Reviewerly/exHarmony huggingface dataset:
Short-Term Solar PV Power Generation Day-Ahead Forecasting Using Artificial Neural Network: Assessment and Validation

Solar photovoltaics (PV) is considered an auspicious key to dealing with energy catastrophes and ecological contamination. This type of renewable energy is based on climatic conditions to produce electrical power. In this article, a multilayer feedforward neural network (MLFFNN) is implemented to predict and forecast the output power for a solar PV power station. The MLFFNN is designed using the module temperature and the solar radiation as the two main only inputs, whereas the expected power is its output. Data of approximately one week (6-days) are obtained from a real PV power station in Egypt. The data of the first five days are used to train the MLFFNN. The training of the designed MLFFNN is executed using two types of learning algorithms: Levenberg-Marquardt (LM) and error backpropagation (EBP). The data of the sixth day, which are not used for the training, are used to check the efficiency and the generalization capability of the trained MLFFNN by both algorithms. The results provide evidence that the trained MLFFNN is running very well and efficiently to predict the power correctly. The results obtained from the trained MLFFNN by LM (MLFFNN-LM) are compared with the corresponding ones obtained by the MLFFNN trained by EBP (MLFFNN-EBP). From this comparison, the MLFFNN-LM has slightly lower performance in the training stage and slightly better performance in the stage of effectiveness investigation compared with the MLFFNN-EBP. Finally, a comparison with other previously published approaches is presented. Indeed, predicting the power correctly using the artificial NN is useful to avoid the fall of the power that maybe happen at any time.
Combining 360° Video and Camera Mapping for Virtual Reality: An Innovative Solution

Changing the framework of traditional video with limited viewing angles, 360° photo/video provides an immersive viewing experience. 360° video is one of the applications and the most important feature of virtual reality for immersion and the feeling of being in another space. However, when viewing 360° videos with a head-mounted display, the viewer feels like a fixed rotatable camera, and the viewer's movement does not change the viewing angle of the object, which greatly reduces the spatial immersion required for virtual reality. Therefore, we propose a solution that maintains high-quality graphics and low hardware demands and supports 6DoF head-mounted displays. Through the camera mapping function in the 3D animation software, 360° surround video is projected into a 3D sphere to create a simple 3D object that corresponds to the shape of the object in the image. With pristine video quality and a realistic 3D spatial perspective, it provides better virtual reality immersion than 360° video and is not as complex as a full 3D virtual reality environment. In the future, 3D scanning and photogrammetry can be integrated to reconstruct a more easily applied 3D virtual reality environment
Fully Decentralized Block Chain with Proxy Re-Encryption Algorithm for Healthcare Security

The medical technology is being evolved for enhancing the healthcare information related to medication, diseases, medical images etc., by using Electronic Healthcare Recording (EHR).The details such as gender, age, and weight of patients are sensitive, and require protection against unauthorized access.Therefore, providing a robust security for medical data is indeed a challenge.The present research work proposed a Fully Decentralized Block chain (FDBC) model with the Proxy Re-Encryption Algorithm (PReA) with an Ethereum smart contract.The proposed FDBC-PReA allows the data to be visible only for the owner and the smart contract as the model is implemented in an Ethereum based test bed.Each time when the data is transformed, the data loss is obtained for the incorrect data which is taken care by the decentralized BC.The fully decentralized BC stores and checks whether each of the entity shares or accesses the real time data.The proposed FDBC-PReA Ethereum smart contracts, and proxy re-encryption algorithms are executed with a faster rate that consumed average block time of 5.48s, which is lesser when compared to average block times of Smart-Contract Ethereum Distributed Ledger-14s, Searchable
Link-ability: A Prediction Method of Web Service Compatibility based on Markov Process

Prediction on compatibility of a service is crucial because it not only provides assurance for service requesters to successfully fulfil the workflow, but also relieves the pressure of the service pool from overload. Existing researches on parameter prediction mainly rely on collaborative filtering and matrix factorization, which are easily exposed to data-sparse problem, and they don't have solid support from mathematical proof. In this paper, we are the first to propose a method to predict compatibility with reliable mathematical proof, and its mechanism prevents data-sparse. Firstly we propose an architecture called Topology Retrievable Service Oriented Architecture, which is capable of collecting individual invocation records in a specific period of time and generate the whole topology of the service pool. Secondly, based on the architecture we presented, we give our definitions of Link-ability, propose Link-ability Generation Algorithm and present our solid mathematical strategies in the aspect of Markov Process. Lastly, we perform a series of experiment to strengthen our point, the result shows that our Link-ability not only reveals compatibility, but also potential compatibility of connecting services.
Master equation emulation and coherence preservation with classical control of a superconducting qubit

