Reviewerly/exHarmony huggingface dataset:
Blending Ensemble of Fine-Tuned Convolutional Neural Networks Applied to Mammary Image Classification

Medical images classification is a challenging research topic in the field of computer vision, especially when applied to diagnosis of breast cancer (BC). Nowadays, histopathological image is marked as the gold standard for diagnosing BC. However, such diagnosis is heavily dependent on the clinician's experience, which is extremely time consuming and is subjected to human error even for experienced doctors. To address those problems, this paper implements an automated method for distinguishing the benign from the malignant tumor based on a convolutional neural network (CNN). Traditional deep CNN and machine learning methods not only lead to poor performance, but also fail to make full use of the long-term dependence between some key features and image tags. To further meet the high accuracy requirement of diagnosis, according to the characteristics of histopathological images, we propose a novel CNN framework. Firstly, a normal image is augmented to solve the problem about having a limited database. Secondly, we introduce transfer learning to obtain more accurate weight parameters that were pre-trained on the ImageNet. Thirdly, we combine various features extracted by many individual models to obtain comprehensive features. Finally, random forest is introduced to enforce classification. The experimental results show that novel CNN frameworks have better performance compared with individual models, including DenseNet and ResNet. Experimental results are able to prove the effectiveness of our strategy.
Variational Generation of Thermofield Double States and Critical Ground States with a Quantum Computer

Finite-temperature phases of many-body quantum systems are fundamental to phenomena ranging from condensed-matter physics to cosmology, yet they are generally difficult to simulate. Using an ion trap quantum computer and protocols motivated by the Quantum Approximate Optimization Algorithm (QAOA), we generate nontrivial thermal quantum states of the transverse-field Ising model (TFIM) by preparing thermofield double states at a variety of temperatures. We also prepare the critical state of the TFIM at zero temperature using quantum-classical hybrid optimization. The entanglement structure of thermofield double and critical states plays a key role in the study of black holes, and our work simulates such nontrivial structures on a quantum computer. Moreover, we find that the variational quantum circuits exhibit noise thresholds above which the lowest depth QAOA circuits provide the best results.
Towards an automation of the fact-checking in the journalistic web context

Is Fact checking automatisable? Apparently, yes, since numerous moved forward noted in the search and the analysis of digital data. However, this task which in priori seemed to be simple, turns out rather binding. Indeed, automate the check of facts combine and requires at the same time very advanced knowledge in analysis of data, in search of web data, the web technologies, in image processing and sometimes in automatic natural language processing (NLP). Nevertheless, the latter years, numerous researches are led to deepen such an analysis. In this article, having revisited the state of the art concerning the question, we identify and diagnose in detail the obstacles before concluding with an explanation of the methods.
Latent Semantic Analysis for Knowledge Management in Construction

Knowledge management (KM) in the Architecture, Engineering and Construction (AEC) industry is hampered by the overload and fragmentation of heterogenous data, information and knowledge. This has led to an inability to identify, capture, manage and reuse the large amount of potential knowledge generated by AEC organizations, underpinned fundamentally by the tacit nature of AEC knowledge, as well as the lack of interoperability of information systems. We address these challenges by evaluating a computational machine learning technique known as Latent Semantic Analysis (LSA) in real world AEC contexts, with reference to KM and knowledge creation theory. Specifically, we investigate whether and how LSA can be used to infer insights from abundant unstructured natural language textual material, that would otherwise have required expert tacit knowledge to achieve. Our investigations are rooted in various AEC domain problem contexts, applicable across the lifecycle: 1) Mapping of Communities of Practice via modelling of personnel knowledge, skills and competences, 2) Management of constructability knowledge and inference of domain topical relationships, and 3) Extraction and processing of change issues from a BIM-based renovation project. For each, we articulate the respective domain problems through the lenses of KM and align LSA to address them. Our principle results demonstrate that valuable insights, traditionally dependant on domain-specific human reasoning, can indeed be inferred from readily accessible textual documentation (be it passively or actively generated) in each AEC context. As such, LSA can help to give quantitative substance to many conceptual KM conjectures and contribute to developing both practical and philosophical elements of KM in AEC. We demonstrate that LSA has a clear role in advancing domain-specific applications of information retrieval and case-based reasoning systems, ontological mediation between data models as a matter of interoperability, as well as reconciliation between the social-technical, the tacit-explicit, and the qualitative-quantitative relationships of KM in AEC. Additionally, we highlight the contributions of our results with respect to the knowledge creation model – constituting processes of socialization, externalization, combination and internalization (SECI). Through demonstrating LSA's capabilities in the AEC context, we hope that this research opens new perspectives on the role of KM in various AEC applications, and likewise, new perspectives in the role of artificial intelligence within KM.
Image Segmentation Method Based on Statistical Parameters of Homogeneous Data Set

