Reviewerly/exHarmony huggingface dataset:
An Evolutionary Distributed Framework for Projection Pursuit

Projection Pursuit is a methodology for deriving meaningful low-dimensional representations of data. It generally aims at extracting features as linear combinations of the original attributes, under an algorithm which optimizes a function that quantifies the interestingness of the projection. The current paper builds on our previous research, extending an algorithmic framework based on evolutionary algorithms in two directions: at its roots a one-dimensional PP framework, we enhance it with two-dimensional search capabilities providing meaningful planar views (rotations) of the data; moreover, we provide a distributed implementation under Apache Spark. The distributed implementation is not meant only to deal with large data, but also to exploit the inherent parallelism of the evolutionary paradigm, enhancing the efficiency of the method not only when run on a cluster, but also on a single machine, exploiting the existing virtual CPUs. Several case studies illustrate the applicability of our framework and demonstrate its efficiency.
Uncertainty Quantification Neural Network from Similarity and Sensitivity

Uncertainty quantification (UQ) from similar events brings transparency. However, the presence of an irrelevant event may degrade the performance of similarity-based algorithms. This paper presents a UQ technique from similarity and sensitivity. A traditional neural network (NN) for the point prediction is trained at first to obtain the sensitivity of different input parameters at different points. The relative range of each input parameter is set based on sensitivity. When the sensitivity of one parameter is very high, a small deviation in that parameter may result in a large deviation in output. While selecting similar events, we allow a small deviation in highly sensitive parameters and a large deviation in less sensitive parameters. Uncertainty bounds are computed based on similar events. Similar events contain exact matches and slightly different samples. Therefore, we train a NN for bound correction. The bound-corrected uncertainty bounds (UB) provide a fair and domain-independent uncertainty bound. Finally, we train NNs to compute UB directly. The end-user need to run the final NN to obtain UB, instead of following the entire process. The code of the proposed method is also uploaded to Github. Also, users need to run only the fifth script to train a NN of a different UB.
Time Series Prediction by Reservoir Neural Networks

The tasks of forecasting time series arise in many areas of computer science. Algorithms based on machine learning do a good job of this task. In this work, we performed a comparative analysis of a number of algorithms for predicting time series by reservoir neural networks (echo-state networks) according to the forecast accuracy and the time it takes to build the forecast. To test forecasting algorithms, data sets obtained from the Mackey-Glass equation were used. The experiments showed that the sigmoidal and radial networks with a SOM projector give the most accurate forecast, but they are also the least fast. A new reservoir optimization algorithm is proposed - a direct version of the Infomax method. The functionality of the mutual information of the input and output of the reservoir is maximized. This algorithm requires non-negativity of data values, but it works much faster than the well-known iterative version of Infomax and a radial network with a SOM projector, although it slightly reduces the forecast accuracy.
High-Level Visual Masking of Image Compression Artefacts

We present the results of a subjective experiment where we measured detection thresholds for 2°wide noise targets placed in 23 different natural scenes. Unlike previous studies on visual masking, we focus particularly on dissociating cases of low-level and high-level masking. That is, cases where the target is not perceived predominantly due to limits of either early or late vision. To that end, we exploit the change blindness paradigm and analyse detection rates, times and primed subjective ratings of target visibility. Our results are of significance for developing advanced models of human vision for signal quality/fidelity assessment, particularly in the context of compression.
VoltJockey

Based on the concept of hardware separation, ARM introduced TrustZone to build a trusted execution environment for applications. It has been quite successful in defending against various software attacks and forcing attackers to explore vulnerabilities in interface designs and side channels. In this article, we propose an innovative software-controlled hardware fault-based attack, VoltJockey, on multi-core processors that adopt dynamic voltage and frequency scaling (DVFS) techniques for energy efficiency. We deliberately manipulate the processor voltage via DVFS to induce hardware faults into the victim cores, and therefore breaking TrustZone. The entire attack process is based on software without any involvement of hardware, which makes VoltJockey stealthy and hard to prevent.
Integrating Smartphone Network Architecture and Data Security Techniques to Mitigate Sharp Practices in Non-Profit Organizations

