Reviewerly/exHarmony huggingface dataset:
Distributed Order Estimation of ARX Model under Cooperative Excitation Condition

In this paper, we consider the distributed estimation problem of a linear stochastic system described by an autoregressive model with exogenous inputs when both the system orders and parameters are unknown. We design distributed algorithms to estimate the unknown orders and parameters by combining the proposed local information criterion with the distributed least squares method. The simultaneous estimation for both the system orders and parameters brings challenges for the theoretical analysis. Some analysis techniques, such as double array martingale limit theory, stochastic Lyapunov functions, and martingale convergence theorems are employed. For the case where the upper bounds of the true orders are available, we introduce a cooperative excitation condition, under which the strong consistency of the estimation for the orders and parameters is established. Moreover, for the case where the upper bounds of true orders are unknown, a similar distributed algorithm is proposed to estimate both the orders and parameters, and the corresponding convergence analysis for the proposed algorithm is provided. We remark that our results are obtained without relying on the independency or stationarity assumptions of regression vectors, and the cooperative excitation conditions can show that all sensors can cooperate to fulfill the estimation task even though any individual sensor cannot.
Covid-19 Mobile Tracking Application Utilizing Smart Sensors

Covid-19 Pandemic continues to spread and a lot of techniques have already been used to help confine it. One of the measures, that has been proven of some efficiency is the use of mobile tracking apps. These apps are addressing the unmet needs of healthcare and public health system, including contact tracing, health information dissemination, symptom checking and providing tools for training healthcare providers. In this paper we are presenting an application that focuses on basic smartphone’s sensors in order to provide information to users about areas of high risk and their corresponding heatmaps and an indoor positioning system using Bluetooth and Wi-Fi, as a form of contact tracing for covid patients.
Dynamic k determination in k-NN classifier: A literature review

One of the widely used classification algorithms is k-Nearest Neighbours (k-NN). Its popularity is mainly due to its simplicity, effectiveness, ease of implementation and ability to add new data in the training set at any time. However, one of its main drawbacks is the fact that its performance is highly dependent on the proper selection of parameter k, i.e. the number of nearest neighbours that the algorithm examines. The most frequently used technique for the “best” k determination is the cross validation as there is no general rule for choosing the k value due to its dependency on the training dataset. However, selecting a fixed k value throughout the dataset does not take into account its special features, like data distribution, class separation, imbalanced classes, sparse and dense neighborhoods and noisy subspaces. A lot of research has been done to date in the specific field, leading to many k-NN variations. In the present research, a thorough literature review is conducted in order to summarize all the achievements made to date in this field. Specifically, a pool of twenty eight (28) approaches with their experimental results are presented, all concerning methods and techniques for dynamic “best” k selection.
BisSiam: Bispectrum Siamese Network Based Contrastive Learning for UAV Anomaly Detection

In recent years, a surging number of unmanned aerial vehicles (UAVs) are pervasively utilized in many areas. However, the increasing number of UAVs may cause privacy and security issues such as voyeurism and espionage. It is critical for individuals or organizations to manage their behaviors and proactively prevent the misbehaved invasion of unauthorized UAVs through effective anomaly detection. The UAV anomaly detection framework needs to cope with complex signals in the noisy-prone environments and to function with very limited labeled samples. This paper proposes <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BisSiam</small> , a novel framework that is capable of identifying UAV presence, types and operation modes. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BisSiam</small> converts UAVs signals to bispectrum as the input and exploits a siamese network based contrastive learning model to learn the vector encoding. A sampling mechanism is proposed for optimizing the sample size involved in the model training whilst ensuring the model accuracy without compromising the training efficiency. Finally, we present a similarity-based fingerprint matching mechanism for detecting unseen UAVs without the need of retraining the whole model. Experiment results show that our approach outperforms other baselines and can reach 92.85% accuracy of UAV type detection in unsupervised learning scenarios. 91.4% accuracy can be achieved when <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BisSiam</small> is used for detecting the UAV type of the out-of-sample UAVs.
Report on UG<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e715" altimg="si23.svg"><mml:mrow><mml:msup><mml:mrow /><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup><mml:mo>+</mml:mo></mml:mrow></mml:math> challenge Track 1: Assessing algorithms to improve video object detection and classification from unconstrained mobility platforms

How can we effectively engineer a computer vision system that is able to interpret videos from unconstrained mobility platforms like UAVs? One promising option is to make use of image restoration and enhancement algorithms from the area of computational photography to improve the quality of the underlying frames in a way that also improves automatic visual recognition. Along these lines, exploratory work is needed to find out which image pre-processing algorithms, in combination with the strongest features and supervised machine learning approaches, are good candidates for difficult scenarios like motion blur, weather, and mis-focus — all common artifacts in UAV acquired images. This paper summarizes the protocols and results of Track 1 of the UG2+ Challenge held in conjunction with IEEE/CVF CVPR 2019. The challenge looked at two separate problems: (1) object detection improvement in video, and (2) object classification improvement in video. The challenge made use of new protocols for the UG2 (UAV, Glider, Ground) dataset, which is an established benchmark for assessing the interplay between image restoration and enhancement and visual recognition. In total, 16 algorithms were submitted by academic and corporate teams, and a detailed analysis of them is reported here.
Secured Data Storage Using Deduplication in Cloud Computing Based on Elliptic Curve Cryptography

