Reviewerly/exHarmony huggingface dataset:
Detection of Breast Cancer by Using Various Machine Learning and Deep Learning Algorithms

Women worldwide are frequently trapped with breast cancer. In nations like Nigeria, 69% of females who have breast cancer frequently die from the disease and having a faster diagnosis can greatly enhance their survival chances due to early detection and proper care. Early detection of breast cancer can greatly improve prognosis and survival chances by promoting the early treatment of patients. Effective classification of benign tumors will minimize the unnecessary medical treatments for patients. There are many ways that cancer can be detected: X-ray, ultrasound, mammogram, etc. In addition, there is also a lot of research on proper evaluations of breast cancer, which includes risk calculators, and measuring extra relative risks of an individual or persons being malignant or benign. Due to its independence and specific advantages in detecting complex characteristics from among thousands of breast cancer patterns from complex data, machine learning (ML) has been widely recognized in the breast tumor pattern classification and forecast modeling as the methodology of choice. A lot of research has been conducted into proper evaluations of breast cancer and understanding the effectiveness of various treatments. In the classification and prediction model of breast cancer, ML is an influential model. Effective services are given by approaches such as the classification of data in the above sentences. These methods are commonly used in medicine as well as for decision-making. This paper covers different algorithms for ML, which can be used for breast cancer detection. Since these techniques are commonly used in many areas, they are also used for making decisions regarding diagnostic diagnosis and clinical studies. These algorithms for detecting breast cancer are built in this paper, which range from simple ML techniques to complex models. In this report, we have explained the advantages of our further study by capturing detailed data on breast cancer patients. In the following analysis, the output is contrasted with other algorithms that consider the accuracy of detecting a problem of the same type. Thanks to these algorithms, doctors will be able to identify cancerous cells in the breast, and then there goes on to treating the patients.
Modeling visual perceptual learning of Contrast Discrimination with Integrated Reweighting

The Integrated Reweighting Model has been used to replicate a variety of phenomena in Visual Perceptual Learning, or VPL. These phenomena include learning, location specificity (Dosher et al 2013, PNAS), and even roving (Dosher et al 2020, JOV). While the phenomena that have been replicated span many of the important characteristics of VPL, the tasks replicated have been primarily constrained to single forced-choice orientation discrimination tasks. Here we demonstrate a twin network modification to the model allows it to perform a 2-AFC (alternative forced choice) contrast discrimination task. Thus, as compared with previous versions of the model that focused primarily on orientation judgements of single images, this modification expands the model in capability both to 1) compare two separate images and to 2) make contrast judgments. The twin network methodology uses two parallel instantiations of a variant of the classic model whose outputs are then compared. This model thus offers each trail, in the case of contrast discrimination, an estimate of which image has higher contrast. Here we test this model in a classic behavioral contrast discrimination protocol inspired by Yu et al in 2004 in JOV. The similarity to classic protocols in VPL is made possible by the twin network instantiation of the integrated reweighting model. Expanding the capability of classic models is critical for determining their utility as models for the human visual system.
Perception of gait motion during multiple lower-limb vibrations in young healthy individuals: a pilot study

In virtual reality (VR), immersion can be created through synchronous visuomotor stimulations and enhanced by adding auditory or kinesthetic stimulations. Multiple patterned vibrations applied at the lower limbs might be a way to induce kinesthetic perception of gait motion that could be combined with VR stimulations to add the perception of self-motion. However, gait motion perception using multiple vibrations has not yet been evaluated. The objective of the study was to quantify the perception of gait motion while applying multiple, patterned vibrations to the lower limbs in healthy individuals. Twenty young healthy participants (25.1 ± 4.4 years) experienced multiple vibrations in 1-min trials. Stimulation consisted of a vibration pattern based on the sequence of muscle lengthening during a 2-s gait cycle. Stimulation was applied on participants in a standing position, under 11 experimental conditions controlling visual information (eyes open/closed), vibration frequency (40-80 Hz), and number and location of the joints stimulated (hips, knees, ankles isolated or combined two by two). Perception of gait motion was quantified for each condition using a 10-point visual analog scale (VAS, 0: "no perception", 10: "Perception of gait movements"). All participants except one achieved a score higher than 5/10 in at least one condition. Great variability was found for perception of gait motion within participants and conditions (VAS ranging from 0 to 9.6/10). Differences were found between conditions (p < 0.01), with higher mean and median scores in conditions that included knee vibration. Inducing gait motion perception is possible using multiple vibrations in healthy individuals. Stimulation of the knees seems to positively influence perception of gait motion.
The Agile