Contemporary research is geared towards integrating multidisciplinary domains and targeted to developing robust solutions. This implies that single-discipline solutions most times do not give the desired results. This study intends to apply this concept statement in mitigating the hydra-headed challenge facing the world today called sharp-practice (corruption) which some believe that it is worse than the current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The sharp practice is a major challenge in the world today and a threat to the development of any country. Sharp practices range from higher offices of the state institutions to the small offices located in remote and rural local areas. Non-profit Organizations (NPOs) are also affected by sharp practices as the office-bearers of these institutions are constantly misappropriating donors’ funds meant for these facilities for their selfish interests. State and private donors offer a large portion of their resources to NPOs to improve the standard of living of needy people in remote and rural areas. However, most NPOs sited in rural areas do not have systems that can be used to monitor and capture the management of resources. In this paper, we attempt to solve this challenge by proposing the integrating Smartphone Network Architecture (SMA) and Data Security Techniques (DST) to mitigate sharp practices in NPOs. In our approach, a smartphone-mobile app algorithm was developed using JavaScript. The app uses the Least Significant Bits (LSB) method to secretly embed a Date Stamp on a PNG image whenever an event is captured by a secret camera at the NPO’s center. The results showed that the LSB method is the most suitable technique to embed a small-scale secret data on the pixels of a PNG image file. The LSB technique does not require the original cover image for decoding the secret data hidden in the image over the network. From the study, these techniques have shown to improve accountability and alleviate corrupt practices in NPOs.
Design of Knowledge Base and Curriculum Planning Based on Artificial Intelligence Teaching System

The application of artificial intelligence in early teaching resulted in the intelligent teaching System. It is a comprehensive subject involving computer science, artificial intelligence, psychology, education and behavior. Its main purpose is to make the computer system the guidance and assistant of learning, to endure the intelligence to the computer system, and to replace the human teacher to some extent, to achieve the best teaching effect. From the perspective of results, it changes the traditional teaching mode and teaching environment, can complete the teaching task in the traditional teaching mode, while playing an irreplaceable good teaching effect. However, in the development of computer network technology, people's demand has gradually increased, intelligent teaching system also began to show its own shortcomings, such as: low degree of intelligence and personalization, waste of a lot of student information resources, lack of knowledge base and other shortcomings. In this paper, the data mining technology is applied to the development of intelligent teaching system, so as to mine more and more important information in the student information database, and the knowledge base is fully applied or reorganized or enriched. By using the data mining technology for reference, the intelligent and personalized service of the intelligent teaching system is strengthened.
Information Visualization Interface on Home Router Traffic Data for Laypersons

With the aim to increase the awareness of the everyday internet user for the own home network traffic, we present two interactive visualization interfaces for visual exploration of home router traffic records. Thereby we differentiate between users with a present intrinsic motivation for the topic and those with absent intrinsic motivation. Therefore, gamification in the first interface is used to maintain motivation of the first type of user, while the storytelling concept based on the hero's journey in the second interface aims at increasing the perceived incentives for the second user group.
Ensemble Learning for Heterogeneous Missing Data Imputation

Missing values can significantly affect the result of analyses and decision making in any field. Two major approaches deal with this issue: statistical and model-based methods. While the former brings bias to the analyses, the latter is usually designed for limited and specific use cases. To overcome the limitations of the two methods, we present a stacked ensemble framework based on the integration of the adaptive random forest algorithm, the Jaccard index, and Bayesian probability. Considering the challenge that the heterogeneous and distributed data from multiple sources represents, we build a model in our use case, that supports different data types: continuous, discrete, categorical, and binary. The proposed model tackles missing data in a broad and comprehensive context of massive data sources and data formats. We evaluated our proposed framework extensively on five different datasets that contained labelled and unlabelled data. The experiments showed that our framework produces encouraging and competitive results when compared to statistical and model-based methods. Since the framework works for various datasets, it overcomes the model-based limitations that were found in the literature review.
MANGA: Method Agnostic Neural-policy Generalization and Adaptation

In this paper we target the problem of transferring policies across multiple environments with different dynamics parameters and motor noise variations, by introducing a framework that decouples the processes of policy learning and system identification. Efficiently transferring learned policies to an unknown environment with changes in dynamics configurations in the presence of motor noise is very important for operating robots in the real world, and our work is a novel attempt in that direction. We introduce MANGA: Method Agnostic Neural-policy Generalization and Adaptation, that trains dynamics conditioned policies and efficiently learns to estimate the dynamics parameters of the environment given off-policy state-transition rollouts in the environment. Our scheme is agnostic to the type of training method used - both reinforcement learning (RL) and imitation learning (IL) strategies can be used. We demonstrate the effectiveness of our approach by experimenting with four different MuJoCo agents and comparing against previously proposed transfer baselines.
Towards a cognitive-inspired automatic unitizing technique