In this paper, we present a new automatic method of image segmentation, which uses statistical parameters of a homogeneous data set. The method can be applied to image sets obtained from CT, MRI, ultrasound, and histological investigations. The result of applying the proposed method is image components and their contours.
Rapid Assessment Procedures Formative Research Approach Used to Design a Mobile-technology Enhanced Social and Behavior Change Communication Nutrition Strategy in Nigeria (P16-053-19)

To design of a tailored social and behavior change communication (SBCC) strategy to improve program coverage and key nutrition behaviors of internally displaced persons (IDP) in northeast Nigeria, To determine preferred and appropriate ways of integrating one- and two-way mobile communication tools into SBCC activities, To demonstrate application of the Rapid Assessment Procedures (RAP) formative research approach in a humanitarian context with severe insecurity. A RAP approach and was conducted over 4 weeks in August, 2018. It included multiple qualitative methods and various participant types for triangulation. In-depth interviews (IDI), open-ended interviews and focus group discussions (FGD) were conducted among mothers and fathers of children under 5 years, local community leaders and health workers. Semi-structured interviews were conducted among professionals (nutrition and technology experts) with contextual programming knowledge. Interviews and FGD were digitally recorded in Hausa or Kanuri before translation and transcription into English. Textual data were managed in NVivo software, where salient themes pertinent to the research questions were identified. A total of 29 IDI, 12 FGD and 10 semi-structured interviews were conducted among diverse stakeholders. An SBCC strategy was designed with preferred communication channels, defined audience segments and tailored messages using emic terms in local languages. Primary caregivers, typically female in this context, were identified as the primary audience. Community health workers, local leaders (e.g., imams) and husbands were identified as secondary audiences who influence the primary caregivers’ nutrition behaviors. Interpersonal (care groups), media (radio) and mobile technologies (interactive voice response calls; SMS; chatbots in low-cost websites) emerged as preferred and viable communication channels. Salient terms and phrases were applied to standard biomedical nutrition messages about breastfeeding and complementary feeding. A RAP approach offers a viable formative research model in humanitarian contexts where security challenges might not allow for desired lengthy, in-depth formative work typical of high-quality SBCC strategy design. World Food Program, Korea International Cooperation Agency (KOICA).
A Mobility Evaluation of Tilt Panning and Offset Sensing Smart Watch Input

Small smartwatch touchscreens restrict input and researchers have long explored how alternative modalities and techniques can enable new possibilities and boost expressiveness. However, these techniques are generally designed in and for static, stable settings; mobility issues are rarely considered. To address this omission, we conducted a mobility study that compares two recently proposed alternative smartwatch input modalities: physical movements of a watch on the wrist, and an offset touch sensor on the edge of the device. We observed high selection errors for tilt input (23.89% to 34.22%) and prolonged times for offset sensing (>1000ms). We propose a combined input technique designed to fit the constraints of mobile watch use: touches to the device edge stabilize and constrain input, while tilt and touch control and trigger it. A second study shows this design can improve target selection time while mobile to less than 800ms with error rates of 10.2%.
An Empirical Study of the Effectiveness of Software Architecture Evaluation Meetings

One of the most important issues in scenario-based software architecture evaluation is the development of scenarios for characterizing a desired set of quality attributes by holding meetings of stakeholders. As such meetings are costly to arrange, studying the effectiveness of such meetings is an important research question. In this paper, we report on the findings from a software architecture evaluation empirical study aimed at investigating the effectiveness of scenario development meetings in terms of the quantity of gained and lost scenarios during the evaluation process. The findings from the experiment data analysis raise the question about the effectiveness of holding meetings for developing scenarios as more existing scenarios were lost than new scenarios were gained as a result of these meetings.
An approach for learning from robots using formal languages and automata

Purpose In this study, human activity with finite and specific ranking is modeled with finite state machine, and an application for human–robot interaction was realized. A robot arm was designed that makes specific movements. The purpose of this paper is to create a language associated to a complex task, which was then used to teach individuals by the robot that knows the language. Design/methodology/approach Although the complex task is known by the robot, it is not known by the human. When the application is started, the robot continuously checks the specific task performed by the human. To carry out the control, the human hand is tracked. For this, the image processing techniques and the particle filter (PF) based on the Bayesian tracking method are used. To determine the complex task performed by the human, the task is divided into a series of sub-tasks. To identify the sequence of the sub-tasks, a push-down automata that uses a context-free grammar language structure is developed. Depending on the correctness of the sequence of the sub-tasks performed by humans, the robot produces different outputs. Findings This application was carried out for 15 individuals. In total, 11 out of the 15 individuals completed the complex task correctly by following the different outputs. Originality/value This type of study is suitable for applications to improve human intelligence and to enable people to learn quickly. Also, the risky tasks of a person working in a production or assembly line can be controlled with such applications by the robots.
Icytomine: A User-Friendly Tool for Integrating Workflows on Whole Slide Images

