Reviewerly/exHarmony huggingface dataset:
Research on Electrical Fire Risk Assessment Technology of Cultural Building Based on Random Forest Algorithm

Refer to the cultural building electrical fire accident case, a survey of typical building electrical fire prevention and control of cultural building, using the fire risk assessment technology, extracted the cultural building potential electrical fire risk characteristics, through the hierarchical entropy method to calculate weight information of risk characteristics, and application of random forest classification algorithm, build the cultural building electrical fire risk assessment model. This paper evaluates the risk of electrical fire in cultural building from multiple dimensions, which provides necessary theoretical basis and method support for on-site investigation, risk assessment and fire prevention of cultural building.
An Approach and Implementation for Knowledge Graph Construction and Q&amp;A System

We investigate and propose a knowledge graph-based method and implementation of the question-and-answer (Q&A) system for COVID-19 cases imported from abroad. It mainly analyzes and organizes the knowledge graph construction methods based on knowledge acquisition and visualization. In addition, this paper implements the knowledge graph-based Q&A system by training term frequency-inverse document frequency (TF-IDF) model and Bidirectional Long Short-Term Memory + Conditional Random Field (Bi-LSTM+CRF) model as well as Cypher query statements using the graph database Neo4j. Finally, the visual intelligent interface of the Q&A system is designed to meet user requirements and realize the function of accurate Q&A.
An ML Bio-inspired Model for improving Security and Speed of FHE for Cybersecurity

Fully homomorphic encryption is the scheme, which allows the users to perform arbitrary computation on encrypted data without decrypting it. Input data can be encrypt ,while the another user, without having decryption key can carry out data processing on the encrypted input. The encrypted output can be recovered by the user having the secret key. ML is one among the foremost exciting technologies that one would have ever encountered. Optimization is an important and crucial a part of machine learning, has attracted researchers attention. Applying the idea of Genetic Algorithms and Machine learning on Cipher system provides much more security for the ciphertext data. In this paper, we implement BGV Fully Homomorphic Encryption (FHE) scheme. Then, we introduce ML based Genetic Algorithm Model to enhance security of BGV FHE.
Penerapan Data Mining Terhadap Data Penjualan Prioduk Kopi Menggunakan Algoritma Apriori

Currently, the business competition in the culinary field is very tight. Second Home is a culinary business that sells various coffee products that are in demand by the public. The large number of various coffee products causes a lot of sales transaction data to be generated by Second Home. The sales data is not managed properly, causing accumulation and useless data. The level of useless data can be minimized by using a data mining approach. The Data Mining approach aims to provide new information from coffee product sales data. Apriori algorithm is one of the algorithms of data mining that is able to find out association rules based on consumer buying patterns. Based on consumer purchasing patterns for coffee products, the owner of Second Home can provide recommendations or promotions for certain products. The dataset used in this study is 566 sales transaction data from March to May 2021. The application of data mining to sales data with the Apriori Algorithm produces 2 (two) association rules with a support value of 10% and confidence 60%. The results showed that the products purchased simultaneously were Banana fields and Es Kopi Lava Yo Lah with a confidence value of 69.43%.
Adoption of Blockchain in Financial Services

This paper aims to investigate the adoption of blockchain in the banking industry. The topic was researched by utilizing previous research papers and blockchain product/ service providers. There are several use cases of the blockchain in the financial sector. However, blockchain adoption is still in its early stages.
Deep Learning on Side-Channel Analysis

This chapter provides an overview of recent applications of deep learning to profiled side-channel analysis (SCA). The advent of deep neural networks (mainly multiple layer perceptrons and convolutional neural networks) as a learning algorithm for profiled SCA opened several new directions and possibilities to explore the occurrence of side-channel leakages from different categories of systems. This is particularly important for designers to verify to what extent an adversary can extract sensitive information when possessing state-of-the-art attack methods. Deep learning is a fast-evolving technology that provides several advantages in profiled SCA and we summarize what are the main directions and results obtained by the research community.
Klasifikasi Jenis Jamur Menggunakan SVM dengan Fitur HSV dan HOG

