Reviewerly/exHarmony huggingface dataset:
Performance Analysis of Breast Cancer Detection Method Using ANFIS Classification Approach

Breast cancer is one of the deadly diseases prevailing in women.Earlier detection and diagnosis might prevent the death rate.Effective diagnosis of breast cancer remains a significant challenge, and early diagnosis is essential to avoid the most severe manifestations of the disease.The existing systems have computational complexity and classification accuracy problems over various breast cancer databases.In order to overcome the above-mentioned issues, this work introduces an efficient classification and segmentation process.Hence, there is a requirement for developing a fully automatic methodology for screening the cancer regions.This paper develops a fully automated method for breast cancer detection and segmentation utilizing Adaptive Neuro Fuzzy Inference System (ANFIS) classification technique.This proposed technique comprises preprocessing, feature extraction, classifications, and segmentation stages.Here, the wavelet-based enhancement method has been employed as the preprocessing method.The texture and statistical features have been extracted from the enhanced image.Then, the ANFIS classification algorithm is used to classify the mammogram image into normal, benign, and malignant cases.Then, morphological processing is performed on malignant mammogram images to segment cancer regions.Performance analysis and comparisons are made with conventional methods.The experimental result proves that the proposed ANFIS algorithm provides better classification performance in terms of higher accuracy than the existing algorithms.
A Steganography Based on Optimal Multi-Threshold Block Labeling

Hiding secret data in digital images is one of the major research fields in information security.Recently, reversible data hiding in encrypted images has attracted extensive attention due to the emergence of cloud services.This paper proposes a novel reversible data hiding method in encrypted images based on an optimal multi-threshold block labeling technique (OMTBL-RDHEI).In our scheme, the content owner encrypts the cover image with block permutation, pixel permutation, and stream cipher, which preserve the in-block correlation of pixel values.After uploading to the cloud service, the data hider applies the prediction error rearrangement (PER), the optimal threshold selection (OTS), and the multithreshold labeling (MTL) methods to obtain a compressed version of the encrypted image and embed secret data into the vacated room.The receiver can extract the secret, restore the cover image, or do both according to his/her granted authority.The proposed MTL labels blocks of the encrypted image with a list of threshold values which is optimized with OTS based on the features of the current image.Experimental results show that labeling image blocks with the optimized threshold list can efficiently enlarge the amount of vacated room and thus improve the embedding capacity of an encrypted cover image.Security level of the proposed scheme is analyzed and the embedding capacity is compared with state-of-the-art schemes.Both are concluded with satisfactory performance.
Machine learning-based re-classification of the geochemical stratigraphy of the Rajahmundry Traps, India

The lavas exposed at the Rajahmundry Traps have similar ages and geochemical compositions compared to those from the Ambenali and Mahabaleshwar formations of the Deccan Traps, which is why they are believed to represent their eastern extensions. However recent geochronological data for the lavas crop out in the Rajahmundry quarries and paleontological evidence from the expanded sediment-lava sequences preserved in the Krishna-Godavari Basin indicate that the lower Rajahmundry Flow could represent a flow of the Poladpur formation. Here we present new major and trace element data for lavas of the lower (five samples) and middle (three samples) Rajahmundry Flows exposed in the Gowripatnam Quarry. To test whether the lower Rajahmundry Flow show geochemical similarities to the Poladpur formation, we applied a machine learning-based classification model which is able to predict the most probable formation affiliation for an unknown sample. The classification method we used is the Random Forest algorithm and training as well as testing our model with new and published geochemical data from the Poladpur and Ambenali formations (n = 73) by using Repeated K-Fold Cross Validation yields a prediction accuracy of ~86%. According to our model results, the lavas of the lower Rajahmundry Flow were assigned to the Poladpur formation, while those from the middle Rajahmundry Flow show geochemical similarities to the Ambenali formation. Flows of each of the voluminous Deccan formations (Poladpur, Ambenali and Mahabaleshwar) are therefore present at Rajahmundry, confirming that these are an eastward extension of the Deccan Traps.
AI accelerator on IBM Telum processor

IBM Telum is the next generation processor chip for IBM Z and LinuxONE systems. The Telum design is focused on enterprise class workloads and it achieves over 40% per socket performance growth compared to IBM z15. The IBM Telum is the first server-class chip with a dedicated on-chip AI accelerator that enables clients to gain real time insights from their data as it is getting processed.
MultiSoma: Motor and Gaze Analysis on Distributed Embodiment With Synchronized Behavior and Perception

