Reviewerly/exHarmony huggingface dataset:
A Study on Quality Prediction Failure Cause Analysis in Batch Process

Quality prediction plays a key role in all kinds of manufacturing. To predict quality products or forecasting a product quality, data should contain time series properties. However, these properties often fail to be contained in the data. This usually happens in many industrial manufacturing environments. For example, lot processing often fails because of many reasons: parts having different lot numbers can be mixed up in the following step; parts are missing, broken, etc; in some cases, the long term data are required but short-term data are only gathered. Without these properties, any machine learning technique should fail to predict data quality. In this paper, we have data collected in short-term period although the total samples are 3,000 and under a condition, we fail to predict a quality of a sample with various machine learning prediction techniques: Gaussian process regression method and auto-regressive integrated moving average. Although hyper-parameters are well-optimized and both predicted results are almost the same, the predicted ones are far from the ground truth. Through these experiments, we concluded that well-designed data collection is the most important in quality prediction.
MSANet: multi-scale attention guided deep neural network for blind image quality assessment

This work explores how to efficiently incorporate both the multi-scale features and attention mechanism into blind image quality assessment modules and proposes an end-to-end multi-scale attention guided deep neural network for perceptual quality assessment. Our method is established on a hierarchical learning framework in which two learning stages including coarse learning upon single-scale and quality refinement upon multi-scale, by which the quality-aware features could effectively extracted and aggregated into quality prediction scores. The proposed MSANet is based on the observation that multi-scale features could provide more flexible and robust features for BIQA whilst attention mechanism are beneficial for quality-aware feature aggregating. Through performance comparison with the state-of-theart approaches, our proposed mothed shows promising potential for blindly measuring the perceptual quality of distorted images.
Counterfactual Embedding Learning for Debiased Recommendation

Recently, recommender system suffers extremely from both interaction bias and sparsity. The conventional unified embedding learning policies fail to consider the imbalanced interaction issue and produce suboptimal representations of users and items for recommendation. Towards the end, this work dedicates to bias-aware embedding learning in a decomposed manner and proposes a Counterfactual Embedding Learning (CEL) for debiased recommendation. Instead of debiasing with sampling uniform interactions, we follow and capitalize the natural bias distribution to model users and items with a counterfactual hypothesis. Concretely, embeddings of users and items are built with common and special causes to model latent frequent and infrequent bias isometrically. Particularly, relying on the frequency gap between common and special causes, CEL introduces a bias-aware counterfactual masking on interactions and forms multiple counterfactual worlds to extract effects of various special causes comparing with the factual world. Experiments show that the proposed CEL performs superior to state-of-the-art baselines.
Development of Data Gloves for Humanoid Robot Hand Simulation and Hand Posture Recognition

Human hand movement data set is valuable in many practical applications. Scientists and engineers study human biometric movement data to understand how to interact with the surroundings to mimic the process in humanoid, to create prosthetic for the disables, or to visualize human’s motions in entertaining products. Therefore, using a sensor integrated gloves is an effective method to collect human hand movement data. In this paper, the construction of data gloves is presented step by step from the hardware design, embedded data collecting to data processing to rebuild the human hand posture in a visualization tool. The gloves are applied a system of inertial measurement units (IMUs) to track the human hand movement to increase the accuracy, reliability, and the flexibility in comparison to those using other types of sensors. The glove built-in embedded system can collect and process the wrist and phalanges orientation data from the IMUs to model a simplified human hand of 22 degrees of freedom. Using the acquired human hand data, the Multilayer Perceptron algorithm (MLP) data is applied for hand posture recognition with fast and high accurate results.
Soundness and Completeness Results for LEA and Probability Semantics

In [2], a logical system called the logic of evidence aggregation (LEA) was introduced, along with an intended semantics for it called probability semantics. The goal was to describe probabilistic evidence aggregation in the setting of formal logic. However, as noted in that paper, LEA is not complete with respect to probability semantics. This leaves open the tasks to find sound and complete semantics for LEA and a proper axiomatization for probability semantics. In this paper we do both. We define a class of basic models called deductive basic models. We show LEA is sound and complete with respect to the class of deductive basic models. We also define an axiomatic system $$\mathsf{LEA}_+$$ extending LEA and show it is sound and complete with respect to probability semantics.
Soil Saturation Level Monitoring in Strawberry Plants for Automatizing Grikulan Watering

