Reviewerly/exHarmony huggingface dataset:
An optimum security enabled System‐on‐Chip selection for Internet of Things applications using genetic algorithm modeling

Summary The System‐on‐Chip (SoC) is the core component in embedded systems that push toward the evolution of the Internet of Things. The quick growth of embedded SoC devices has a substantial influence to overcome security issues in embedded system design. Since microcontroller‐based systems are security and safety‐critical, this article focuses on security aspects of state‐of‐the‐art microcontrollers through performing design space analysis of several crypto‐core algorithms. These crypto functions are analyzed and compared to identify and implement the appropriate algorithm for on‐chip security. The genetic algorithm (GA) optimization model is utilized for the optimal selection of secured SoC from the commercial devices that are stored in the database. In this work, the fitness function is proposed with various levels such as performance‐centric, security enabled, low operating power, and application‐specific SoC selection. We also validate the GA results by implementing the security benchmarks on resultant devices by analyzing the execution time and performance. The accuracy of the GA‐based optimum SoC selection is estimated using confusion matrix method that produces the accuracy of .
On a Possible Quantum Variational Autoencoder Circuit

Generative Models have always attracted the attention of Machine Learning research community; they are useful and also generally harder than their discriminative counterparts. In these models, we would be looking into learning the probability distribution of the input and sampling from that to generate new data samples. Since quantum computing and algorithms are inherently random, they can facilitate a natural framework in this situation. But, getting a suitable gate circuit to achieve the requisite quantum state which by repeated preparation and measurement leads to the sought-after data samples is not trivial. In this paper, we propose a quantum circuit which has a flavor of Variational Autoencoder with the usual visible and hidden nodes for input data and latent distribution. The encoder portion comprises of a suitably chosen parameterized phase ansatz and Inverse Quantum Fourier Transform blocks. Depending on whether the measurement is carried out on the hidden nodes or not, the decoder circuit, which is just not the inverse of the encoder in our case, is configured. The Kullback-Leibler Divergence is used train the circuit towards the required input distribution. Numerical results presented demonstrate the correct functionality of the approach.
Automated hyperparameter selection for the PC algorithm

The PC algorithm infers causal relations using conditional independence tests that require a pre-specified Type I α level. PC is however unsupervised, so we cannot tune α using traditional cross-validation. We therefore propose AutoPC, a fast procedure that optimizes α directly for a user chosen metric. We in particular force PC to double check its output by executing a second run on the recovered graph. We choose the final output as the one which maximizes stability between the two runs. AutoPC consistently outperforms the state of the art across multiple metrics.
Architecture Analysis Methods

Abstract Architecture as a discipline is gaining prominence in dealing with the size, sophistication, and complexity of human‐made entities. Organizations evaluate architectures to determine: fitness for purpose; presence or absence of characteristics; satisfaction of requirements; goodness, completeness, and consistency; effectiveness and suitability; coping with change; stakeholder concerns are addressed adequately and so on. Organizations find it difficult to evaluate architectures as there are few publicly known architecture analysis methods, most of the methods are proprietary, architecture descriptions use different terminologies, architecture/system characteristics are wide and varying in nature and there is considerable manual skill and effort involved. In this paper, a few architecture analysis methods that can be codified and used to evaluate a wide variety of architectures across different domains are presented. Further, the efficacy of these analysis methods is illustrated with appropriate examples.
Differential Privacy in Personalized Pricing with Nonparametric Demand Models

With the rapid development of artificial intelligence and big data, the application of data-driven personalized pricing has been increasingly prevalent in real practices such as finance, insurance, and retailing. However, with the public’s growing concern of the abuse of their personal data, legislation efforts are being taken to guarantee data privacy. In this work, we guarantee customers’ data privacy from the algorithm design of our dynamic personalized pricing policies. Two algorithms are developed with different levels of privacy guarantee. The first algorithm protects customers’ data in a centralized manner, meaning that the data aggregator (the pricing platform) is trusted, and the attacker is unlikely to know customers’ personal information. The second algorithm has a stronger privacy guarantee, which is mathematically proved to be able to protect customers’ data even when the data set is hacked. Besides privacy protection, both of our algorithms are effective in achieving near-optimal revenue maximization.
Investigation of Facial Preference Using Gaussian Process Preference Learning and Generative Image Model

