Reviewerly/exHarmony huggingface dataset:
Human activity recognition based on integration of multilayer information of convolutional neural network architecture

Summary Human activity recognition (HAR) has gained researcher's interest due to its increasing demand in automated monitoring applications. Development of efficient HAR algorithm is still an open research area due to the challenges like inter and intra‐class variations, diversity in lighting conditions, view point changes, and complex object motions. Convolutional neural network (CNN) based methods have achieved significant improvement in HAR. However, CNN implementations have drawback that it require a lot of computational resources due to the use of large number of learnable parameters. To overcome this drawback, we propose a simple and computationally efficient deep CNN architecture using multi‐layer information fusion for HAR. In this study, we explore the impact of information fusion at intermediate layers of the network, as each convolutional layer of the network hierarchically extracts information at different level of abstraction of the objects from the video frames. In this work, first we designed a simple and computationally efficient deep CNN architecture and then we introduce a feature fusion strategy to integrate the complementary information of intermediate layers to the layer of the proposed CNN architecture. The proposed architecture is fine‐tuned and trained from scratch with raw RGB data. Softmax classifier is used at the last layer of network for activity classification. Benefits of the proposed architecture over standard deep learning architectures is it's high computational efficiency and reduced requirement of computational resources. To prove the effectiveness of the proposed method, we performed several extensive experiments on publically available datasets. The experimental results of the proposed method have demonstrated its superiority over other existing state‐of‐the‐art methods.
Reporting likelihood ratio for casework in firearm evidence identification

Likelihood ratio (LR) plays an important role in estimating the weight of evidence in firearm evidence identifications. LR is computed from a statistical model including the distribution of the known-matching (KM or within) and known-nonmatching (KNM or between) comparison scores. Current LR procedures rely on KM/KNM scores from existing reference firearm toolmark data sets or alternatively from generating a set of test fires using multiple firearms. Both procedures may contain theoretical or practical issues which may hinder the LR procedures from reporting an unbiased LR estimation in casework. In this paper, a reference data set was established from a set of firearms, each test-fired two cartridge cases, resulting in a basic data set and a control data set. The congruent matching cells (CMC) method was used to generate CMC scores that are used to fit in the KM/KNM statistical distributions for LR estimation. In the initial test, 130 firearms from eight manufacturers were used for generating a reference data set consisting of 260 cartridge cases representing 130 KM and 8385 KNM pairwise breech face images. Test results showed that the KM and KNM distribution intersect at CMC = 2, which is equivalent to LR = 1 (equally to support both the prosecutor and the defense propositions). When the CMC threshold is increased to 6 or more, the LR values are higher than a million, which can provide extremely strong support to the conclusion of the same firearm (or the prosecutor's proposition) in the casework of firearm evidence identification.
Analysis of using Newton’s ring to test lens surface radius of curvature

When using Newton's ring to test lens surface radius of curvature, the air gap in between the master plate and the lens can have either a positive or a negative meniscus shape. The optics for the negative meniscus shape air gap has been widely analyzed and the results are assumed to be valid for the positive meniscus shape air gap. Accurate and approximate analysis are performed in this paper for the positive meniscus shape air gap. Analysis results of this paper show that for the same parameters involved, the Newton's ring number for the positive meniscus shape air gap is larger than the Newton's ring number for the negative meniscus shape air gap. The difference between these two Newton's ring numbers increases as the value of h / r increases, where h is the lens half diameter and r is the master plate surface radius of curvature. For h / r = 0.87, the ratio of these two numbers can be as large as 2.
Monitoring River Sediment by Optimizing Arduino Capabilities Controlled by the PID Algorithm

Sediment causes serious water problems, including flooding, water pollution, and other issues related to water sediment. The data found that 82% of the 550 rivers in Indonesia were polluted and in critical condition. Hence, river maintenance is essential, especially for monitoring the existence of river sediment. This research makes a device to monitor sediment in the river and measure its volume. The device consists of a small boat in which an Arduino Mega 2560 RS microcontroller was an Arduino Mega 2560 RS microcontroller which will control sensors, motors, and a rotary encoder in monitoring and measuring sediment. This paper explained how Arduino could move the boat looking for sediment, detect sediment with infrared sensors, raise and lower the sensor by adjusting the motor in front and behind the boat and finally calculate the volume of sediment. The electronic circuits, block diagrams, and programs used are described in detail in this paper and discuss sensor accuracy and accuracy of measurement results. The result is the device can detect the sediment, measure the height of the sediment, trace the sediment to measure its length, and then rotate to measure the width of the sediment. All movements are carried out by utilizing the capabilities of Arduino. The PID algorithm can precisely determine the initial position of the sensors. It can detect sediment accurately. The measurement results show that the device can work well with a relatively small error.
On the classicality of quantum dephasing processes

