Reviewerly/exHarmony huggingface dataset:
Deep-Learning-Based Channel Estimation for Multi-wavelength Visible Light Communication System

This paper presents a method for channel modeling of multi-wavelength Visible Light Communication (VLC) system, and compares the effects of long short-term memory (LSTM), gated recurrent unit (GRU), sparse autoencoder-s (SAEs) algorithms in channel modeling. The results show that the SAEs algorithm fits the best with a mean square error of only 3 ×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−6</sup> . Besides, we construct the multi-wavelength channel of the RGB tricolor LED light source and model it. Finally, after analyzing the influence of many factors on the channel, it is concluded that the modeling effect of the short-wavelength signal source is the best.
Mutation-Based Algebraic Artificial Bee Colony Algorithm for Computing the Distance of Linear Codes

Finding the minimum distance of linear codes is a non-deterministic polynomial-time-hard problem and different approaches are used in the literature to solve this problem.&#x0D; Although, some of the methods focus on finding the true distances by using exact algorithms, some of them focus on optimization algorithms to find the lower or upper bounds of the distance. In this study,&#x0D; we focus on the latter approach. We first give the swarm intelligence background of artificial bee colony algorithm, we explain the algebraic approach of such algorithm and call it the algebraic artificial bee colony algorithm (A-ABC). Moreover, we develop the A-ABC algorithm by integrating it with the algebraic differential mutation operator. We call the developed algorithm the mutation-based algebraic artificial bee colony algorithm (MBA-ABC). We apply both; the A-ABC and MBA-ABC algorithms to the problem of finding the minimum distance of linear codes. The achieved results indicate that the MBA-ABC algorithm has a superior performance when compared with the A-ABC algorithm when finding the minimum distance of Bose, Chaudhuri, and Hocquenghem (BCH) codes (a special type of linear codes).
Linguistic Capacity as a Factor of Communicative Behavior in the Internet Space: the Findings of Experimental Research

The paper focuses on the potential of linguistic capacity as one of the key adaptive mechanisms of a communicant in the Internet-mediated communication. The methodological platform of the research is the autopoiesis theory developed by Chilean scientists U. Maturana and Fr. Varela which allows to model communication as an interaction between a communicant and his/her niche. It seems that the process of adaptation, its orientation and nature are primarily determined by the adaptive mechanisms that a communicant employs such as linguistic capacity. Receiving stimuli from the niche, a communicant chooses a response strategy in terms of the hierarchy of factors, and then uses one or more adaptive mechanisms to implement it. Linguistic capacity of a communicant plays a significant role both in the process of perception and understanding of the original message, and in the formulation of a response. Within this format, it seems relevant to study the features of communicants’ behavior with a certain level of linguistic capacity and identify plausible patterns of their communicative behavior. This paper puts forward a hypothesis about the influence of linguistic capacity on the perception and further communicants’ behavior, dwells on an experimental research aimed at identifying the specifics of the influence of linguistic capacity on communicative behavior. The participants of this experimental study are Russian-speaking bilinguals and native English-speakers. The study has unveiled some features of the influence of the level of linguistic capacity on the perception and understanding of the original message as well as the choice of a strategy for a response. The revealed discrepancies appear to be culturally-based which confirms the hypothesis and acts as a basis for further investigation.
SA24 Feasibility of a Patient Diagnosis Vetting Method to Identify and Discard Fraudulent Online Patient Survey Responses: A Case Study in Cholangiocarcinoma

This case study describes the analysis of a large, rapidly recruited cholangiocarcinoma patient survey dataset, with the objective of determining the veracity of patient self-reported diagnoses and discarding fraudulent responses. The feasibility of utilizing two external sources as a vetting mechanism for validating patient responses is evaluated. Investigators recruited cholangiocarcinoma patients into a patient-reported outcomes study via email and social media. A $25 gift card was offered for participation. The survey included reCAPTCHA fraud and abuse protection service. 2392 survey responses were received within 3 weeks; 1079 were analyzed after eliminating duplicate or incomplete responses. Respondents were then vetted against two known sources of cholangiocarcinoma patients: registration with the Cholangiocarcinoma Foundation (CCF) or Ciitizen, a patient-mediated health records and RWE platform. Self-reported mean age was 45 years, whereas the known mean age of cholangiocarcinoma diagnosis is 65 years. Patient self-reported treatment features did not align with known treatment patterns for this disease, with 108 (10%) patients self-reporting liver transplantation and over 50% self-reporting neoadjuvant chemotherapy(n=572) and neoadjuvant radiotherapy(n=560). When the study population was restricted to those registered with CCF or Ciitizen, the sample size decreased to 208. This vetted sample more accurately reflected known age distributions for the disease, with a mean age of 58 years. The vetted sample also matched known treatment patterns more accurately, with n=1 (0.5%) reporting liver transplantation, n=42 (20%) reporting receipt of neoadjuvant chemotherapy, and n=15 (17%) reporting neoadjuvant radiotherapy. Fraudulent responses are common among online patient surveys and there are limited methods available to verify patient diagnoses. This study utilized registration with a CCF or Ciitizen to validate survey responses. With this vetting structure applied, responses were more consistent with known disease and treatment patterns, suggesting that this method is feasible for validating survey respondents as actual members of the study's targeted patient population.
A Hybrid Transformer Network for Detection of Risk Situations on Multimodal Life-Log Health Data

