Reviewerly/exHarmony huggingface dataset:
The Use of Conversational Interfaces in Long Term Patient Care

With recent technology advancements conversational interfaces such as Google Home, Siri, and Amazon Echo have gained much popularity among all kinds of ages. Conversational interfaces include chatbots where it's available on-screen and as a voice assistant. With the increasing number of long-term patients such as elderly and disabled people, it is essential to use the technology to fulfill their needs and make them more independent. Therefore, this study is initiated with the motivation to investigate how to facilitate the communication and psychological requirements of long-term patients using conversational interfaces. For that, a comprehensive Interview study based on long term patients in Sri Lanka was conducted to identify the requirements of the elderly long-term patients and done a thematic analysis on the use of voice integrated personal assistants. Proof of concept designed from the identified needs in a user-centric manner. Further, the Developed solution is quantitatively evaluated by elderly long-term patients mainly under five criteria. Based on the results, appropriateness and acceptance of conversational interfaces in the research domain are presented.
Genetic-GAN: Synthesizing Images Between Two Domains by Genetic Crossover

Synthesizing an interpolated image between two real images can be achieved by a simple interpolation on the latent space of the images, so that the resulting image inherits features from both. The task becomes more difficult when two images are in different domains, because an interpolated image whose latent representation lies near the middle of two distant input images may not be realistic and may end up in either domain. In this paper, we present a novel technique called Genetic-GAN that solves a novel problem of synthesizing a set of images that inherit features from both of the domains, while at the same time allowing control of which domain the resulting images fall into. We experiment on human face images using female and male genders as two different domains. We show that our method can take two images with very different attributes and synthesize images between them, and can perform domain transformations.
SIoT Framework: Towards an Approach for Early Identification of Security Requirements for Internet-of-things Applications

Context: Software start-ups have shown their ability to develop and launch innovative software products and services. Small, motivated teams and uncertain project scope makes start-ups good candidates for adopting Agile practices. Objective: We explore how start-ups use Agile practices and what effects can be associated with the use of those practices. Method We use a case survey to analyze 84 start-up cases and 56 Agile practices. We apply statistical methods to test for statistically significant associations between the use of Agile practices, team, and product factors. Results: Our results suggest that development of the backlog, use of version control, code refactoring, and development of user stories are the most frequently reported practices. We identify 22 associations between the use of Agile practices, team, and product factors. The use of Agile practices is associated with effects on source code and overall product quality. A teams’ positive or negative attitude towards best engineering practices is a significant indicator for either adoption or rejection of certain Agile practices. To explore the relationships in our findings, we set forth a number of propositions that can be investigated in future research. Conclusions: We conclude that start-ups use Agile practices, however without following any specific methodology. We identify the opportunity for more fine-grained studies into the adoption and effects of individual Agile practices. Start-up practitioners could benefit from Agile practices in terms of better overall quality, tighter control over team performance, and resource utilization.
Object Recognition and Security Improvement by Enhancing the Features of CCTV

In the today's scenario, security is the biggest concern. Everybody wants the best security for their homes, offices or companies. We have various options for the security including CCTV (Closed-Circuit Television). In our proposed system, we have tried to improve the security provided by the CCTVs. With our system, CCTV will not only capture the things but also recognize the threat and take actions accordingly. In our system, we have implemented OR (Object Recognition), alarm system etc. to enhance the security provided by CCTVs. In current CCTV, the system is only capable of capturing the shots. They do not have object recognition facility. However, we need a system that is capable of capturing as well as recognizing any kind of threat. In our system various types of technologies and API's are used like Tensorflow Object Detection, Twilio (Communication API), OpenCV (Open Source Computer Vision), MySQL Connector. So with this we tried to improve the security as well as reliability of CCTV system.
3 Handwritten Digit Recognition Using Convolutional Neural Networks

Optical character recognition (OCR) systems have been used for extraction of text contained in scanned documents or images. This system consists of two steps: character detection and recognition. One classification algorithm is required for character recognition by their features. Character can be recognized using neural networks. The multilayer perceptron (MLP) provides acceptable recognition accuracy for character classification. Moreover, the convolutional neural network (CNN) and the recurrent neural network (RNN) are providing character recognition with high accuracy. MLP, RNN, and CNN may suffer from the large amount of computation in the training phase. MLP solves different types of problems with good accuracy but it takes huge amount of time due to its dense network connection. RNNs are suitable for sequence data, while CNNs are suitable for spatial data. In this chapter, a CNN is implemented for recognition of digits from MNIST database and a comparative study is established between MLP, RNN, and CNN. The CNN provides the higher accuracy for digit recognition and takes lowest amount of time for training the system with respect to MLP and RNN. The CNN gives better result with accuracy up to 98.92% as the MNIST digit dataset is used, which is spatial data.
Data-driven insights into correlation among geophysical setting, topography and seafloor sediments in the Ross Sea, Antarctic

