Reviewerly/exHarmony huggingface dataset:
Evolutionary Automated Feature Engineering

Effective feature engineering serves as a prerequisite for many machine learning tasks. Feature engineering, which usually uses a series of mathematical functions to transform the features, aims to find valuable new features that can reflect the insight aspect of data. Traditional feature engineering is a labor-intensive and time-consuming task, which depends on expert domain knowledge and requires iterative manner with trial and error. In recent years, many automated feature engineering (AutoFE) methods have been proposed. These methods automatically transform the original features to a set of new features to improve the performance of the machine learning model. However, existing methods either suffer from computational bottleneck, or do not support high-order transformations and various feature types. In this paper, we propose EAAFE, to the best of our knowledge, the first evolutionary algorithm-based automated feature engineering method. We first formalize the AutoFE problem as a search problem of the optimal feature transformation sequence. Then, we leverage roulette wheel selection, subsequence-exchange-based DNA crossover, and $$\epsilon $$ -greedy-based DNA mutation to achieve evolution. Despite its simplicity, EAAFE is flexible and effective, which can not only support feature transformations for both numerical and categorical features, but also support high-order feature transformations. Extensive experimental results on public datasets demonstrate that EAAFE outperforms the existing AutoFE methods in both effectiveness and efficiency.
Path Planning of Long-reach Coupled Tendon-driven Manipulator

Tendon-driven mechanism enables to realize a long-reach robot arm to inspect in industrial plants. However, a large elastic elongation of tendons due to gravitational joint torque makes joint control unstable. In this paper, we proposed a planning method to search for a path that includes only stable postures. The stability of the joint angle was formulated using the balance between tendon elasticity and gravitational torque. As a planning algorithm, Rapidly exploring Random Tree was used. The effectiveness of the proposal was demonstrated by manipulation of 5 kg payload by 10-m-long robotic arm.
A Novel Exploration Technique For Multi-Agent Reinforcement Learning

Multi-agent reinforcement learning (MARL) involves interacting with their environment as well as among themselves, as opposed to single-agent reinforcement learning (RL) which involves one agent interacting with the environment and operated by itself. In most studies on MARL, the calculation of global action-reward value is used to enhance exploration. However, agents' contributions cannot be quantified. By combining curiosity-based intrinsic reward with hierarchical prioritized experience replay, in this paper we propose a technique that quantifies individual agents' contributions to the global achievement. A comparison was made between our proposed algorithm and the existing state-of-the-art algorithms using the StarCraft 2 learning environment. Numerical results demonstrate that our proposed algorithm achieves a higher test win rate and outperforms the existing algorithms under all scenarios tested.
Enhanced Intrusion Detection System for Remote Healthcare

The Internet of Medical Things (IoMT) is an Internet of Things (IoT) application, where medical devices associated with the human body collect physiological data and then transfer them using wireless communication to a remote medical server. This technology enables the remote healthcare system to monitor the patient’s health condition continuously and in real-time. However, due to the sensitive nature of the data processed within the IoMT and wireless communication, an attacker can perform Man-In-The-Middle (MITM) attacks to intercept and modify the data, whose consequences for the victim can vary from privacy violation to life-threatening. The use of traditional security solutions such as cryptography is ineffective due to limited memory and processing capabilities of medical devices. In the light of this constraint, we proposed an Intrusion Detection System (IDS) based on Machine Learning (ML) that uses physiological and network data to detect MITM attacks. First, we prepared the data using an oversampling technique based on Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP), then we evaluated and tested several ML algorithms. The results obtained show the effectiveness of our solution, which was able to detect all attacks in real-time.
A Design of Reinforcement Learning Accelerator Based on Deep Q-learning Network

Reinforcement Learning (RL) is widely adopted in robotic controlling and decision making due to its ability of adapting to the changing. However, the Deep Q-learning Network (DQN) RL systems heavily rely on CPU and GPU to accelerate the computation, which cannot meet the requirement. In this paper, we proposed a design of RL accelerator based on DQN in CPU-FPGA architecture. In this paper, we propose a design of RL accelerator based on DQN in CPU-FPGA architecture. Compared with other CPU architecture, the accelerator we proposed achieves 1.84x computing speed improvement and reduces hardware resources significantly.
PECULIARITIES OF THE UNIVERSITY REPUTATION FORMATION IN THE APPLICATION OF MODERN DIGITAL TECHNOLOGIES