This study introduces a novel approach to investigate human facial attractiveness's intrinsic psychophysical function using a sequential experimental design with a combination of Bayesian optimization (BO) and StyleGAN2. To estimate a facial attractiveness function from pairwise comparison data, we used a BO that incorporates Gaussian process preference learning (GPPL). Fifty female Japanese university students provided facial photographs. We embedded each female facial image into a latent representation (\(18\times 512\) dimensions) in the StyleGAN2 network trained on the Flickr-Faces-HQ (FFHQ) dataset. Using PCA, the latent representations' dimension is reduced to an 8-dimensional subspace, which we refer to here as the Japanese female face space. Nine participants participated in the pairwise comparison task. They had to choose the more attractive facial images synthesized using StyleGAN2 in the face subspace and provided their evaluations in 100 trials. The stimuli for the first 80 trials were created from randomly generated parameters in the face subspace, while the remaining 20 trials were created from the parameters calculated using the acquisition function. We estimated the facial parameters corresponding to the most, the least, 25, 50, 75 percentile rank of attractiveness and reconstructed the faces based on the results. The results show that a combination of StyleGAN2 and GPPL methodologies is an effective way to elucidate human kansei evaluations of complex stimuli such as human faces.
Query bot for retrieving patients’ clinical history: A COVID-19 use-case

With increasing patient complexity whose data are stored in fragmented health information systems, automated and time-efficient ways of gathering important information from the patients' medical history are needed for effective clinical decision making. Using COVID-19 as a case study, we developed a query-bot information retrieval system with user-feedback to allow clinicians to ask natural questions to retrieve data from patient notes.We applied clinicalBERT, a pre-trained contextual language model, to our dataset of patient notes to obtain sentence embeddings, using K-Means to reduce computation time for real-time interaction. Rocchio algorithm was then employed to incorporate user-feedback and improve retrieval performance.In an iterative feedback loop experiment, MAP for final iteration was 0.93/0.94 as compared to initial MAP of 0.66/0.52 for generic and 1./1. compared to 0.79/0.83 for COVID-19 specific queries confirming that contextual model handles the ambiguity in natural language queries and feedback helps to improve retrieval performance. User-in-loop experiment also outperformed the automated pseudo relevance feedback method. Moreover, the null hypothesis which assumes identical precision between initial retrieval and relevance feedback was rejected with high statistical significance (p ≪ 0.05). Compared to Word2Vec, TF-IDF and bioBERT models, clinicalBERT works optimally considering the balance between response precision and user-feedback.Our model works well for generic as well as COVID-19 specific queries. However, some generic queries are not answered as well as others because clustering reduces query performance and vague relations between queries and sentences are considered non-relevant. We also tested our model for queries with the same meaning but different expressions and demonstrated that these query variations yielded similar performance after incorporation of user-feedback.In conclusion, we develop an NLP-based query-bot that handles synonyms and natural language ambiguity in order to retrieve relevant information from the patient chart. User-feedback is critical to improve model performance.
The Countless Lives of Newspapers and the Right to Repurpose

For the past decade, the environmental agenda has been unavoidable for social actors such as governments, NGOs, companies, and individuals. The climate crisis has also reached the field of design, demanding production and disposal processes that do not take a toll on the planet. This article focuses on the overlooked strategy of repurposing, through which everyday consumers transform and subvert simple objects, providing them with novel functions and a new lifespan. To understand how certain elements of design may affect consumers’ willingness to reinvent things, we take newspapers as a case study, giving their plasticity and their many diverse and unexpected functions. Throughout the analysis, a range of physical and social features are singled out, offering novel paths to sustainable design and reflecting on how the industry may prepare objects in advance to be reconfigured at the hands of their users.
Research on Path planning of Three-Dimensional UAV Based on Levy Flight Strategy and Improved Particle Swarm Optimization Algorithm

The original particle swarm optimization (PSO) has insufficient population diversity at the initial stage of evolution, and it is easy to fall into local minima and poor global search ability in multidimensional space. In this paper, a particle swarm optimization algorithm with improved learning factor is proposed, which is combined with Levy flight strategy. The random walk property of Levy flight strategy is used to increase population diversity and jump out of local optimal value. In the research of path planning of three-dimensional space UAV. Firstly, the three-dimensional space terrain is modeled, and various constraint models are established; secondly, smooth the generated path. The improved algorithm significantly improves the global optimization ability and precision of particle swarm optimization algorithm, avoids falling into local optimization, and successfully obtains a smooth and effective path.
Utilization of MAVROS for electro optical seeker subsystem in the design of ROS based architecture for autonomous guided platform