The paper is focused on the development of hybrid transformer architectures for the detection of risk events on multimodal data recorded on a person with visual and signal sensors. The proposed two-stream architecture consists of a visual transformer and linear transformer of time series. The linear transformer is benchmarked on the publicly available dataset UCI-HAR. The experiments with our architecture have been conducted on the in-the-wild dataset BIRDS. The hybrid transformer architecture has better empirical performance than the 3D CNNs and RNNs in previous work. The accuracy of detection of risk situations shows an improvement of 10% over the single-stream transformers.
Research on Psychology Evaluation Method of Nuclear Power Plant Operators

Nuclear power plant operators play a more and more important part to plant safe operation. The paper analyzes and discusses the qualitative and quantitative evaluation methods for the operators. The comparison-analysis of the scope and result of application has been done between the method of outline figure fitted and the method of fuzzy synthetic evaluation. The research can be referenced in the evaluation of operators.
MSR32 Data Sampling Methods for Imbalanced Classification: A Random Forest Study for Predicting Treatment Switching in Multiple Sclerosis

Imbalanced data remains a challenge for utilizing random forest (RF) algorithms in healthcare research. This study evaluated different sampling methods in RF models for predicting treatment switching among patients with multiple sclerosis (MS). This study involved electronic medical records of adults with ≥1 Disease-Modifying Agent (DMA) and ≥1 MS diagnosis from September 2010-May 2017 TriNetX data. The earliest DMA date was assigned as the index date, and patients receiving DMA other than their index DMA prescription during follow-up were considered as switched. Patients were also required to have ≥1 outpatient visit and ≥1 prescription in 12 months pre- and 24 months post-index. RF models involving 72 baseline variables were trained using 70% of the randomly split data. Three sampling methods were evaluated, including up-sampling, down-sampling, and synthetic minority over-sampling techniques (SMOTE). RF classifiers and parameter tuning were implemented among resampled data to train RF models. The model performance of different sampling methods was examined using the Area Under the Curves (AUC), accuracy, recall, and F-1 score. The analytical sample consisted of 6,097 (84.0%) unswitched and 1,161(16.0%) switched patients with MS. The three leading factors associated with treatment switching were: age, type of the index DMA, and year of the index date. The up-sampling method achieved the best model performance with an AUC of 0.65 (accuracy 61%, recall 60%, and F1 score 72%), followed by down-sampling with an AUC of 0.63 (accuracy 62%, recall 63%, and F1 score 74%), and SMOTE with an AUC of 0.60 (accuracy 80%, recall 94%, and F1 score 89%). All sampling methods alleviated data imbalance problems. However, the over-sampling method provided the best AUC over other methods for predicting treatment switch in MS. Therefore, multiple sampling methods should be evaluated based on the extent of imbalance for increasing the performance of RF models.
Transactional Blockchain and Ecosystem

Blockchain is a ledger that does not require any central authority and it is not only used for cryptocurrencies such as Bitcoins, Ethereum. In this paper it is shown that how blockchain help for jobseekers and how they can make use of it and blockchain provides security, integrity, and anonymity. It creates interesting research areas in the perspective of technical challenges. Our objective is to understand the current research topic. This blockchain will also keep of transactions, the purpose of each transaction and document reference if any to implement business transactions.
Parallel Sensor-Space Lattice Planner for Real-Time Obstacle Avoidance

This paper presents a parallel motion planner for mobile robots and autonomous vehicles based on lattices created in the sensor space of planar range finders. The planner is able to compute paths in a few milliseconds, thus allowing obstacle avoidance in real time. The proposed sensor-space lattice (SSLAT) motion planner uses a lattice to tessellate the area covered by the sensor and to rapidly compute collision-free paths in the robot surroundings by optimizing a cost function. The cost function guides the vehicle to follow a vector field, which encodes the desired vehicle path. We evaluated our method in challenging cluttered static environments, such as warehouses and forests, and in the presence of moving obstacles, both in simulations and real experiments. In these experiments, we show that our algorithm performs collision checking and path planning faster than baseline methods. Since the method can have sequential or parallel implementations, we also compare the two versions of SSLAT and show that the run time for its parallel implementation, which is independent of the number and shape of the obstacles found in the environment, provides a speedup greater than 25.
Crypto Currency a Tale of Innovation, Scam and Uncertainty

