Reviewerly/exHarmony huggingface dataset:
Defining key pharmacist and technician roles in response to a hospital downtime or cyberattack

The occurrence of ransomware, or "cyberattacks," on hospital institutions has steadily increased in recent years. Pharmacy departments that rely on automation and software applications are greatly affected when those systems are offline. Pharmacy workflow without automation can be manually intensive and unsafe for patients. More challenges may be present if the hospital pharmacy is not prepared for a cyberattack or does not have standardized downtime procedures for such an event. This article describes a specific event that took place at a 350-bed acute care hospital located in the United States during the summer of 2021. The hospital lost access to the electronic health record, admitting and registration system, financial systems, pharmacy information systems, barcode medication administration systems, server for the automated dispensing cabinets or inventory management applications, diversion software, compliance applications, and all clinical decision support tools. The goal is to describe a standardized downtime procedure for medication management by identifying specific pharmacist and technician roles when automated processes are offline.
Software Defect Prediction Based on SMOTE-Tomek and XGBoost

The use of machine learning techniques to predict software defects has received extensive attention over the years. In practice, there are far fewer defective software samples than non-defective software samples, resulting in a high imbalance of sample categories. The problem of imbalanced data classification is a hotspot and difficulty in machine learning and data mining. In order to effectively solve the problem of software defect prediction, we introduce a classification model that combines SMOTE-Tomek sampling and ensemble learning algorithm XGBoost. Our initial experimental results on ten NASA software defect datasets indicate a clear superiority of the proposed techniques when compared to other hybrid model regarding various assessment metrics, such Accuracy, F-Measure, and AUC.
Generation of meteorological year for the assessment of photovoltaic systems performance in Togo, West Africa

In this paper, the Sandia method, based on the Finkelstein-Schafer (FS) statistical techniques was used to develop a Typical Meteorological Year (TMY) for five cities in Togo (Lomé, Atakpamé, Sokodé, Kara and Mango) with a view to assessing the performance of the 72-cell polycrystalline solar photovoltaic (PV) module in the study areas. Seven daily measurements of meteorological parameters (relative humidity, dew point temperature, minimum and maximum temperatures, mean temperature, wind velocity as well as global solar radiation intensity) obtained from the National Aeronautics and Space Administration (NASA) for 1987–2016 period were used. The TMY predictions were compared with the 30-year Long-Term (LT) average values of the dataset. Two statistical indicators, the Mean Percentage Error (MPE) and the Root Mean Square Error (RMSE), were adopted to evaluate the performance of the PV energy system. Results show close agreements between TMY and LT values for all the parameters considered. The TMY in most cases underestimates the LT mean and fluxes are relatively small. Although the PV energy simulations were found to be underestimated, the generated solar PV energy obtained with TMY compares fairly well with those obtained with LT data. The performance of the TMY data is found to vary over the country but considered ‘very good’ with MPE values ranging between 0.24% and 2.46% as well as RMSE ranging between 8.29 and 16.71 kWh. Slight but insignificant biases (positive in the southern cities of Togo but negative in the north) were found between the estimated PV energy with the TMY when higher weight for global solar radiation was used. This study has implications in building energy simulation for comparative energy efficiency studies.
MixedGAN: Facilitating GAN for Domain Adaptation Learning based on Mixing Different Domain Images for Semantic Segmentation

Convolutional neural network-based semantic segmentation methods rely on the supervision of pixel-level labels, but since pixel-level labelling is time-consuming and labor-intensive, research has shifted to Unsupervised domain adap-tation (UDA), which is mainly used to migrate the learned knowledge from one domain to another, so that synthetic data can be used to help the model learn. Synthetic data can be used to help the model to learn, but the accuracy is not high compared to supervised learning. The reason for the low accuracy of UDA is the gap between domains. In this paper, a new UDA model is proposed to reduce the gap between domains in two steps. The first step is to mix images from two domains and generate pseudo-labels for the generated images, which helps to close the image distribution between domains. In the second step, the Generating Adversarial Neural Network (GAN) is used to distinguish the source of the feature map to further reduce the inter-domain gap. After several experiments, the accuracy of this model is much better than some classical DA models.
OMNI at the Edge