This paper discusses the use of MAVROS as one of the basic components in the design of ROS-based autonomous vehicle control architecture. The MAVROS implementation in the architecture that was designed, focuses on its application in vehicle control and visual target-based pointing. Target visualization is realized by using a stabilized electro optical seeker (using a 3-axis gimbal and an IP-based digital zoom camera) that can be controlled by the operator by using a joystick to aim and select a target. The designed architecture is implemented on an Ardupilot-based multirotor platform, and flight testing has been carried out to test the functionality of the vehicle control and electro optical seeker control.
A Survey on Online Spam Review Detection

In recent years, online reviews reflecting customers' opinion play a significant role in the unprecedented success of online marketing system. Consumers can make justifiable judgement about the quality of products or services based on the large volume of user-generated reviews even when the provider is unknown. Therefore, this online review platform faces frequent abuse by fraudsters' activities that often mislead potential customers and organizations thereby reshaping their businesses. Consequently, with immense technological advancement and diversity of products, the organizations are becoming competitors for each other, and hence, there is a growing tendency among merchants to hire professionals for writing deceptive reviews to promote their own products and defame others. Hence, trustworthiness of those reviewers and authenticity of their reviews are crucial from the perspective of e-commerce. This paper reviews several methodologies to identify spam or false reviews. We have also discussed different feature extraction techniques and parameters used in those algorithms.
Adoption of Libra as a Digital Currency in Thailand

Libra, Facebook's cryptocurrency, is expected to have an impact on the global monetary system; therefore, it is interesting to investigate Libra's adoption in Thailand. The objectives of this research are to examine the key factors in the acceptance of Libra and to gain an understanding of Libra. Logistic regression analysis was used for data analysis. The results indicate that Libra coins have not been accepted among Thais yet. Factors in influencing the acceptance of Libra are age, career, saving, social media, and knowledge scores. To encourage the adoption of Libra, the chapter suggests that gaining knowledge about cryptocurrency nationwide should be prioritised. Moreover, financial institutions should encourage the usage of cryptocurrency among low-saving clients and small enterprises as these groups tend to accept Libra rather than those with a large amount of savings. If cryptocurrencies are accepted nationwide, it might attract investors and boost Thai economic growth.
Development of the Psychological System of Pedagogical Abilities of Students – Future Primary School Teachers

Introduction. Psychological and pedagogical support for the professionalization of a teacher should be based on modern knowledge in the development of pedagogical abilities. The most popular strategy for studying abilities is still the analytical approach, which does not allow to consider abilities as a holistic, continuously developing psychological education. The purpose of the article is to present the results of research into the development of the psychological system of pedagogical abilities of students – future primary school teachers in the context of continuous pedagogical education at secondary school, pedagogical college and pedagogical university. Materials and Methods. The survey involved 201 subjects, including students of the pedagogical class (n = 15), students of the vocational pedagogical college (n = 82), students of the pedagogical university (n = 104). In order to study the psychological system of pedagogical abilities, test methods, questionnaires, self-assessment scales, as well as an analysis of indicators of academic performance were used. The analysis of the psychological system of abilities was made by using systemic indices (coherence, divergence, organization), assessing the homogeneity/ heterogeneity of structures, identifying the basic and leading components of the system. Results. The uneven development of the psychological system of pedagogical abilities at different levels of pedagogical education was shown. It has been established that the development of the system of pedagogical abilities during the period of study at school, college and university cannot be characterized as continuous. Students of the pedagogical class are characterized by a higher organization of the ability system than those in college and university; for college students – their continuous restructuring and a decrease in organization by the end of their studies; for university students – a more stable and progressively developing system of abilities. Qualitative differences in the structural organization of the system are determined by the different contributions of the types of abilities (individual, subjective, personal) to its functioning and achievement of academic results. The organization of the system of abilities of schoolchildren is largely determined by the abilities of an individual, college students – by the abilities of a subject of activity and personality, university students – by the abilities of an individual. Discussion and Conclusion. The conclusions formulated in the research paper serve the basis for the organization of special activities on the purposeful formation of the system of pedagogical abilities in the conditions of continuous pedagogical education.
A Framework for Estimating Gaze Point Information for Location-Based Services

