Reviewerly/exHarmony huggingface dataset:
Virtual Microscopy in Undergraduate Pathology Education

Whole-slide imaging and virtual microscopy (VM) have revolutionised teaching, diagnosis and research in histopathology. This study aimed to establish the feasibility of achieving early integration of clinical reasoning with undergraduate pathology teaching on a VM platform and to determine its student-centricity through student feedback.This study was conducted at the Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, United Arab Emirates, between August and December 2017. A total of 38 VM-centred clinical cases were introduced to 49 students in an integrated undergraduate medical curriculum. The cases were aligned to curricular objectives, reinforced the pathologic basis of disease with critical thinking and were delivered across 15 interactive small-group sessions. A simulated cross-disciplinary integration and judicious choice of pertinent diagnostic investigations were linked to principles of management. Feedback was obtained through a mixed-methods approach.User-friendliness, gradual learning curve of VM and annotation-capacity were scored as 4-5 (on a Likert scale of 1-5) by 91.84%, 87.76% and 83.67% of the participants, respectively. Most students agreed that the content matched the stage of learning (81.63%), theme of the week (91.84%) and development of a strong clinical foundation (77.55%). Integration (85.71%) and clinico-pathological correlation (83.67%) were the strengths of this educational effort. High student attendance (~100%) and improved assessment scores on critical thinking (80%) were observed. Software lacunae included frequent logouts and lack of note-taking tools. Easy access was a significant student-centric advantage.A VM-centred approach with a clinico-pathological correlation has been successfully introduced to inculcate integrated learning. Using the pathologic basis of disease as a fulcrum and critical reasoning as an anchor, a digitally-enabled generation of medical students have embraced this educational tool for tutor-guided, student-centred learning.
SETUCOM: Secure and Trustworthy Context Management for Context-Aware Security and Privacy in the Internet of Things

IoT technologies facilitate the development and the improvement of pervasive computing by enabling effective context-awareness features. These features enable the IoT applications to detect the user’s situation and adapt their behavior. They also enable context-aware security and privacy, which consist in adapting security and privacy mechanisms’ deployment to the user’s situation. Research studies on context-aware security and privacy focus on security and privacy mechanisms’ implementation but do not consider the secure and trustworthy context management. In this paper, we introduce a new secure and trustworthy context management system for context-aware security and privacy in the smart city: “SETUCOM.” SETUCOM is the implementation of the DTM (Device Trust Management) module of the CASPaaS (Context-Aware Security and Privacy as a Service) architecture. It secures context information exchange by using a lightweight hybrid encryption system adapted to IoT devices and manages trust through artificial intelligence techniques such as Bayesian networks and fuzzy logic. A detailed description of the proposed system is provided, and its main performances are evaluated. The results prove SETUCOM feasibility in context-aware security and privacy for the smart city.
Self-Supervised Representation Learning With Path Integral Clustering for Speaker Diarization

Automatic speaker diarization techniques typically involve a two-stage processing approach where audio segments of fixed duration are converted to vector representations in the first stage. This is followed by an unsupervised clustering of the representations in the second stage. In most of the prior approaches, these two stages are performed in an isolated manner with independent optimization steps. In this paper, we propose a representation learning and clustering algorithm that can be iteratively performed for improved speaker diarization. The representation learning is based on principles of self-supervised learning while the clustering algorithm is a graph structural method based on path integral clustering (PIC). The representation learning step uses the cluster targets from PIC and the clustering step is performed on embeddings learned from the self-supervised deep model. This iterative approach is referred to as self-supervised clustering (SSC). The diarization experiments are performed on CALLHOME and AMI meeting datasets. In these experiments, we show that the SSC algorithm improves significantly over the baseline system (relative improvements of 13% and 59% on CALLHOME and AMI datasets respectively in terms of diarization error rate (DER)). In addition, the DER results reported in this work improve over several other recent approaches for speaker diarization.
Fast and robust particle shuttling for quantum science and technology

Abstract We review methods to shuttle quantum particles fast and robustly. Ideal robustness amounts to the invariance of the desired transport results with respect to deviations, noisy or otherwise, from the nominal driving protocol for the control parameters; this can include environmental perturbations. “Fast” is defined with respect to adiabatic transport times. Special attention is paid to shortcut-to-adiabaticity protocols that achieve, in faster-than-adiabatic times, the same results of slow adiabatic driving.
The Effectiveness of Morphology-aware Segmentation in Low-Resource Neural Machine Translation

