Reviewerly/exHarmony huggingface dataset:
Artificial intelligence-assisted interpretation of Ki-67 expression and repeatability in breast cancer

Ki-67 standard reference card (SRC) and artificial intelligence (AI) software were used to evaluate breast cancer Ki-67LI. We established training and validation sets and studied the repeatability inter-observers.A total of 300 invasive breast cancer specimens were randomly divided into training and validation sets, with each set including 150 cases. Breast cancer Ki-67 standard reference card ranging from 5 to 90% were created. The training set was interpreted by nine pathologists of different ages through microscopic visual assessment (VA), SRC, microscopic manual counting (MC), and AI. The validation set was interpreted by three randomly selected pathologists using SRC and AI. The intra-group correlation coefficient (ICC) were used for consistency analysis.In the homogeneous and heterogeneous groups of validation sets, the consistency among the pathologists that used SRC and AI was very good, with an ICC of>0.905. In the validation set, using SRC and AI, three pathologists obtained results that were very consistent with the gold standard, having an ICC above 0.95, and the inter-observer agreement was also very good, with an ICC of>0.9.AI has satisfactory inter-observer repeatability, and the true value was closer to the gold standard, which is the preferred method for Ki-67LI reproducibility; While AI software has not been popularized, SRC may be interpreted as breast cancer Ki-67LI's standard candidate method.
Back to Fukushima: Perceptions and effects of an immersive journalism story

News media organizations have been experimenting with immersive journalism formats in recent years. The search for new ways to tell stories is driven by technology and has given rise to new –and not so new– forms of expression. The initial enthusiasm has dissipated over the years, so the uncertainty of the future of immersive journalism justifies the study of the most potential ways of using this technology in journalism. This research was carried out in Finland and Spain, and it is based on a textual narrative analysis and a reception study comparing the online article and 360º video versions of the report Fukushima: Contaminated Lives from El país. The results identify the affordances of both formats and confirm that immersive journalism has the capacity to generate a greater emotional effect based on a greater perception of presence, realism and involvement, also having a positive impact on increasing interest and changing opinions on related topics. The narration of online article seems to work better only if there is large amount of information that has to be understood and contextualized. However, authors claim that, instead of an essential break, there are similarities between both formats, which are situated on the same continuum of emotionality and rationality. These findings also indicate that immersive storytelling can be an important part of the emotional or affective turn in contemporary journalism. The sensation of presence and empathy towards distant and complex realities emerge as the main distinguishing values of the kind of immersive journalism that Fukushima: Contaminated lives represents.
Critical Correlation of Predictors for an Efficient Risk Prediction Framework of ICU Patient Using Correlation and Transformation of MIMIC-III Dataset

Abstract Various predictive frameworks have evolved over the last decade to facilitate the efficient diagnosis of critical diseases in the healthcare sector. Some have been commercialized, while others are still in the research and development stage. An effective early predictive principle must provide more accurate outcomes in complex clinical data and various challenging environments. The open-source database system medical information mart for intensive care (MIMIC) simplifies all of the attributes required in predictive analysis in this regard. This database contains clinical and non-clinical information on a patient’s stay at a healthcare facility, gathered during their duration of stay. Regardless of the number of focused research attempts employing the MIMIC III database, a simplified and cost-effective computational technique for developing the early analysis of critical problems has not yet been found. As a result, the proposed study provides a novel and cost-effective machine learning framework that evolves into a novel feature engineering methodology using the MIMIC III dataset. The core idea is to forecast the risk associated with a patient’s clinical outcome. The proposed study focused on the diagnosis and clinical procedures and found distinct variants of independent predictors from the MIMIC III database and ICD-9 code. The proposed logic is scripted in Python, and the outcomes of three common machine learning schemes, namely Artificial Neural Networks, K-Nearest Neighbors, and Logistic Regression, have been evaluated. Artificial Neural Networks outperform alternative machine learning techniques when accuracy is taken into account as the primary performance parameter over the MIMIC III dataset.
Attribute-Based Expressive and Ranked Keyword Search Over Encrypted Documents in Cloud Computing

