Reviewerly/exHarmony huggingface dataset:
The Macro-Economics of Crypto-Currencies: The Role of Private Moneys in Monetary Policy

Cryptocurrencies provide an important dimension of innovation to the evolution of the exchange medium we call money. There are now over 4,000 such currencies, and their potential and volume is growing. The impact of such currencies for money laundering, law enforcement, and banking supervision have been extensively discussed on the transaction level. But this is the micro level of analysis. What has been rare is a macro level discussion of the impact on the monetary system of a country. Central banks, which are institutions tasked with providing monetary stability, will see their problems rise while the power of their traditional tools to control money supply and interest rates - such as reserve requirements and the discount rates - is declining. But the new digital technologies - such as distributed ledgers - and new approaches provide regulatory bodies also with new and potentially powerful tools. The task for central banks and policy makers is to create new approaches to use, regulate, and incent them in shaping the macro-economic path of their economy. The paper will propose several of these approaches. This is of particular importance in an economic recovery post coronavirus. In the process, central banks will also, predictably, issue their own digital currencies, and a tiny number of those will become global super-currencies. This will create a new type of issues.
ASSISTING THE STUDENTS MASTERING LITERARY TEXT COMPREHENSION THROUGH SCHEMATA

Reading comprehension presents one of the ways of getting some information and the information that readers read differs one from another. Understanding different information is influenced by reader’s previous knowledge: the more previous knowledge readers have, the better they understand the text read. Considering that there are different texts genres, the texts within one genres are designed in a manner that each of them has their own writing style. As an assistance to memorize the facts related on the reading comprehension and to understand what is read, and when it is about all texts genres, the schemata technique is used. Some researchers states that in the readers’ memory there is predictable structure of knowledge which can be taken as a base that the readers, using these structures in reading, understand all texts genres easily. For improving students’ abilities to master the literature texts, especially the prose and poetic texts, the schemata technique is applied in this research. In the preliminary study, it is determined that the student’s achievements in mastering the prose texts were below the set criteria of the success, and that students were less motivated to learn, too. Students achieved an average of 70.20 points, while the criteria of the success set by the STIBA Academy were 75.00. After applying the schemata technique, students achieved an average of 85.85 points in the prose course and were more motivated to learn. In the preliminary study, in the poetry course, students achieved an average of 66.05 points, and the motivation to learn was low. After applying the schemata technique, students achieved an average of 84.15 points, and the motivation to learn increased. Based on the obtained results, it can be concluded that the application of the schemata technique affects the improvement of students' abilities to understand literary texts, especially prose and poetry, as well as their motivation to study these contents.
A Survey on Recent Trends of PIO and Its Variants Applied for Motion Planning of Dynamic Agents

Pigeon Inspired Optimization (PIO) algorithm is gaining popularity since its development due to faster convergence ability with great efficiencies when compared with other bio-inspired algorithms. The navigation capability of homing pigeons has been precisely used in Pigeon Inspired Optimization algorithm and continuous advancement in existing algorithms is making it more suitable for complex optimization problems in various fields. The main theme of this survey paper is to introduce the basics of PIO along with technical advancements of PIO for the motion planning techniques of dynamic agents. The survey also comprises of findings and limitations of proposed work since its development to help the research scholar around the world for particular algorithm selection especially for motion planning. This survey might be extended up to application based in order to understand the importance of algorithm in future studies.
What is the metaverse? A high-tech plan to Facebookify the world

This article explains and critiques the concept of the metaverse. It describes the metaverse in relation to AR, VR, and XR, using examples. It critiques the techno-deterministic nature of current debate about the metaverse. It critiques the implicit ontological standpoint of those advocating for a metaverse.
LIBS2ML: A library for scalable second order machine learning algorithms

LIBS2ML is a library based on scalable second order learning algorithms for solving large-scale problems, i.e., big data problems in machine learning. LIBS2ML has been developed using MEX files, i.e., C++ with MATLAB/Octave interface to take the advantage of both the worlds, i.e., faster learning using C++ and easy I/O using MATLAB. Most of the available libraries are either in MATLAB/Python/R which are very slow and not suitable for large-scale learning, or are in C/C++ which does not have easy ways to take input and display results. So LIBS2ML is completely unique due to its focus on the scalable second order methods, the hot research topic, and being based on MEX files. Thus it provides researchers a comprehensive environment to evaluate their ideas and it also provides machine learning practitioners an effective tool to deal with the large-scale learning problems. LIBS2ML is an open-source, highly efficient, extensible, scalable, readable, portable and easy to use library. The library can be downloaded from the URL: \url{https://github.com/jmdvinodjmd/LIBS2ML}.
SlowFast Rolling-Unrolling LSTMs for Action Anticipation in Egocentric Videos