In this paper, we present and assess a novel technique for unitizing inspired by a cognitive theory on event structure perception. Unitizing indicates the process of dividing an observation into smaller units. Unitizing is often performed automatically, e.g., by selecting fixed-length windows. Although fast, such approach might result in unit boundaries being placed mid-interaction, eventually affecting observation, annotation, and labeling. We conceived a unitizing technique based on the Event Segmentation theory. In brief, changes drive the perception of boundaries between events (or units): an unexpected change in the observed situation might mean the current event ended and a new one begun. Our technique relies on observed changes for identifying unit boundaries. The first sketch of our technique was recently tested, proving it effective in overcoming the aforementioned shortcomings of fixed-window unitizing. Here, we further explore its feasibility by testing it in a different domain, i.e., solo stage performances, in order to explore the feasibility of adopting our unitizing approach across domains. Our results further support the idea of leveraging the Event Segmentation Theory for the design of an automatic technique for video unitizing.
Fusion of External and Internal Prior Information for the Removal of Gaussian Noise in Images

In this paper, a new method for the removal of Gaussian noise based on two types of prior information is described. The first type of prior information is internal, based on the similarities between the pixels in the noisy image, and the other is external, based on the index or pixel location in the image. The proposed method focuses on leveraging these two types of prior information to obtain tangible results. To this end, very similar patches are collected from the noisy image. This is done by sorting the image pixels in ascending order and then placing them in consecutive rows in a new two-dimensional image. Henceforth, a principal component analysis is applied on the patch matrix to help remove the small noisy components. Since the restored pixels are similar or close in values to those in the clean image, it is preferable to arrange them using indices similar to those of the clean pixels. Simulation experiments show that outstanding results are achieved, compared to other known methods, either in terms of image visual quality or peak signal to noise ratio. Specifically, once the proper indices are used, the proposed method achieves PSNR value better than the other well-known methods by &gt;1.5 dB in all the simulation experiments.
On the Exact Round Complexity of Best-of-Both-Worlds Multi-party Computation

The two traditional streams of multiparty computation (MPC) protocols consist of– (a) protocols achieving guaranteed output delivery ( $$\textsf {god}$$ ) or fairness ( $$\textsf {fn}$$ ) in the honest-majority setting and (b) protocols achieving unanimous or selective abort ( $$\textsf {ua}$$ , $$\textsf {sa}$$ ) in the dishonest-majority setting. The favorable presence of honest majority amongst the participants is necessary to achieve the stronger notions of $$\textsf {god}$$ or $$\textsf {fn}$$ . While the constructions of each type are abound in the literature, one class of protocols does not seem to withstand the threat model of the other. For instance, the honest-majority protocols do not guarantee privacy of the inputs of the honest parties in the face of dishonest majority and likewise the dishonest-majority protocols cannot achieve $$\textsf {god}$$ and $$\textsf {fn}$$ , tolerating even a single corruption, let alone dishonest minority. The promise of the unconventional yet much sought-after species of MPC, termed as 'Best-of-Both-Worlds' (BoBW), is to offer the best possible security depending on the actual corruption scenario. This work nearly settles the exact round complexity of two classes of BoBW protocols differing on the security achieved in the honest-majority setting, namely $$\textsf {god}$$ and $$\textsf {fn}$$ respectively, under the assumption of no setup (plain model), public setup (CRS) and private setup (CRS + PKI or simply PKI). The former class necessarily requires the number of parties to be strictly more than the sum of the bounds of corruptions in the honest-majority and dishonest-majority setting, for a feasible solution to exist. Demoting the goal to the second-best attainable security in the honest-majority setting, the latter class needs no such restriction. Assuming a network with pair-wise private channels and a broadcast channel, we show that 5 and 3 rounds are necessary and sufficient for the class of BoBW MPC with $$\textsf {fn}$$ under the assumption of 'no setup' and 'public and private setup' respectively. For the class of BoBW MPC with $$\textsf {god}$$ , we show necessity and sufficiency of 3 rounds for the public setup case and 2 rounds for the private setup case. In the no setup setting, we show the sufficiency of 5 rounds, while the known lower bound is 4. All our upper bounds are based on polynomial-time assumptions and assume black-box simulation. With distinct feasibility conditions, the classes differ in terms of the round requirement. The bounds are in some cases different and on a positive note at most one more, compared to the maximum of the needs of the honest-majority and dishonest-majority setting. Our results remain unaffected when security with abort and fairness are upgraded to their identifiable counterparts.
IO-aware Factorization Machine for User Response Prediction