Mushrooms are one of the plants that have so many varieties. Every variety has a different shape and color. But most people still feel difficult to know and classify every mushroom. Therefore, classification for mushroom is needed. Method for this research are Hue Saturation Value (HSV) as color segmentation, then Histogram of Oriented Gradient (HOG) as feature extraction, and Support Vector Machine (SVM) as a classification method. Mushrooms that being use are Agaricus, Amanita, Boletus, Cortinarius, Entoloma, Hygrocybe, Lactarius, Russula, Suillus. Total of mushrooms for this research are 900, with 100 each genus. This research using the k-fold Cross Validation method for 4-fold. From 900 images there are 675 for the training phase and 225 for the testing phase. Overall for this research got precision, recall, accuracy respectively 23.80%, 22.94%, and 82.69%. The best mushroom was Boletus with precision, recall, accuracy respectively 55.37%, 46.84%, and 89.69%.
Hierarchical Knowledge Distillation for Dialogue Sequence Labeling

This paper presents a novel knowledge distillation method for dialogue sequence labeling. Dialogue sequence labeling is a supervised learning task that estimates labels for each utterance in the target dialogue document, and is useful for many applications such as dialogue act estimation. Accurate labeling is often realized by a hierarchically-structured large model consisting of utterance-level and dialogue-level networks that capture the contexts within an utterance and between utterances, respectively. However, due to its large model size, such a model cannot be deployed on resource-constrained devices. To overcome this difficulty, we focus on knowledge distillation which trains a small model by distilling the knowledge of a large and high performance teacher model. Our key idea is to distill the knowledge while keeping the complex contexts captured by the teacher model. To this end, the proposed method, hierarchical knowledge distillation, trains the small model by distilling not only the probability distribution of the label classification, but also the knowledge of utterance-level and dialogue-level contexts trained in the teacher model by training the model to mimic the teacher model's output in each level. Experiments on dialogue act estimation and call scene segmentation demonstrate the effectiveness of the proposed method.
Cluster for a Web Application Hosting

Modern businesses need to store and process large amounts of data: customer orders, inventory items, internal and external email correspondence, business website traffic, and so on. There are multiple ways to handle the IT operations associated with these activities and loosely they can be divided into the following:
CROSS-BORDER HEALTH DATA FROM LEGISLATION TO IMPLEMENTATION A CRITICAL DISCURSIVE APPROACH TO COVID-19 RESPONSES1

With a focus on health datafication in the European Union, this article sets out to investigate a few highlights from the EU’s pronouncements on issues of public health and technology, through the tools of Critical Discourse Studies. As an unprecedented public health crisis, the COVID-19 pandemic has revealed that, when it comes to healthcare, EU countries are disconnected from one another. In fact, health datafication is misaligned between Member States and even within national health systems themselves. However, the tech solutionist position that strives for full interoperability of systems in public health (as for contact tracing apps) often disregards the ethical, legal and social issues related to the use of technology itself, i. e. data protection, impact and trust. The aim of the analysis is to illustrate the role of the linguistic and discursive framing of the values and priorities that inform the debate about pandemic response management, to which millions of EU citizens have been exposed in the last two years.
Translation memory systems

This chapter offers an overview of the Translation Memory (TM) tools that have revolutionized the work of translators over the last three decades. It outlines the history and the concept of these widely used translation tools and presents the most popular TM systems. The limitations of current commercial TM tools are discussed and the latest research towards the development of new generation TM programs is outlined.
Real Time Handling Occlusion in Augmented Reality Based on Photogrammetry

Augmented Reality (AR) technology has become an effective tool in many domains as visualization, planning, operations design in construction, manufacturing, surgery, and other applications. Incorporating virtual objects in a physical world to obtain a realistic augmentation requires correct relationships between real and virtual objects. The traditional static vision of AR consists of interaction and navigation through human beings using computer screens. However, the new technology based on photogrammetry and AR allows strengthening the possibilities in 3D data visualization, navigation, and interaction. This paper proposes a close-range photogrammetry method for 3D reconstruction based on imagery acquisition to get a highly accurate 3D model. The proposed method will deal with real-time orientation and tracking, merging close-range photogrammetry and AR. We validated our approach on data sets representing realistic scenes. Obtained results show the proposed approach’s efficiency and accuracy for solving the occlusion problem in AR systems.
Graph-Order Optimization Algorithm Based on Equal-in-Space Distance Model for High-Resolution Image Matting

Image matting is an essential image processing technology. optimized-based image matting methods can significantly improve the alpha matte quality of high-resolution images. However, the local information of the foreground may be similar to the background, which causes the inversion problem of the alpha matte in the single-point optimized. In this paper, we propose an image matting mathematical model of the equal-in-space distance. The model transforms the high-resolution image matting problem into several small-scale combinatorial optimization problems according to the similarity among pixel features. Inspired by spanning tree, we propose a graph-order optimization strategy, which generates the optimization sequence of small-scale optimization problems according to the edge weight among graph nodes. In addition, we designed a graph-order optimization algorithm based on optimized information transfer to solve each node in the graph. Experimental results show that the proposed model solves the alpha matte inversion problem of single-point optimization matting. Besides, the proposed algorithm outperforms the state-of-the-art optimization algorithms for the high-resolution image matting problem.
Concept Drift Detection Based on Restricted Boltzmann Machine in Multi-class Classification System