The practices we employ to ensure that the nodes in our tree conform to these primary principles are key to the success of the Agile Codex. In this section, I will provide examples of the practices I use. They are by no means dogma, and depending on your organization, business, market, product, and people you may find other, more suitable methods for you.
Case Study: The Automation of an over the Counter Financial Derivatives Transaction Using the CORDA Blockchain

We implemented the automation of a real-world Bilateral Derivatives Over-The-Counter Post Trade Confirmation financial transaction model adopting the Corda permissioned blockchain technology; this kind of transaction is currently performed manually by specialized clerks in most private banking organizations. The proposed solution design is an innovative certified sequence of the workflow states of the transaction, based on the asynchronized interaction among two parties through a smart contract coordinated and validated by the notary node. We show the business logic model of the application and the flow diagrams compatible with the current Corda R3 distribution.
Digital Contact Tracing – An Examination of Uptake in UK and Germany

At the start of the pandemic, our research on community disquiet surrounding tracking and tracing surveillance used as COVID-19 control led us to our conclusion that a failure to engage with the public in the development and execution of the technology had a negative influence on the way it has been received. In this paper, we sought to test our view: that damaged or absent trust, relating to the technology or its sponsors (particularly governments), was key in understanding the way community disquiet constrained efficacy of the control policy. However, initial findings have demonstrated instances where trust relationships were damaged this did not always or consistently appear to deter significant rates of downloads. Conversely, initial public engagement and approval of the technology likewise did not always or consistently result in requisite uptake rates being met, for the technology to work as planned. Through our survey of the UK and German app, how trust is created and maintained is neither simple not inevitable. Externalities beyond community engagement effected trust in various ways depending on the wider socio-political control environment prevailing. What can be said of trust and engagement is that their absence, along with other influences of public permission and approval, can have an impact on how control initiatives are received by data subjects. Therefore if trust and engagement are not magic bullets for efficacy, their absence will produce disquiet and this can impact on the sustainability of pandemic control policy.
Blockchain et DLT dans le système bancaire (Blockchain and DLT in the Banking System)

French Abstract: La technologie blockchain est un reseau distribue sur des machines decentralisees qui permet d'effectuer et valider des transactions. Le terme blockchain recouvre des usages divers sur le marche bancaire depuis quelques annees. En considerant l’architecture de la blockchain et l’infrastructure bancaire, nous visons d’abord a clarifier les utilisations de ces technologies en distinguant les notions de registre distribue (DLT) et de blockchain, en insistant aussi sur les risques induits par la normalisation internationale des terminologies. Ensuite, nous differencions les blockchains par rapport a leurs niveaux de permissions afin de mieux cerner leurs realites dans l'environnement bancaire. Pour ce faire, nous considerons notamment le cas du projet blockchain (Madre) de la Banque de France pour illustrer les pratiques en cours. Enfin, nous nous tournons vers l’analyse approfondie des DLT et leur place dans le systeme bancaire. Il ressort de l'analyse que la DLT du consortium est le protocole le plus adapte actuellement pour repondre aux attentes et contraintes du marche bancaire.English Abstract: Blockchain technology is a network distributed on decentralized computers that allows transactions to be carried out and validated. The term blockchain has covered various uses in the banking market for several years. By considering the architecture of the blockchain and the banking infrastructure, we first aim to clarify the uses of these technologies by distinguishing the concepts of distributed ledger (DLT) and blockchain, while also insisting on the risks induced by standardization international terminology. Next, we differentiate blockchains according to their permission levels in order to better understand their realities in the banking environment. To do this, we consider especially the case of the blockchain project (Madre) of the Banque de France to illustrate current practices. Finally, we turn to the in-depth analysis of DLTs and their place in the banking system. The analysis shows that the DLT of the consortium is the most suitable protocol currently to meet the expectations and constraints of the banking market.]
Graph Signal Processing over Multilayer Networks -- Part II: Useful Tools and Practical Applications

This work introduces a tensor-based framework of graph signal processing over multilayer networks (M-GSP) to analyze high-dimensional signal interactions. Following Part I's introduction of fundamental definitions and spectrum properties of M-GSP, this second Part focuses on more detailed discussions of implementation and applications of M-GSP. Specifically, we define the concepts of stationary process, convolution, bandlimited signals, and sampling theory over multilayer networks. We also develop fundamentals of filter design and derive approximated methods of spectrum estimation within the proposed framework. For practical applications, we further present several MLN-based methods for signal processing and data analysis. Our experimental results demonstrate significant performance improvement using our M-GSP framework over traditional signal processing solutions.
Ethno-Cultural Characteristics of the Educational System of Pedagogical University: Experience of Comprehension