Open quantum systems are a topic of intense theoretical research. The use of master equations to model a system's evolution subject to an interaction with an external environment is one of the most successful theoretical paradigms. General experimental tools to study different open system realizations have been limited, and so it is highly desirable to develop experimental tools which emulate diverse master equation dynamics and give a way to test open systems theories. In this paper we demonstrate a systematic method for engineering specific system-environment interactions and emulating master equations of a particular form using classical stochastic noise in a superconducting transmon qubit. We also demonstrate that non-Markovian noise can be used as a resource to extend the coherence of a quantum system and counteract the adversarial effects of Markovian environments.
A New Strategy for the Morphological and Colorimetric Recognition of Erythrocytes for the Diagnosis of Forms of Anemia based on Microscopic Color Images of Blood Smears

The detection of red blood cells based on morphology and colorimetric appearance is very important in improving hematology diagnostics.There are automatons capable of detecting certain forms, but these have limitations with regard to the formal identification of red blood cells because they consider certain cells to be red blood cells when they are not and vice versa.Other automata have limitations in their operation because they do not cover a sufficient area of the blood smear.In spite of their performance, biologists have very often resorted to the manual analysis of blood smears under an optical microscope for a morphological and colorimetric study.In this paper, we present a new strategy for semi-automatic identification of red blood cells based on their isolation, their automatic color segmentation using Otsu's algorithm and their morphology.The algorithms of our method have been implemented in the programming environment of the scientific software MATLAB resulting in an artificial intelligence application.The application, once launched, allows the biologist to select a region of interest containing the erythrocyte to be characterized, then a set of attributes are computed extracted from this target red blood cell.These attributes include compactness, perimeter, area, morphology, white and red proportions of the erythrocyte, etc.The types of anemia treated in this work concern the iron-deficiency, sickle-cell or falciform, thalassemia, hemolytic, etc. forms.The results obtained are excellent because they highlight different forms of anemia contracted in a patient.
Surface Assessment of Transparent Glass Plate with Wavelength-Modulated Interferometry and Harmonic Phase-Iterative Method

Multiple reflections and nonlinearity of phase modulating need to be compensated to accurately profile the surface of the glass plate using wavelength-modulated interferometry. In this study, to assess the glass plate surface using wavelength-modulated interferometry, a new phase-iterative method insensitive to the harmonic signals and nonlinear errors was developed. We developed the harmonic phase-iterative method by combining the phase-iterative method with irradiance signal considering the harmonic components. Moreover, for suppression of the harmonic-related errors in the iterative calculation, the noble pixel-selection technique and convergence formula were combined with the phase-iterative method. For estimation of the developed iterative method, we conducted the numerical simulation and assessed the glass plate surface with the Fizeau interferometer. The simulation and assessment results demonstrated that the proposed method could perform phase extraction with improved iterative calculation and suppression of the effects of the harmonics and nonlinearity.
FPGA-Based High-Throughput Key-Value Store Using Hashing and B-Tree for Securities Trading System

Field-Programmable Array (FPGA) technology is extensively used in Finance. This paper describes a high-throughput key-value store (KVS) for securities trading system applications using an FPGA. The design uses a combination of hashing and B-Tree techniques and supports a large number of keys (40 million) as required by the Trading System. We have used a novel technique of using buckets of different capacities to reduce the amount of Block-RAM (BRAM) and perform a high-speed lookup. The design uses high-bandwidth-memory (HBM), an On-chip memory available in Virtex Ultrascale+ FPGAs to support a large number of keys. Another feature of this design is the replication of the database and lookup logic to increase the overall throughput. By implementing multiple lookup engines in parallel and replicating the database, we could achieve high throughput (up to 6.32 million search operations/second) as specified by our client, which is a major stock exchange. The design has been implemented with a combination of Verilog and high-level-synthesis (HLS) flow to reduce the implementation time.
Emojis that work! Incorporating visual cues from facial expressions in emojis can reduce ambiguous interpretations