In the era of digital transformation not only the commercial en- terprises business models are changing but also the universities ones, in par- ticular, their communication activities change. The article studies the current approaches of higher education institutions to forming a positive business rep- utation and managing reputational risks in the digital media field. It defines the expectations of various stakeholders from the activities of higher education institutions and also considers what universities expect from interaction with partners. Possible points of contact with target audiences are looked into, tak- ing into account such concepts as mission, brand, image and reputation of the university. The activity of several leading foreign universities in the Internet- environment, including social networks, as well as some other tools to form the reputation of the university are reviewed. It was found that despite the im- portance and prevalence of social networks in the world’s leading universities, they are not the only point of contact with target audiences and do not have a decisive role in shaping the reputation of the university. In particular, it was determined what important role publishing and library activities play for the image of the university. The reputational risks of universities in the digital age are also viewed. The article defines the necessity of marketing approach to the evaluation and formation of the university reputation.
Bridging Images and Videos: A Simple Learning Framework for Large Vocabulary Video Object Detection

Scaling object taxonomies is one of the important steps toward a robust real-world deployment of recognition systems. We have faced remarkable progress in images since the introduction of the LVIS benchmark. To continue this success in videos, a new video benchmark, TAO, was recently presented. Given the recent encouraging results from both detection and tracking communities, we are interested in marrying those two advances and building a strong large vocabulary video tracker. However, supervisions in LVIS and TAO are inherently sparse or even missing, posing two new challenges for training the large vocabulary trackers. First, no tracking supervisions are in LVIS, which leads to inconsistent learning of detection (with LVIS and TAO) and tracking (only with TAO). Second, the detection supervisions in TAO are partial, which results in catastrophic forgetting of absent LVIS categories during video fine-tuning. To resolve these challenges, we present a simple but effective learning framework that takes full advantage of all available training data to learn detection and tracking while not losing any LVIS categories to recognize. With this new learning scheme, we show that consistent improvements of various large vocabulary trackers are capable, setting strong baseline results on the challenging TAO benchmarks.
Location Data Anonymization Retaining Data Mining Utilization

Location information, such as customers’ home addresses, is essential for data mining tasks. On the other hand, it is sensitive private information. In most countries, when requesting a third party for data mining, it is legally required to anonymize personal information such as home addresses in the database. However, conventional anonymization methods significantly lose the usefulness that location information has inherently. In this paper, we proposed an anonymization method of location retaining important locational features. In the proposed method, each address is replaced with a ranking value of the distance from each facility that is important in terms of location, such as a station or a supermarket. We examined our method and confirmed that the important rules mined from non-anonymized data could also be mined from our anonymized data.
Epoch Confidentiality in Updatable Encryption

In this paper we formalise public-key Updatable Encryption (PKUE), a primitive so far studied formally only in the symmetric setting. Defining UE in the public-key setting enables us to establish a new notion of security we call epoch confidentiality (EC) which considers the ability of an adversary to distinguish the public keys used in periods known as epochs and in turn reflects the leakage of the time in which a ciphertext was created. We propose a public-key UE construction and prove that it satisfies our new notion of security alongside a notion of ciphertext confidentiality such that efficiency is not affected by moving to the public-key setting.
Interpretation on the Google Cloud Platform and Its Wide Cloud Services

Cloud computing is growing wide and first-rate promising technology. There are three forms of cloud computing: infrastructure as a service (IaaS), software as a service (SaaS), and platform as a service (PaaS). There are many cloud service providers. Among these, AWS (Amazon Web Service), Azure (Microsoft Cloud), IBM Cloud, Oracle Cloud, and VMware are immensely popular. In this paper, the authors study the Google Cloud Platform (GCP) and a few principally used popular services. Google Cloud Platform could be a cloud provider; they supply servers and services that are used on-demand and at scale. Google Cloud Platform is the fastest growing cloud service supplier now.
Adversarial Attack Using Sparse Representation of Feature Maps

Deep neural networks can be fooled by small imperceptible perturbations called adversarial examples. Although these examples are carefully crafted, they involve two major concerns. In some cases, adversarial examples generated are much larger than minimal adversarial perturbations while in others the attack method involves an extensive number of iterations making it infeasible. Moreover, the sparse attacks are either too complex or are not sparse enough to achieve imperceptibility. Therefore, attacks designed should be fast and minimum in terms of ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm. In this research, we used a dictionary learning technique to generate sparse adversarial examples based on feature maps of target images. We present two novel algorithms to tune the dictionary learning process and feature map selection. The results on MNIST and Imagenet show our attack is better or competitive with the state-of-the-art methods. We also compared our method with sparse attacks recently introduced in literature. As a result, we have achieved comparable attack success rate when compared to the state-of-the-art with smaller ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm. We also tested the efficacy of our attack in the presence of defense mechanisms and none of the defenses were able to combat the effect of our proposed attack.
Use of Blockchain Enabled IoT in Insurance: A Case Study of Calamity Based Crop Insurance