With the increasing scale of High Performance Computing (HPC) systems and their support infrastructure coupled with the growing number of sensors and systems monitoring software, a deluge of operational data has become available for analysis and real time observation. NERSC uses a data warehouse called Operations Monitoring and Notification Infrastructure (OMNI) to store this data. However, there are challenges to gathering heterogeneous data from multiple sources such as latency, bandwidth, privacy, infrastructure or availability. Considerations for cybersecurity also need to be addressed. Some challenges associated with mitigating this data and making the data more available to users are resolved using edge computing technologies. This chapter will discuss how NERSC uses edge computing services to securely send data to OMNI from the heterogeneous sources available throughout the data center. The chapter will provide examples of how we use edge computing, our successes and challenges implementing the solutions and the prospective future work planned.
Explaining the gradient: Requirements for theories of visual awareness

We propose that any theory of visual awareness must explain the gradient of different awareness measures over experimental conditions, especially when those measures form double dissociations among each other. Theories meeting this requirement must be specific to the measured facets of awareness, such as motion, contrast, or color. Integrated information theory (IIT) lacks such specificity because it is an underconstrained theory with unspecific predictions.
Analysis of Performance Improvement for Speaker Verification by Combining Feature Vectors of LPC Spectral Envelope, MFCC and pLPC Pole Distribution

This paper examines the performance improvement via combining feature vectors for speaker verification. As feature vectors, we use LPC (linear predictive coding) spectral envelope (LPCSE), Mel-frequency cepstral coefficients (MFCC) and piecewise LPC (pLPC) pole distribution (pLPCPD). Here, pLPCPD is the feature which we have introduced and developed, and this paper shows a relationship of the underlying pLPC speech signal model for speaker verification with ARMA and AR models for speech production. We execute experiments of speaker verification with the combinations of the features. We examine the experimental results and show that (1) the smallest AER (average error rate) is achieved by MFCC followed in order by pLPCPD and LPCSE, (2) the AER achieved by LPCSE, MFCC and LPCSE+MFCC (combination of LPCSE and MFCC) is reduced by combining pLPCPD, (3) there are conflicting features in LPCSE, MFCC and pLPCPD to increase the AER, and the smallest AER is obtained by MFCC+pLPCPD among the combinations of LPCSE, MFCC and pLPCPD. We also investigate the relationship between pLPCPD and the aperiodicity used for vocal cord excitation in recent vocoders, which seems to explain the above result (2).
A Study on Various Phishing Techniques and Recent Phishing Attacks

Now-a-days internet has become a very unsafe space to deal with. Hackers are constantly trying to gain the user's personal information, and detailed credentials. So many websites on the internet, even though safe, this safety cannot be assured by all websites. These rule breakers avoid abiding by rules, and try to employ methods like trickery and hacking to gain illegal access to private information. To be able to overcome this problem, we need to first understand the intricacies of how the virus is designed. This paper mainly deals with the different phishing techniques and recent phishing attacks that took place during COVID 19. like Link Manipulation, Filter Evasion, Website Forgery, Phone Phishing and Website Forgery. We have also studied a subtle method to perform phishing attacks that makes links appear legitimate, but actually redirect a victim to an attackers website called Convert Redirect. In this paper, we present some phishing examples like Paypal phishing which involves sending an email that fraudulently claims to be from a well known company and Rapidshare Phishing where in the spoofed web page, phishers attempt to confuse their victims just enough to entice them to enter their login name and password. To perform these types of phishing the Phishers uses so many phishing techniques like Link Manipulation, Filter Evasion, Website Forgery, Phone Phishing and Website Forgery. Phishing techniques include the domain of email messages. Phishing emails have hosted such a phishing website, where a click on the URL or the malware code as executing some actions to perform is socially engineered messages. Lexically analyzing the URLs can enhance the performance and help to differentiate between the original email and the phishing URL. As assessed in this study, in addition to textual analysis of phishing URL, email classification is successful and results in a highly precise anti phishing. From the thorough analysis of the research paper, we have understood how phishing attacks work and the different methods employed to carry out the attack. Also, we have studied some of the most recent phishing attacks and measures taken by the authorities to overcome and prevent any such attacks in future.
Transformer Networks for Trajectory Classification