Attribute-based keyword search (ABKS) has been proposed to realize fine-grained access control and provide search service in cloud computing. However, most ABKS schemes focus on single or conjunctive keyword search, while the recent Boolean keyword search schemes only support monotonic query formula mainly involving AND, OR and threshold operators. How to support more expressive Boolean query formulas and return the corresponding accurate search results to users have become challenges for practical ABKS over ciphertexts. In this paper, we introduce an attribute-based expressive and ranked keyword search scheme over encrypted documents named ABERKS, which allows authorized users to submit expressive Boolean query formulas involving AND, OR, NOT and threshold operators. ABERKS utilizes a non-monotonic access tree structure to construct the query formula, and further leverages extended Boolean model to rank the search results. Specifically, the users are able to define the weights in the query formula, and get the relevance score of each matched ciphertext if the attributes and keywords are both satisfied. We prove the security of ABERKS against chosen keyword attack under selective ciphertext policy model and against keyword guessing attack, and also conduct extensive experiments to show the efficiency and practicality of ABERKS.
Two-step image dehazing with intra-domain and inter-domain adaptation

Intra-domain and inter-domain gaps are widely presented in image processing tasks due to data distribution differences. In the field of image dehazing, particular previous works have paid attention to the inter-domain gap between the synthetic domain and the real domain. However, those methods only establish the connection from the real domain to the synthetic domain without considering the significant distribution shift within the synthetic domain (intra-domain gap). In this work, we propose a Two-Step Dehazing Network (TSDN) with an intra-domain adaptation and a constrained inter-domain adaptation. First, we subdivide the distributions within the synthetic domain into subsets and mine the optimal subset (easy samples) by loss-based supervision. To alleviate the intra-domain gap of the synthetic domain, we propose an intra-domain adaptation to align distributions of other subsets to the optimal subset by adversarial learning. Finally, we conduct the constrained inter-domain adaptation from the real domain to the optimal subset of the synthetic domain, alleviating the domain shift between domains as well as the distribution shift within the real domain. Extensive experimental results demonstrate that our framework performs favorably against the state-of-the-art algorithms both on the synthetic datasets and the real datasets.
Hands-Free Authentication for Virtual Assistants with Trusted IoT Device and Machine Learning

Virtual assistants, deployed on smartphone and smart speaker devices, enable hands-free financial transactions by voice commands. Even though these voice transactions are frictionless for end users, they are susceptible to typical attacks to authentication protocols (e.g., replay). Using traditional knowledge-based or possession-based authentication with additional invasive interactions raises users concerns regarding security and usefulness. State-of-the-art schemes for trusted devices with physical unclonable functions (PUF) have complex enrollment processes. We propose a scheme based on a challenge response protocol with a trusted Internet of Things (IoT) autonomous device for hands-free scenarios (i.e., with no additional user interaction), integrated with smart home behavior for continuous authentication. The protocol was validated with automatic formal security analysis. A proof of concept with websockets presented an average response time of 383 ms for mutual authentication using a 6-message protocol with a simple enrollment process. We performed hands-free activity recognition of a specific user, based on smart home testbed data from a 2-month period, obtaining an accuracy of 97% and a recall of 81%. Given the data minimization privacy principle, we could reduce the total number of smart home events time series from 7 to 5. When compared with existing invasive solutions, our non-invasive mechanism contributes to the efforts to enhance the usability of financial institutions' virtual assistants, while maintaining security and privacy.
Female Higher Education Teachers Use Digital Technologies More and Better than They Think

The unequal status of female teachers in higher education is a reality. However, are digital technologies another reason for their exclusion? The main aim of this study was to analyze the integration of digital technologies in teaching from a gender perspective. The research was a multiple case study applying the quantitative method. The sample comprised 527 teachers from four Spanish universities. The instrument utilized was a self-administered survey. The results confirm both types of segregation and the fact that female teachers see themselves as less digitally competent than their male counterparts; ironically, the findings also show that the former use information and communication technologies in teaching more than the latter. In conclusion, we highlight the need to study more deeply the intersection between digital breach and gender in higher education, and to take measures during the early stages of education to overcome female teachers’ tendency of underestimating their digital competence.
Sparse Transcription

Abstract The transcription bottleneck is often cited as a major obstacle for efforts to document the world’s endangered languages and supply them with language technologies. One solution is to extend methods from automatic speech recognition and machine translation, and recruit linguists to provide narrow phonetic transcriptions and sentence-aligned translations. However, I believe that these approaches are not a good fit with the available data and skills, or with long-established practices that are essentially word-based. In seeking a more effective approach, I consider a century of transcription practice and a wide range of computational approaches, before proposing a computational model based on spoken term detection that I call “sparse transcription.” This represents a shift away from current assumptions that we transcribe phones, transcribe fully, and transcribe first. Instead, sparse transcription combines the older practice of word-level transcription with interpretive, iterative, and interactive processes that are amenable to wider participation and that open the way to new methods for processing oral languages.
Medically-oriented design for explainable AI for stress prediction from physiological measurements

