Reviewerly/exHarmony huggingface dataset:
Compatibility Evaluation of Clustering Algorithms for Contemporary Extracellular Neural Spike Sorting

Deciphering useful information from electrophysiological data recoded from the brain, in-vivo or in-vitro, is dependent on the capability to analyse spike patterns efficiently and accurately. The spike analysis mechanisms are heavily dependent on the clustering algorithms that enable separation of spike trends based on their spatio-temporal behaviours. Number of clustering algorithms are reported in the literature over several decades focused on different applications. Although a small subset of clustering algorithms is adopted for spike analysis, however, not much work has been reported on the compliance and suitability of such clustering algorithms for spike analysis. In this work, we have attempted to comment on the suitability of available clustering algorithms and how do they actually perform when exposed to spike analysis. In this regard, we have performed a compatibility assessment on algorithms already employed in spike sorting as well as the algorithms yet to be investigated for application in sorting neural spikes. The performance of the algorithms is compared in terms of their accuracy, confusion matrix and well accepted internal and external indices. Three data sets comprising of easy (low noise), difficult (noisy) and a real data with known ground truth are chosen for performance comparison, ensuring a uniform test bed. The procedure also employs two feature sets, PCA and wavelets. A statistical score scheme is provided to evaluate the performance individually and overall. The open nature of the datasets, the clustering algorithms and the evaluation criteria make the proposed evaluation framework widely accessible to the research community. This is an attempt to provide a reference guide for emerging neuroscientists to select the most suitable algorithms for their spike analysis requirements.
MANTRA: development and localization of a mobile educational health game targeting low literacy players in low and middle income countries

Abstract Background Mobile technology is increasingly important for delivering public health interventions to remote populations. This research study developed, piloted, and assessed a serious game for mobile devices that teaches geohazard, maternal, and neonatal health messages. This unique mHealth intervention aimed at low-literacy audiences in low resource settings is part of the Maternal and Neonatal Technologies in Rural Areas (MANTRA) project: Increasing maternal and child health resilience before, during, and after disasters using mobile technology in Nepal. Methods The serious game was developed through a co-creation process between London and Kathmandu based researchers by email and video-calling, and face-to-face with local stakeholders in Nepal. The process identified core needs, developed appropriate pictograms and mechanics, and tailored the pilot serious game to the local cultural context. Evaluations and feedback from end users took place in rural villages and suburban Kathmandu in Province Three. Field evaluation sessions used mixed methods. Researchers observed game play and held focus group discussions to elicit qualitative feedback and understand engagement, motivation, and usability, and conducted a paired pre- and post-game knowledge assessment. Results The MANTRA serious game is contextualized to rural Nepal. The game teaches 28 learning objectives in three modules: maternal health, neonatal health, and geohazards, through picture matching with immediate audio and visual feedback. User feedback from focus groups demonstrated high engagement, motivation, and usability of the game. Conclusions This MANTRA study is a unique mHealth intervention of a serious game to teach core health and geohazards messages to low-literacy audiences in rural Nepal. Although the mobile game is tailored for this specific context, the developmental process and insights could be transferable to the development of other games-based interventions and contextualized for any part of the world. Successfully targeting this low-literacy and illiterate audience makes the MANTRA development process the first of its kind and a novel research endeavor with potential for widespread impact and adoption following further game development. Trial registration This project was approved by the University College London Ethics Committee in London, United Kingdom [10547/001], and the Nepal Health Research Council in Kathmandu, Nepal [Reg. No. 105/2017]. All participants provided informed written consent.
Challenges and Issues of Resource Allocation Techniques in Cloud Computing

In a cloud computing paradigm, allocation of various virtualized ICT resources is a complex problem due to the presence of heterogeneous application (MapReduce, content delivery and networks web applications) workloads having contentious allocation requirements in terms of ICT resource capacities (resource utilization, execution time, response time, etc.).This task of resource allocation becomes more challenging due to finite available resources and increasing consumer demands.Therefore, many unique models and techniques have been proposed to allocate resources efficiently.However, there is no published research available in this domain that clearly address this research problem and provides research taxonomy for classification of resource allocation techniques including strategic, target resources, optimization, scheduling and power.Hence, the main aim of this paper is to identify open challenges faced by the cloud service provider related to allocation of resource such as servers, storage and networks in cloud computing.More than 70 articles, between year 2007 and 2020, related to resource allocation in cloud computing have been shortlisted through a structured mechanism and are reviewed under clearly defined objectives.Lastly, the evolution of research in resource allocation techniques has also been discussed along with salient future directions in this area.
IIIT-AR-13K: A New Dataset for Graphical Object Detection in Documents