With the development of information technology, more and more data are generated from social life. Concept drift detection in multi-class classification system has gradually become a research hotspot in the field of data mining. To solve this problem, a multi-class concept drift detection algorithm based on Restricted Boltzmann Machine is proposed in this paper. Based on the probability distribution of RBM, the KL divergence and concept drift detection coefficients are constructed to detect concept drift and judge its type. The performance of the algorithm is tested and analyzed on simulation and real data sets.
Handwritten digit generation based on generative adversarial networks

This paper introduced a method to generate handwritten digit images by using the Generative Adversarial Networks model. Alternating loss and unstable accuracy were persistent problems during the training process. Human handwritten digit image generation plays a crucial role in criminal investigation and machine learning. There are many excellent and effective ordinary deep networks in machine learning programs for recognizing different pieces of images. However, only a few models can be used to construct new patterns. The experiment used Generative Adversarial Networks as the core algorithm to generate data that look like human-written digits. A Convolutional Neural Networks model is used as Discriminator to evaluate whether an image is genuine or created. The inverse convolutional layers in the code turn input into a two-dimensional pixel values picture. The MNIST provides as much as 60,000 data samples for training the program, where thousands of handwritten single digits, between 0 and 9, are displayed by 28*28 pixels images in greyscale. The pictures created by the algorithm initially looked blurry. But after thousands of training steps, the statistics appeared much more realistic. With more iterations carried out, the output images became more and more distinguishable.
A new approach to the traveling salesman problem

The intrinsic difficulty of the Traveling Salesman Problem (TSP) is associated with the combinatorial explosion of potential solutions in the solution space. The significant difficulty with search algorithms for the TSP is how to quickly find the optimal tour and make sure it is optimal. Scamming of all possible solutions requires exponential computing time. Do we need exploring all the possibilities to find the optimal solution? How can we narrow down the search space effectively and efficiently for an exhausted search? This paper describes a new search strategy for solving the TSP. The search strategy combines local search and exhausted search. Local search is used to reduce the search space quickly for exhausted search. In this paper, local search is treated as a discrete dynamical system. A set of local search trajectories converges to small area of the solution space. Then exhausted search is used to search the small area to find the optimal solution. The global optimization features and computing complexity of this new search system are also discussed.
Return-oriented programming protection in the IBM POWER10

Return-oriented programming (ROP) is a technique for hijacking the control-flow of a program and forcing it to perform computations that were never originally intended. ROP is achieved by modifying the values of return addresses saved to memory, causing a failure of control-flow integrity. In the POWER10 processor, we have adopted a cryptographic mechanism for ROP protection. Return addresses are cryptographically hashed when control-flow enters a function and the hash is saved in memory. The hash is recomputed and compared to the saved value just before a return from the function. Any mismatch is flagged as a violation and generates an exception to the supervisor. POWER10 was augmented with instructions to generate and verify those hashes. We minimize performance impact on running programs by implementing the cryptographic hash with dedicated functional units.
Investigating technological risks and mitigation strategies in software projects

Risks are present in any software project. In particular, technological risks are complex, volatile, and difficult to predict accurately. Despite this, the literature is scarce about such risks. This paper address this gap by identifying technological risk factors and strategies to mitigate them in software projects. We conducted and analyzed semi-structured interviews with 25 experts from ten organizations that execute software projects. Participants work with emerging technologies in academia-industry collaboration projects. Our analysis, using grounded theory, led to nine technological risk factors, classified into Research risk or Product risk. Further, for each technological risk, we identified the main mitigation strategy adopted by the practitioners. The results of our study can be used as a catalog to assist organizations in managing technological risks on software projects.
Hybrid CNN Based Attention with Category Prior for User Image Behavior Modeling

