation system show the usefulness and feasibility of information flows.
Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations

How can we learn a classier that is “fair” for a protected or sensitive group, when we do not know if the input to the classier belongs to the protected group? How can we train such a classier when data on the protected group is dicult to aain? In many settings, nding out the sensitive input aribute can be prohibitively expensive even during model training, and sometimes impossible during model serving. For example, in recommender systems, if we want to predict if a user will click on a given recommendation, we oen do not know many aributes of the user, e.g., race or age, and many aributes of the content are hard to determine, e.g., the language or topic. us, it is not feasible to use a dierent classier calibrated based on knowledge of the sensitive aribute. Here, we use an adversarial training procedure to remove information about the sensitive aribute from the latent representation learned by a neural network. In particular, we study how the choice of data for the adversarial training eects the resulting fairness properties. We nd two interesting results: a small amount of data is needed to train these adversarial models, and the data distribution empirically drives the adversary’s notion of fairness.  Alex Beutel, Jilin Chen, Zhe Zhao, Ed H. Chi. 2017.  Adversarially Learning Fair Representations. In Proceedings of 2017Workshop on Fairness, Accountability, and Transparency in Machine Learning, Halifax, Canada, August 2017 (FAT/ML ’17), 5 pages.
MuVE: Efficient Multi-Objective View Recommendation for Visual Data Exploration

To support effective data exploration, there is a well-recognized need for solutions that can automatically recommend interesting visualizations, which reveal useful insights into the analyzed data. However, such visualizations come at the expense of high data processing costs, where a large number of views are generated to evaluate their usefulness. Those costs are further escalated in the presence of numerical dimensional attributes, due to the potentially large number of possible binning aggregations, which lead to a drastic increase in the number of possible visualizations. To address that challenge, in this paper we propose the MuVE scheme for Multi-Objective View Recommendation for Visual Data Exploration. MuVE introduces a hybrid multi-objective utility function, which captures the impact of binning on the utility of visualizations. Consequently, novel algorithms are proposed for the efficient recommendation of data visualizations that are based on numerical dimensions. The main idea underlying MuVE is to incrementally and progressively assess the different benefits provided by a visualization, which allows an early pruning of a large number of unnecessary operations. Our extensive experimental results show the significant gains provided by our proposed scheme.
Context-Based Chinese Word Segmentation using SVM Machine-Learning Algorithm without Dictionary Support

This paper presents a new machine-learning Chinese word segmentation (CWS) approach, which defines CWS as a break-point classification problem; the break point is the boundary of two subsequent words. Further, this paper exploits a support vector machine (SVM) classifier, which learns the segmentation rules of the Chinese language from a context model of break points in a corpus. Additionally, we have designed an effective feature set for building the context model, and a systematic approach for creating the positive and negative samples used for training the classifier. Unlike the traditional approach, which requires the assistance of large-scale known information sources such as dictionaries or linguistic tagging, the proposed approach selects the most frequent words in the corpus as the learning sources. In this way, CWS is able to execute in any novel corpus without proper assistance sources. According to our experimental results, the proposed approach can achieve a competitive result compared with the Chinese knowledge and information processing (CKIP) system from Academia Sinica.