We introduce a new dataset for graphical object detection in business documents, more specifically annual reports. This dataset, iiit-ar-13k, is created by manually annotating the bounding boxes of graphical or page objects in publicly available annual reports. This dataset contains a total of 13k annotated page images with objects in five different popular categories—table, figure, natural image, logo, and signature. It is the largest manually annotated dataset for graphical object detection. Annual reports created in multiple languages for several years from various companies bring high diversity into this dataset. We benchmark iiit-ar-13k dataset with two state of the art graphical object detection techniques using faster r-cnn [20] and mask r-cnn [11] and establish high baselines for further research. Our dataset is highly effective as training data for developing practical solutions for graphical object detection in both business documents and technical articles. By training with iiit-ar-13k, we demonstrate the feasibility of a single solution that can report superior performance compared to the equivalent ones trained with a much larger amount of data, for table detection. We hope that our dataset helps in advancing the research for detecting various types of graphical objects in business documents ( http://cvit.iiit.ac.in/usodi/iiitar13k.php ).
Learning Algebra using Augmented Reality: A Preliminary Investigation on the Application of Photomath for Lower Secondary Education

This study aims to determine the effect of Photomath mobile application on the achievement and attitude of learning algebraic equations for lower secondary school students in Malaysia. Photomath is a free mobile application, for both ios and android platforms, which uses computer vision and image understanding technology to scan math equations using the mobile camera to subsequently provide worked solutions. The application recognises equations of both printed and handwritten in nature and provides step-by-step calculations and graphical solutions. In this study, data were collected from 33 lower secondary students through pre and post-test and questionnaire. Data from pre-test and post-test were used to investigate the performance of students after the application of Photomath, whereas the questionnaire was used to identify the attitude of students towards the application of Photomath in learning algebraic equations, regarding belief and readiness aspects. Different mean scores for pre-test and post-test were tested, and hypothesis testing using a t-test was used. Data from the questionnaire were analysed using descriptive statistics. This study has found that the application of Photomath significantly enhances the learning of algebraic equations among Form Two students. However, the participants’ belief and readiness towards Photomath are still at a moderate level.
Trufflehunter

This paper presents and evaluates Trufflehunter, a DNS cache snooping tool for estimating the prevalence of rare and sensitive Internet applications. Unlike previous efforts that have focused on small, misconfigured open DNS resolvers, Trufflehunter models the complex behavior of large multi-layer distributed caching infrastructures (e.g., such as Google Public DNS). In particular, using controlled experiments, we have inferred the caching strategies of the four most popular public DNS resolvers (Google Public DNS, Cloudflare Quad1, OpenDNS and Quad9). The large footprint of such resolvers presents an opportunity to observe rare domain usage, while preserving the privacy of the users accessing them. Using a controlled testbed, we evaluate how accurately Trufflehunter can estimate domain name usage across the U.S. Applying this technique in the wild, we provide a lower-bound estimate of the popularity of several rare and sensitive applications (most notably smartphone stalkerware) which are otherwise challenging to survey.
Combining BERT with Static Word Embeddings for Categorizing Social Media

Pre-trained neural language models (LMs) have achieved impressive results in various natural language processing tasks, across different languages.Surprisingly, this extends to the social media genre, despite the fact that social media often has very different characteristics from the language that LMs have seen during training.A particularly striking example is the performance of AraBERT, an LM for the Arabic language, which is successful in categorizing social media posts in Arabic dialects, despite only having been trained on Modern Standard Arabic.Our hypothesis in this paper is that the performance of LMs for social media can nonetheless be improved by incorporating static word vectors that have been specifically trained on social media.We show that a simple method for incorporating such word vectors is indeed successful in several Arabic and English benchmarks.Curiously, however, we also find that similar improvements are possible with word vectors that have been trained on traditional text sources (e.g.Wikipedia).
Learning Music Helps You Read: Using Transfer to Study Linguistic Structure in Language Models