Detailed mapping based on the high-resolution grids, such as GEBCO, ETOPO1, GlobSed, EGM-2008 is crucial for various domains of Earth sciences: geophysics, glaciology, Quaternary, sedimentology, geology, environmental science, geomorphology, etc. The study presented a GMT-based scripting techniques of the cartographic data processing aimed at the comparative analysis of the bathymetry, sediment thickness, geologic objects and geophysical settings in the study area based on various datasets. The study area is located in the Ross Sea, Antarctic. The highest values of the sediment thickness over 7,500 m are dominating in the southwest segment of the Ross Sea closer to the Victoria Land, followed by the region over the Ross Ice Shelf with values between 5,500 to 7,000 m (170°-175°W). The increased sediment thickness (2,500 to 3,000 m) was also mapped seen in the region NE off the Sulzberger Bay (70-75°S to 140-155°W), caused by the closeness of the Marie Bird Land ice coasts. A remarkable correlation between the gravity and the topography of the sea-land border in the Marie Bird Land area is well reflected in the coastal line and a set of the higher values in the free-air gravity. On the contrary, negative values (–60 to -80 mGal) are notable along the submarine toughs stretching parallel in the western part of the basin: e.g. the trough stretching in NW-SE direction in the 170°W-175°E, 65°S-68°S, between the 167°W-175°W, 70°S-72°S. Such correlations are clearly visible on the map, indicating geological lineaments and bathymetric depressions correlating with gravity grids. The paper contributes to the regional studies of the Ross Sea, the Antarctic and Polar region, and development of the cartographic technical methodologies by presenting an application of the GMT for thematic mapping.
MiCE: Mixture of Contrastive Experts for Unsupervised Image Clustering

We present Mixture of Contrastive Experts (MiCE), a unified probabilistic clustering framework that simultaneously exploits the discriminative representations learned by contrastive learning and the semantic structures captured by a latent mixture model. Motivated by the mixture of experts, MiCE employs a gating function to partition an unlabeled dataset into subsets according to the latent semantics and multiple experts to discriminate distinct subsets of instances assigned to them in a contrastive learning manner. To solve the nontrivial inference and learning problems caused by the latent variables, we further develop a scalable variant of the Expectation-Maximization (EM) algorithm for MiCE. Empirically, we evaluate the clustering performance of MiCE on four widely adopted natural image datasets. MiCE achieves significantly better results than various previous methods and a strong contrastive learning baseline.
Solutions to the Cold Start Problem in Recommender System: A survey

Collaborative Filtering (CF) is one of the techniques of recommender system to recommend the items to user based on previously collected preferences of other users. The efficiency of recommendation is mainly depending on how much information about target user and target item available in the system. When this information are not available sufficiently then it creates problem in the system for the new user and new item, which is known as the cold start problem. This problem is very challenging for CF as it deteriorates the performance of recommendations. In literature, many authors have tried to overcome this problem. This study surveys the work done till date for the solution of cold start problem. Keywords : Recommender System, Cold Start Problem, New Item Cold Start, New User Cold Start, Data Sparsity Cite this Article Shital N. Gondaliya, Kiran Amin. Solutions to the Cold Start Problem in Recommender System: A Survey. Journal of Software Engineering Tools & Technology Trends. 2018; 5(3): 14–21p.
An Enhanced WordNet Query Expansion Approach for Ontology Based Information Retrieval System

Ontology-based information retrieval is described as a cutting-edge approach capable to enhance the returns of semantic results from documents. This approach works better when similar and relevant terms are added to user’s initial query terms using data sources such as wordnet; such technique is known as query expansion. However, the precision of the added term(s) tends to be inaccurate because of the existing WordNet’s deficit to handle inflected forms of words. In lieu of this development, this research aims to design Rule based Web Ontology Language (OWL) Information Retrieval System with an enhanced wordnet for query expansion but only limited to the noun subnet database. A combined ontology development methodology was implored; and OWL-2 to develop the ontology for a novel domain of maize crop considering primarily soil, fertilizer and irrigation knowledge. Its rule-based ontology because Competency Questions were modeled using First-Order-Logic (FOL) and encoded with Semantic Web Rule Language (SWRL). Similarly, the wordnet was enhanced on python environment considering the lemmatization’s lookup table and the third party modules of Natural Language Tool Kits (NLTK), pattern.en and enchant. Therefore, in this research, the improved wordnet can handle inflected word without stemming it to the root word. It also correctly suggested related words in the case of user’s wrong spelt word thereby; reduces minimally time wastage and fatigue. This development invariably aids ontology validation along with the other forms of validations carried out. The research ultimately offers an effective ontology-based information retrieval system based on the proposed algorithmic framework.
Физиология и психология – диалектика взаимодействия при решении психофизиологической проблемы