It raises the question of the necessity and importance of retaining the ethno-cultural component in the educational systems of pedagogical universities in the context of the dialectics of unification and differentiation, originality, as well as the specifics of dual professionalization in the educational process of a pedagogical university. The purpose of the article is to consider the ethno-cultural conditionality of the educational system of a pedagogical university, to justify the relevance and ambiguity of retaining the ethno-cultural characteristics of educational systems in modern conditions. The research methodology is based on systematic, socio-cultural and ethno-cultural approaches. The methods used are a set of methods of theoretical research (analysis, synthesis, generalization, analogy, interpretation), as well as included observation, comparative analysis of information and video series presented on the websites of universities. Attention is drawn to the essential inconsistency of the characteristic “ethno-cultural”, the meaning of personal choice and the process of forming ethno-cultural identity. It is emphasized that ethno-cultural characteristics are objectively present in the educational system, since they are set both essentially (characteristics of the external environment and the environment of the university, attitudes, preferences, experience and identity of subjects) and purposefully (in the process of goal-setting and choosing system-forming activities). Due to the retention of ethno-cultural identity, it becomes possible to maintain the balance of globalization and glocalization at the level of local organizations and communities; to protect the growing personality (due to the effect of “double professionalization” of both the student mastering the teaching profession and his future pupils) from the threat of cultural marginalization and the destruction of ethnic (ethno-cultural) identity; to provide additional driving forces for the development of the educational system itself, oriented by its nature not on standardization, but on the cultivation of uniqueness.
Synthesizing Skeletal Motion and Physiological Signals as a Function of a Virtual Human's Actions and Emotions.

Round-the-clock monitoring of human behavior and emotions is required in many healthcare applications which is very expensive but can be automated using machine learning (ML) and sensor technologies. Unfortunately, the lack of infrastructure for collection and sharing of such data is a bottleneck for ML research applied to healthcare. Our goal is to circumvent this bottleneck by simulating a human body in virtual environment. This will allow generation of potentially infinite amounts of shareable data from an individual as a function of his actions, interactions and emotions in a care facility or at home, with no risk of confidentiality breach or privacy invasion. In this paper, we develop for the first time a system consisting of computational models for synchronously synthesizing skeletal motion, electrocardiogram, blood pressure, respiration, and skin conductance signals as a function of an open-ended set of actions and emotions. Our experimental evaluations, involving user studies, benchmark datasets and comparison to findings in the literature, show that our models can generate skeletal motion and physiological signals with high fidelity. The proposed framework is modular and allows the flexibility to experiment with different models. In addition to facilitating ML research for round-the-clock monitoring at a reduced cost, the proposed framework will allow reusability of code and data, and may be used as a training tool for ML practitioners and healthcare professionals.
Analysing Customer Buying Habits with Visual Data Mining

The ultimate aim of every industry and organization is to make profits by attracting a greater number of customers. To to achieve this motto they need to analyze the priorities of the customer. This is usually done by doing marketing everywhere such as in social media, newspaper, sites, etc. The marketers keep advertising at many sites without even knowing whether the advertisement is useful at that platform or not. Hence, making such huge investments in advertisements at wrong platforms will lead to less profits. There is a need for the companies to provide customer services in such a way that the customer doesn’t lose his interest and trust and must maintain a healthy relationship. If such services are provided equally to every customer, then there is a possibility that the company might provide its service to those customers who bring low profits and keep the customers who make high profits in waiting. To avoid such discrepancies, categorization of customers can be done based on their priority. This can be achieved using the Clustering technique, k-means algorithm. Since the customer data is unsupervised, k-means helps us to cluster them. If we use supervised data, then prediction of new customer’s priority can also be done using K nearest neighbors’ algorithm. The exploration of deep insights of data using exploratory data analysis makes it easy to understand data using visual representations [3][4][6]. These visual representations also lead to less time consumption for exploratory data analysis.
Towards Unbiased Random Features with Lower Variance For Stationary Indefinite Kernels

Random Fourier Features (RFF) demonstrate well-appreciated performance in kernel approximation for large-scale situations but restrict kernels to be stationary and positive definite. And for non-stationary kernels, the corresponding RFF could be converted to that for stationary indefinite kernels when the inputs are restricted to the unit sphere. Numerous methods provide accessible ways to approximate stationary but indefinite kernels. However, they are either biased or possess large variance. In this article, we propose the generalized orthogonal random features, an unbiased estimation with lower variance. Experimental results on various datasets and kernels verify that our algorithm achieves lower variance and approximation error compared with the existing kernel approximation methods. With better approximation to the originally selected kernels, improved classification accuracy and regression ability is obtained with our approximation algorithm in the framework of support vector machine and regression.
Sound Field Projection System using Optical See-Through Head Mounted Display

