Reviewerly/exHarmony huggingface dataset:
Modeling of ringdown cavity maladjustment based on the extreme learning machine

In the cavity ringdown technique, cavity maladjustment may affect the accuracy of measurement. The effect of maladjustment is complex and nonlinear. Therefore, a neural network method of an extreme learning machine is presented to model the nonlinear relation between the intracavity loss and the cavity maladjustments. This method was tested by two-dimensional angular scanning simulations and experiments. After dataset training, this method finely predicted the intracavity loss at certain maladjustments. The root mean square values of the prediction deviation were about 0.27 ppm in simulation and about 0.44 ppm in experiment. This method was further applied to a cavity ringdown system for high reflectivity measurement. The measurement uncertainty was improved from ±0.0025% to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mo>±<!-- ± --></mml:mo><mml:mrow class="MJX-TeXAtom-ORD"><mml:mn>0.0019</mml:mn></mml:mrow><mml:mi mathvariant="normal">%<!-- % --></mml:mi></mml:math> .
Health System Efficiency of OECD Countries with Data Envelopment Analysis

Purpose: This study is aimed at measuring the efficiency of 37 OECD countries for 2020 using the Data Envelopment Analysis (DEA) method. Besides, it is aimed at ranking the efficient decision making units by using the super-efficiency DEA model. Design/methodology/approach: In the study, analyses were carried out with input-oriented Charnes, Cooper and Rhodes (CCR), input-oriented Banker, Charnes and Cooper (BCC) models and super-efficiency models of these models by using 4 inputs and 3 outputs. Findings: As a result of the analysis, 14 countries in the CCR model and 20 countries in the BCC model were efficient. According to the results of the super-efficiency models, the efficient countries were ranked. Research limitations/implications: The limitations of the study are the analyses are based on input-oriented DEA models and the research was conducted in OECD countries. Originality/value: Performance evaluation of health systems has gained importance in recent years. Many countries are making efforts to improve their health systems. Due to epidemics such as COVID-19, OECD countries, like many countries around the world, have increased the share of health expenditures in GDP. Because of this situation, the evaluation of the performance of OECD countries in the field of health has emerged as a very important research topic.
Effectiveness of Decision Support System for Hydroponic Plant Nutrient Selection Using Apriori Algorithm Method

Hydroponics is an agricultural system that uses water as its growing medium by adding nutrients to plants without using soil. In hydroponic cultivation, the thing that needs to be considered is the provision of an optimal dose of nutrient solution as a food source for plants. The provision of nutrients that are not in accordance with the needs of plants results in plants stopping growing, so that plants do not take care of each other. The type of nutrition commonly used in hydroponic plants is AB mix. However, the use of AB mix as a hydroponic nutrient also has disadvantages, namely synthetics and the price of AB mix nutrients are quite expensive. Therefore, alternative nutrients are needed that have the potential to be used as hydroponic nutrients. By analyzing hydroponic plant assessment data so that the rules of linkage between combinations of goods are found and form a pattern of itemset combinations with apriori algorithm. The association technique aims to find matching links in the database. From the results of data mining calculations using apriori algorithms, nutrient assessment data on hydroponic plants with the minimum support 20% and the minimum confidence 70%, formed five rules on hydroponic plant nutrition. One of the best rules is that AB mix nutrition is used on cucumber, spinach, celery, and kailan plants, so with 100% probability that POC nutrients will be good to also use on cucumber, spinach, celery, and kailan plants.
ALGORITHM COMPARISON AND FEATURE SELECTION FOR CLASSIFICATION OF BROILER CHICKEN HARVEST

Broiler chickens are the result of superior breeds that produce a lot of meat. In practice, however, many breeders experience crop failure, which has a serious impact on the economy and can also affect farmer quality, resulting in sanctions. The value of the performance index produced at harvest indicates the success rate of harvesting broiler chickens. Broiler crop yield data can be used to help classify broiler crop yield data using an approach method. The CRISP-DM (Cross Industry Standard Process for Data Mining) method was used in this study's data mining technique. This study compares 3 classification algorithms to determine the best algorithm and 3 feature selection methods to determine the best method for improving algorithm performance. According to the findings of this study, the Random Forest algorithm is the best algorithm for classifying harvest data, with an accuracy rate of 89.14 percent. The best way to improve the algorithm's performance is to use the Backward Elimination method, which can increase the accuracy by 7.53 percent. As a result, the Random Forest + Backward Elimination algorithm yields an accuracy value of 96.67 percent. According to this study, the factors that influence crop yield increase are FCR, number of harvests, and body weight.
Benchmarking Learning-based Bitrate Ladder Prediction Methods for Adaptive Video Streaming