Research related to Trajectory Classification is actively underway, and its application fields are also very diverse. Existing studies related to trajectory classification mainly used RNN-based models such as SimpleRNN, LSTM, GRU, etc. However, these Seq2Seq models cause a bottle neck problem that does not reflect all information when the length of the input sequence increases during the encoding process. Therefore, we propose a Transformer model for more accurate trajectory classification even in situations where the trajectory input sequence is long. As a dataset, we use MNIST stroke sequence dataset, which expresses the stroke of the numbers of the MNIST as a unit vector trajectory. As a result, Transformer achieved comparable performance to LSTM.
An energy-effective altitude controller of a pusher-type hybrid UAV in transition phases

Recent decades have witnessed an increasing interest in QuadPlane UAVs (unmanned aerial vehicles) thanks to its automatic ability in vertical takeoff and landing (VTOL) without a pilot and a runway. However, pusher-type QuadPlane UAVs show a disadvantage in the sense of maintaining a useless payload (quad motors) during the flight time; that is, the quad motors are only used about 5 minutes in only VTOL phases while a flight time is usually about 3 hours. Reducing the quad rotors' weight requires an energy-effective altitude control law, which also plays an essential role in guaranteeing flight safety because there is redundant energy to deal with an emergency. This paper introduces an energy-effective altitude control law for a pusher-type QuadPlane UAV in transition phases based on the sliding mode control technique. The proposed control law estimates the aerodynamic force in the longitude channel to reduce required quad energy based on UAV state and aerodynamic coefficients. Simulation and actual flight test results show that the proposed control law is better than the standard PID controller in terms of altitude tracking errors (about four times) and energy (about 1.5 times).
Language Learning Strategies, Gender, and Motivation in Foreign Language Context

How the students deal with their learning strategies has been investigated in the last few decades. The main points in this article focused on the profile of learning strategies used by the students of SMA N 16 Samarinda in the academic year 2020/ 2021 from the tenth to twelfth grade in learning English as a foreign language, gender, and motivation types. This study is quantitative research. The data was taken from two main questionnaires: SILL and motivation questionnaire. In analyzing the data, SPSS version 26 was used in this study. The number of participants was 99 students that consisting of 41 males and 58 females. The finding in this study found that the participants mostly used metacognitive strategies in their language learning and cognitive strategies were the least used. There was no significant difference between LLS usage with gender. Students’ intrinsic and intrinsic motivation play a significant role in their interest in learning English as a foreign language.
Overview of Cloud Security

Cloud computing is defined to compute or process the data that is stored in a particular cloud. This cloud has several layers, each layer meant for its unique functionality and computes the data based on the same functionalities, and based on those functionalities it has certain risks. When we store any amount of data in a particular cloud there will be certain security threats and storage aspects and controversies based on which particular type of cloud deployment models, we choose to store the data. Also, the various kinds of security techniques that we can implement to resolve those threats of security and storage challenges.
A Self-Learning Fuzzy System for Automotive Fault Diagnosis

This paper describes a self-learning fuzzy system for automotive fault diagnosis. We first present a formal model for fuzzy engineering diagnosis including problem modeling, selecting optimal parameters, and a learning algorithm for adjusting fuzzy membership functions. A fuzzy diagnostic system developed under this model is expected to produce better diagnostic results, requires minimum process time, and can be easily adapted to different vehicle models. A fuzzy system for detecting Lambse fails in an Electronic Engine Controller(EEC) in a vehicle is developed and implemented. The system is designed to be a component of an End-of-Line test system at automobile assembly plant.
Artificial Intelligence-Enabled Knowledge Management

As the basic law of guiding knowledge and innovation activities, knowledge management (KM) has long been regarded as an important process of organization management. It is believed that an organization’s competitive advantage can be enhanced through effective use and management of knowledge. The development of artificial intelligence (AI) has brought new ways to facilitate enterprise knowledge creation. Knowledge and innovation activities have begun to appear some unprecedented new phenomenon, and KM 3.0 era has come. This chapter reviews AI and KM, and elaborates what happens in the KM process under the influence of AI. Specifically, the introduction of algorithms has greatly improved the efficiency of KM, such as accelerating knowledge creation; it brings simultaneously social and ethical considerations (algorithm discrimination, etc.). Furthermore, it has produced new phenomenon, new technologies, and new theories in the KM process driven by AI. By presenting the latest phenomenon and research trends of AI-enabled KM, this chapter hopes to provide some guidance for future research.
DAPP to Store Electronic Medical Health Records on Ethereum Blockchain and IPFS