Emojis are included in more than half of all text-based messages shared on digital platforms. Evidence is emerging to suggest that many emojis are ambiguous, which can lead to miscommunication and put a strain on social relations. We hypothesized that emojis that incorporate visual cues that distinguish facial expressions of emotions, known as facial action units (AUs), might offer a solution to this problem. We compared interpretation of novel emojis that incorporate AUs with interpretations of existing emojis and face photographs. Stimuli conveyed either sadness, happiness, anger, surprise, fear, or neutrality (i.e., no emotion). Participants (N = 237) labelled the emotion(s) conveyed by these stimuli using open-ended questions. They also rated the extent to which each stimulus conveyed the target emotions using rating scales. Seven out of ten emojis with action units were interpreted more accurately or as accurately when compared to existing emojis and face photographs. A critical difference between these novel emojis and existing emojis is that the emojis with action units were never seen before by the participants - their meaning was entirely derived from the presence of AUs. We conclude that depicting visual cues from real-world facial expressions in emojis has the potential to facilitate emotion communication in text-based digital interactions.
Active Feature Acquisition via Human Interaction in Relational domains

We consider the problem of interactive and explainable active feature elicitation in relational domains in which a small subset of data is fully observed while the rest of the data is minimally observed. The goal is to identify the most informative set of entities for whom acquiring additional relations would yield a more robust model. We assume the presence of a human expert who can interactively provide the relations. Thus there is a need for an explainable model. Consequently, we employ an relational tree-based distance metric to identify the most diverse set of relational examples (entities) to obtain more relational feature information on. The model that is learned iteratively is an interpretable and explainable model that is presented to the human expert for eliciting additional features. Our empirical evaluation demonstrates both the efficiency and the interpretability of the proposed approach.
Benin

On July 8, 2022, the IMF Executive Board approved 42-month Extended Fund Facility (EFF) and Extended Credit Facility (ECF) arrangements under High Combined Credit Exposure (HCCE) (391 percent of quota, about US$650 million) to help Benin meet pressing financing needs and support the country’s National Development Plan centered on achieving SDGs. The program is off to a strong start notwithstanding elevated uncertainty. While there is broad consensus that sound macroeconomic management in recent years is generating tangible dividends for the economy as a whole, the public is frustrated over the fact that this is taking time to translate into improved socioeconomic conditions for all. This sentiment has been compounded by temporary import price pressures since Russia’s invasion of Ukraine. Legislative elections will be held in early January 2023, with related risks to the program expected to be limited.
Disentangled representations: towards interpretation of sex determination from hip bone

By highlighting the regions of the input image that contribute the most to the decision, saliency maps have become a popular method to make neural networks interpretable. In medical imaging, they are particularly well-suited to explain neural networks in the context of abnormality localization. However, from our experiments, they are less suited to classification problems where the features that allow to distinguish between the different classes are spatially correlated, scattered and definitely non-trivial. In this paper we thus propose a new paradigm for better interpretability. To this end we provide the user with relevant and easily interpretable information so that he can form his own opinion. We use Disentangled Variational Auto-Encoders which latent representation is divided into two components: the non-interpretable part and the disentangled part. The latter accounts for the categorical variables explicitly representing the different classes of interest. In addition to providing the class of a given input sample, such a model offers the possibility to transform the sample from a given class to a sample of another class, by modifying the value of the categorical variables in the latent representation. This paves the way to easier interpretation of class differences. We illustrate the relevance of this approach in the context of automatic sex determination from hip bones in forensic medicine. The features encoded by the model, that distinguish the different classes were found to be consistent with expert knowledge.
A General Theory for Client Sampling in Federated Learning

While client sampling is a central operation of current state-of-the-art federated learning (FL) approaches, the impact of this procedure on the convergence and speed of FL remains under-investigated. In this work, we provide a general theoretical framework to quantify the impact of a client sampling scheme and of the clients heterogeneity on the federated optimization. First, we provide a unified theoretical ground for previously reported sampling schemes experimental results on the relationship between FL convergence and the variance of the aggregation weights. Second, we prove for the first time that the quality of FL convergence is also impacted by the resulting covariance between aggregation weights. Our theory is general, and is here applied to Multinomial Distribution (MD) and Uniform sampling, two default unbiased client sampling schemes of FL, and demonstrated through a series of experiments in non-iid and unbalanced scenarios. Our results suggest that MD sampling should be used as default sampling scheme, due to the resilience to the changes in data ratio during the learning process, while Uniform sampling is superior only in the special case when clients have the same amount of data.
Streaming Punctuation: A Novel Punctuation Technique Leveraging Bidirectional Context for Continuous Speech Recognition