User historical behaviors are proved useful for Click Through Rate (CTR) prediction in online advertising system. In Meituan, one of the largest e-commerce platform in China, an item is typically displayed with its image and whether a user clicks the item or not is usually influenced by its image, which implies that user's image behaviors are helpful for understanding user's visual preference and improving the accuracy of CTR prediction. Existing user image behavior models typically use a two-stage architecture, which extracts visual embeddings of images through off-the-shelf Convolutional Neural Networks (CNNs) in the first stage, and then jointly trains a CTR model with those visual embeddings and non-visual features. We find that the two-stage architecture is sub-optimal for CTR prediction. Meanwhile, precisely labeled categories in online ad systems contain abundant visual prior information, which can enhance the modeling of user image behaviors. However, off-the-shelf CNNs without category prior may extract category unrelated features, limiting CNN's expression ability. To address the two issues, we propose a hybrid CNN based attention module, unifying user's image behaviors and category prior, for CTR prediction. Our approach achieves significant improvements in both online and offline experiments on a billion scale real serving dataset.
Quantum Incoherence Based Simultaneously on k Bases

Quantum coherence is known as an important resource in many quantum information tasks, which is a basis-dependent property of quantum states. In this paper, we discuss quantum incoherence based simultaneously on k bases using Matrix Theory Method. First, by defining a correlation function m(e,f) of two orthonormal bases e and f, we investigate the relationships between sets I(e) and I(f) of incoherent states with respect to e and f. We prove that I(e)=I(f) if and only if the rank-one projective measurements generated by e and f are identical. We give a necessary and sufficient condition for the intersection I(e)⋂I(f) to include a state except the maximally mixed state. Especially, if two bases e and f are mutually unbiased, then the intersection has only the maximally mixed state. Secondly, we introduce the concepts of strong incoherence and weak coherence of a quantum state with respect to a set B of k bases and propose a measure for the weak coherence. In the two-qubit system, we prove that there exists a maximally coherent state with respect to B when k=2 and it is not the case for k=3.
Pedestrian Detection Using Integrated Aggregate Channel Features and Multitask Cascaded Convolutional Neural-Network-Based Face Detectors

Pedestrian detection is a challenging task, mainly owing to the numerous appearances of human bodies. Modern detectors extract representative features via the deep neural network; however, they usually require a large training set and high-performance GPUs. For these cases, we propose a novel human detection approach that integrates a pretrained face detector based on multitask cascaded convolutional neural networks and a traditional pedestrian detector based on aggregate channel features via a score combination module. The proposed detector is a promising approach that can be used to handle pedestrian detection with limited datasets and computational resources. The proposed detector is investigated comprehensively in terms of parameter choices to optimize its performance. The robustness of the proposed detector in terms of the training set, test set, and threshold is observed via tests and cross dataset validations on various pedestrian datasets, including the INRIA, part of the ETHZ, and the Caltech and Citypersons datasets. Experiments have proved that this integrated detector yields a significant increase in recall and a decrease in the log average miss rate compared with sole use of the traditional pedestrian detector. At the same time, the proposed method achieves a comparable performance to FRCNN on the INRIA test set compared with sole use of the Aggregated Channel Features detector.
Privacy-from-Birth: Protecting Sensed Data from Malicious Sensors with VERSA

With the growing popularity of the Internet-of-Things (IoT), massive numbers of specialized devices are deployed worldwide, in many everyday settings, including homes, offices, vehicles, public spaces, and factories. Such devices usually perform sensing and/or actuation. Many of them handle sensitive and personal data. If left unprotected, ambient sensing (e.g., of temperature, motion, audio, or video) can leak very private information. At the same time, some IoT devices use low-end computing platforms with few (or no) security features.There are many well-known techniques to secure sensed data, e.g., by authenticating communication end-points, encrypting data before transmission, and obfuscating traffic patterns. Such techniques protect sensed data from external adversaries, while assuming that the sensing device itself is secure. Meanwhile, both the scale and frequency of IoT-focused attacks are growing. This prompts a natural question: how to protect sensed data even if all software on the device is compromised? Ideally, in order to achieve this, sensed data must be protected from its genesis, i.e., from the time when a physical analog quantity is converted into its digital counterpart and becomes accessible to software. We refer to this property as PfB: Privacy-from-Birth.In this work, we formalize PfB and design Verified Remote Sensing Authorization (VERSA) – a provably secure and formally verified architecture guaranteeing that only correct execution of expected and explicitly authorized software can access and manipulate sensing interfaces, specifically, General Purpose Input/Output (GPIO), which is the usual boundary between analog and digital worlds on IoT devices. This guarantee is obtained with minimal hardware support and holds even if all device software is compromised. VERSA ensures that malware can neither gain access to sensed data on the GPIO-mapped memory nor obtain any trace thereof. VERSA formally verified and its open-sourced implementation targets resource-constrained IoT edge devices, commonly used for sensing. Experimental results show that PfB is both achievable and affordable for such devices.