Blockchain and cryptocurrency are two terms that go parallelly. we are hearing it in our daily life. Cryptocurrencies also known as cryptos are game changers of our lives. Unlike money that is issued and regulated by the government crypto isn’t controlled by any government or any persons. This is accomplished by the use of blockchain that makes the topic more curious. This work explains about the impact of the cryptocurrencies in the world. This paper says more about the non-technological aspects of cryptocurrencies that includes how it came into existence, why it used now, how it is seen by the world.
Significance of Cyber Security in Cyber World

Cyber security has its unique role in securing information in every sector. Protecting information from is hackers have become more challenging. Almost everyone recognizes the important of cyber world as a fact of daily life. Various policies and regulation acts were being implemented by organizations and governments to prevent cyber crimes. The cybersecurity the main thing that originates in mind is ’cybercrime’ which are aggregate colossally daily. This paper focuses on cyber security challenges, the world is facing and required techniques and technologies to prevent them. No technology we use is ever infallible
Contrast Enhancement of Gray Image Using Discrete Cosine Transform

In this paper a new method for contrast enhancement based on the discrete cosine transform is discussed and implemented. The technique converts the image into DCT domain and the DCT coefficients are modified using proposed mask then the enhanced image is reconstructed using inverse DCT. The discrete cosine transform outperforms with better image quality and with highest PSNR value.
Detection of Malaria Using Image Processing and Machine Learning

Malaria is a severe worldwide health issue that requires rapid and accurate diagnosis in order to control the disease. Malaria is easy to treat if discovered early and followed up with correct medical therapy. Computer-aided diagnosis is becoming more prominent since it can be used as a basic screening test in the absence of an expert microbiologist. To automate malaria diagnosis, an image processing algorithm necessary. In order to construct picture characteristics based on colour, texture, and the geometry of the cells and parasites. It incorporates many representations of the data for imaging, image processing, segmentation, and classification of test images.
A Survey on Sentiment Analysis

The rapid rise of online-based applications, such as forums and blogs, has resulted in comments and updates related to daily activities. Emotional analysis is a process of collecting and analyzing people's ideas, thoughts, and ideas about a variety of topics, products, topics and services. Opinion polls often benefit companies, governments, and individuals by gathering information and making decisions based on ideas. However, emotional analysis and the assessment process face many challenges. These challenges create barriers to accurately defining emotions and determining acceptable emotional differences. Emotional analysis identifies and extracts specific information from the text using language processing and text digging. This article discusses an overview of the strategy for completing this project and for the use of emotional analysis. Then, it evaluates, compares, and investigates common ways to gain a complete understanding of their benefits and disadvantages. Finally, emotional analysis challenges are explored to define future indicators.
Text Pattern Searching Algorithm: Naive, KMP, Rabin Karp Comparative Study

The Pattern Searching algorithms are sometimes also referred to as String Searching Algorithms and are considered as a part of the String algorithms.These algorithms are useful in the case of searching a string within another string.String matching is the problem of finding all occurrences of a character pattern in a text.This paper provides an overview of different string-matching algorithms and comparative study of these algorithms.In this paper, we have evaluated several algorithms, such as Naive string-matching algorithm, Brute Force algorithm, Rabin-Karp algorithm, Boyer-Moore algorithm, Knuth-Morris-Pratt algorithm, Aho-Corasick Algorithm and Commentz Walter algorithm.
Significance of Search Engine Optimization

As the Internet grows in popularity, hundreds of thousands of people use search engines such as Google and Yahoo to search for information. However, search engine users are most interested in some result pages. Therefore, selling a website with search engine results is an important task in website development. Search engine optimization (SEO) needs to complete this task. However, from time to time, the black hat SEO strategy behind search engines is used, and the page rank of search engine results is higher than it deserves. This research paper implies a new methodology for search engine optimization (SEO) without sandboxing via serps such as Google and Bing. Previously, rulebooks were based solely on the number of backlinks on a page. This technique employs a secure link building technique where link speed is key, without sacrificing website optimization. Today's algorithm updates are considered. This white paper describes the characteristics of search engine PageRank algorithms, SEO techniques, and strategies for ranking keywords.
Survey on Various Image Compression Techniques Used in Image Processing to Improve the Quality of Image