Human behavior and perception are optimized for a single body. Yet, the human brain has plasticity, which allows us to extend our body schema. By utilizing technology like robotics or virtual reality (VR), we can modify our body parts or even add a new body to our own while retaining control over these parts. However, the update of body cognition when controlling multiple bodies has not been well examined. In this study, we explore the task performance and body cognition of humans when they have multiple full bodies as an extended embodiment. Our experimental system allows a participant to control up to four bodies at the same time and perceive sensory information from them. The participant experiences synchronizing behavior and vision perception in a virtual environment. We set up three tasks for multiple bodies and evaluated the cognition of these bodies with their gazing information, task performances, and subjective ratings. We found that humans can have the sense of body ownership and agency for each body when controlling multiple bodies simultaneously. Furthermore, it was observed that people manipulate multiple bodies by actively switching their attention in a static environment and passively switching their attention in a dynamic environment. Distributed embodiment has the potential to extend human behavior in cooperative work, parallel work, group behavior, and so on.
Research Trends on the Use of Augmented Reality Technology in Teaching English as a Foreign Language

The main aim of this research is to display research trends in studies on augmented reality (AR) in teaching English as a foreign language by using bibliometric mapping and content analysis. For this purpose, 64 studies in total published up to 2019 were accessed for bibliometric analysis. In addition, 49 articles published between 2007 and 2019 were reached for content analysis. The bibliometric mapping results indicated that related studies mostly focused on the effectiveness of mobile and ubiquitous learning. In addition, early childhood education, mobile learning, and gamification have become the focus of recent research. While Thorne, Azuma and Squire are the mostly cited authors in this field, Computers &amp;amp; Education, Educational Technology &amp;amp; Society, British Journal of Educational Technology and Computers in Human Behavior are among the journals cited most. Content analysis results revealed that academic achievement and motivation were the most commonly examined variables in the studies. These results revealed that most of the studies were grounded on quantitative research designs, especially experimental designs. Questionnaires and achievement tests were the most used data collection tools. While purposive sampling was the most preferred sampling method, there was an increase in the adoption of random sampling in 2011. Finally, descriptive statistics, T-tests, ANOVA-ANCOVA, and correlation analysis were mostly performed quantitative analysis methods, and content analysis was mostly used in qualitative analysis.
A TinyML Approach to Human Activity Recognition

Abstract Human Activity Recognition has been a favorite topic for the scholars not only because of its wide scale acceptance in the industry but areas which may help in medical and in our normal household works as well. Since to make this technology available to the last person standing in the queue it is important that models compiled and trained in this field are not just high performing but optimized as such with incurs the least overhead. And thus bringing TinyML into the picture which has specialty in the field of optimizing the model w.r.t. the size of the model, energy consumption, network bandwidth usage etc. Thus this work includes using optimizing techniques such as pruning and quantization on the pre-proposed models and analyze the changes it causes in such models w.r.t accuracy and size. Our work is able infer that by using both Pruning and Quantization techniques on a human activity recognition model we can compress a model up to 10 time without hampering severe diversion to the accuracy of the model. We have taken three models and UCI-HAR dataset and compare the outcomes of the experiment.
Explainable AI for Security of Human-Interactive Robots

This article considers the ways that explainable AI can be used to help secure human-interactive robots. To do so, we acknowledge that robots interact with a variety of people. For example, some people may operate robots that perform tasks in their homes or offices, while other people may be tasked with defending robots from potential attackers. We describe how explainable AI can be used to help the human operators of robots appropriately calibrate the trust they have in their systems, and we demonstrate this through an implementation. We also describe a novel generalizable human-in-the-loop framework based on control loops to characterize and explain attacks on robots to a robot defender. We explore the utility of such a framework through an analysis of its application in the incident management process, applied to robots. This framework allows formal definition of explainability, and the necessary condition for explainability in robots. The overarching goal of this article is to introduce the application of explainability for security of robotics as a novel area of research, therefore, we also discuss several open research problems we uncovered while applying explainable AI to security of robots.
Validity Arguments for AI‐Based Automated Scores: Essay Scoring as an Illustration

Abstract In this article, we argue that automated scoring engines should be transparent and construct relevant—that is, as much as is currently feasible. Many current automated scoring engines cannot achieve high degrees of scoring accuracy without allowing in some features that may not be easily explained and understood and may not be obviously and directly relevant to the target assessment construct. We address the current limitations on evidence and validity arguments for scores from automated scoring engines from the points of view of the Standards for Educational and Psychological Testing (i.e., construct relevance, construct representation, and fairness) and emerging principles in Artificial Intelligence (e.g., explainable AI, an examinee's right to explanations, and principled AI). We illustrate these concepts and arguments for automated essay scores.
Recognize the bias? News media partisanship shapes the coverage of facial recognition technology in the United States