This paper evaluates the performance of several modern subword segmentation methods in a low-resource neural machine translation setting.We compare segmentations produced by applying BPE at the token or sentence level with morphologically-based segmentations from LMVR and MORSEL.We evaluate translation tasks between English and each of Nepali, Sinhala, and Kazakh, and predict that using morphologically-based segmentation methods would lead to better performance in this setting.However, comparing to BPE, we find that no consistent and reliable differences emerge between the segmentation methods.While morphologically-based methods outperform BPE in a few cases, what performs best tends to vary across tasks, and the performance of segmentation methods is often statistically indistinguishable.
Regularized bi-directional co-clustering

The simultaneous clustering of documents and words, known as co-clustering, has proved to be more effective than one-sided clustering in dealing with sparse high-dimensional datasets. By their nature, text data are also generally unbalanced and directional. Recently, the von Mises–Fisher (vMF) mixture model was proposed to handle unbalanced data while harnessing the directional nature of text. In this paper, we propose a general co-clustering framework based on a matrix formulation of vMF model-based co-clustering. This formulation leads to a flexible framework for text co-clustering that can easily incorporate both word–word semantic relationships and document–document similarities. By contrast with existing methods, which generally use an additive incorporation of similarities, we propose a bi-directional multiplicative regularization that better encapsulates the underlying text data structure. Extensive evaluations on various real-world text datasets demonstrate the superior performance of our proposed approach over baseline and competitive methods, both in terms of clustering results and co-cluster topic coherence.
Development of E-Learning Management Model for Teaching System at The Police Academy

This study aims to describe the development of e-learning management model for the teaching system at the Police Academy through management of educational information systems. The method used is research and development (R&amp;D). The development of e-learning management model for the teaching system as a research focus based on the Technology Accepted Model (TAM) theory and the education management information system input process output. The resulting model product is in accordance with the needs of police academy cadets in the use of e-learning. The product developed is an e-learning management system through an input output process. The results of a questionnaire to 100 Akpol cadets show that the e-learning management model of the teaching system is effective in providing convenience and information related to teaching at the Police Academy.
A systematic mapping study about DevOps

DevOps is a very trendy term these days in the software development companies (SDC), term that emerges as a possible solution to finally reach an acceleration and a rise of productivity expected with the appearance of agile development approaches, but that until now had not materialized, through automation practices, continuous integration, continuous build, and continuous deployment. This paper aims to show current knowledge about the process of adopting DevOps in SDC through a systematic mapping of the literature. However, the results obtained show that there is little detailed information regarding activities, tasks, roles, and other important process elements for the adoption of DevOps. Similarly, it has been concluded that there is no a unified terminology, therefore, it is important to standardize it to simplify the understanding and application of DevOps. Furthermore, this paper shows the preview of the framework that it is being developed for the adoption of DevOps in the SDC.
Elastic weight consolidation for better bias inoculation

The biases present in training datasets have been shown to affect models for sentence pair classification tasks such as natural language inference (NLI) and fact verification.While fine-tuning models on additional data has been used to mitigate them, a common issue is that of catastrophic forgetting of the original training dataset.In this paper, we show that elastic weight consolidation (EWC) allows finetuning of models to mitigate biases while being less susceptible to catastrophic forgetting.In our evaluation on fact verification and NLI stress tests, we show that fine-tuning with EWC dominates standard fine-tuning, yielding models with lower levels of forgetting on the original (biased) dataset for equivalent gains in accuracy on the fine-tuning (unbiased) dataset.
A reproduction of Apple’s bi-directional LSTM models for language identification in short strings

Language Identification is the task of identifying a document's language.For applications like automatic spell checker selection, language identification must use very short strings such as text message fragments.In this work, we reproduce a language identification architecture that Apple briefly sketched in a blog post.We confirm the bi-LSTM model's performance and find that it outperforms current open-source language identifiers.We further find that its language identification mistakes are due to confusion between related languages.
Multi-scale Feature Fusion UAV Image Object Detection Method Based on Dilated Convolution and Attention Mechanism