We analyze the multitime statistics associated with pure dephasing systems repeatedly probed with sharp measurements, and search for measurement protocols whose statistics satisfies the Kolmogorov consistency conditions possibly up to a finite order. We find a rich phenomenology of quantum dephasing processes which can be interpreted in classical terms. In particular, if the underlying dephasing process is Markovian, we find sufficient conditions under which classicality at every order can be found: this can be reached by choosing the dephasing and measurement basis to be fully compatible or fully incompatible, that is, mutually unbiased bases (MUBs). For non-Markovian processes, classicality can only be proven in the fully compatible case, thus revealing a key difference between Markovian and non-Markovian pure dephasing processes.
A Closer Look at the Kernels Generated by the Decision and Regression Tree Ensembles

Tree ensembles can be interpreted as implicit kernel generators, where the ensuing proximity matrix represents the data-driven tree ensemble kernel. Focus of our work is the utility of tree based ensembles as kernel generators that (in conjunction with a regularized linear model) enable kernel learning. We elucidate the performance of the tree based random forest (RF) and gradient boosted tree (GBT) kernels in a comprehensive simulation study comprising of continuous and binary targets. We show that for continuous targets (regression), this kernel learning approach is competitive to the respective tree ensemble in higher dimensional scenarios, particularly in cases with larger number of noisy features. For the binary target (classification), the tree ensemble based kernels and their respective ensembles exhibit comparable performance. We provide the results from several real life datasets for regression and classification relevant for biopharmaceutical and biomedical applications, that are in line with the simulations to show how these insights may be leveraged in practice.
Experimental determination of a multiqubit ground state via a cluster mean-field algorithm

A quantum eigensolver method has been designed under a multilayer cluster mean-field algorithm, which can be used to determine the eigenstates of a large-scale quantum system in a series of much-smaller-scale quantum computations.
Experimenting with non-fiction VR storytelling: micronarrative, abstraction and interactive navigation. The case of <i>In Pieces VR</i>

This article presents In Pieces VR, a VR-based artwork and experimental documentary on political prison, and discusses its main design challenges, goals, and creative approach. This project aims at creating a documentary and artistic experience that departs from conventional immersive journalism by presenting to its viewers a story made out of very small narrative units, and with subjects presented in the form of virtual sculptures devoid of any specific identity. The idea is that by leaving much of the making sense left open, viewers will have to fill in the gaps. The working hypothesis is that this will help create a different emotional, intellectual and political connection with the piece than that a conventional documentary would achieve, particularly to an audience unfamiliar with, or even politically alien to its specific context. Public exhibition of the work and user evaluation showed that the piece was successful in creating such connections.
Did Aristotle Endorse Aristotle’s Thesis? A Case Study in Aristotle’s Metalogic

Since McCall (1966), the heterodox principle of propositional logic that it is impossible for a proposition to be entailed by its own negation—in symbols, ¬(¬φ→φ)—has gone by the name of Aristotle’s thesis, since Aristotle apparently endorses it in Prior Analytics 2.4, 57b3–14. Scholars have contested whether Aristotle did endorse his eponymous thesis, whether he could do so consistently, and for what purpose he endorsed it if he did. In this article, I reconstruct Aristotle’s argument from this passage and show that he accepts this thesis. Further, I show that the argument he gives is, making plausible assumptions, a correct proof in a consistent fragmentary nonclassical metalogic for a metatheorem he previously states concerning his assertoric syllogistic. In this way, Aristotle’s argument emerges as a fascinating case study in the use of a nonclassical metalogic to prove a result about a nonclassical object system.
A multi-objective cloud energy optimizer algorithm for federated environments

Energy is a considerable portion of the cost of a cloud data center. In addition to energy, carbon emission tax imposes another cost on data centers, which have different policies in different cities around the world. Most of the cloud data centers are geographically distributed, which can have a huge advantage in reducing such costs. In addition, in recent years, cloud federation, in which multiple cloud providers share their IT infrastructures voluntarily, could play a crucial role in minimizing the energy consumption of cloud data centers. On the other hand, improper VM placement results frequent VM migration, constant turning off/on physical machines, service quality degradation and energy consumption increase. To tackle these weaknesses, we propose a multi-objective algorithm, Called OUR-ACS, to minimize data centers' energy consumption, carbon emission, and the total expenses (energy cost + carbon tax) in a federated environment considering both initial VM placement and VM consolidation approaches. We evaluate the efficiency of our proposed algorithm by using the ClousSim Plus simulator toolkit using real-world datasets. Compared to the competing algorithms, our simulation results indicate that our proposed algorithm could reduce the total energy consumption, carbon emission, and the total costs of multiple cloud data centers by an average of 37.6%, 41%, and 25%, respectively, in a federated environment.
An asymmetric data hiding using binary pattern detected with metaheuristics algorithm