HTTP adaptive streaming (HAS) is increasingly adopted by over-the-top (OTT)-based video streaming services, it allows clients to dynamically switch among various stream representations. Each of these representations is encoded to target a specific bitrate providing a wide range of operating bitrates known as the bitrate ladder. Several approaches with different levels of complexity are currently used to build such a bitrate ladder. The most straightforward method is to use a fixed bitrate ladder for all videos, which is a set of bitrate-resolution pairs, called “one-size-fits-all”, and the most complex is based on the intensive encoding of all resolutions over a wide bitrate range to construct the convex-hull. This latter is then used to obtain a per-title bitrate ladder. Recently, various methods relying on machine learning (ML) techniques have been proposed to predict content-based ladder without performing exhaustive search encoding. In this paper, we conduct a benchmark study of several handcrafted and deep learning (DL)-based approaches for predicting content-optimized bitrate ladder, which we believe provides baseline methods and will be useful for future research in this field. The obtained results, based on 200 video sequences compressed with the high-efficiency video coding (HEVC) encoder, reveal that the most efficient method predicts the bitrate ladder without performing any encoding process at the cost of a slight Bjøntegaard delta bitrate (BD-BR) loss of 1.43% compared to the exhaustive approach. The dataset and the source code of the considered methods are made publicly available at: https://github.com/atelili/Bitrate-Ladder-Benchmark.
FALSIFIKOVANJE I ZLOUPOTREBA BEZGOTOVINSKIH INSTRUMENATA PLAĆANJA I EVROPSKI STANDARDI

General social digitization and dynamic lifestyle changes, with the increasing use and spread of “smart” phones, with many different contents they offer, lead to the emergence of new relationships between banks, buyers and sellers, which relate to modern business with payment cards. Payments are made, regardless of whether it is a traditional or online business, and modern banking is constantly searching for the most optimal way of payment with the use of new technological and communication solutions. Payment card transactions are a frequent target for the commission of criminal acts, which is not only a challenge for the legislator to set up a legal framework for protection, but also for detection authorities and jurisprudence. The European standards that are being formulated try to respond to these challenges, which also requires implementation into domestic legislation. Directive 2019/713/EU of the European Parliament and the Council on the fight against fraud and counterfeiting in connection with cashless means of payment precisely provides the basis for the protection of cashless payment instruments, in accordance with the requirements set in connection with payments today and the criminal law reaction in domestic legislation.
Mitigating the Digital Divide by Leveraging On-Campus Partnerships

COVID-19 disrupted all existing educational processes at North Carolina Agricultural University, the largest public historically Black college and university (HBCU). More specifically, students and faculty were required to complete all coursework in an online virtual setting. Many students faced a “digital divide” and needed access to technology. New virtual technology services and software applications were tested and launched to provide extended services to the students. A Laptop Lending Program was created to fully support distance and virtual learning initiatives. The success of this program is not only focused on the number of laptops distributed, the self-reported benefits to students, and validation of Library Services’ impact on campus, but also by the effectiveness of the collaborations and partnerships quickly formed to mitigate the effects of the pandemic.
Slice-and-Forge

Graph convolutional networks (GCNs) are becoming increasingly popular as they can process a wide variety of data formats that prior deep neural networks cannot easily support. One key challenge in designing hardware accelerators for GCNs is the vast size and randomness in their data access patterns which greatly reduces the effectiveness of the limited on-chip cache. Aimed at improving the effectiveness of the cache by mitigating the irregular data accesses, prior studies often employ the vertex tiling techniques used in traditional graph processing applications. While being effective at enhancing the cache efficiency, those approaches are often sensitive to the tiling configurations where the optimal setting heavily depends on target input datasets. Furthermore, the existing solutions require manual tuning through trial-and-error or rely on sub-optimal analytical models.
Sentiment Analysis on Mixed Language Facebook Comments: A Food and Beverages Case Study

Millions of people in the world shared their thoughts on products and services in different mediums. Social media is one of the avenues through which people express their opinions. Digital marketers extract and analyze content from social media sites so that they can best promote and package their products or services to potential buyers. Our work involved analyzing emotions on the opinionated comments posted on the Food and Beverages Facebook fan pages. This research investigated emotion-carried words and slang as well as emoticons expressed in Chinese, English, and Bahasa Malaysia posts and comments that cannot be found in the lexicons made available in the West and China where only locals can understand them. Since a high percentage of the comments are typically not written and structured well, we use the lexicon approach in this research to provide a word-level sentiment score. We applied the concept of sentic computing in the sentiment analysis. This research computes emotional labels and sentiment scores for Chinese, English, and Bahasa Malaysia for mixed-language posts and comments. The outcomes of the mixed language sentiment analysis are a set of emotion tags: pleasantness, attention, sensitivity, and aptitude. This research highlights many interesting challenges for future works.
High-fidelity continuous-variable quantum key distribution via the proper combination of zero-photon catalysis and quantum scissors