This paper presents study of assorted lossy compression techniques. the 2 techniques are Wavelet Difference Reduction (WDR) based compression and Singular Value Decomposition (SVD) based compression and SVD based compression reduces the psycho visual redundancies present within the image through rank reduction technique. WDR may be a lossy compression technique. It gains compression by taking the discrete wavelet transform of the input image so encodes the transform values using difference compression method. Singular Value Decomposition (SVD) is one in every of the simplest compression techniques. SVD based compression technique gives better visual quality at higher singular values. Various compression parameters like PSNR, MSE and compression ratio are evaluated for the assorted techniques. during this survey, compare how SVD is applied to colour images, the technique of compression and maintain the standard of the image using SVD and also the algorithm to compress a picture using image processing tool MATLAB and compared the WDR SVD lossy compression techniques.
Pengenalan Sis-Log in Apps sebagai E-Commerce untuk Kelompok Usaha Tani Karawang pada Masa Pandemi Covid-19

The problem that is happening in the field in the form of agricultural products is information that is often found today. From these problems linking farmers and distributors, of course, has an impact on long shipping flow. In addition, coupled with the presence of Pandemi Covid-19 which caused agricultural distribution to be hampered. So the purpose of this service designs applications that are the needs of farmers, so that vegetables can be directly to consumers. Sis-Log in Apps (Agricultural Logistics System Application) is an application designed by researchers and can be used for the needs of farmers and consumers. The method used in this service is a field method. The results in this service are in the form of sis-log design in apps. Applications are useful to facilitate the process of buying and selling directly between farmers and consumers. Without going through other intermediaries that make the shipping network long. Then the quality of the freshness of vegetables is maintained and directly to the hands of consumers. In addition, this application uses technology that is looking for the closest distance between farmers and consumers. So this makes the effectiveness and efficiency of vegetable delivery.
Different Forms of Online-Education in a Technical University

The article discusses the questions of higher education digitalization in modern conditions and the usage of different online courses. The timeliness of the education digitalization is directly related to the need for the entire world to move to a new level of online courses development. The technologies of distance learning, blended learning, inverted learning, their pros and cons, and their possibilities for the transition to digital education are considered. The article provides examples of Massive open online courses such as Coursera, national platforms for open education, Stepik. The diagram of people’s attitude to obtain online education is also presented in the article.
Distributed weather sensing incentivization

&amp;lt;p&amp;gt;Short-term weather forecasting, i.e., nowcasting, is an important area of research, with application to logistics, insurance, and environmental and social governance. More accurate forecasting is required to achieve nowcasting, which implies the need for improvement in either the acquisition of weather data or the development of better forecasting algorithms. While much of the literature focuses on algorithmic improvements, we consider how one may incentivize better data acquisition. Our idea is to implement a market-based incentive scheme that rewards meteorological data acquisition in proportion to how useful that data is in improving weather forecasts.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Our scheme incentivizes optimal placement of meteorological sensors by issuing blockchain-backed digital currency in direct proportion to the sensitivity of the weather forecast to raw data inputs. The scheme consists of an algorithm whose outputs are weather forecasts and whose inputs are data from weather sensors. The algorithm issues digital tokens in direct proportion to the sensitivity of outputs, i.e., the errors in forecasting, to inputs, i.e., sensor data. The issuance has the effects of incentivizing better sensor placement and improved sensor quality. Placement is incentivized because poor placement results in a lower forecast sensitivity; for example, this could happen due to placement near a non-representative microclimate, causing an aberration, or near another, well-placed sensor, resulting in redundancy. Quality is incentivized because higher certainty results in higher forecast sensitivity. With improved sensor placement and quality, the scheme therefore incentivizes better weather forecasts.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The incentivization therefore encourages better data assimilation and data availability and also enables the development of solutions that encourage better forecasting with market mechanisms. For example, a digital token may be traded between market participants and tokens may be issued by some authority to incentivize improved data acquisition. This latter example can be extended to a situation where an authority seeks to improve data acquisition in an area with low monitoring frequency, such as the world's oceans.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We present the results of a demonstration performed using a network of three Intellisense weather stations with forecasts based on a modification of the NAM&amp;amp;#8209;NMM, performing nowcasts for locations in Torrance, California. The model was implemented in an oracle that performs forecasts and uses the results of the forecasts to issue rewards on the Solana blockchain testnet. The three weather stations were place in two different locations, with the two sensors placed in the same location having different update frequencies, resulting in higher uncertainty in the data coming from the lower-fidelity sensor. The two sensors in the same location cannibalize each other's earnings, and the higher-fidelity sensor is rewarded with the larger share of earnings.&amp;lt;/p&amp;gt;