Action anticipation in egocentric videos is a difficult task due to the inherently multi-modal nature of human actions. Additionally, some actions happen faster or slower than others depending on the actor or surrounding context which could vary each time and lead to different predictions. Based on this idea, we build upon RULSTM architecture, which is specifically designed for anticipating human actions, and propose a novel attention-based technique to evaluate, simultaneously, slow and fast features extracted from three different modalities, namely RGB, optical flow, and extracted objects. Two branches process information at different time scales, i.e., frame-rates, and several fusion schemes are considered to improve prediction accuracy. We perform extensive experiments on EpicKitchens-55 and EGTEA Gaze+ datasets, and demonstrate that our technique systematically improves the results of RULSTM architecture for Top-5 accuracy metric at different anticipation times.
Research on the Development of Blockchain in Supply Chain

At the present stage, China’s economy is at a critical stage of transformation and development, and supply chain finance has also entered a deep stage of development. In order to promote supply chain finance to get rid of hollowing out and other traps, it is necessary to improve the way of financial information service. Some characteristics of blockchain technology are consistent with the development of supply chain finance, which can improve the industry’s ability to control risks related to supply chain finance to a certain extent, and make supply chain finance better serve the real economy.On the basis of previous basic research, this paper creatively puts forward how to use blockchain to solve the problems existing in supply chain finance, and discusses in detail the obstacles to the popularization of blockchain technology in the field of supply chain finance, thus providing some schemes for realizing the application of blockchain in the field of supply chain finance.
Image-to-Graph Convolutional Network for Deformable Shape Reconstruction from a Single Projection Image

Shape reconstruction of deformable organs from two-dimensional X-ray images is a key technology for image-guided intervention. In this paper, we propose an image-to-graph convolutional network (IGCN) for deformable shape reconstruction from a single-viewpoint projection image. The IGCN learns relationship between shape/deformation variability and the deep image features based on a deformation mapping scheme. In experiments targeted to the respiratory motion of abdominal organs, we confirmed the proposed framework with a regularized loss function can reconstruct liver shapes from a single digitally reconstructed radiograph with a mean distance error of 3.6mm.
Frozen Pretrained Transformers for Neural Sign Language Translation

One of the major challenges in sign language translation from a sign language to a spoken language is the lack of parallel corpora. Recent works have achieved promising results on the RWTH-PHOENIX-Weather 2014T dataset, which consists of over eight thousand parallel sentences between German sign language and German. However, from the perspective of neural machine translation, this is still a tiny dataset. To improve the performance of models trained on small datasets, transfer learning can be used. While this has been previously applied in sign language translation for feature extraction, to the best of our knowledge, pretrained language models have not yet been investigated. We use pretrained BERT-base and mBART-50 models to initialize our sign language video to spoken language text translation model. To mitigate overfitting, we apply the frozen pretrained transformer technique: we freeze the majority of parameters during training. Using a pretrained BERT model, we outperform a baseline trained from scratch by 1 to 2 BLEU-4. Our results show that pretrained language models can be used to improve sign language translation performance and that the self-attention patterns in BERT transfer in zero-shot to the encoder and decoder of sign language translation models.
Context-Aware Probabilities (CAP) for Semantic Image Segmentation

Semantic image segmentation based on Convolutional Neural Networks (CNNs) is one of the main approaches in computer vision area. The methods based on deep convolutional neural networks, typically use a pre-trained CNN trained on the large image classification datasets as a backend to extract features (image descriptors) from the images. Whereas, the special size of output features from CNN backends are smaller than the input images, by stacking multiple deconvolutional layers on the last layer of backend network, the dimension of the output will be the same as the input image. Segmentation using local image descriptors without involving relationships between these local descriptors yield weak and uneven segmentation results. Inspired by these observations, in this research we propose Context-Aware Probabilities (CAP) unit. CAP unit generates probabilities for classes using local-image descriptors. This unit can be used in any semantic image segmentation architectures. We used CAP unit in Fully Convolutional Network (FCN) and DeepLab-v3-plus architectures and propose the new FCN-CAP and DeepLab-v3-plus-CAP architectures. Training the proposed architectures on PASCAL VOC2012 dataset shows 1.9% and 0.4% accuracy improvement compared to the corresponding basic architectures, respectively.
Face Identification and Verification using Histogram Based Approach in Spatial Domain

The present paper relates to “Face Identification and Verification”. Histogram based image feature extraction approach is used for the system implementation. The database taken for the work comprises of 40×10 size images from forty different objects, that’re limited to 92×112 pixels. Firstly the eyes and face are detected and then cropped for the recognition purpose. The histogram approach performs cumulative distribution to identify the images of eye and face. Therefore a non-linear change that reflects the “accumulative distribution” of the test image produces an image having uniform histogram. Euclidean distance is also calculated for the processing to make the comparison between the test and database image. Some of the difficulties are found in the work excessive changeability in head revolution and slope, intensity and angle of light, facial characteristics, etc. It is observed that the method is best suitable in case of eye recognition.
Tight Security of Twin-DH Hashed ElGamal KEM in Multi-User Setting

