Reviewerly/exHarmony huggingface dataset:
New chaotic map development and its application in encrypted color image

This paper traces the process of constructing a new one-dimensional chaotic map, and will provide a simple application in color image encryption.The use of Sarkovskii's theorem will make it possible to determine the existence of chaos and restrict all conditions to ensure the existence of this new sequence.In addition, the sensitivity to initial conditions will be proved by Lyapunov's index value.Similarly, the performance of this new chaotic map will be illustrated graphically and compared with other chaotic maps most commonly used in cryptography.Finally, a humble color image encryption application will show the power of this new chaotic map.
An Image Classification Model that Learns Image Features and Numerical Information

In recent years, deep neural network technology has been developing rapidly, especially in the field of image recognition. However, since deep neural networks learn images based on pixel values, they can only learn the features of the image and not the meta-information that the image has. In this paper, we focused on the differences between image features and meta-information. For example, 0 and 9 are relatively similar in terms of image characteristics, but there is significant difference in terms of the numbers they actually mean. In contrast, 3 and 4 are relatively dissimilar in terms of image features, but the difference is small in terms of the values they actually mean. In order to solve problems like this example, this paper proposes a method for learning based not only on the features of the image, but also on the numerical information that the image has. Experiments were conducted on the MNIST and Kannada-MNIST datasets using three different models: DNN, CNN, and RNN. As a result, the numerical error is smaller in the proposed model than in the baseline.
An Evaluation of Strong-Motion Parameters at the S-net Ocean-Bottom Seismograph Sites Near the Kanto Basin for Earthquake Early Warning

We analyzed strong-motion records at the ground and borehole in and around the Kanto Basin and the seafloor in the Japan Trench area from three nearby offshore earthquakes of similar magnitudes (Mw 5.8–5.9). The seafloor strong-motion records were obtained from S-net, which was established to enhance tsunami and earthquake early warnings after the 2011 great Tohoku-oki earthquake disaster. The borehole records were obtained from MeSO-net, a dense network of seismometers installed at a depth of 20 m in the Tokyo metropolitan area. The ground records were obtained from the K-NET and KiK-net networks, established after the 1995 great Hanshin-Awaji earthquake disaster. The MeSO-net and S-net stations record the shakings continuously, while the K-NET and KiK-net records are based on triggering thresholds. It is crucial to evaluate the properties of strong motions recorded by the S-net for earthquake early warning (EEW). This paper compared the peak ground accelerations (PGAs) and peak ground velocities (PGVs) between the S-net and K-NET/KiK-net stations. Because the MeSO-net records were from the borehole, we compared the PGAs and significant durations of the low-frequency motions (0.1–0.5 Hz) between the S-net and MeSO-net stations from identical record lengths. We found that the horizontal PGAs and PGVs at the S-net sites were similar to or larger than the K-NET/KiK-net sites for the S wave. In contrast, the vertical PGAs and PGVs at the S-net sites were similar to or smaller than those at the K-NET/KiK-net sites for the S wave. Particularly, the PGAs and PGVs for the P-wave parts on the vertical-component records of S-net were, on average, much smaller than those of K-NET/KiK-net records. The difference was more evident in the PGAs. The average ratios of S-wave horizontal to vertical PGAs were about 2.5 and 5 for the land and S-net sites, respectively. The low-frequency PGAs at the S-net sites were similar to or larger than those of the MeSO-net borehole records. The significant durations between the two-networks low-frequency records were generally comparable. Quantification of the results from a larger dataset may contribute to ground-motion prediction for EEW and the design of the offshore facilities.
Virtual Fieldwork: Designing Augmented Reality Applications Using Virtual Reality Worlds

AR technology continues to develop and is expected to be used in a much wider range of fields. However, existing head-mounted displays for AR are still inadequate for use in daily life. Therefore, we focused on using VR to develop AR services and explored the possibility of virtual fieldwork. By comparing fieldwork using AR and virtual fieldwork using VR, we revealed the feasibility and limitations of virtual fieldwork. It was shown that virtual fieldwork is effective in an indoor environment where no other people are present. By using virtual fieldwork, researchers can obtain qualitative results more easily than with traditional fieldwork. On the other hand, it was suggested that it is difficult to realize virtual fieldwork in a large environment such as outdoors and in the presence of others. In particular, participants do not perceive the virtual fieldwork as fieldwork in the real world because the experience of walking in a large space is difficult to experience in conventional VR.
Design and Evaluation of a Privacy-preserving Supply Chain System Based on Public Permissionless Blockchain