The review is devoted to the history of solving one of the most fundamental problems of humanity: how does the material brain give birth to a psyche devoid of the attributes of materiality? This is a so-called or psychophysiological problem (mind-body problem). The article provides an analytical analysis of various approaches and concepts used by representatives of different Sciences for more than two thousand years to solve this riddle. The emphasis is on two Sciences – psychology and physiology, in which psychologists and physiologists sought a solution to a psychophysical problem based on the principles and methods of their science. The article shows that advances in the field of neurophysiology and neurobiology are slowly but inevitably bringing us closer to solving the difficult problem of consciousness, while the descriptive and speculative approaches of philosophers and psychologists have not yet allowed us to reveal the principles of the psyche and understand the mechanisms of its formation by the brain. Nevertheless, the authors of the article believe that only the joint work of physiologists and psychologists on a common plan, with the development of a single concept of the search for truth and in close cooperation with representatives of other neuroscience, can lead humanity to unravel the great mystery of nature: the discovery of the principles and mechanisms of the psyche and consciousness.
Space services market development

The article discusses the economic processes that occur during the commercialization and provision of a new type of consumer and production services in the field of space activities, explores the concept and structure of the global and Russian space services markets, which are not a single whole and include a number of areas: launch services market, geoinformation market services, the market for navigation and geodetic services, the market for space communications, television and telecommunications, the market for space services to strengthen defense s countries and the development of basic sciences; the role of Russia in the space activity market is revealed, special attention is paid to the commercial use of space tools for remote sensing of the Earth, the prospects for the development of the space activity market and its transformation into a popular business area through the formation of space tourism markets and interplanetary spacecraft are determined.
Hepatitis classification using support vector machines and random forest

&lt;span id="docs-internal-guid-e57881bf-7fff-62db-2c1e-192664c8e8a8"&gt;&lt;span&gt;Hepatitis is a medical condition defined by inflammation of the liver. It can be caused by infection of the liver by hepatitis viruses or is of unknown aetiology. There are 5 main hepatitis viruses, such as virus types A, B, C, D and E. The infection may occur with limited or no symptoms, but also may include some symptoms like abdominal pain, dark urine, extreme fatigue, jaundice, nausea or vomiting. Because Indonesia is a large archipelago, the prevalence of viral infections varies greatly by region of acute hepatitis patients. This research uses data of hepatitis examination result with amount of 113 data and 5 features. The method that used is support vector machines (SVM) and random forest method. SVM is the classification method that uses discriminant hyper-plane, dividing to classes. meanwhile, random forest is a tree-based ensemble depending on a collection of random variables. SVM and random forest (RF) are applied to predict hepatitis data, and then the results will be compared.&lt;/span&gt;&lt;/span&gt;
Exploring Routing Strategies for Multilingual Mixture-of-Experts Models

Sparsely-Gated Mixture-of-Experts (MoE) has been a successful approach for scaling multilingual translation models to billions of parameters without a proportional increase in training computation. These models, however, are prohibitively large for serving deployment and there is no easy way to extract a sub-network to decode for a particular language pair. This work proposes improved strategies to route MoE models by tasks instead of tokens, thus enabling separation of network structures at decoding time while enjoying the benefits of scale and task sharing at training time. We compare routing strategies at multiple levels (token, sentence, task) in both, the encoder and the decoder, and conduct extensive experiments on two benchmarks: the public WMT dataset of 30 language pairs and an in-house web-scale dataset of 200 language pairs. On WMT, with a Transformer base model with 32 experts, our task-level MoE outperforms the best performing token-level MoE model by +1.0 BLEU on average over all language pairs. When scaling up to Transformer big model with 128 experts on the large-scale massively multilingual benchmark, our task-level MoE is competitive with token-level MoE while being able to reduce the decoder model size by a factor of 32.34 and increase peak throughput by 2.6 times at inference.
Intelligent Tutoring Systems, Learning and Cognitive Styles of Dyslexic Students