This work presents an asymmetric data hiding method using binary patterns detected with a metaheuristic algorithm. The technique uses a pheromone matrix and two stage decision process to detect binary patterns. The binary pattern acts as a secret key to isolate the complex area of the cover image from the smooth area, both the process of information hiding and information retrieval. The method hides confidential data in the complex area of cover media using the information present in the detected binary pattern and leaves the smooth area's pixel unaffected. The confidential data is hidden asymmetrically, and the asymmetry is ensured using a stego key of varying configuration and size. The binary pattern, stego key, and asymmetry of the number of bits hidden in the cover image's pixels enhance the security of confidential data by many folds. The visual analysis of the results demonstrated that the presented technique does not add any detectable modification to the resulting images and keeps the hidden data innocent for observers. The quantitative results obtained in terms of the evaluation metrics of peak signal to noise ratio, mean square error, structure similarity index, and hiding capacity, show that the proposed technique has a high value of peak signal to noise ratio and structural similarity index with a significant hiding capacity, while the mean square error is also significantly low. The high security and the quality of resulting images make it impossible for unauthorized users to obtain hidden information. Hence, the existence of confidential information is not compromised. Moreover, the comparative analysis shows that the performance of the presented technique, in terms of the evaluation metrics, is better than or comparable with state-of-the-art techniques. The binary pattern, large size, and large variety of the stego key make the proposed technique superior to the state-of-the-art techniques, in terms of security.
On fine-grained visual explanation in convolutional neural networks

Existing explanation methods for Convolutional Neural Networks (CNNs) lack the pixel-level visualization explanations to generate the reliable fine-grained decision features. since there are inconsistencies between the explanation and the actual behavior of the model to be interpreted, we propose a Fine-Grained Visual Explanation for CNN, namely F-GVE, which produces a fine-grained explanation with higher consistency to the decision of the original model. The exact backward class-specific gradients with respect to the input image is obtained to highlight the object-related pixels the model used to make prediction. In addition, for better visualization and less noise, F-GVE selects an appropriate threshold to filter the gradient during the calculation and the explanation map is obtained by element-wise multiplying the gradient and the input image to show fine-grained classification decision features. Experimental results demonstrate that F-GVE has good visual performances and highlights the importance of fine-grained decision features. Moreover, the faithfulness of the explanation in this paper is higher and it is effective and practical on troubleshooting and debugging detection.
A novel hybrid authentication protocol utilizing lattice-based cryptography for IoT devices in fog networks

The Internet of Things (IoT) has taken the interconnected world by storm. Due to their immense applicability, IoT devices are being scaled at exponential proportions worldwide. But, very little focus has been given to securing such devices. As these devices are constrained in numerous aspects, it leaves network designers and administrators with no choice but to deploy them with minimal or no security at all. We have seen distributed denial-of-service attacks being raised using such devices during the infamous Mirai botnet attack in 2016. Therefore we propose a lightweight authentication protocol to provide proper access to such devices. We have considered several aspects while designing our authentication protocol, such as scalability, movement, user registration, device registration, etc. To define the architecture we used a three-layered model consisting of cloud, fog, and edge devices. We have also proposed several pre-existing cipher suites based on post-quantum cryptography for evaluation and usage. We also provide a fail-safe mechanism for a situation where an authenticating server might fail, and the deployed IoT devices can self-organize to keep providing services with no human intervention. We find that our protocol works the fastest when using ring learning with errors. We prove the safety of our authentication protocol using the automated validation of Internet security protocols and applications tool. In conclusion, we propose a safe, hybrid, and fast authentication protocol for authenticating IoT devices in a fog computing environment.
Minimum Mixture Error Entropy-Based Robust Cubature Kalman Filter for Outlier-Contaminated Measurements

This letter investigates the robust state estimation of the nonlinear systems with outlier-contaminated measurements. Due to the advantage of mixture error entropy with two kernel bandwidths in handling non-Gaussian noise caused by the outliers, a novel minimum mixture error entropy (MMEE) criterion-based robust cubature Kalman filter is proposed, in which the cost function is constructed by MMEE criterion, and the nonlinear measurement model is linearized by the statistical linear regression method. By a benchmark target tracking scenario with non-Gaussian measurement noise and INS/GNSS loose combination vehicle tracking experiment, the effectiveness of the proposed filter is demonstrated.
A Study on the Convergence Curricula of Major Art Colleges in Domestic and Foreign Countries