In this digital world, information technology is growing day by day. Due to this, a large amount of data is generated every day from various domains, and one of the domains is medical health records. A large amount of medical data is generated every day. Such as electronic medical records, medical images, diagnostic reports, X-rays, MRI scans, etc. These medical records can help in treating a patient when needed and can be shared with different medical institutions. There are systems built that are used to store all the medical records of patients. But they are centralized and may not be secured, and a user may not know how and where these records are shared. However, if these medical records are leaked or shared with a third party, the owner of that medical record may not know how and where these medical records are used, thus sabotaging the patient's privacy. Therefore, controlling the access rights to medical data is an urgent issue. On the other hand, patients do not have any proper application that will help them store and view their history of medical records and have control over them. This project aims to build a decentralized application to store the medical records of patients on the Ethereum Blockchain and Inter Planetary File System (IPFS). This app will help users to keep electronic medical records in one place, and the user will have full control over their data. This app will help doctors diagnose the patients by seeing their medical history. This will also help researchers to research various diseases. This app will store the data of patients from doctors and pathology labs. Users will be able to control who can add the medical details and see them. They can give access and revoke it. This application will store the file in DICOM, JPEG, JPG, PNG, and PDF format. In this application, there is no centralized authority. This application is secured because of peer-to-peer and distributed networks, it is tamper-proof. User has control over their data. They can choose whom to share their data with, and blockchain is reliable.
Text-Guided Image Inpainting

Given a corrupted image, image inpainting aims to complete the image and outputs a plausible result. When we complete the missing region, we always borrow the information from a known area, which is aimless and causes unsatisfactory results. In our daily life, some other information is often used for corrupted images, such as text descriptions. Therefore, we introduce the use of text information to guide image inpainting. To fulfill this task, We introduce an inpainting model named TGNet (Text-Guided Inpainting Network). We provide a text-image gated feature fusion module to fuse text feature and image feature deeply. A mask attention module is proposed to enhance the consistency of known areas and the repaired area. Extensive quantitative and qualitative experiments on three public datasets with captions demonstrate the effectiveness of our method.
A Blockchain-based Security Framework for Secure and Resilient Smart Grid

Abstract Smart grid integrates the information and communication technologies with the physical grid. It can not only improve security and efficiency of traditional power grids, but also has advantages in promoting environmental protection and sustainable development. However, the existing smart grid systems suffer some security concerns such as privacy leakage and data tampering. In order to address these security issues, we propose a security framework for smart grid using Blockchain. It protects data privacy through cryptographic tools, and it applies Blockchain technologies to prevent data tampering. Compared with the existing smart grid systems, our proposed system enjoys better security and resilience.
End-to-End Dialogue Generation Using a Single Encoder and a Decoder Cascade With a Multidimension Attention Mechanism

Human dialogues often show underlying dependencies between turns, with each interlocutor influencing the queries/responses of the other. This article follows this by proposing a neural architecture for conversation modeling that looks at the dialogue history of both sides. It consists of a generative model where one encoder feeds three decoders to process three successive turns of dialogue for predicting the next utterance, with a multidimension attention mechanism aggregating the past and current contexts for a cascade effect on each decoder. As a result, a more comprehensive account of the dialogue evolution is obtained than by focusing on a single turn or the last encoder context, or on the user side alone. The response generation performance of the model is evaluated on three corpora of different sizes and topics, and a comparison is made with six recent generative neural architectures, using both automatic metrics and human judgments. Our results show that the proposed architecture equals or improves the state-of-the-art for adequacy and fluency, particularly when large open-domain corpora are used in the training. Moreover, it allows better tracking of the dialogue state evolution for response explainability.
Global trajectory optimization of mobile robot based on Minimum Snap

Since the path planned by the traditional A * algorithm consists of a series of discrete spatial points and does not contain time and control quantities, it cannot be directly used for trajectory tracking of mobile robots. To address this problem, this paper uses the Minimum Snap method to transform the traditional path planning algorithm and the planned path into a continuous smooth trajectory containing both time, velocity and acceleration information. The trajectory is optimized by introducing the trajectory shape constraint and velocity and acceleration constraint, so as to generate a global trajectory suitable for the path tracking of mobile robots, while the generated trajectory differs too much from the original path shape and the robot motion trajectory is not smooth due to the robot's own physical constraints are not considered. Finally, the effectiveness of the proposed method is verified by simulation experiments, and the method is applied to a robot autonomous navigation system under ROS (Robot Operating System) to confirm the practicality and reliability of the method.