While speech recognition Word Error Rate (WER) has reached human parity for English, continuous speech recognition scenarios such as voice typing and meeting transcriptions still suffer from segmentation and punctuation problems, resulting from irregular pausing patterns or slow speakers. Transformer sequence tagging models are effective at capturing long bi-directional context, which is crucial for automatic punctuation. Automatic Speech Recognition (ASR) production systems, however, are constrained by real-time requirements, making it hard to incorporate the right context when making punctuation decisions. Context within the segments produced by ASR decoders can be helpful but limiting in overall punctuation performance for a continuous speech session. In this paper, we propose a streaming approach for punctuation or re-punctuation of ASR output using dynamic decoding windows and measure its impact on punctuation and segmentation accuracy across scenarios. The new system tackles over-segmentation issues, improving segmentation F0.5-score by 13.9%. Streaming punctuation achieves an average BLEUscore improvement of 0.66 for the downstream task of Machine Translation (MT).
The Mediated Voice

This chapter aims to introduce perspectives for understanding the mediated and then disembodied voice in human–computer interaction. My argument starts with two basic notions. First, I introduce the mediated voice as an electrified space of vocal expression. As voices “from me” or as “clones of myself”, they are modified through electrified processes and become reflections and distortions of the original voice. Secondly, the mediated voice is explored in prerecorded vocal elements and spatialised disembodied vocal clips as “myths of disembodied voices” that can animate a body in our imagination. Next, I discuss four vocal domains that contribute to meaning in vocal music in human–computer interaction. To do this, I will introduce a brief theoretical framework for understanding persona references in the mediated voice. These illustrate vocal phenomena associated with identities such as articulation, affect, physicality and language. The persona references each describe a significant area of the voice in electroacoustic music and thus the mediated voice. Through analysing some examples, this chapter will illuminate the nature of the mediated voice using insight from listening examples and practice-based research.
Enhancing Graph Convolutional Networks by Topology Sampling

Graph Neural Networks (GNNs) play a very important role today. It does analyze not only the graph data itself, but also the data connectivity of the graph. The quality of a GNN is thus altered by the result of extracted graph structure information. The extraction could be enhanced by GNN model design or directly from the training dataset with a GNN-decoupled method. In this paper, we propose RankedDrop, a new sampling method to improve the extraction of graph structure information. This approach is based on droppingout technique, and it adopts a spatial-aware selection of edges to drop. It takes into account structure information of the graph to control the dropping-out, and its random selection of edges to be dropped is under the control of a probability generated with respect to graph’s topological importance. Our experiments point out that RankedDrop provides high-quality and robust training results compared to the leading solutions. Furthermore, RankedDrop could be a framework plugin and combined with GNN model improvements to maximize GNN quality. Furthermore, RankedDrop could be a plugin for AI frameworks like MindSpore and combined with GNN model improvements to maximize GNN quality.
Disinhibition in a CSCL Environment

In this paper, we explore the IRC Français foreign language learning environment. This environment offers little more than a text-based chat system with a few features to make writing in a foreign language easier. Despite the limited structure, conversations online exhibit strong differences from conversations in the classroom, even when the teachers and students remain constant. We offer some explanations for these findings based on interviews conducted with a number of the teachers and students.
Analisis Penerapan Normalisasi Data Dengan Menggunakan Z-Score Pada Kinerja Algoritma K-NN

The large volume of information in the data causes a lot of data to be stored in the dataset. The dataset consists of various attributes and attribute values which contain information stored in the dataset. Data mining is a process that can be used to search for information on datasets. However, the problems encountered in the dataset are often found to have abnormal data such as the range of values that are too far and different between dataset attributes. The value range that is too far causes the results of the information obtained to be not optimal, in data mining itself the process or results are good based on the quality of the data stored in the dataset. Data normalization is a preprocessing stage, where data normalization is scaled back to the range of values in the attribute. Z-Score Normalization is a statistical technique that can be used in data mining to preprocess data by performing data transformations. Z-Score Normalization can be combined with data mining classification techniques, where the role of Z-Score Normalization is to normalize data which is useful for improving the performance of data mining classification algorithms, especially the K-NN algorithm in this study. The results of the study show that Z-Score Normalization is useful for improving performance than the K-NN algorithm. This can be seen from the increase in the accuracy value obtained from the K-NN process before normalizing the dataset and after normalizing the dataset. The accuracy values respectively before normalizing the dataset were 95.13%, 95.83%, 96.11%, 95.77% and 95.81% after normalizing the dataset there was an increase in the accuracy value, namely 97.87%, 98, 57%, 98.77%, 97.23% and 98.11%.