We present Icytomine, a user-friendly software platform for processing large images from slide scanners. Icytomine integrates in one unique framework the tools and algorithms that were developed independently on Icy and Cytomine platforms to visualise and process digital pathology images. We illustrate the power of this new platform through the design of a dedicated program that uses convolutional neural network to detect and classify glomeruli in kidney biopsies coming from a multicentric clinical study. We show that by streamlining the analytical capabilities of Icy with the AI tools found in Cytomine, we achieved highly promising results.
From Emoji Usage to Categorical Emoji Prediction

Emoji usage drastically increased recently, they are becoming some of the most common ways to convey emotions and sentiments in social messaging applications. Several research works automatically recommend emojis, so users do not have to go through a library of thousands of emojis. In order to improve emoji recommendation, we present and distribute two useful resources: an emoji embedding model from real usage, and emoji clustering based on these embeddings to automatically identify groups of emojis. Assuming that emojis are part of written natural language and can be considered as words, we only used unsupervised learning methods to extract patterns and knowledge from real emoji usage in tweets. Thereby, emotion categories of face emojis were obtained directly from text in a fully reproductible way. These resources and methodology have multiple usages; for example, they could be used to improve our understanding of emojis or enhance emoji recommendation.
A closed-form general solution for the distance of point-to-ellipse in two dimensions

This paper resolved a closed-form general solution for the distance of one point and one ellipse in a two dimensional plane. Even such problem is simple as apparently to be understood, the solutions provided from literature are all about iterative algorithms or approximating approaches. None of O(1) methods or general roots solutions are found. The author takes several geometrical transformations to reduce the power of the equation to simplify the problem. Eventually, a closed-form general solution is resolved in this paper. This can be a contribution both for academic and for in practice and should be archived. Based on this general solution, we can develop the algorithm computing the shortest path touring n sequenced ellipses soon. Since the results of this paper is sufficiently fundamental, it can be conducted in a variety of applications: in the fields of computer-aided design and manufacturing, computer graphics, layered manufacturing, prototyping, robot motion (path) planning, wireless sensor networking, multimedia animation, geodesy, astronomy, physics, molecular geometry, electromagnetics, and Fluid mechanics.
THE EFFECT OF TASK-BASED LANGUAGE TEACHING (TBLT) STRATEGY ON STUDENTS’ SPEAKING ABILITY IN DEMONSTRATIVE SPEECH

The aim of conducting this research was to find out whether or not TBLT strategy has the effect of students’ speaking ability. The population was all of the eleventh grades students of MAN 2 Kota Bengkulu in academic year 2017/2018 that consist of 281 students. The sample of the research of this research was taken from class XI IPS 3 which consisted of 35 students and class XI IPS 4 which consisted of 35 students. The students were divided into two class, the experiment class and the control class. The experiment was given treatment by using TBLT strategy in teaching speaking about demonstrative speech, while the control class was given by using conventional technique. The pretest was given to the two groups before giving the treatment. The mean score of experimental group was 57.786 and the mean score of control class was 58.057. The result showed that sig.(2-tailed) 0.927 was higher than α-value (0.05). It means that the ability of both classes are not significant effect.  After given the treatment for ten meeting in experimental class, the mean score of experimental class was 68.92 and the mean score of control class was 63.54. The result showed that sig.(2tailed) 0.02 was less than (0.05). It means that the alternative hypothesis accepted and null hypothesis is rejected. So the researcher concluded the TBLT strategy gave positive effect towards students’ speaking ability in demonstrative speech of MAN 2 Kota Bengkulu. Moreover, the problem of this research formulated in the question: Is there any significant effect on students’ speaking ability in demonstrative speech between the students who are taught using TBLT strategy and those of that who are not?  The hypothesis of this research is there is significant effect on students’ speaking ability in demonstrative speech between the students who are taught using TBLT strategy and those of that who are not.
Application of Hierarchical Clustering for Object Tracking with a Dynamic Vision Sensor

Monitoring public space with imaging sensors to perform an object- or person-tracking is often associated with privacy concerns. We present a Dynamic Vision Sensor (DVS) based approach to achieve this tracking that does not require the creation of conventional grey- or color images. These Dynamic Vision Sensors produce an event-stream of information, which only includes the changes in the scene. The presented approach for tracking considers the scenario of fixed mounted sensors. The method is based on clustering events and tracing the resulting cluster centers to accomplish the object tracking. We show the usability of this approach with a first proof-of-concept test.
Categories with families and first-order logic with dependent sorts