Securing the traceability of products in a supply chain is an urgent issue. Recently, supply-chain systems that use a blockchain have been proposed. In these systems, the blockchain is used as a common database shared among supply chain parties to secure the integrity and reliability of distribution information such as ownership transfer records. These systems thus secure a high level of traceability in the supply chain. Considering future scalability of supply chains, public permissionless blockchain (PPBC) is a promising approach. In this approach, however, distribution information that should be kept private is made public since the information recorded in PPBC can be read by anyone. We therefore propose a method for preserving privacy while securing traceability in a supply chain system using PPBC. The proposed method preserves privacy by concealing distribution information via encryption. In addition, the proposed method ensures distribution among legitimate supply chain parties while concealing their blockchain addresses by using zero-knowledge proofs.We implement the proposed method on Ethereum smart contracts and verify the system behavior. The results show that the proposed method works as expected, and that system usage cost per distribution party is at most 2.2 × 106 gas units in terms of blockchain transaction fees.
Differentially Private Naïve Bayes Classifier Using Smooth Sensitivity

Abstract There is increasing awareness of the need to protect individual privacy in the training data used to develop machine learning models. Differential Privacy is a strong concept of protecting individuals. Naïve Bayes is a popular machine learning algorithm, used as a baseline for many tasks. In this work, we have provided a differentially private Naïve Bayes classifier that adds noise proportional to the smooth sensitivity of its parameters. We compare our results to Vaidya, Shafiq, Basu, and Hong [1] which scales noise to the global sensitivity of the parameters. Our experimental results on real-world datasets show that smooth sensitivity significantly improves accuracy while still guaranteeing ɛ -differential privacy.
BFV, CKKS, TFHE: Which One is the Best for a Secure Neural Network Evaluation in the Cloud?

We provide clear and concise guidelines for the use of three of the most popular homomorphic cryptosystems: BFV, CKKS and TFHE. Because they are unified under the Chimera framework and it is now possible to switch a ciphertext from one cryptosystem to another, such a comparison is essential to better understand which cryptosystem to use in which use-case or for which part of a secure computation on the cloud. We do this by comparing the application of the three cryptosystems to the evaluation phase of standard feed-forward neural networks tested on the MNIST ( http://yann.lecun.com/exdb/mnist/ ) database. We tested their application in the case where both the query and the neural network model are encrypted and in the case when only the query is encrypted. We evaluated the results obtained using the three homomorphic schemes in terms of precision, memory usage and execution time for a minimal security of 128 bits.
What are you optimizing for? Aligning Recommender Systems with Human Values

We describe cases where real recommender systems were modified in the service of various human values such as diversity, fairness, well-being, time well spent, and factual accuracy. From this we identify the current practice of values engineering: the creation of classifiers from human-created data with value-based labels. This has worked in practice for a variety of issues, but problems are addressed one at a time, and users and other stakeholders have seldom been involved. Instead, we look to AI alignment work for approaches that could learn complex values directly from stakeholders, and identify four major directions: useful measures of alignment, participatory design and operation, interactive value learning, and informed deliberative judgments.
Making the digital economy “fit for Europe”

Abstract Over the past three decades, cyberspace has gradually become an engine of unsustainable outcomes from an economic, social and environmental perspective. The European Commission has launched several new initiatives, in the attempt to restore public control over cyberspace, remedy the distributional imbalances generated by the rise of large‐scale digital platforms, and promote Europe's digital sovereignty. The paper argues that only by embedding rules and values in “code” and preserving openness towards the rest of the world will the EU manage to achieve its desired goals. Current initiatives such as the data strategy, the AI regulation, the Digital Services Act and the European Cloud Federation appear still too sparse and uncoordinated to really deliver on Europe's ambition to lead the world in the sustainable use of technology.
A Chatbot Solution for eGLU-Box Pro: The Usability Evaluation Platform for Italian Public Administrations

This paper shows a chatbot solution for eGLU-box Pro, a usability testing platform for Italian Public Administration (PA). eGLU-box Pro is a web-based tool designed to help PA practitioners in creating remote usability tests and analyzing participants’ answers and interaction data after they complete the usability tasks. The impact of the chatbot solution on users’ experience was assessed by bio-behavioral evaluation methods such as eye tracking, electroencephalography, and facial expression recognition. This work describes the platform and its integrated chatbot solution and shows the results of a preliminary laboratory study involving 20 end-users. The study is part of an ongoing design and development project based on a user-centered approach.
E-SIP: Website-Based Scheduling Information System to Increase the Effectivity of Lecturer's Performance and Learning Process