Strawberry is a subtropical fruit that thrives in Indonesia with soil as its growing media. Soil treatment becomes essential for strawberry farmers, in which the soil has a saturation nature and clump due to routine watering activity. The farmers do watering Grikulan fertilizer as a recovery step to improve soil quality. This study develops a system in the Android software or mobile application that can monitor the level of soil saturation in strawberry plants with output is the information of soil saturation indication based on the results of calculations using Fuzzy Inference System Mamdani method. The application can be connected with the actuator to water Grikulan fertilizer to give order automation. This research produces a feasibility value of 90.14% through the testing of R Validation.
AN APPROACH TO SOLVE THE PROBLEM OF DATA FUSION FOR MULTI-TARGET TRACKING USING FUZZY LOGIC

This paper presents a multitarget data fusion (identification) algorithm called the Fuzzy data fusion algorithm (FDFA) for radar target tracking. This approach is formulated using the Kalman filter and FDF, the algorithm is accomplished by using fuzzy logic. Simulation results for cluttered conditions show that the proposed algorithm's performance is better than the probability data fusion (PDF) and the joint probability data fusion (JPDF) filters, which were presented in [2-4, 6].
An Enhanced Dynamic Image Watermarking Framework based on Phase Congruency and Saliency

Perceptual watermarking approaches achieve better image fidelity with the advantages of the Human Visual System (HVS). Saliency uses the advantages of this feature by identifying different visually perceptual regions of an image. Additionally, Phase congruency (PC) exploits those features of digital images by detecting the corner points. In the proposed method, the information hiding Capacity is generated from both bit depth maps created by comparing phase congruency results and saliency outcomes of an image that ascertains the low perceptually visible pixels (utilizing the Least Significant Bit (LSB) technique). The key idea is to ensure an additional layer of security using Symmetric Key Cryptography (SKC) and to hide information in a less observant area that is not easily perceptible inside the cover image. The experimental result analysis ensures better imperceptibility and robustness as it resists different attacks along with superior data hiding capacity.
A Metaheuristic Optimization Algorithm for Solving Higher-Order Boundary Value Problems

An effective metaheuristic algorithm to solve the higher-order boundary value problems, called a genetic programming technique is presented. In this paper, a genetic programming algorithm, which depends on the syntax tree representation, is employed to obtain the analytical solutions of higher- order differential equations with the boundary conditions. The proposed algorithm can be produce an exact or approximate solution when the classical methods lead to unsatisfactory results. To illustrate the efficiency and accuracy of the designed algorithm, several examples are tested. Finally, the obtained results are compared with the existing methods such as the homotopy analysis method, the B-Spline collocation method and the differential transform method.
Fair and Stable Matching Virtual Machine Resource Allocation Method

In order to unify the management and scheduling of cloud resources, cloud platforms use virtualization technology to re-integrate multiple computing resources in the cloud and build virtual units on physical machines to achieve dynamic provisioning of resources by configuring virtual units of various sizes. Therefore, how to reasonably determine the mapping relationship between virtual units and physical machines is an important research topic for cloud resource scheduling. In this paper, we propose a fair cloud virtual machine resource allocation method of using the stable matching theory. Our allocation method considers the allocation of resources from both user’s demand and cloud computing resource provider’s request. When multiple users apply for resources, firstly select a user by user priority, and then deal with this user’s task. Because the user priority is dynamic, so as to avoid a user’s long-term share of resources. This strategy makes user task scheduling is relatively fair. On the basis of weighing the fair allocation of user resources, the stable matching between physical machines and virtual machines is achieved. Our simulation experiments especially given that the main focus of the paper is not to develop a very novel algorithm, but to demonstrate our virtual machine resource allocation method, which effectively improves the average utilization rate of computing resources and reduces the operating costs of cloud providers.
Covid-19 Prediction in Indonesia Capital Buffer with K Nearest Neighbor Regression

Covid-19 is an infectious disease caused by the SARS-CoV-2 virus. This virus first appeared in the city of Wuhan, China in December 2019. Since the appearance of this first case, around 25 countries have confirmed the emergence of similar cases in their countries. On March 2, 2020, the Indonesian government announced the emergence of the first Covid-19 case. Since then, the growth of the Covid-19 case in Indonesia has continued to increase day by day and has spread to all provinces in Indonesia. Tangerang regency, as one of Indonesia&#x0027;s Capital Buffer, has an important role in supporting the needs of the capital. This study will discuss how to predict the number of Covid-19 cases in Tangerang regency using the K-Nearest Neighbors Regression method because the increase in positive cases in the capital Jakarta and Tangerang could occur due to the mobility between two regions. This method was chosen because its performance is considered good and can analyze the predicted value based on the relationship between the existing variables, although it requires large enough data. The prediction model made has been evaluated using several error rate calculations and produces the best accuracy value of 96.98&#x0025; and the worse accuracy value given 79.97&#x0025;.
Distance Based Joint Probability Density Estimation For Unsupervised Outlier Detection