The insurance process contains multiple policies and activities and is mainly dependent on the closing of policies on initiating, maintaining, and closing the claims. The major concerns in insurance are processing time, security, and settlement time of payments. Blockchain can help the insurance industry to mitigate and improve the processing time. Blockchain has the characteristics of immutability, integrity, security, and no third-party dependency. In this paper, we present the use case of blockchain along with IoT to improve the insurance processes. Further, we propose a framework for automatic claim settlement of crop insurance in case of yield loss due to natural calamity. The proposed framework is based on a smart contract-enabled blockchain to reduce the claim processing time and maintain data integrity.
Object Recognition and Localization Based on Binocular Stereo Vision

In order to obtain the three-dimensional position of the target, and then measure the trajectory of the moving target, the problem of target recognition and positioning based on binocular stereo vision is studied. A binocular stereo vision system is built and MATLAB software is used to complete the calibration of the system. Through the checkerboard calibration method, the internal and external para-meters, relative attitude and distortion parameters of the left and right cameras are obtained. In the stereo matching stage, the image is preprocessed first, the RANSAC algorithm is used to eliminate the mismatch, and then the SIFT and SURF feature recognition algorithms are used to match the left and right images, and the pixel coordinates of the corresponding points are obtained, and then the three-dimensional space coordinates of the target point are obtained. and compared the measurement accuracy of the two feature recognition algorithms. The experimental results show that the method can accurately locate the target point and has high precision.
Prediction of exchange rate using improved particle swarm optimised radial basis function networks

In this paper, a radial basis function neural network (RBFN) model has been trained by canonical particle swarm optimisation (PSO) and improved particle swarm optimisation (IMPSO) algorithms to efficiently predict the exchange rate of Indian rupees against the exchange rate of G-7 countries for future days. We have used two variants of PSO such as canonical PSO and IMPSO for optimising the parameters of radial basis function neural network through learning from the past data of exchange rate prediction. Here, we have considered 43 countries' exchange rates to predict the Indian rupees against the G-7 countries. Forty-three exchange rates have been collected and based on their correlation analysis a dataset has been prepared to validate the proposed model. In addition, a fair comparison has been carried out between IMPSO tuned RBFN and canonical PSO tuned RBFN with respect to the results obtained by varying the number of iterations for future days' prediction. From the experimental results, it is observed that the predictive performance of IMPSO tuned RBFN modelling the case of higher number of iterations is promising vis-à-vis canonical PSO tuned RBFNs model.
A Blockchain Communication Resource Optimization Consensus Method

Since the emergence of Bitcoin, the blockchain technology behind it has been gradually gaining attention from all walks of life. As the core of blockchain technology, the consensus algorithm determines the security, scalability and decentralization of the blockchain and many other important characteristics. The efficiency of the current blockchain consensus algorithm still needs to be improved. To address this issue, this paper proposes a consensus algorithm for communication resource optimization (CCRO), which divides consensus nodes into different domains, calculates the trust of nodes by several trust factors, and assigns different roles to nodes according to the trust. It also introduces a new class of nodes, i.e., communication nodes, which are responsible for the delivery of messages in the consensus process, and achieves the communication resource optimization of the blockchain system. By building an experimental platform, we verify the performance of CCRO, which downscales the inter-node consensus communication protocol, optimizes the regional data synchronization protocol, reduces the network communication overhead, and effectively improves the consensus efficiency.
Rayleigh EigenDirections (REDs): Nonlinear GAN Latent Space Traversals for Multidimensional Features

We present a method for finding paths in a deep generative model’s latent space that can maximally vary one set of image features while holding others constant. Crucially, unlike past traversal approaches, ours can manipulate arbitrary multidimensional features of an image such as facial identity and pixels within a specified region. Our method is principled and conceptually simple: optimal traversal directions are chosen by maximizing differential changes to one feature set such that changes to another set are negligible. We show that this problem is nearly equivalent to one of Rayleigh quotient maximization, and provide a closed-form solution to it based on solving a generalized eigenvalue equation. We use repeated computations of the corresponding optimal directions, which we call Rayleigh EigenDirections (REDs), to generate appropriately curved paths in latent space. We empirically evaluate our method using StyleGAN2 and BigGAN on the following image domains: faces, living rooms and ImageNet. We show that our method is capable of controlling various multidimensional features: face identity, geometric and semantic attributes, spatial frequency bands, pixels within a region, and the appearance and position of an object. Our work suggests that a wealth of opportunities lies in the local analysis of the geometry and semantics of latent spaces.
Short-term Solar Irradiance Forecasting based on Self-Attentive Transformers