The development of internet and information technology made a system to the practical, effective, and efficient. One of the impacts is ease to make scheduling information system in university. This research is research and development (R&amp;D) models which have aim to develop the website-based scheduling information system to increase the effectivity of lecturer’s performance and learning process in IAIN Tulungagung. E-SIP program develop by using software development life cycle in term of waterfall model. Waterfall model was selected because it was easy and efficient. This system development consisted of system need analysis, system design, implementation, testing, dissemination, and maintenance. Data collecting system using literature review, field study, and interview. Furthermore, data also collected from questionnaire scores of E-SIP validations conducted by the validator and respondents, in terms of admins. After doing some development phase and trial, the E-SIP was developed and ready to use to make scheduling process in IAIN Tulungagung. E-SIP possibly runs with using any browser supporting the java-script system. The development result of E-SIP is in terms of login page for users in three levels. The first level is Super Admin which is responsible to input all databases needed for E-SIP. The second level is Department Admin, which is responsible to arrange the individual schedule of departments. The third level is Faculty Admin, which only able to see the schedule arranged by Department Admin. The similarity of all levels is to print and see the schedule. The advantage of E-SIP is that the scheduling system is able to be controlled by online and performed simultaneously at one time as well as to check overlapping or nonautomatic schedules in the website. Besides, the disadvantage happens when there is a bug or an error resulting in that the system does not function properly and needs to be immediately resolved.
Development of Online Based Smart House Renting Web Application

The house rental issue is one of the elemental parts of society.Nowadays, it is extremely difficult to find suitable accommodation in city areas if people search for it physically.On the other hand, the land owner also needs to rent the house.It can be difficult to find tenants just to hang a lease sign on a building, and as a result, they lose money.An online common platform can play a vital role in this case.The purpose of the study is to develop a common web-based online platform for both tenants and house owners so that both tenants and landowners will mutually benefit from the system.This paper presents the development of web applications for the people of Bangladesh where both house owners and tenants can register and tenants can have houses for rent via sophisticated contact with the house owner.In this paper, a common online-based smart house rental web application has been developed both for tenants and for house owners.This web application is very user-friendly, efficient and it has got many unique features that are not offered by other currently available house rental websites here in Bangladesh.Tenants can register using their phone number, store information about their identity, search for available houses, send messages to house owners, and choose a suitable house using developed web applications.House owners can also register for the system, which will manually verify and authenticate the knowledge provided by the house owner can view a tenant's information history whenever a tenant makes contact through text and supply house-related information accordingly.The proposed online smart house system has been tested and validated.It works very efficiently with many features.The application provided faster and improved opportunities to get houses, as well as ensuring the availability of houses for rent in the greatest number of areas.The system will help to spread trustworthy services nationwide and supply users with the chance to speak and improve the house rent in Bangladesh.Because it has many smart features, this developed online smart house rental
Leafmap: A Python package for interactive mapping and geospatial analysis with minimal coding in a Jupyter environment

Leafmap is a Python package for interactive mapping and geospatial analysis with minimal coding in a Jupyter environment.It is a spin-off project of the geemap Python package (Wu, 2020), which was designed specifically to work with Google Earth Engine (GEE) (Gorelick et al., 2017).However, not everyone in the geospatial community has access to the GEE cloud computing platform.Leafmap is designed to fill this gap for non-GEE users.It is a free and open-source Python package that enables users to analyze and visualize geospatial data with minimal coding in a Jupyter environment, such as Google Colab, Jupyter Notebook, and JupyterLab.Leafmap is built upon several open-source packages, such as folium (Filipe et al., 2021)and ipyleaflet (Renou et al., 2021) (for creating interactive maps), WhiteboxTools (Lindsay, 2018) and whiteboxgui (for analyzing geospatial data), and ipywidgets (Grout & et al., 2021) (for designing interactive graphical user interface [GUI]).Leafmap has a toolset with various interactive tools that allow users to load vector and raster data onto the map without coding.In addition, users can use the powerful analytical backend (i.e., WhiteboxTools) to perform geospatial analysis directly within the leafmap user interface without writing a single line of code.The WhiteboxTools library currently contains 468 tools for advanced geospatial analysis, such as GIS Analysis, Geomorphometric Analysis,
Co‐teaching based pseudo label refinery for cross‐domain object detection