We propose transfer learning as a method for analyzing the encoding of grammatical structure in neural language models. We train LSTMs on non-linguistic data and evaluate their performance on natural language to assess which kinds of data induce generalizable structural features that LSTMs can use for natural language. We find that training on non-linguistic data with latent structure (MIDI music or Java code) improves test performance on natural language, despite no overlap in surface form or vocabulary. To pinpoint the kinds of abstract structure that models may be encoding to lead to this improvement, we run similar experiments with two artificial parentheses languages: one which has a hierarchical recursive structure, and a control which has paired tokens but no recursion. Surprisingly, training a model on either of these artificial languages leads the same substantial gains when testing on natural language. Further experiments on transfer between natural languages controlling for vocabulary overlap show that zero-shot performance on a test language is highly correlated with typological syntactic similarity to the training language, suggesting that representations induced by pre-training correspond to the cross-linguistic syntactic properties. Our results provide insights into the ways that neural models represent abstract syntactic structure, and also about the kind of structural inductive biases which allow for natural language acquisition.
Venue Topic Model–enhanced Joint Graph Modelling for Citation Recommendation in Scholarly Big Data

Natural language processing technologies, such as topic models, have been proven to be effective for scholarly recommendation tasks with the ability to deal with content information. Recently, venue recommendation is becoming an increasingly important research task due to the unprecedented number of publication venues. However, traditional methods focus on either the author’s local network or author-venue similarity, where the multiple relationships between scholars and venues are overlooked, especially the venue–venue interaction. To solve this problem, we propose an author topic model–enhanced joint graph modeling approach that consists of venue topic modeling, venue-specific topic influence modeling, and scholar preference modeling. We first model the venue topic with Latent Dirichlet Allocation. Then, we model the venue-specific topic influence in an asymmetric and low-dimensional way by considering the topic similarity between venues, the top-influence of venues, and the top-susceptibility of venues. The top-influence characterizes venues’ capacity of exerting topic influence on other venues. The top-susceptibility captures venues’ propensity of being topically influenced by other venues. Extensive experiments on two real-world datasets show that our proposed joint graph modeling approach outperforms the state-of-the-art methods.
Deep learning‐based pilot‐assisted channel state estimator for OFDM systems

This study proposes an online deep learning-based channel state estimator for OFDM wireless communication systems by employing the deep learning long short-term memory (LSTM) neural networks. The proposed algorithm is a pilot-assisted estimator type. The proposed estimator is initially offline trained using simulated data sets, and then it follows the channel statistics in an online deployment, where finally the transmitted data can be recovered. A comparative investigation is performed using three different optimisation algorithms for deep learning to evaluate the performance of the proposed estimator at each. The proposed estimator provides a superior performance in comparison to least square (LS) and minimum mean square error (MMSE) estimators when limited pilots are used, thanks to the outstanding learning and generalisation capabilities of deep learning LSTM neural networks. Also, it does not require any prior knowledge of channel statistics. So, the proposed estimator is promising for channel state estimation in OFDM communication systems.
Using Genre-based Approach to Overcome Students’ Difficulties in Writing

Because of the importance of the English language, the government of Indonesia always aims to apply approaches that are anticipated to be appropriate for education levels and students’ ability, not only in attaining knowledge but also in relation to their personality and integrity in regard to using the knowledge gained in their future day-to-day life. A genre-based approach (GBA) is used to conduct classroom instruction and genre-based learning practices. One expert, Yan, argues that this approach has been popular since the 1980s, alongside the idea that students and writers could benefit from researching various types of written language. The GBA plays an important role in overcoming students’ difficulties in writing achievement. Because a descriptive quantitative method was adopted in this research, observation, document analysis (writing test), and interviews were conducted as data collection for the implementation of this GBA. After analysis of the data, the researchers came to the conclusion that this paper would be beneficial as a tutorial in helping students decide what ideas could be included in their writing, especially in classroom practice, to create meaningful texts. This GBA model can be used as a framework to enable teachers and students to develop their writing skills. It is hoped that this model can become a classroom strategy in allowing teachers and students to achieve a higher level of writing skill.
The Impact of the Covid-19 Pandemic on the World of Education in Indonesia