The purpose of this study is to categorize and compare the characteristics of the convergence form in the curriculum of 44 cases of major domestic and foreign art Colleges. As a result of the study, I analyzed the cases into three types. First, multi-major systems (15 Colleges), interdisciplinary open (15 Colleges), and convergence majors (17 Colleges) belong to the institutional dimension. Second, digital reinforcement types (11 Colleges) and new technology combined majors (9 Colleges) belong to the major dimension. Third, practical education (22 Colleges for arts-related practical education and 5 Colleges for general practical education) belongs to the program level. Comparing the characteristics of the cases, domestic universities were relatively insufficient in opening interdisciplinary and convergence majors. In addition, there was a lack of digital reinforcement and new technology combination majors, and in particular, general practice was not provided at all. Characteristically, a total of 50% of Colleges overseas and domestic Colleges provide practical arts education.
Aircraft Target Detection in Satellite Remote Sensing Images Based on Improved YOLOv5

Compared with natural images, remote sensing targets have small and dense target shape and complex background, resulting in low detection accuracy and inaccurate identification of target positions. To better extract the features and locations of aircraft targets, this paper proposes a YOLOv5-absorbed algorithm based on the YOLOv5 algorithm. The YOLOv5-absorbed algorithm removes the low-resolution feature layers of the Backbone and the Neck and prunes the prediction head to reduce the loss of position information. At the same time, a new up-sampling module is added to enlarge the feature map in the PAN (Path Aggregation Network) and improve the detection accuracy of aircraft targets in remote sensing images. On this basis, the Coordinate Attention mechanism is introduced to make the network pay attention to a larger area, and DIOU NMS (Distance IoU Non-Maximum Suppression) is introduced to improve the detection accuracy of dense targets. The experimental results of the test data set show that compared with the YOLOv5 algorithm, the YOLOv5-absorbed algorithm has a faster convergence speed and smaller loss, mAP (mean Average Precision) increased from 89.6% to 95.3% and the number of parameters decreased from 92.216M to 36.046M.
LSSO: Long Short-term Scaling Optimizer

Demand forecast-based resource allocation is a key element of both application and network service management and resource cost optimization in cloud environments. A central mechanism called scaling or auto-scaling is responsible for adjusting the allocated resources dynamically to current or predicted demands. Most of today's solutions support short-term optimization, where the long-term effect of scaling actions is not considered, therefore the overall operational cost can be sub-optimal. In this paper, we address this issue and provide a long and short-term scaling optimization method called LSSO. Our contribution is threefold. First, we introduce the formal description of the LSSO scaling problem motivated by real cloud native applications. Second, we transform the problem into a tractable graph representation and show that the optimal solution of the original problem emerges as a shortest path problem in the graph. As a result, the transformation and the optimal solution can be computed in polynomial time. Third, we evaluate and validate the proposed algorithm by numerical analysis on multiple datasets using a cost model which considers the running and scaling costs. Results suggest that if the dynamics of the traffic is fairly predictable and the scaling cost is not negligible, LSSO is able to reduce the operation costs significantly. The exact cost gain depends on the ratio of the running and scaling costs, but in realistic operation regimes it can reach even 18 % and the method can also tolerate some inaccuracies in the forecast.
Distributed Computations with Layered Resolution

Modern computationally-heavy applications are often time-sensitive, demanding distributed strategies to accelerate them. On the other hand, distributed computing suffers from the bottleneck of slow workers in practice. Distributed coded computing is an attractive solution that adds redundancy such that a subset of distributed computations suffices to obtain the final result. However, the final result is still either obtained within a desired time or not, and for the latter, the resources that are spent are wasted. In this paper, we introduce the novel concept of layered-resolution distributed coded computations such that lower resolutions of the final result are obtained from collective results of the workers – at an earlier stage than the final result. This innovation makes it possible to have more effective deadline-based systems, since even if a computational job is terminated because of timing, an approximated version of the final result can be released. Based on our theoretical and empirical results, the average execution delay for the first resolution is notably smaller than the one for the final resolution. Moreover, the probability of meeting a deadline is one for the first resolution in a setting where the final resolution exceeds the deadline almost all the time, reducing the success rate of the systems with no layering.
Predictable Open Source FaaS Function Chains

Function as a Service (FaaS) provides a way to execute modular pieces of code in cloud environments. FaaS also supports the implementation of distributed cloud applications by deploying function chains.In this paper, we introduce a simulation framework for function chains using asynchronous invocations. By using our simulator the completion time distribution of the individual function invocations can be estimated as well as the completion time of the distribution of the whole function chain.We evaluate the results of our simulation framework by using compute-intensive, Python3 based function chains that implement image transforming operations. We also propose cost decreasing methods for function chains with asynchronous invocations.