In this paper, we examine how the combination of zero-photon catalysis (ZPC) and a quantum scissors (QS) intensifies the range and the fidelity of a continuous-variable quantum key distribution CV-QKD protocol. Using a non-Gaussian calculation, we realized that the QS significantly boosts the range of the protocol and enhances its fidelity at long distances. At negligible values of excess noise, ZPC operation leads to incredibly high fidelity values, even at large distances. In this case, an optimal strength of the ZPC operation exists that maximizes the secure distance of the protocol. Finally, our study of a system with a nonzero value of excess noise reveals that the fidelity generally increases with ZPC, as opposed to the range, which decreases with this Gaussian operation.
Competency Assessment on Disaster Risk Reduction and Management (DRRM) Among Senior High School Students of Um Digos College

The Philippines is geographically located in the Pacific ring of fire, making the country one of the most vulnerable countries to natural disasters. With this, proper mitigations such as disaster preparedness and awareness among the citizens are essential to minimize the casualties whenever disaster strikes. Thus, this study aimed to assess Senior High School Students' competency level (SHS) on Disaster Risk Reduction and Management (DRRM) topics. A descriptive-comparative quantitative design was used to attain the objectives of the study. Moreover, an adopted questionnaire was used in this study. A total of twohundred twenty-two (222) Senior High School Students from the University of Mindanao - Digos participated in the survey. The study results revealed that Senior High School Students’ DRRM competency level needs improvement and showed no significant differences when analyzed according to their grade level, strand, and age, except for the topic, “Application of DRRM Concepts,” when analyzed by age. In contrast, it was found that there is a significant difference in students’ competency according to their sex. Therefore, the findings of the study implied that there is a need for curriculum enhancement and training programs for teachers to address the gaps pertaining to DRRM.
Metamorphic testing of oracle quantum programs

Quantum Computing is regarded with a growing sense of excitement and expectation. While the preliminary evidences of its practical interest accumulate, there are numerous challenges that must be addressed before quantum computing can reach generalized adoption and realize its full potential. One such challenge concerns assessing whether a quantum program is correct, namely in the sense that it implements the desired functionality. In the classical realm, program testing is a well-established approach to find defects in programs. While this approach should inspire quantum software development, porting classical testing techniques to the quantum world is by no means trivial. We propose a novel approach to test quantum programs. Our approach is based on the use of metamorphic relations, which allows us to overcome the well-known quantum measurement problem. We describe a series of exploratory experiments whose results provide evidence of the usefulness of our approach.
An Evolutionary Concept Analysis of Precision Medicine, and Its Contribution to a Precision Health Model for Nursing Practice

Precision medicine is a new concept that has been routinely encountered in the literature for little more than a decade. With increasing use, it becomes crucial to understand the meaning of this concept as it is applied in various settings. An evolutionary concept analysis was conducted to develop an understanding of the essential features of precision medicine and its use. The analysis led to a comprehensive list of the antecedents, attributes, and consequences of precision medicine in multiple settings. With this understanding, precision medicine becomes part of the broader practice of precision health, an important process proposed by nursing scholars to provide complete, holistic care to our patients. A model for precision health is presented as a framework for care.
BrightFlow: Brightness-Change-Aware Unsupervised Learning of Optical Flow

Unsupervised optical flow estimation relies on the assumption that pixels characterizing the same observed object should exhibit a stable appearance across video frames. With this assumption, the long-standing principle behind flow estimation consists in optimizing a photometric loss that maximizes the similarity between paired pixels in successive frames. However, these frames could be subject to strong brightness changes due to the radiometric properties of scenes as well as their viewing conditions.In this paper, we present BrightFlow, a new method to train any optical flow estimation network in an unsupervised manner. It consists in training two networks that jointly estimate optical flow and brightness changes. These changes are then compensated in the photometric loss so that reconstruction errors due to shadows or reflections will not affect negatively the training. As this compensation mechanism is only used at training stage, our method does not impact the number of parameters or the complexity at inference. Extensive experiments conducted on standard datasets and optical flow architectures show a consistent gain of our method. Source code is available at https://github.com/CEA-LIST/BrightFlow.
Energy and Performance-Efficient Dynamic Consolidate VMs Using Deep-Q Neural Network