Due to the influence of the shooting angle of view and the flight height, the images taken by UAV often have complex backgrounds and contain a large number of small and unevenly distributed objects. In order to solve the problem that it is difficult to accurately locate and recognize small objects in UAV images under complex backgrounds, this paper proposes an multi-scale feature fusion algorithm D-A-FS SSD (Dilated-Attention-Feature Fusion SSD) based on the combination of dilated convolution and attention mechanism. In the process of feature extraction, the receptive field of the feature is expanded through the dilated convolution, which improves the network's feature expression of object distribution and scale difference information. And a attention network is used in our method to effectively suppresse the background information. In the multi-scale detection stage, our method fuses the low-level feature map responsible for detecting small objects with the high-level feature map which have much higher semantic information to improve the recognition accuracy of small objects. Experimental results show that our method effectively improves the accuracy of UAV image object detection.
MTStemmer: A multilevel stemmer for effective word pre-processing in Marathi

In natural language processing, it is important that the context and the meaning of words are retained while also ensuring the efficacy of the data modelling process. During human-to-human interactions, special care is taken regarding the tense and phrasing of the words by taking into consideration the rules of grammar of the specific language. While this modification of words is necessary for framing consistent sentences, these appendages do not add significant value to the original meaning of the word. Stemming is the process of converting words back to their root form for efficient and accurate modelling of the data. In this paper, MTStemmer, a new stemmer for the Marathi language is proposed. It focuses on the stripping of suffixes for obtaining the root word form. The proposed stemmer applies a multilevel approach by taking into consideration both auxiliary verb-based suffixes and gender-based suffixes. The presented approach intends to improve upon the limitations of the previously proposed stemmers for this language. The stemming performed by the stemmer is found to be more accurate in terms of mapping to the root words. Stemming is often an important pre-processing step before processing the data further for the main task. The benefit of the proposed stemmer is demonstrated by using it for an extractive Marathi text summarization task. A significant improvement in the performance of multiple performance metrics is achieved owing to the stemming done by MTStemmer. The working of the proposed stemmer shows promising signs for the development of similar engines for other Indic languages.
Movement Tube Detection Network Integrating 3D CNN and Object Detection Framework to Detect Fall

Unlike most of the existing neural network-based fall detection methods, which only detect fall at the time range, the algorithm proposed in this paper detect fall in both spatial and temporal dimension. A movement tube detection network integrating 3D CNN and object detection framework such as SSD is proposed to detect human fall with constrained movement tubes. The constrained movement tube, which encapsulates the person with a sequence of bounding boxes, has the merits of encapsulating the person closely and avoiding peripheral interference. A 3D convolutional neural network is used to encode the motion and appearance features of a video clip, which are fed into the tube anchors generation layer, softmax classification, and movement tube regression layer. The movement tube regression layer fine tunes the tube anchors to the constrained movement tubes. A large-scale spatio-temporal (LSST) fall dataset is constructed using self-collected data to evaluate the fall detection in both spatial and temporal dimensions. LSST has three characteristics of large scale, annotation, and posture and viewpoint diversities. Furthermore, the comparative experiments on a public dataset demonstrate that the proposed algorithm achieved sensitivity, specificity an accuracy of 100%, 97.04%, and 97.23%, respectively, outperforms the existing methods.
Simulating the Same Physics with Two Distinct Hamiltonians

We develop a framework and give an example for situations where two distinct Hamiltonians living in the same Hilbert space can be used to simulate the same physics. As an example of an analog simulation, we first discuss how one can simulate an infinite-range-interaction one-axis twisting Hamiltonian using a short-range nearest-neighbor-interaction Heisenberg XXX model with a staggered field. Based on this, we show how one can build an alternative version of a digital quantum simulator. As a by-product, we present a method for creating many-body maximally entangled states using only short-range nearest-neighbor interactions.
Design of English Learning Application for Children Early Childhood

Early childhood development includes several aspects such as physical, motor, language, social and cognitive. Cognitive factors are important role because most learning activities are always related to thinking and remembering problems. Besides language development is very important, it is because language is a communication tool of children with their environment, with good communication it will result in good interactions. Therefore, children need to be given the right stimulus so that during the child's growth and development to the maximum. One stimulus that can be given for cognitive development and language of early childhood is by providing an interesting medium, because in the development of early childhood the way of thinking requires visualization of abstract thinking. The purpose of this research is to design an application of learning English for mobile-based early childhood which is expected to be used as an \alternative as a medium to assist cognitive development and communication of early childhood. English based learning applications for mobile children are made with Construct 2 software. The development method uses the Multimedia Development Life Cycle. While testing the application using ISO 9126, the results of testing the quality of the application using ISO 9126 which includes the Functionality and Usability characteristics obtained Functionality test results obtained value of 100%, and the Usability test results obtained value of 91.11% so it can be concluded that the design of English learning applications is appropriate to be used as a medium to assist cognitive development and communication of early childhood.
COVID-19 waves in Africa: Effects of outbreak response and interventions