In the last decade, a lot of attention has been given to develop artificial intelligence (AI) solutions for mental health using machine learning. To build trust in AI applications, it is crucial for AI systems to provide for practitioners and patients the reasons behind the AI decisions. This is referred to as Explainable AI. While there has been significant progress in developing stress prediction models, little work has been done to develop explainable AI for mental health.In this work, we address this gap by designing an explanatory AI report for stress prediction from wearable sensors. Because medical practitioners and patients are likely to be familiar with blood test reports, we modeled the look and feel of the explanatory AI on those of a standard blood test report. The report includes stress prediction and the physiological signals related to stressful episodes. In addition to the new design for explaining AI in mental health, the work includes the following contributions: Methods to automatically generate different components of the report, an approach for evaluating and validating the accuracies of the explanations, and a collection of ground truth of relationships between physiological measurements and stress prediction.Test results showed that the explanations were consistent with ground truth. The reference intervals for stress versus non-stress were quite distinctive with little variation. In addition to the quantitative evaluations, a qualitative survey, conducted by three expert psychiatrists confirmed the usefulness of the explanation report in understanding the different aspects of the AI system.In this work, we have provided a new design for explainable AI used in stress prediction based on physiological measurements. Based on the report, users and medical practitioners can determine what biological features have the most impact on the prediction of stress in addition to any health-related abnormalities. The effectiveness of the explainable AI report was evaluated using a quantitative and a qualitative assessment. The stress prediction accuracy was shown to be comparable to state-of-the-art. The contributions of each physiological signal to the stress prediction was shown to correlate with ground truth. In addition to these quantitative evaluations, a qualitative survey with psychiatrists confirmed the confidence and effectiveness of the explanation report in the stress made by the AI system. Future work includes the addition of more explanatory features related to other emotional states of the patient, such as sadness, relaxation, anxiousness, or happiness.
Sparse-attentive meta temporal point process for clinical decision support

In the study of clinical decision-making, prediction of future clinical events of patients has become an important task, especially for variant disease predictions. In previous studies, the disease prediction problems are considered as binary classification based on the patients’ electronic health records (EHRs), which lack the capacity to predict multiple types of diseases. In this paper, we propose a method which can predict both the patients’ disease types among various candidate diseases and patients’ next hospital visit time. The next hospital visit time is crucial for medical experts in making decisions, because it reflects the onset time information of disease and provides sufficient information on the severity of the disease. Our proposed method is implemented based on the point process framework, which utilizes meta-learning to gain the prior knowledge of the individual patient’s clinical data with context information, adopts sparse-attention to determine the importance of past major clinical events, and simulates the intensity of clinical events through Hawkes process to predict the types of diseases diagnosed by the doctor and patient’ next hospital visit time. The experimental data are extracted from the public datasets: Multiparameter Intelligent Monitoring in Intensive Care (MIMIC-II) and Medical Information Mart for Intensive Care (MIMIC-III). Compared with the baseline time series models, our proposed method has achieved superior results, with a higher F1-score (66.67%) and a lower root-mean-square error (RMSE) (6.69) on the test set, which proves the effectiveness of the proposed method. We further study the self-attention mechanism based on Transformer and sparse-attention methods to demonstrate the validity of our model. Our proposed method provides empirical evidence of its ability in facilitating the decision-making process of clinicians, which can be potentially utilized as effective clinical decision support tools to better improve the quality of medical services and reduce medical errors.
An iterative approach to unsupervised outlier detection using ensemble method and distance-based data filtering

Abstract Outlier or anomaly detection is the process through which datum/data with different properties from the rest of the data is/are identified. Their importance lies in their use in various domains such as fraud detection, network intrusion detection, and spam filtering. In this paper, we introduce a new outlier detection algorithm based on an ensemble method and distance-based data filtering with an iterative approach to detect outliers in unlabeled data. The ensemble method is used to cluster the unlabeled data and to filter out potential isolated outliers from the same by iteratively using a cluster membership threshold until the Dunn index score for clustering is maximized. The distance-based data filtering, on the other hand, removes the potential outlier clusters from the post-clustered data based on a distance threshold using the Euclidean distance measure of each data point from the majority cluster as the filtering factor. The performance of our algorithm is evaluated by applying it to 10 real-world machine learning datasets. Finally, we compare the results of our algorithm to various supervised and unsupervised outlier detection algorithms using Precision@n and F-score evaluation metrics.
Evaluation of the Oculus Rift S tracking system in room scale virtual reality