First-order logic with dependent sorts, such as Makkai's first-order logic with dependent sorts (FOLDS), or Aczel's and Belo's dependently typed (intuitionistic) first-order logic (DFOL), may be regarded as logic enriched dependent type theories. Categories with families (cwfs) is an established semantical structure for dependent type theories, such as Martin-L\"of type theory. We introduce in this article a notion of hyperdoctrine over a cwf, and show how FOLDS and DFOL fit in this semantical framework. A soundness and completeness theorem is proved for DFOL. The semantics is functorial in the sense of Lawvere, and uses a dependent version of the Lindenbaum-Tarski algebra for a DFOL theory. Agreement with standard first-order semantics is established. Applications of DFOL to constructive mathematics and categorical foundations are given. A key feature is a local propositions-as-types principle.
Forecasting of Solar Irradiance using Probability Distributions for a PV System: A Case Study

Photo Voltaic (PV) generation is intermittent which cannot carry out the constant electric power. The amount of solar irradiance received for a particular area is one of the most important climatic conditions for forecasting PV generation. The solar irradiance data used for analysis is collected from Koneru Lakshmaiah Education Foundation (KLEF) and the data is collected for the year (2017-18). In this paper, four Probability Distribution Functions (PDFs) such as Normal, Weibull, Rayleigh, Log normal are used to investigate the best fit probability distribution of the collected solar irradiance data. Root Mean Square Error (RMSE) method is used as a goodness of fit test for identifying the best suited probability distribution function for the collected solar irradiance data. With this analysis, solar irradiance and PV power generation can be predicted for the next year. The efficacy of the proposed method is validated using MATLAB.
JCTDHS at SemEval-2019 Task 5: Detection of Hate Speech in Tweets using Deep Learning Methods, Character N-gram Features, and Preprocessing Methods

In this paper, we describe our submissions to SemEval-2019 contest. We tackled subtask A - “a binary classification where systems have to predict whether a tweet with a given target (women or immigrants) is hateful or not hateful”, a part of task 5 “Multilingual detection of hate speech against immigrants and women in Twitter (hatEval)”. Our system JCTDHS (Jerusalem College of Technology Detects Hate Speech) was developed for tweets written in English. We applied various supervised ML methods, various combinations of n-gram features using the TF-IDF scheme and. In addition, we applied various combinations of eight basic preprocessing methods. Our best submission was a special bidirectional RNN, which was ranked at the 11th position out of 68 submissions.
Mixed Reality Extension System Using Beam Projectors : Beyond the Sight

Recently commercial mixed-reality devices have be launched and a variety of mixed-reality content has produced, but narrow field of view, which appear to be hardware technical limitations, are mentioned as an important issue for hindering immersion and limiting the scope of use. We propose a new innovative system that cooperate multiple beam projectors and a number of mixed reality devices. Using this technology, users can maximize immersion and minimize frustration of narrow viewing angles through 3D object rendering on background of large 2D screens. This system, named BtS (Beyond the Sight), is implemented on a client-server basis and includes the ability to calibrate between devices, share spatial coordinate systems, and synchronize real-time renderings as core modules. In this paper, each configuration module is described in detail and the possibility of its performance and application is shown through the introduction of mixed reality content case created using BtS system.
The Effect of Onomatopoeia to Enhancing User Experience in Virtual Reality

Onomatopoeia refers to a word that phonetically imitates, resembles, or suggests the sound (or motion that accompanies sound) that it describes. It is often used in comics, games, and videos, along with the actual sound in caption. This usage is a way to emphasize, exaggerate, dramatize and draw attention the situation. In this paper we explore if the use of onomatopoeia, associated with sound feedback, could also bring about similar effects and improve the user experience in immersive virtual reality. We present an experiment comparing the user’s subjective experiences and attentive performance in two virtual worlds, each configured in two test conditions: (1) sound feedback with no onomatopoeia and (2) sound feedback with it. Our experiment has found that the moderate and strategic use of onomatopoeia can indeed help direct user attention, offer object affordance and thereby enhance user experience and even the sense of presence and immersion.
Enabling finger pointing based text selection on touchscreen mobile devices

Text selection relying on adjustment handles is a frequently used method on touchscreen mobile devices. However, this method suffers from limitations such as slow and unconformable interactions when dragging handles and being inadequate to select non-adjacent text for editing together. To address these limitations, this study proposes Text Pin, a novel technique that specifies the two ends of intended text by finger tapping rather than finger dragging. We designed mode-augmented handles to enable such operation. Experimental results show Text Pin was faster than default handle-based techniques on Android while maintaining comparable selection accuracy.