With cloud computing facing higher levels of Big Data than ever, the processor scale is rapidly expanding. Large clusters place a heavy burden on cloud service providers and the environment. High energy consumption decreases the economic benefits of cloud service providers while enormous power demands pressure on the environment. The dynamic consolidation of virtual machines (VMs), which uses live migration technology to optimize resource usage and reduce energy consumption, is sufficient for saving energy while ensuring high performance with the desired level of quality of service (QoS) between cloud providers and users. In this article, we propose a novel machine-learning algorithm called deep-Q neural network VM consolidation (DQNVMC) that combines the Q-leaning approach with deep learning neural network to find an approximately optimal solution. Furthermore, based on the real workload trace in the cloud environment, the experiments show that DQNVMC effectively reduces energy consumption while meeting the high performance of QoS requirements.
SuperFormer: Continual learning superposition method for text classification

One of the biggest challenges in continual learning domains is the tendency of machine learning models to forget previously learned information over time. While overcoming this issue, the existing approaches often exploit large amounts of additional memory and apply model forgetting mitigation mechanisms which substantially prolong the training process. Therefore, we propose a novel SuperFormer method that alleviates model forgetting, while spending negligible additional memory and time. We tackle the continual learning challenges in a learning scenario, where we learn different tasks in a sequential order. We compare our method against several prominent continual learning methods, i.e., EWC, SI, MAS, GEM, PSP, etc. on a set of text classification tasks. We achieve the best average performance in terms of AUROC and AUPRC (0.7% and 0.9% gain on average, respectively) and the lowest training time among all the methods of comparison. On average, our method reduces the total training time by a factor of 5.4-8.5 in comparison to similarly performing methods. In terms of the additional memory, our method is on par with the most memory-efficient approaches.
Development of an Automatic Pill Image Data Generation System

Objectives: Since the easiest way to identify pills and obtain information about them is to distinguish them visually, many studies on image processing technology exist. However, no automatic system for generating pill image data has yet been developed. Therefore, we propose a system for automatically generating image data by taking pictures of pills from various angles. This system is referred to as the pill filming system in this paper.Methods: We designed the pill filming system to have three components: structure, controller, and a graphical user interface (GUI). This system was manufactured with black polylactic acid using a 3D printer for lightweight and easy manufacturing. The mainboard controls data storage, and the entire process is managed through the GUI. After one reciprocating movement of the seesaw, the web camera at the top shoots the target pill on the stage. This image is then saved in a specific directory on the mainboard.Results: The pill filming system completes its workflow after generating 300 pill images. The total time to collect data per pill takes 21 minutes and 25 seconds. The generated image size is 1280 × 960 pixels, the horizontal and vertical resolutions are both 96 DPI (dot per inch), and the file extension is .jpg.Conclusions: This paper proposes a system that can automatically generate pill image data from various angles. The pill observation data from various angles include many cases. In addition, the data collected in the same controlled environment have a uniform background, making it easy to process the images. Large quantities of high-quality data from the pill filming system can contribute to various studies using pill images.
On the Use of Semantically-Aligned Speech Representations for Spoken Language Understanding

In this paper we examine the use of semantically-aligned speech representations for end-to-end spoken language understanding (SLU). We employ the recently-introduced SAMU-XLSR model, which is designed to generate a single embedding that captures the semantics at the utterance level, semantically aligned across different languages. This model combines the acoustic frame-level speech representation learning model (XLS-R) with the Language Agnostic BERT Sentence Embedding (LaBSE) model. We show that the use of the SAMU-XLSR model instead of the initial XLS-R model improves significantly the performance in the framework of end-to-end SLU. Finally, we present the benefits of using this model towards language portability in SLU.
SET: Sampling-Enhanced Exploration Tree for Mobile Robot in Restricted Environments

Mobile robots generally work in harsh and restricted environments, which poses challenges for mobile robots to find a feasible path efficiently. This article presents a planning method, namely, sampling-enhanced exploration tree (SET), to improve computational efficiency in restricted environments while guaranteeing high-quality performance. The core of SET is sampling-enhanced exploration, which consists of critical areas identification, guiding-exploration, and rectifying-exploration. In the critical areas identification phase, the restricted areas are identified based on the distribution of the hybrid samples. Next, the critical samples in restricted areas are selected as the origins of the sampling-enhanced exploration. In the guiding-exploration phase, the sampling-enhanced exploration starts from the origins and marches quickly with the guidance of the leader-samples to capture the spatial feature and connectivity of the restricted areas. The spatial information provides essential guidance for efficient biased sampling. In the rectifying-exploration phase, the directions of sampling-enhanced exploration are rectified to transit the problematic areas and supplement samples. Theoretical analysis is provided to shed light on the properties of SET. Moreover, the generality and effectiveness of SET are verified through a series of mobile robot simulations and real-world experiments.