Introduction: Public health safety measures have been advocated for towards the effective containment of COVID-19. This study aimed to assess the effects of public health safety measures (PHSM) implemented during different phases of the COVID-19 pandemic in Africa. Methods: We conducted a retrospective analysis of the COVID-19 control measures implemented across Africa. We extracted and collated data on COVID-19 cases, deaths, and stringency level (SL) from online data repositories between 18 July, 2020 and 18 December, 2020 in Africa. Results: In the South African region, COVID-19 cases increased when there was partial closure of work sectors (SL = 41.67), but decreased with complete closure of work sectors (SL=46.30) in South Africa. In North Africa, COVID-19 cases increased when there was limited contact tracing (SL = 23.15), but decreased with comprehensive contact tracing (SL = 75.00) in Tunisia. In West Africa, COVID-19 cases increased when there was partial school closure (SL = 50.93), but decreased with complete school closure (SL = 65.74) in Nigeria. In East Africa, COVID-19 cases increased when contact tracing was limited (SL = 28.70), but decreased with comprehensive contact tracing (SL = 51.85) in Sudan. In Central Africa, COVID-19 cases increased when there was partial school closure (SL = 43.52), but decreased with complete school closure (SL = 56.48) in Congo. Conclusion: PHSM should be intensified to ensure the effective containment of COVID-19 in Africa. Evidence-based decisions should be made by policy makers and contextualized in each African country as the COVID-19 pandemic evolves.
An Improved Method for Combine (LSB and MSB) Based on Color Image RGB

Image steganography is the art of hiding data into an image by using the secret key.This paper presents two techniques that combine the most significant bit (MSB) as well as the least significant bit (LSB) based on a color image (24bit for RGB).The presented study proposes a novel method to combine (LSB and MSB) bits based on check MSB values and replace bits from LSB with a secret message.The result of this proposed method that made not affect quality stego -image based on the resulting histogram that shows a match between the cover image and stego-image and more secure because not hidden in all image.The factors were used Mean Square Error (MSE), Compute Payload, in addition to Peak Signal to Noise Ratio (PSNR).The PSNR's rate is high and MSE is less.The result of this paper when applying on the different image gives high PSNR of 87.141 and less MSE of 0.00012 when inserting message 80 bits and reduction value PSNR of 72.023 and MSE of 0.0040 when inserting message 1200 bits and measure entropy is the same value for cover image and stego -image then this method is more security for the attacker.
Impact of Social Media on Youth

Social media contribute an online statement that attaches cluster of community, who network and share the information. There are well known social media like Facebook, Wikipedia, twitter, Google+, Pinterest, LinkedIn, Instagram, Tumblr and reddita. They are all social networking websites where users can share their information to the public. Business people also can promote their products through social media and get crowd funding. Social media analytics collects and analyze the data from blogs and social media website that helps business people to frame a wise decision. Social media also influence students positively to understand human behaviours and negatively become selfish and fanatic. Thus social media is used both for construction and destruction purpose for people from different walks of life
Review of Human Gesture Recognition Based on Computer Vision Technology

Over the years, with continuous expansion of the application fields of intelligent video surveillance, technologies related to human gesture recognition have received more attention and become a research hotspot. This paper first shows the architecture of the intelligent surveillance system, and the corresponding computer vision task of human gesture recognition, such as target detection, feature fusion and scene understanding. Then, in order to better understand related technologies, typical methods based on statistics, template and deep learning are summarized, the characteristics and implementation of convolutional neural networks are introduced in detail. Third, some data sets are listed, and we compare existing models in different projects of human body pose estimation from performance indicators. Finally, the flow chart of crowd posture estimation is designed, with the key modules being explained, which helps better understand the mechanism of gesture recognition. Therefore, this paper has certain theoretical value and application significance.
Exploiting Definitions for Frame Identification

Frame identification is one of the key challenges for frame-semantic parsing.The goal of this task is to determine which frame best captures the meaning of a target word or phrase in a sentence.We present a new model for frame identification that uses a pre-trained transformer model to generate representations for frames and lexical units (senses) using their formal definitions in FrameNet.Our frame identification model assesses the suitability of a frame for a target word in a sentence based on the semantic coherence of their meanings.We evaluate our model on three data sets and show that it consistently achieves better performance than previous systems.