It is an important research area to construct a cryptosystem that satisfies the security for multi-user setting. In addition, it is desirable that such a cryptosystem is tightly secure and the ciphertext size is small. For IND-CCA public key encryption schemes for multi-user setting with constant-size ciphertexts tightly secure under the DH assumptions, in 2020, Y. Sakai and G. Hanaoka firstly proposed such a scheme (implicitly based on hybrid encryption paradigm) under the DDH assumption. More recently, Y. Lee et al. proposed such a hybrid encryption scheme (with slightly stronger security) where the assumption for the KEM part is weakened to the CDH assumption. In this paper, we revisit the twin-DH hashed ElGamal KEM with even shorter ciphertexts than those schemes, and prove that its IND-CCA security for multi-user setting is in fact tightly reducible to the CDH assumption.
Adversarial Robustness for Unsupervised Domain Adaptation

Extensive Unsupervised Domain Adaptation (UDA) studies have shown great success in practice by learning transferable representations across a labeled source domain and an unlabeled target domain with deep models. However, previous works focus on improving the generalization ability of UDA models on clean examples without considering the adversarial robustness, which is crucial in real-world applications. Conventional adversarial training methods are not suitable for the adversarial robustness on the unlabeled target domain of UDA since they train models with adversarial examples generated by the supervised loss function. In this work, we leverage intermediate representations learned by multiple robust ImageNet models to improve the robustness of UDA models. Our method works by aligning the features of the UDA model with the robust features learned by ImageNet pre-trained models along with domain adaptation training. It utilizes both labeled and unlabeled domains and instills robustness without any adversarial intervention or label requirement during domain adaptation training. Experimental results show that our method significantly improves adversarial robustness compared to the baseline while keeping clean accuracy on various UDA benchmarks.
Joint Use of Skip Connections and Synthetic Corruption for Anomaly Detection with Autoencoders

In industrial vision, the anomaly detection problem can be addressed with an autoencoder trained to map an arbitrary image, i.e. with or without any defect, to a clean image, i.e. without any defect. In this approach, anomaly detection relies conventionally on the reconstruction residual or, alternatively, on the reconstruction uncertainty. To improve the sharpness of the reconstruction, we consider an autoencoder architecture with skip connections. In the common scenario where only clean images are available for training, we propose to corrupt them with a synthetic noise model to prevent the convergence of the network towards the identity mapping, and introduce an original Stain noise model for that purpose. We show that this model favors the reconstruction of clean images from arbitrary real-world images, regardless of the actual defects appearance. In addition to demonstrating the relevance of our approach, our validation provides the first consistent assessment of reconstruction-based methods, by comparing their performance over the MVTec AD dataset [1], both for pixel- and image-wise anomaly detection. Our implementation is available at https://github.com/anncollin/AnomalyDetection-Keras .
Library Guides: REDCap at Berkeley: Basic Features

This guide has information about REDCap at Berkeley including campus availability, training videos, and how to get help.
Lift: An Educational Interactive Stochastic Ray Tracing Framework with AI-Accelerated Denoiser

Real-time physically based rendering has long been looked at as the holy grail in Computer Graphics. With the introduction of Nvidia RTX-enabled GPUs family, light transport simulations under real-time constraint started to look like a reality. This paper presents Lift, an educational framework written in C++ that explores the RTX hardware pipeline by using the low-level Vulkan API and its Ray Tracing extension, recently made available by Khronos Group. Furthermore, to accomplish low variance rendered images, we integrated the AI-based denoiser available from the Nvidia ́s OptiX framework. Lift’s development arose primarily in the context of the graduate 3D Programming course taught at Instituto Superior Técnico and Master Theses focused on Real-Time Ray Trac- ing and provides the foundations for laboratory assignments and projects development. The platform aims to make easier students to learn and to develop, by programming the shaders of the RT pipeline, their physically-based ren- dering approaches and to compare them with the built-in progressive unidirectional and bidirectional path tracers. The GUI allows a user to specify camera settings and navigation speed, to select the input scene as well as the rendering method, to define the number of samples per pixel and the path length as well as to denoise the generated image either every frame or just the final frame. Statistics related with the timings, image resolution and total number of accumulated samples are provided too. Such platform will teach that nowadays physically-accurate images can be rendered in real-time under different lighting conditions and how well a denoiser can reconstruct images rendered with just one sample per pixel.
Thermobarometry and origin of andesitic lavas in Zoolesk area based on mineral chemistry (northeast of Sarbisheh, east Iran)