An accurate short-term solar forecasting system is crucial for a stable, efficient, and reliable energy system. Although deep learning-based models have shown excellent performance in time series forecasting challenges, their sequential nature, each state is dependent on the preceding state, leads to the loss of previous knowledge over very long input sequences. This paper proposes a novel architecture based on the self-attentive transformer with no risk of forgetting previous information, faster training time, and lower computing complexity. First, a self-attentive transformer architecture is designed and developed. Then, the performance of the proposed structure is compared to the state-of-the-art methods such as convolutional neural network (CNN), long short-term memory networks (LSTM), a combination of LSTM and attention mechanism (AttLSTM), and residual Network (ResNet) using seventeen years of data from twelve different solar sites in Philadelphia, Pennsylvania. The numerical result illustrates that the proposed method outperforms baseline models for a half-day forecasting horizon.
Promise and Peril of the Data Deluge for Historians

Historical analyses are inevitably based on data – documents, fossils, drawings, oral traditions, artifacts, and more. Recently, historians have been urged to embrace the data deluge (Guldi and Armitage 2014) and teams are now systematically assembling large digital collections of historical data that can be used for rigorous statistical analysis (Slingerland and Sullivan 2017; Turchin et al. 2015; Whitehouse et al. 2019; Slingerland et al. 2018–2019). The promise of large, widely accessible databases is the opportunity for rigorous statistical testing of plausible historical models. The peril is the temptation to ransack these databases for heretofore unknown statistical patterns. Statisticians bearing algorithms are a poor substitute for expertise.
THE CEILING EFFECT AND FLIGHT INSIGHT OF UNMANNED AERIAL VEHICLES DURING PROXIMITY INSPECTION OF BRIDGES VIA COMPUTATIONAL FLUID DYNAMICS MODELING AND SIMULATIONS

UAVs face some critical challenges in pervasive applications, such as navigation in GPS-denying areas, obstacle avoidance, and tradeoff between payload and flight time. The INSPIRE UTC at Missouri S&amp;T is developing a clamping UAV, a hybrid flying and traversing robot, to make it possible that the UAV flies under a bridge girder, grabs the girder, and moves along the girder for inspection. This heavy UAV is difficult to control underneath the deck when equipped with multiple cameras. In this study, CFD modeling and simulation are conducted to understand the flight behavior and ceiling effect of the UAV under the bridge deck. First, 3D scanning and reverse engineering are used to build CFD models. Second, a series of single propeller tests are conducted to validate the process of CFD modeling and the simulation results. Third, based on the workflow and technique validated through single propeller CFD analysis, the CFD model of an entire UAV is established and analyzed to predict the behavior of the UAV and understand its interaction with upper boundaries as it approaches the bridge ceiling vertically. The CFD simulation results show that the ceiling effect of the designed clamping UAV is insignificant when the UAV approaches the standard bridge deck with enough depth. These results provide a technical reference for the design and control of the clamping UAV for bridge inspection.
Abstract Argumentation Goes Quantum: An Encoding to QUBO Problems

We present an encoding of some NP-Complete problems in Abstract Argumentation to Quadratic Unconstrained Binary Optimization (QUBO) problems. A solution for a QUBO problem corresponds to minimize a quadratic function over binary variables (0/1), whose coefficients can be represented by a symmetric square matrix. Being this problem NP-Complete as well, there exist approximate solvers: from the D-Wave Ocean SDK we test a simulated annealing algorithm and a real quantum annelaer provided by the Leap $$^{TM}$$ Quantum Cloud Service. Hence, we propose a new encoding for Abstract Argumentation problems, which will benefit from the future development of quantum computation.
Tab this folder of documents

In the midst of digital transformation, automatically understanding the structure and composition of scanned documents is important in order to allow correct indexing, archiving, and processing. In many organizations, different types of documents are usually scanned together in folders, so it is essential to automate the task of segmenting the folders into documents which then proceed to further analysis tailored to specific document types. This task is known as Page Stream Segmentation (PSS). In this paper, we propose a deep learning solution to solve the task of determining whether or not a page is a breaking-point given a sequence of scanned pages (a folder) as input. We also provide a dataset called TABME (TAB this folder of docuMEnts) generated specifically for this task. Our proposed architecture combines LayoutLM and ResNet to exploit both textual and visual features of the document pages and achieves an F1 score of 0.953. The dataset and code used to run the experiments in this paper are available at the following web link: https://github.com/aldolipani/TABME.