Outlier detection is a vital preprocessing step in data mining and it holds a great importance for Machine Learning (ML) algorithms. If a ML model is learned without removing the outliers from the data, the outliers present in the data can influence the prediction accuracy of a ML model and the outcome of such a model can be misleading. Keeping in view the importance of outliers detection, this paper proposes an unsupervised outlier detection mechanism. The proposed outlier detection mechanism is based on the Joint Probability Density Estimation (JPDE) with an integration of a Distance Measure (DM). The proposed approach has an advantage of utilizing only a single dimensional distance vector to compute the outliers in a dataset. This enables the proposed algorithm to find the outliers from a high dimensional dataset with low computational complexity. Furthermore, three different approaches based on JPDE-DM are proposed and evaluated using some complex benchmark synthetic datasets.
Design of Personalized Recommendation Algorithms based on Big Data Framework

Abstract The issue of information overload has become increasingly prominent since there are various kinds of data generated daily. A good recommendation systems can better deal with such problems. However, traditional recommendation systems for a single machine are suffering from the computing bottleneck in the environment of massive data. An individual recommendation algorithm is unable to gratify desiring users. To tackle this problem, we designed and implemented three kinds of recommendation algorithms based on big data framework in this paper. Besides, we improved the traditional recommendation algorithms leveraging the prevailing big data processing technologies. Finally, we evaluated the efficiency of the algorithm through recall rate, precision rate and coverage. Experiments show that the hybrid model-based recommendation algorithms which can be applied to the bulk data environment are better than the single recommendation algorithms.
Cultural Tourism, Internet of Things, and Smart Technologies in Museums

In this chapter, the author explores the application of the internet of things (IoT) in museums. IoT technology typically combines physical objects with hardware and software. For museums, the simplest example is 3D virtual tours, which need a computer and an internet connection. Today, however, museums have become more complicated with virtual and augmented technologies. Virtual and augmented reality devices, such as virtual reality (VR) glasses, and related applications, such as Google Arts and Culture, provide interactive museum tour experiences for visitors. For all these experiences, they only need to connect to the internet with their devices. Virtual museum tours range from history to space technologies. This chapter explores the nature of using IoT technologies in cultural tourism, especially in museums.
Triad of Split Learning: Privacy, Accuracy, and Performance

Split learning is a new machine learning model, considering the distributed users' privacy. While preserving the privacy of user data, split learning can leverage an amount of training data from multiple users. This paper presents how split learning can efficiently trade privacy, prediction accuracy, and training overhead. We devise three practical implementation models of split learning with different levels of privacy. Our experiment shows that privacy, accuracy, and training overhead are differently presented according to the implementation model. The result supports that privacy-preserving layer in split learning enhances privacy with marginal processing overhead, and we can achieve reasonably high accuracy, compared with the local model with limited dataset.
Development and verification of Immersive contents to alleviate mental illness: Focusing on mild depressive disorder

Depressive disorder is one of the most common diseases that lower the quality of life. Early treatment and intervention for patients with a depressive disorder are very important, and treatments include drug therapy and psychotherapy. We intend to achieve emotional control and short-term emotional stability in patients with the mild depressive disorder by creating a psychological stability treatment space using immersion factors. We tried to increase immersion further and maximize the stability effect by selecting and producing spaces considering the color sense. For the produced immersive content, the sense of presence was measured using ITQ and PQ to evaluate the completeness of the content. In future studies, we plan to recruit patient groups and conduct verification experiments using QIDS-SR to verify the validity of the produced content.
Stochastic Euler Heavy Ball Method

Stochastic Heavy Ball method (SHB) has been widely used in various machine learning and deep learning tasks due to its superior generalization performance. However, a large number of effort need to be spent in tuning the learning rates of SHB, which is costly and inefficient in practical applications. Towards this end, this paper proposes the Stochastic Euler Heavy Ball method (SEHB), which simultaneously achieves good generalization like SHB and obtains rapid convergence. Our method adopts new adaptive learning rates which is different from classical adaptive methods like Adam. Convergence analysis is discussed in both convex and non-convex situations. Furthermore, we conduct numerical experiments and deep learning experiments to test the performance of SEHB. Empirical results demonstrate that our method shows better generalization performance than classical stochastic optimization methods such as SHB and Adam.
Interpretable Fine-grained BI-RADS Classification of Breast Tumors