Media exists as the primary route through which the public learns about new technologies and thus plays an important role in shaping public sentiments. This article examines the influence of news media partisanship on the coverage of the controversial artificial intelligence (AI) technology facial recognition. A mixed-methods content analysis of news articles ( N = 451) from 23 US-based news outlets highlights the emergence of several frames in coverage of facial recognition pertaining to issues of privacy and surveillance, bias, technology’s ability to provide solutions, and its problematic development and implementation. Coverage was differentiated by partisanship, whereby left-leaning media focused more on ethical problems associated with the technology compared to their right-leaning peers who highlighted its abuses by foreign governments. Right-leaning media also referred more to technology’s positive uses, such as helping law enforcement, compared to left-leaning media. Finally, AI companies were the most dominant suppliers of information to the media regarding the technology.
Security Augmentation Approach for Internet of Things Enhanced Cryptographic Algorithm

The Internet of Things (IoT) is continuously achieving pragmatic attention for research and investigation as this technology have achieved considerable popularity worldwide because of rapidly connecting billions of apparatus to internet and capable enough to make a pragmatic connection among diverse devices for data communication as desired in modern world. Several investigations have been carried out on IoT secrecy in recent years for protecting the confidential data as well as information but due to rapid technological advancements in modern devices as well as communication systems along with new technologies, the protection of the confidential data or information is becoming a critical threat all over globe. In this paper, authors suggested a novel security augmentation approach for IoT utilizing an enhanced cryptographic algorithm that is capable enough to secure confidential data as well as information in faster and pragmatic manner in comparison to the conventional approaches and establish pragmatic connectivity in smart city model as demanded in present era. This experimentation work was carried out over a personal computer (PC) installed with the MATLAB R2019b software kit, 16 GB of RAM (Random Access Memory), Window 10 along with the 64-bit operating system (OS). The measured results were observed with high degree of accuracy and precision for pragmatic validation. The measured outcomes are pragmatic and improved in comparison to diverse kinds of conventional strategies. Although, multifarious investigators have suggested diverse strategies in previous years to improve secrecy of confidentiality data but there are still gigantic possibilities in this arena for more investigation.
Fibre-optic measurement of strain and shape on a helicopter rotor blade during a ground run: 2. Measurement of shape

Abstract Optical fibre strain and shape measurement sensors were deployed on a rotor blade during a full-speed helicopter ground run, with real-time data wirelessly streamed from rotor hub-mounted sensor interrogators. In part 2 of a 2-part paper series, two-dimensional direct fibre-optic shape sensing (DFOSS), using fibre segment interferometry-based interrogation is investigated. The concept of blade shape change visualisation over one rotation period using rotation displacement surfaces is introduced and the usefulness of DFOSS data to gain additional insights by determining operational modal frequencies independently for both horizontal and vertical vibration directions of the blade is demonstrated.
Empirical Analysis of Parallel Corpora and In-Depth Analysis Using LIWC

The machine translation system aims to translate source language into target language. Recent studies on MT systems mainly focus on neural machine translation. One factor that significantly affects the performance of NMT is the availability of high-quality parallel corpora. However, high-quality parallel corpora concerning Korean are relatively scarce compared to those associated with other high-resource languages, such as German or Italian. To address this problem, AI Hub recently released seven types of parallel corpora for Korean. In this study, we conduct an in-depth verification of the quality of corresponding parallel corpora through Linguistic Inquiry and Word Count (LIWC) and several relevant experiments. LIWC is a word-counting software program that can analyze corpora in multiple ways and extract linguistic features as a dictionary base. To the best of our knowledge, this study is the first to use LIWC to analyze parallel corpora in the field of NMT. Our findings suggest the direction of further research toward obtaining the improved quality parallel corpora through our correlation analysis in LIWC and NMT performance.
ExeBench: an ML-scale dataset of executable C functions

Machine-learning promises to transform compilation and software engineering, yet is frequently limited by the scope of available datasets. In particular, there is a lack of runnable, real-world datasets required for a range of tasks ranging from neural program synthesis to machine learning-guided program optimization. We introduce a new dataset, ExeBench, which attempts to address this. It tackles two key issues with real-world code: references to external types and functions and scalable generation of IO examples. ExeBench is the first publicly available dataset that pairs real-world C code taken from GitHub with IO examples that allow these programs to be run. We develop a toolchain that scrapes GitHub, analyzes the code, and generates runnable snippets of code. We analyze our benchmark suite using several metrics, and show it is representative of real-world code. ExeBench contains 4.5M compilable and 700k executable C functions. This scale of executable, real functions will enable the next generation of machine learning-based programming tasks.
Hiding multiple images into a single image via joint compressive autoencoders