This paper discusses how teachers may not always be available for supporting students, especially if students live in areas which are relatively inaccessible or too time consuming for teachers to reach. An alternative is intelligent tutoring systems (ITS) offering learners course support. However, it is evident that learners with special educational needs (SEN) such as dyslexia, may not have the cognitive facility to progress at levels equivalent to their peers and so may require support in strategies in which they could grasp concepts. One way of helping students to learn would be to harness strategies of teaching in ITS. It is suggested that bringing students together with ITS using mnemonic strategies may be a way of helping students achieve. The paper discusses the benefits of using mnemonic strategies by dyslexic learners and ways in which mnemonics could be harnessed in ITS.
Sampling Sparse Representations with Randomized Measurement Langevin Dynamics

Stochastic Gradient Langevin Dynamics (SGLD) have been widely used for Bayesian sampling from certain probability distributions, incorporating derivatives of the log-posterior. With the derivative evaluation of the log-posterior distribution, SGLD methods generate samples from the distribution through performing as a thermostats dynamics that traverses over gradient flows of the log-posterior with certainly controllable perturbation. Even when the density is not known, existing solutions still can first learn the kernel density models from the given datasets, then produce new samples using the SGLD over the kernel density derivatives. In this work, instead of exploring new samples from kernel spaces, a novel SGLD sampler, namely, Randomized Measurement Langevin Dynamics (RMLD) is proposed to sample the high-dimensional sparse representations from the spectral domain of a given dataset. Specifically, given a random measurement matrix for sparse coding, RMLD first derives a novel likelihood evaluator of the probability distribution from the loss function of LASSO, then samples from the high-dimensional distribution using stochastic Langevin dynamics with derivatives of the logarithm likelihood and Metropolis–Hastings sampling. In addition, new samples in low-dimensional measuring spaces can be regenerated using the sampled high-dimensional vectors and the measurement matrix. The algorithm analysis shows that RMLD indeed projects a given dataset into a high-dimensional Gaussian distribution with Laplacian prior, then draw new sparse representation from the dataset through performing SGLD over the distribution. Extensive experiments have been conducted to evaluate the proposed algorithm using real-world datasets. The performance comparisons on three real-world applications demonstrate the superior performance of RMLD beyond baseline methods.
Vision Powered Conversational AI for Easy Human Dialogue Systems

In this paper, we propose an end to end goal-oriented conversational AI agent that can provide contextual information from a potential hazard site. We posit the conversational agent as a FloodBot capable of seeing, sensing, assessing hazard condition, and ultimately conversing about them. We present our domain-specific FloodBot design-solution and learning-experience from the real-time deployment in a flash flood devastated city that uses state-of-the-art deep learning models. We specifically used computer vision and pertinent natural language processing technologies to empower the conversation power of the FloodBot. To deliver such practical and usable AI, we chain multiple deep learning frameworks and create a human-friendly question-answer based dialogue system. We present our deployment details from the last five months and validate the results using ongoing COVID19's impact on the area as well.
A multi-scale conditional deep model for tumor cell ratio counting

We propose a method to accurately obtain the ratio of tumor cells over an entire histological slide. We use deep fully convolutional neural network models trained to detect and classify cells on images of H&amp;E-stained tissue sections. Pathologists' labels consisting of exhaustive nuclei locations and tumor regions were used to trained the model in a supervised fashion. We show that combining two models, each working at a different magnification allows the system to capture both cell-level details and surrounding context to enable successful detection and classification of cells as either tumor-cell or normal-cell. Indeed, by conditioning the classification of a single cell on a multi-scale context information, our models mimic the process used by pathologists who assess cell neoplasticity and tumor extent at different microscope magnifications. The ratio of tumor cells can then be readily obtained by counting the number of cells in each class. To analyze an entire slide, we split it into multiple tiles that can be processed in parallel. The overall tumor cell ratio can then be aggregated. We perform experiments on a dataset of 100 slides with lung tumor specimens from both resection and tissue micro-array (TMA). We train fully-convolutional models using heavy data augmentation and batch normalization. On an unseen test set, we obtain an average mean absolute error on predicting the tumor cell ratio of less than 6%, which is significantly better than the human average of 20% and is key in properly selecting tissue samples for recent genetic panel tests geared at prescribing targeted cancer drugs. We perform ablation studies to show the importance of training two models at different magnifications and to justify the choice of some parameters, such as the size of the receptive field.
SARS-CoV-2 Diagnostic Tests for Reopening of Borders: A Systematic Review and Meta-Analysis

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