Object detection is one of the main tasks in computer vision and has made great progress in recent years. However, the performance of target detectors is significantly dropped by the differences between existing datasets and application scenarios, leading to the so-called domain shift problem. To address such an issue, a novel co-teaching based pseudo label refinery framework for cross-domain object detection is developed, which cooperates with two models to select data from target domain for each other. This strategy can effectively purify the predicted pseudo labels and resist noisy labels. Specifically, the framework consists of two encoders (i.e. structure encoder and global encoder), two classifiers and one discriminator, in which structure encoder is used to extract structural features that are not disturbed by colour, and the global encoder is used to extract the complete discriminant features. The two encoders are each followed by a classifier. In training, the structure and global encoder with labelled source samples are first trained, so that it has the initial recognition ability. Then the samples assigned are used with pseudo labels by the classifier following the structure encoder to fine-tune the global encoder which pre-trained on the labelled source domain and obtain the refined labels for the target data. With the refined labels, the structure encoder is further optimised on the target domain. During this process, the proposal is to cross use the two classifiers to promote the mutual transfer of complementary capabilities of the two encoders. Moreover, a novel residual channel attention block (RCA) embedded with salient features is designed to pay more attention to the target regions. Extensive experiments demonstrate that the developed framework can generate clean labels for unlabelled target data and boost the performance of cross domain object detection. The code is available at http://www.msp-lab.cn:1436/msp/cbplr-master.
Private Stream Aggregation with Labels in the Standard Model

Abstract A private stream aggregation (PSA) scheme is a protocol of n clients and one aggregator. At every time step, the clients send an encrypted value to the (untrusted) aggregator, who is able to compute the sum of all client values, but cannot learn the values of individual clients. One possible application of PSA is privacy-preserving smart-metering, where a power supplier can learn the total power consumption, but not the consumption of individual households. We construct a simple PSA scheme that supports labels and which we prove to be secure in the standard model. Labels are useful to restrict the access of the aggregator, because it prevents the aggregator from combining ciphertexts with different labels (or from different time-steps) and thus avoids leaking information about values of individual clients. The scheme is based on key-homomorphic pseudorandom functions (PRFs) as the only primitive, supports a large message space, scales well for a large number of users and has small ciphertexts. We provide an implementation of the scheme with a lattice-based key-homomorphic PRF (secure in the ROM) and measure the performance of the implementation. Furthermore, we discuss practical issues such as how to avoid a trusted party during the setup and how to cope with clients joining or leaving the system.
MT-YOLOv5: Mobile terminal table detection model based on YOLOv5

Abstract Table detection is an important task of optical character recognition(OCR). At present, table detection for desktop applications has basically reached commercial requirements. With the advancement of informatization, personal demand for table detection has gradually increased. There is an urgent need to establish a table detection method that can be deployed on handheld devices. This paper proposes a mobile terminal table detection model based on YOLOv5. First, we used YOLOv5 as the main framework of the model. However, considering the problem of connection redundancy in the backbone of YOLOv5, on the basis of retaining the YOLOv5 multi-scale detection head, we replaced the backbone of YOLOv5 with the same excellent Mobilenetv2. In addition, considering the non-linear defects of the lightweight model, we use deformable convolution to make up for it. This paper has been evaluated on the ICDAR 2019 dataset, and the results show that compared with the baseline model, the model reduces the number of parameters by half and increases the detection speed by 47%. At the same time, the model can reach 35.25 FPS on ordinary Android phones.
Quantum computing for classical problems: variational quantum eigensolver for activated processes

Abstract The theory of stochastic processes impacts both physical and social sciences. At the molecular scale, stochastic dynamics is ubiquitous because of thermal fluctuations. The Fokker–Plank–Smoluchowski equation models the time evolution of the probability density of selected degrees of freedom in the diffusive regime and it is, therefore, a workhorse of physical chemistry. In this paper we report on the development and implementation of a variational quantum eigensolver to solve the Fokker–Planck–Smoluchowski eigenvalue problem. We show that such an algorithm, typically adopted to address quantum chemistry problems, can be effectively applied to classical systems, paving the way to new applications of quantum computers. We compute the conformational transition rate in a linear chain of rotors with nearest-neighbour interactions. We provide a method to encode the probability distribution for a given conformation of the chain on a quantum computer and assess its scalability in terms of operations. A performance analysis on noisy quantum emulators and quantum devices (IBMQ Santiago) is provided for a small chain which shows results in good agreement with the classical benchmark without any further addition of error mitigation techniques.
The Universal Approximation Property: Characterizations, Existence, and a Canonical Topology for Deep-Learning

The universal approximation property (UAP) of feed-forward neural networks issystematically studied for arbitrary families of functions in general functionspaces. Two characterizations of the UAP are found, conditions for theexistence of a small family of functions with the UAP are given, and acanonical topology guaranteeing that a set of functions has the UAP isexplicitly constructed. These general results are applied to two concreteproblems in learning theory. First, it is shown that neural networkarchitectures with a sigmoid activation function achieving the values 0 and 1are capable of approximating any set function between two Euclidean spaces forthe canonical topology. As a second application of our results, it is shownthat any continuous function accepting an arbitrary number of inputs can beapproximated by a neural network receiving an arbitrary number of inputs. Thismakes these networks suitable for learning problems where the dimension of thedata is diverging, such as in ultra-high dimensional situations.