Since the beginning of 2020, the world has been shocked by the Corona Virus Diseases (Covid-19) pandemic which has spread rapidly throughout the world, infecting more than forty million people, causing more than one million deaths by October 2020. This pandemic has an impact on the education world where all levels of education from kindergarten to postgraduate are required to carry out distance learning through online-based classes. Various problems then arise in distance learning because not all students have the facilities and infrastructure needed in its implementation, even causing casualties. This research examines and discusses the phenomenon of Covid-19 pandemic and its impact on the education world in Indonesia. The purpose of this research is to find out government policies in overcoming learning problems in a pandemic situation and finding the right policies so that learning can still be carried out effectively, how the supervision needs to be done by the government so that teaching and learning activities can run effectively and how the teaching communication strategies in using online media so that the delivery of material by lecturers can be easily understood by students. From the research results, several conclusions were obtained. The Minister's policy to carry out the online learning process still faces various challenges in its implementation; The learning process from home must always be monitored through good cooperation between students, parents, and educators, especially for primary and secondary education; and by using some appropriate learning communication strategies, the online teaching and learning process can be carried out more effectively because it does not take up a lot of time and space.
How to evaluate deep learning for cancer diagnostics – factors and recommendations

The large volume of data used in cancer diagnosis presents a unique opportunity for deep learning algorithms, which improve in predictive performance with increasing data. When applying deep learning to cancer diagnosis, the goal is often to learn how to classify an input sample (such as images or biomarkers) into predefined categories (such as benign or cancerous). In this article, we examine examples of how deep learning algorithms have been implemented to make predictions related to cancer diagnosis using clinical, radiological, and pathological image data. We present a systematic approach for evaluating the development and application of clinical deep learning algorithms. Based on these examples and the current state of deep learning in medicine, we discuss the future possibilities in this space and outline a roadmap for implementations of deep learning in cancer diagnosis.
Understanding automated conversational agent as a decision aid: matching agent's conversation with customer's shopping task

Purpose To provide better services to customers, especially immediate responses and 24/7 availability, businesses are implementing text-based automated conversational agents, i.e. chatbots on their social platforms and websites. Chatbots are required to not only provide customers with necessary consultancy and guidance but also communicate friendly and socially. Based on the cognitive fit theory, this study attempts to examine the role of chatbot as a decision aid and how the match between information presentation in forms of decisional guidance and communication style and the shopping task influences consumers' perceived cognitive fit and decision performance outcomes. Design/methodology/approach A 2 x 2 x 2 between subject online experiment was conducted to identify which kind of decisional guidance (suggestive and informative guidance) and communication style (task-oriented vs social-oriented style) are the most appropriate for each type of shopping task (searching vs browsing task). Findings The findings show that when customers interact with chatbots, they will perceive higher cognitive fit if the chatbots provide them with suggestive guidance and communicate in a friendly style especially when they perform a searching task. Originality/value This study is the first attempt to understand the role of chatbots as a decision aid to customers using the communicative language. This study also tries to explore the cognitive fit theory in a novel way, and we propose the information presentation in forms of communicative language rather than matrices, tables and graphs.
SuperHistopath: A Deep Learning Pipeline for Mapping Tumor Heterogeneity on Low-Resolution Whole-Slide Digital Histopathology Images

High computational cost associated with digital pathology image analysis approaches is a challenge towards their translation in routine pathology clinic. Here, we propose a computationally efficient framework (SuperHistopath), designed to map global context features reflecting the rich tumor morphological heterogeneity. SuperHistopath efficiently combines i) a segmentation approach using the linear iterative clustering (SLIC) superpixels algorithm applied directly on the whole-slide images at low resolution (5x magnification) to adhere to region boundaries and form homogeneous spatial units at tissue-level, followed by ii) classification of superpixels using a convolution neural network (CNN). To demonstrate how versatile SuperHistopath was in accomplishing histopathology tasks, we classified tumor tissue, stroma, necrosis, lymphocytes clusters, differentiating regions, fat, hemorrhage and normal tissue, in 127 melanomas, 23 triple-negative breast cancers, and 73 samples from transgenic mouse models of high-risk childhood neuroblastoma with high accuracy (98.8%, 93.1% and 98.3% respectively). Furthermore, SuperHistopath enabled discovery of significant differences in tumor phenotype of neuroblastoma mouse models emulating genomic variants of high-risk disease, and stratification of melanoma patients (high ratio of lymphocyte-to-tumor superpixels (p = 0.015) and low stroma-to-tumor ratio (p = 0.028) were associated with a favorable prognosis). Finally, SuperHistopath is efficient for annotation of ground-truth datasets (as there is no need of boundary delineation), training and application (~5 min for classifying a whole-slide image and as low as ~30 min for network training). These attributes make SuperHistopath particularly attractive for research in rich datasets and could also facilitate its adoption in the clinic to accelerate pathologist workflow with the quantification of phenotypes, predictive/prognosis markers.