Fine-grained classification of breast tumors is crucial for early diagnosis and timely treatment. Most fine-grained visual classification approaches focus on learning 'informative' visual patterns, which depend on the attention of the network, instead of 'discriminative' patterns, which interpretably contribute to classification. In this paper, we propose to extract discriminative patterns from informative patterns by utilizing the prior information of the dataset. The proposed method can detect the rough contour of the tumor area without boundary ground-truth guidance. At the same time, different masks are generated from the rough contour to reflect prior information on breast cancer. Moreover, a soft-labeling approach is utilized to replace the original BI-RADS label. Our model is trained using image-level object labels and interprets its results via a rough segmentation of tumor parts. Extensive experiments show that our approach achieves a significant performance increase on our BI-RADS classification dataset.
Lightweight VLSI Architectures for Image Encryption Applications

Lightweight cryptography offers significant security service in constrained environments such as wireless sensor networks and Internet of Things. The focus of this article is to construct lightweight SPN block cipher architectures with substitution box based on finite fields. The paper also details the FPGA implementation of the lightweight symmetric block cipher algorithm of SPN type with combinational S-box. Restructuring of traditional look-up-table Substitution Box (S-Box) sub-structure with a combinational logic S-box is attempted. Elementary architectures namely the basic round architecture and reduced datawidth architecture incorporating look-up-table and combinational S-Box substructure are compared in terms of area and throughput. Proposed restructure mechanism occupies less FPGA resources with no comprise in the latency and also demonstrates performance efficiency and low power consumption in Xilinx FPGAs. Robustness of the proposed method against various statistical attacks has been analyzed through comparison with other existing encryption mechanisms.
Risk analysis and safety protection of automobile maintenance information disclosure network

A website deployed on the Internet may face various risks such as malicious tampering of the website homepage, failure of the website to open or log in normally, and core data being locked or stolen. In order to ensure the normal operation of the website, the security of the website should be strengthened. Based on the actual security maintenance experience of an in-use website, this article analyzes possible risks from the various stages of website development, deployment, operation, and maintenance, and proposes security countermeasures. Judging from the actual operation effect of the website, after the website is deployed to the cloud and properly configured for security, the robustness and security of the website are guaranteed, and the expected requirements are met.
Build a Real-Time Flight Control Algorithm for the Lighter than Air Indoor Robot Using PX4 Autopilots Support from UAV Toolbox

With the widespread use of robots, there is a need to develop new designs that are suitable for special applications. These designs can differ mechanically introducing new dynamic characteristics not found in common designs. For these designs, developing adequate control algorithms that run in real-time is considered to be challenging, especially for those who do not have sufficient experience dealing with low-level embedded software. We seek to show the flexibility offered by using MathWorks provided PX4 autopilot support to build customized real-time control flight and vehicle management algorithms. We show the pipeline of autopilot&#x0027;s algorithm design to stably fly an in-house developed lighter than air indoor robot (LAIDR). In our study, we deal with highly nonlinear problems to fly LAIDR due to the properties of the physical body. However, the &#x201C;PX4 autopilot support for UAV toolbox&#x201D; allows dealing with these challenges smoothly through developing the algorithm in a modular but integrated form. We evaluated LAIDR by broadcasting live clips of an indoor (theater) event. The results show how efficient LAIDR performs to achieve different shooting angles (e.g., sky view) while following the desired flying path.
A Novel Encryption Algorithm Using Random Number Generation for Ciphertext Expansion

Information security has a major role to play in this ever-increasing digital world. One of the many important aspects in information security is cryptography, which is used to encrypt and decrypt information thereby protecting it from eavesdroppers. The modern cryptographic techniques make it difficult to decipher a message for even the most powerful computers. With advances in supercomputing, it is getting easier to break down these complex codes within minutes. This paper introduces a new algorithm for symmetric key encryption which will increase the length of the ciphertext as per the requirement and make it harder to decode back the original plaintext without knowing the secret key. The proposed algorithm in this paper generates a large random private key by using two relatively small random numbers: A natural number and a binary number, which is then used to encrypt the plaintext message.