In this study, a novel framework for estimating a user's gaze point is proposed. If it is possible to detect what a user is looking at, appropriate services can be provided accordingly. Most existing methods for gaze estimation using image processing are classified into two types: those using the third-person viewpoint and those using the first-person viewpoint. However, the former approach lacks accurate estimation and the latter approach can cause privacy issues. In the proposed framework, sensor information from acceleration and gyro sensors installed in mobile devices is utilized instead of the first-person camera. From the images obtained from the third-person camera, a heatmap showing the possibility of objects that the user is looking at is estimated using machine learning techniques. This information is combined with the position of the user's head, which is obtained from the sensor information, to estimate the location of the user's gaze point. Experimental results show that the proposed method achieves a much higher accuracy than existing techniques. Obtaining user gaze information is very helpful in providing advanced location-based services (LBSs). The proposed framework can increase the added value of various types of LBSs.
Bug Prediction Using Source Code Embedding Based on Doc2Vec

Bug prediction is a resource demanding task that is hard to automate using static source code analysis. In many fields of computer science, machine learning has proven to be extremely useful in tasks like this, however, for it to work we need a way to use source code as input. We propose a simple, but meaningful representation for source code based on its abstract syntax tree and the Doc2Vec embedding algorithm. This representation maps the source code to a fixed length vector which can be used for various upstream tasks – one of which is bug prediction. We measured this approach’s validity by itself and its effectiveness compared to bug prediction based solely on code metrics. We also experimented on numerous machine learning approaches to check the connection between different embedding parameters with different machine learning models. Our results show that this representation provides meaningful information as it improves the bug prediction accuracy in most cases, and is always at least as good as only using code metrics as features.
Fall Detection System Using Wearable Sensors with Automated Notification

Nowadays, elderly people mostly are living independently in their hometowns. Hence, their activities of daily living (ADL) are not monitored by their family and may lead to accident cases such as falling or slipping. This situation can cause trauma such as brain injury or other side effects on their health. In this project, a prototype of fall detection system was developed using wearable sensors via the internet of things (IoT) platform. Wearable sensors which are gyroscope and accelerometer are attached to the elderly person to obtain significant data of falling detection. Several ADLs i.e., walking, standing, sitting on a chair, sitting on the floor, laying on a bed, and sitting to standing will be monitored among the elders. Data analysis is performed to identify the condition between selected ADLs and imitated falls scenarios. From the experimental results, the proposed system can detect falls and send a notification when a fall occurrence is detected with accuracy of 97%.
Architectural scheme of a multi-blockchain in the Agricultural field

The blockchain represents structured and shared data. Instantly, it is a modern method of distributed databases controlled by a group of individuals. The goal is to store information, create a digital ledger of data, and dispatch them to each network-independent party. Blockchain technology gets used in many fields like government, transportation, healthcare, etc. This article will focus on the blockchain in the agricultural supply chain, especially the multi-blockchain scheme to enhance efficiency and track products. This kind of scheme requires the existence of two chains at least in one system. The article analyzes previous researches on how implementing this model and cites its various steps. Also, this research will offer a novel theoretical architecture inspired by the previous ones. The novel structure will avoid the errors that exist in previous experiments. This novel theoretical scheme uses sensors to provide us with environmental data. Subsequently, we use the multi-blockchain structure to stock our data in blocks. After that, we build Smart Contracts to control all the transactions and make decisions based on the conditions inside the source code of these automated contracts. This scheme would be more effective than the cloud and the simple blockchain storage.
X-ray Synthesis Based on Triangular Mesh Models Using GPU-Accelerated Ray Tracing for Multi-modal Breast Image Registration

For image registration of breast MRI and X-ray mammography we apply detailed biomechanical models. Synthesizing X-ray mammograms from these models is an important processing step for optimizing registration parameters and deriving images for multi-modal diagnosis. A fast computation time for creating synthetic images is essential to enable a clinically relevant application. In this paper we present a method to create synthetic X-ray attenuation images with an hardware-optimized ray tracing algorithm on recent graphics processing units’ (GPU) ray tracing (RT) cores. The ray tracing algorithm is able to calculate the attenuation of the X-rays by tracing through a triangular polygon-mesh. We use the Vulkan API, which enables access to RT cores. One frame for a triangle mesh with over 5 million triangles in the mesh and a detector resolution of \(1080\times 1080\) can be calculated and transferred to and from the GPU in about 0.76 s on NVidia RTX 2070 Super GPU. Calculation duration of an interactive application without the transfer overhead allows real time application with more than 30 frames per second (fps) even for very large polygon models. The presented method is able to calculate synthetic X-ray images in a short time and has the potential for real-time applications. Also it is the very first implementation using RT cores for this purpose. The toolbox will be available as an open source.