Abstract In specific virtual reality applications that require high accuracy it may be advisable to replace the built-in tracking system of the HMD with a third party solution. The purpose of this research work is to evaluate the accuracy of the built-in tracking system of the Oculus Rift S Head Mounted Display (HMD) in room scale environments against a motion capture system. In particular, an experimental evaluation of the Oculus Rift S inside-out tracking technology was carried out, compared to the performance of an outside-in tracking method based on the OptiTrack motion capture system. In order to track the pose of the HMD using the motion capture system the Oculus Rift S was instrumented with passive retro-reflective markers and calibrated. Experiments have been performed on a dataset of multiple paths including simple motions as well as more complex paths. Each recorded path contained simultaneous changes in both position and orientation of the HMD. Our results indicate that in room-scale environments the average translation error for the Oculus Rift S tracking system is about $$1.83$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>1.83</mml:mn> </mml:mrow> </mml:math> cm, and the average rotation error is about $$0.77^\circ$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>0</mml:mn> <mml:mo>.</mml:mo> <mml:msup> <mml:mn>77</mml:mn> <mml:mo>∘</mml:mo> </mml:msup> </mml:mrow> </mml:math> , which is 2 orders of magnitude higher than the performance that can be achieved using a motion capture system.
An efficient approach for quantum entanglement purification

Entanglement plays a major role in quantum information process and is a principal resource for various quantum applications like quantum teleportation, quantum key distribution, quantum communication, etc. Strong entangled pairs are required for efficient information process but system impurities during the transportation diminishes the entanglement by reducing the fidelity of the entangled pair. In order to reduce this, purification techniques can be used. In this paper, we propose an efficient entanglement purification method to distill the entanglement using entanglement swapping. The proposed method increases the fidelity of the entanglement and can be a proficient for various applications of quantum computing.
Machine Learning Techniques for Sentiment Analysis of Code-Mixed and Switched Indian Social Media Text Corpus - A Comprehensive Review

A comprehensive review of sentiment analysis for code-mixed and switched text corpus of Indian social media using machine learning (ML) approaches, based on recent research studies has been presented in this paper.Code-mixing and switching are linguistic behavior shown by the bilingual/multilingual population, primarily in spoken but also in written communication, especially on social media.Code-mixing involves combining lower linguistic units like words and phrases of a language into the sentences of other language (the base language) and code-switching involves switching to another language, for the length of one sentence or more.In code-mixing and switching, a bilingual person takes one or more words or phrases from one language and introduces them into another language while communicating in that language in spoken or written mode.People nowadays express their views and opinions on several issues on social media.In multilingual countries, people express their views using English as well as their native languages.Several reasons can be attributed to code-mixing.Lack of knowledge in one language on a particular subject, being empathetic, interjection and clarification are some to name.Sentiment analysis of monolingual social media content has been carried out for the last two decades.However, during recent years, Natural Language Processing (NLP) research focus has also shifted towards the exploration of code-mixed data, thereby, making code mixed sentiment analysis an evolving field of research.Systems have been developed using ML techniques to predict the polarity of code-mixed text corpus and to fine tune the existing models to improve their performance.
Remarks on fixed point assertions in digital topology, 2

Several recent papers in digital topology have sought to obtain fixed point results by mimicking the use of tools from classical topology, such as complete metric spaces. We show that in many cases, researchers using these tools have derived conclusions that are incorrect, trivial, or limited.
ShortcutFusion: From Tensorflow to FPGA-Based Accelerator With a Reuse-Aware Memory Allocation for Shortcut Data

Residual block is a very common component in recent state-of-the art CNNs such as EfficientNet or EfficientDet. Shortcut data accounts for nearly 40% of feature-maps access in ResNet152 [8]. Most of the previous DNN compilers, accelerators ignore the shortcut data optimization. This paper presents ShortcutFusion, an optimization tool for FPGA-based accelerator with a reuse-aware static memory allocation for shortcut data, to maximize on-chip data reuse given resource constraints. From TensorFlow DNN models, the proposed design generates instruction sets for a group of nodes which uses an optimized data reuse for each residual block. The accelerator design implemented on the Xilinx KCU1500 FPGA card 2.8x faster and 9.9x more power efficient than NVIDIA RTX 2080 Ti for 256x256 input size. . Compared to the result from baseline, in which the weights, inputs, and outputs are accessed from the off-chip memory exactly once per each layer, ShortcutFusion reduces the DRAM access by 47.8-84.8% for RetinaNet, Yolov3, ResNet152, and EfficientNet. Given a similar buffer size to ShortcutMining [8], which also mine the shortcut data in hardware, the proposed work reduces off-chip access for feature-maps 5.27x while accessing weight from off-chip memory exactly once.