Interest in image hiding has been continually growing. Recently, deep learning-based image hiding approaches improve the hidden capacity significantly. However, the major challenges of the existing methods are that they are difficult to balance between the errors of the modified cover image and those of the recovered secret image. To solve this problem, in this paper, we develop an image hiding algorithm based on a joint compressive autoencoder framework. Further, we propose a novel strategy to enlarge the hidden capacity, i.e., hiding multi-images in one container image. Specifically, our approach provides an extremely high image hidden capacity coupled with small reconstruction errors of the secret image. More importantly, we tackle the trade-off problem of earlier approaches by mapping the image representations in the latent spaces of the joint compressive autoencoder models, leading to both high visual quality of the container image and low reconstruction error the secret image. In an extensive set of experiments, we confirm our proposed approach to outperform several state-of-the-art image hiding methods, yielding high imperceptibility and steganalysis resistance of the container images with high recovery quality of the secret images, while improving the image hidden capacity significantly (four times higher than full-image hiding capacity).
Clustering and the efficient use of cognitive resources

A central component of human intelligence is the ability to make abstractions, to gloss over some details in favor of drawing out higher-order structure. Clustering stimuli together is a classic example of this. However, the crucial question remains of how one should make these abstractions—what details to retain and what to throw away? How many clusters to form? We provide an analysis of how a rational agent with limited cognitive resources should approach this problem, considering not only how well a clustering fits the data but also by how 'complex' it is, i.e. how cognitively expensive it is to represent. We show that the solution to this problem provides a way to reinterpret a wide range of psychological models that are based on principles from non-parametric Bayesian statistics. In particular, we show that the Chinese Restaurant Process prior, ubiquitous in rational models of human and animal clustering behavior, can be interpreted as minimizing an intuitive formulation of representational complexity.
Improving the accuracy of hydrothermal alteration mapping based on image fusion of ASTER and Sentinel-2A data: a case study of Pulang Cu deposit, Southwest China

Mapping hydrothermal alteration is an important means of mineral exploration, therefore, effective improvement of the accuracy of identification of hydrothermal alteration minerals is a hot topic. In this study, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Sentinel-2A data were selected for the Pulang copper deposit, to improve the accuracy of hydrothermal alteration mineral mapping. Three data fusion methods of Principal Component Analysis (PCA), Gram–Schmidt (GS) and High-Pass Filtering (HPF) were employed, and method of spectral matching was introduced to obtain high spatial resolution and high spectral fidelity fused data. The comparison showed that the HPF fusion method had the best effect. The fused data by the HPF fusion method were then implemented to map hydrothermal alteration minerals by using a multifractal-based method of PCA + Spectrum–Area. Through the verification of a field investigation, the results exhibited a similar hydrothermal alteration distribution, compared with the results obtained from the original ASTER data, but with higher identification accuracy. Therefore, the case study strongly suggests that the image fusion method with spectral matching is an effective tool to increase spatial resolution, while maintaining high spectral fidelity. Thus, it is a valuable and economic method for improving the identification accuracy of hydrothermal alteration minerals. HIGHLIGHTSThe spectral matching method was introduced to unify the surface reflectance of ASTER and Sentinel-2A data.Principal Component Analysis (PCA), Gram-Schmidt (GS) and High-Pass Filtering (HPF) methods were employed to fuse AST ER and Sentinel-2A data.The results show that the extraction accuracy of hydrothermal alteration minerals based on AST ER - Sentinel-2A fused data has 6.98% higher than that of original ASTER data.
A Comparison of Immersive and Non-Immersive VR for the Education of Filmmaking

Nowadays, both IVR (Immersive VR) and NIVR (Non-immersive VR) have already been adopted by filmmakers and used in the education of filmmaking, but few studies have shown their differences in supporting learning. This article aims to compare these two forms of technology as educational tools for learning filmmaking and give suggestions on how to choose between them. Two applications with the same purpose and content were developed using IVR and NIVR technologies respectively. An experiment of within subject design was implemented using these two versions as experimental material. 39 subjects participated in experiment and the quantitative measures include presence, motivation and usability. SPSS was used for data analysis and the statistical results, together with interview reports showed that both technologies led to positive learning experience while IVR had better performance in the presence (especially in the “sensory & realism” and “involvement” subscales) and intrinsic motivation (especially in the “enjoyment” subscale) while NIVR was more accessible to the public and may provide more complex and powerful functions with sophisticated GUI. In conclusion, both technologies are capable of supporting the learning of filmmaking effectively when chosen for proper educational missions.