There are various ways to grasp the spatial and temporal structures of sound field. Sound field visualization is an effective technique to understand spatial sound information. For example, acoustical holography, optical methods, and beam-forming have been proposed and studied. In recent years, augmented reality (AR) technology has rapidly developed and is now more familiar. Many sensors, display devices, and ICT technologies have been implemented in AR equipment, which enable interaction between real and virtual worlds. In this paper, we propose an AR display system, which displays the results obtained by the beam-forming method. The system consists of 16ch microphone array, real-time sound field visualization system and optical see-through head mounted display (OST-HMD). Real-time sound field visualization system analyses sound signals recorded by 16ch microphone array by beam-forming method. Processed sound pressures data are sent to OST-HMD by using transmission control protocol (TCP), and colormap is projected on real world. Settings property of real-time sound field visualization system can be changed by using virtual user interface (UI) and TCP. In addition, multi-users can experience the system by sharing sound pressures and settings property data. Using this system, users wearing OST-HMD can observe sound field information intuitively.
Layered Neural Atlases for Consistent Video Editing

We present a method that decomposes, or unwraps, an input video into a set of layered 2D atlases, each providing a unified representation of the appearance of an object (or background) over the video. For each pixel in the video, our method estimates its corresponding 2D coordinate in each of the atlases, giving us a consistent parameterization of the video, along with an associated alpha (opacity) value. Importantly, we design our atlases to be interpretable and semantic, which facilitates easy and intuitive editing in the atlas domain, with minimal manual work required. Edits applied to a single 2D atlas (or input video frame) are automatically and consistently mapped back to the original video frames, while preserving occlusions, deformation, and other complex scene effects such as shadows and reflections. Our method employs a coordinate-based Multilayer Perceptron (MLP) representation for mappings, atlases, and alphas, which are jointly optimized on a per-video basis, using a combination of video reconstruction and regularization losses. By operating purely in 2D, our method does not require any prior 3D knowledge about scene geometry or camera poses, and can handle complex dynamic real world videos. We demonstrate various video editing applications, including texture mapping, video style transfer, image-to-video texture transfer, and segmentation/labeling propagation, all automatically produced by editing a single 2D atlas image.
THE EFFECTIVENESS OF SPEED READING AS TECHNIQUE IN TEACHING READING COMPREHENSION OF TENTH GRADE AT MA. SUNAN CENDANA MADURA

The objective of the research is to know the students’ achievement using speed reading technique in teaching reading comprehension. The research is carried out at MA Sunan Cendana Madura. The method non-randomized experimental study and the number of experimental class is 22 and 23 for control class. The researcher uses ANCOVA (Analysis of Covariance. The result shows that the pretest average score of each group is different. The pretest score of experimental group is 61.36 and control group is 44.35. Then, the post-test average score of the experimental group is 87,27 and the control group is 56,52. It is found that the value of F is 33,18. It is found that F critical with df 43 at 0.01 level of significance to 7,08. It means that F value is higher than F critical score. The result of data analysis above shows that: (1) speed reading technique is more effective than the conventional technique on reading comprehension.
Sparse PointPillars: Maintaining and Exploiting Input Sparsity to Improve Runtime on Embedded Systems

Bird's Eye View (BEV) is a popular representation for processing 3D point clouds, and by its nature is fundamentally sparse. Motivated by the computational limitations of mobile robot platforms, we create a fast, high-performance BEV 3D object detector that maintains and exploits this input sparsity to decrease runtimes over non-sparse baselines and avoids the tradeoff between pseudoimage area and runtime. We present results on KITTI, a canonical 3D detection dataset, and Matterport-Chair, a novel Matterport3D-derived chair detection dataset from scenes in real furnished homes. We evaluate runtime characteristics using a desktop GPU, an embedded ML accelerator, and a robot CPU, demonstrating that our method results in significant detection speedups (2 × or more) for embedded systems with only a modest decrease in detection quality. Our work represents a new approach for practitioners to optimize models for embedded systems by maintaining and exploiting input sparsity throughout their entire pipeline to reduce runtime and resource usage while preserving detection performance. All models, weights, experimental configurations, and datasets used are publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://vedder.io/sparse_point_pillars.