As a supervised learning method, Factorization Machine (FM) is famous for its capability of modeling feature interactions. However, FM's performance might be bad if we assign the same weight to all feature interactions, as not all of them are equally useful and productive. Attentional Factorization Machine (AFM) improves FM by discriminating the importance of distinctive feature interactions via a neural attention network. Nevertheless, the neural attention network in AFM is not fine-grained enough and it ignores the information of the fields implied by the features, which limits the performance of the model. In this work, we propose a novel model named IO-aware Factorization Machine (IOFM), which enhances the feature representation ability of attention mechanism in estimating weights via two awareness auxiliary matrices. To make the model more efficient, we further reduce the model parameters using canonical decomposition for the two auxiliary matrices and design a shared matrix to correlate the decomposed matrices. Extensive experiments on two real-world datasets indicate the superiority of our IOFM model over the state-of-the-art methods.
Gaussian-Dirichlet Random Fields for Inference over High Dimensional Categorical Observations

We propose a generative model for the spatio-temporal distribution of high dimensional categorical observations. These are commonly produced by robots equipped with an imaging sensor such as a camera, paired with an image classifier, potentially producing observations over thousands of categories. The proposed approach combines the use of Dirichlet distributions to model sparse co-occurrence relations between the observed categories using a latent variable, and Gaussian processes to model the latent variable's spatio-temporal distribution. Experiments in this paper show that the resulting model is able to efficiently and accurately approximate the temporal distribution of high dimensional categorical measurements such as taxonomic observations of microscopic organisms in the ocean, even in unobserved (held out) locations, far from other samples. This work's primary motivation is to enable deployment of informative path planning techniques over high dimensional categorical fields, which until now have been limited to scalar or low dimensional vector observations.
Representation Reconstruction Head for Object Detection

There are two kinds of detection heads in object detection frameworks. Between them, the heads based on full connection contribute to mapping the learned feature representation to the sample label space, while the heads based on full convolution facilitate preserving location sensitivity information. However, to enjoy the benefits from both detection heads is still underexplored. In this paper, we propose a generalized Representation Reconstruction Head (RRHead) to break through the limitation that most detection heads focus on unilateral self-advantage while ignoring another one. RRHead enhances multi scale feature representation for better feature mapping, and employs location sensitivity representation for better location preservation. These optimize fully-convolutional-based heads and fully-connected-based heads separately. RRHead can be embedded in existing detection frameworks to heighten the rationality and reliability of the detection head representation without any additional modification. Extensive experiments show that our proposed RRHead improves the detection performance of the existing frameworks by a large margin on several challenging benchmarks, and achieves new state-of-the-art performance.
Research on Harmonic Overvoltage Suppression Technology of Single-core Cable Shielding Layer

Traditional shielding layer protector of single-core cable can only suppress lightning and fault overvoltage, and can not solve the induced overvoltage and harmonic overvoltage caused by harmonics. When a discharge damping technology for shielding layer of single-core cable is proposed. Firstly, establish the coupling model of shielding layer by analyzing the mechanism of shielding layer induced voltage; then, study the induced overvoltage and resonance overvoltage caused by harmonics based on the model; furthermore, a technical scheme of reducing the overvoltage caused by harmonics by grounding the cable shielding layer through discharge capacitance and damping resistance in series is proposed; finally, give the method of choosing parameters of discharge capacitance and damping resistance. Theoretical results and simulated analysis confirm effectiveness of this method.
A Bayesian View of Frame Interpolation and a Comparison with Existing Motion Picture Effects Tools

Frame interpolation is the process of synthesising a new frame in-between existing frames in an image sequence. It has emerged as a key module in motion picture effects. Previous work either relies on two frame interpolation based entirely on optic flow, or recently DNNs. This paper presents a new algorithm based on multiframe motion interpolation motivated in a Bayesian sense. We also present the first comparison using industrial toolkits used in the post production industry today. We find that the latest Convolutional Neural Network approaches do not significantly outperform explicit motion based techniques.
Two-Round Symmetric Cryptography for Medical Image Infosecurity Against-Hacker Attacks in a Picture Archiving and Communication System

Digitalizing medical images, such as images in ultrasonography or mammography, or magnetic resonance imaging, can be applied for telemedicine applications in telediagnosis and telesurgery, and can be stored in a cloud database via computer networking transmission or wireless communications. Besides, these images contain the patient privacy information. Thus, their reliability and availability should be protected to ensure medical image infosecurity in public channels or open spaces. Medical images can also be hacked by unauthorized people. Therefore, in the picture archiving and communication system (PACS), this study proposes against-hacker attacks with two-round symmetric cryptography models for medical image infosecurity. Hash transformation with multi secret keys is performed to change the pixel values and produce dynamic errors for the two-round encryption processes. In image decryption, two-round decryption processes are employed to estimate the possible hacker attacks at the routing path and to determine the decryption key parameter by using an optimization-based controller. For a case study of mammographic images consisting of 50 benign tumors and 50 malignant tumors, the peak signal-to-noise ratio (PSNR) is employed to evaluate the decryption quality between the plain and decrypted images.