Thermobarometry and origin of andesitic lavas in Zoolesk area based on mineral chemistry (northeast of Sarbisheh, east Iran)
Algorithmic Analysis on Optimization Oriented Hot Event Detection and Product Recommendation

“A hot topic is said to be a topic that people widely talk about during a specific time”. However, there was no precise description of the hot topic and hotness evaluation standards. This paper presents a novel system that concerns on Hot Topic Detection (HTD) integrated with the recommendation o products as well.  The proposed hot event detection model is performed via (i) Pre-processing (ii) Feature Selection and weighting (iii) Text Model Construction (iv) Cluster-based topic Identification. Initially, the keywords are extracted from every tweet. Subsequently, the feature vector space is evaluated that is then subjected for portraying text model construction. At last, clustering is carried out via optimization logic that meant for detecting the hot topic. Particularly, two diverse clustering processes are carried out: micro-clustering and macro-clustering, where the optimal centroid is selected by Monarch Butterfly Optimization (MBO). Following the hot event detection, the adopted model concerns the recommendation of the product associated with the discussed topic. Finally, algorithmic analysis is done by varying the weighting element of the MBO model from 0.2, 0.4, 0.6 and 0.8.
MULTI-LAYER INFERENCE FUZZY TSUKAMOTO DETERMINING LAND SUITABILITY CLASS OF COCOA PLANTS

Determining the land suitability class of plants specifically cocoa (Theobroma cacao) is significant to do because each plant has a different characteristic of growth. This research aims at implementing the algorithm to determine the land suitability class of cocoa plants using the Multi-Layer Inference Fuzzy Tsukamoto (MLIFT). This research uses 18 input variables including 15 non-linguistic variables or crisp and the rest are linguistic ones or fuzzy as the data of growth requirements of cocoa plants. Generally, the algorithm used consists of three main steps those are fuzzification, Tsukamoto inference machine, and defuzzification consisting of three layers. The first layer covers seven inference engines, while each of the second and the third ones only consists of one inference engine. The concept of inference process in Fuzzy Tsukamoto is calculating the weighted average of each result of the nference process. Based on the testing result, it can be concluded that the multi-layer inference Fuzzy Tsukamoto for determining the land suitability class of cocoa plants has an accuracy level amounted 97%.
Use of WhatsApp in academic activities of Tourism Administration students in Peru during Covid-19

The whole world and Peru are experiencing a critical situation as a result of an unknown disease called Covid-19. Faced with this situation, the use of WhatsApp has played a leading role in the field of education, driving a remarkable development in academic activities among students and teachers. In this research we reviewed a diverse number of articles from the last 5 years. The objective of the study was to determine the influence of the use of WhatsApp on academic activities during the Covid-19 pandemic in students of Tourism Administration, Amazonas-Peru-2021. The research was quantitative, descriptive, with a non-experimental design, considering a sample of 120 university students. A questionnaire with 22 dichotomous questions was applied. 82.5% of the students said that the use of WhatsApp influenced academic support, 70.8% to make queries and absolve doubts, and 70% in collaborative work, 61.7% in communication between teachers and students, and 60% in participation, while 56.7% to present academic papers. With regard to the tools used, 47.5% of the students used email, 46.7% WhatsApp, 5.8% Facebook. In relation to the type of information shared through WhatsApp, 59% indicated academic information, 24% photographs, 10% thoughts-feelings and emotions. 80.8% of respondents believed that the use of WhatsApp influenced learning-teaching; 60% in academic performance. Regarding the reason for use, 44.2% of the students said they were in contact with their peers and friends, while 28.3% to communicate between peers and teachers, 16.7% to exchange information, 7.5% to perform tasks, and finally 3.3% indicated for entertainment. As for the frequency of use of WhatsApp, 26.7% used more hours, 24.2% used 3 hours a day, 17.5% used an hour a day. It was evidenced that the use of WhatsApp influenced the academic activities during the Covid-19 pandemic of the students of the Professional School of Administration in Tourism, Amazonas-Peru-2021.
Privacy-preserving Machine Learning for Medical Image Classification

With the rising use of Machine Learning (ML) and Deep Learning (DL) in various industries, the medical industry is also not far behind. A very simple yet extremely important use case of ML in this industry is for image classification. This is important for doctors to help them detect certain diseases timely, thereby acting as an aid to reduce chances of human judgement error. However, when using automated systems like these, there is a privacy concern as well. Attackers should not be able to get access to the medical records and images of the patients. It is also required that the model be secure, and that the data that is sent to the model and the predictions that are received both should not be revealed to the model in clear text. In this study, we aim to solve these problems in the context of a medical image classification problem of detection of pneumonia by examining chest x-ray images.