The tremendous development of cloud computing with related technologies is an unexpected one. However, centralized cloud storage faces few challenges such as latency, storage, and packet drop in the network. Cloud storage gets more attention due to its huge data storage and ensures the security of secret information. Most of the developments in cloud storage have been positive except better cost model and effectiveness, but still data leakage in security are billion-dollar questions to consumers. Traditional data security techniques are usually based on cryptographic methods, but these approaches may not be able to withstand an attack from the cloud server's interior. So, we suggest a model called multi-layer storage (MLS) based on security using elliptical curve cryptography (ECC). The suggested model focuses on the significance of cloud storage along with data protection and removing duplicates at the initial level. Based on divide and combine methodologies, the data are divided into three parts. Here, the first two portions of data are stored in the local system and fog nodes to secure the data using the encoding and decoding technique. The other part of the encrypted data is saved in the cloud. The viability of our model has been tested by research in terms of safety measures and test evaluation, and it is truly a powerful complement to existing methods in cloud storage.
Design-Driven Requirements for Computationally Co-Creative Game AI Design Tools

Game AI designers must manage complex interactions between the AI character, the game world, and the player, while achieving their design visions. Computational co-creativity tools can aid them, but first, AI and HCI researchers must gather requirements and determine design heuristics to build effective co-creative tools. In this work, we present a participatory design study that categorizes and analyzes game AI designers' workflows, goals, and expectations for such tools. We evince deep connections between game AI design and the design of co-creative tools, and present implications for future co-creativity tool research and development.
ART: Automated Reclassification for Threat Actors based on ATT&amp;CK Matrix Similarity

Given the perniciousness of threats posed by state-sponsored advanced persistent threats (APTs), identifying cyber threat attribution of the cyber threat actors (CTA) is of paramount importance for deterring cyber-attacks by APTs. As state-sponsored APT groups have been especially active in the past decade, recent studies have attempted to establish attribution with the limited set of information of the APT groups. Various government agencies and SOC vendors have utilized Indicators of Compromise (IoC) and Tactic, Technique, Procedures (TTPs) to collect intelligence pertaining to the adversaries, to no avail. Recently, MITRE’s ATT&CK® framework has been widely adopted for collecting and documenting the TTPs of the various CTAs. This paper presents an Automated Reclassification for Threat Actors (ART) that quantitatively compares the TTPs from different APT groups. ART crawls cyber threat reports and retrieves the ATT&CK matrix of APT groups. Then, it vectorizes the ATT&CK matrix and calculates the cosine similarity. By reexamining the various aliases of the CTAs with the ATT&CK framework, we believe that ART can help classify the indiscriminately established APT groups.
Modeling optimization method based on Gamma test and NSGA II for forecast of PV power output

Forecasting can overcome the shortcomings of volatility and intermittent nature of photovoltaic (PV) power. The PV power output is affected by many factors. To improve the forecasting accuracy, the forecasting model needs considering as many relevant factors as possible, but this makes the input feature vector contain redundant information and leads to high complexity forecasting algorithm, low system stability, and poor application practicability. In this paper, we develop a novel optimization method based on Gamma test and Non-dominated Sorting Genetic Algorithm to optimize the input feature vector space and reduce the information redundancy. Meanwhile, to build targeted models for different weather conditions, a new weather variability and smoothed clearness index are deduced in this paper to measure the weather fluctuation. Based on them, the weather is classified into A and B microweather types. Two sets of input vector for the two types are optimized, and only half of the impact factors (21/51) are selected by this optimization method. Two sets of support vector machine, ELMAN artificial neural network and multivariate autoregressive models, are built. The method can greatly simplify the computational complexity as well as improve the accuracy and efficiency of the models.
Conversion of CVSS Base Score from 2.0 to 3.1

In this paper an application of machine learning algorithms (MLA) to convert the Common Vulnerability Scoring System (CVSS) ratings from version 2.0 to 3.1 is discussed. CVSS is an international industry standard that describes vul-nerabilities and provides measurable risk indicators. By 2015, only version 2.0 of CVSS was used, while the latest and most advanced CVSS version - 3.1, was introduced in 2019. Due to a large number of vulnerabilities found to date, not all publicly known vulnerabilities have been assigned a CVSS 3.1 score. This situation hinders many organizations in moving from CVSS 2.0 to 3.1 standard. The application of machine learning algorithms presented in this contribution allows calculation of the missing CVSS 3.1 scores and thus should facilitate for many organisations the transition to CVSS 3.1 standard. The method additionally provides repeatable conversions of base metrics parameters to newer standard with efficiency higher than 90% (median).
Computer-Aided Teaching System Based on Data Mining

The traditional teaching model cannot adapt to the teaching needs of the era of smart teaching. Based on this, this paper combines data mining technology to carry out teaching reforms, constructs a computer-aided system based on data mining, and constructs teaching system functions based on actual conditions. The constructed system can carry out multisubject teaching. Moreover, this paper uses a data mining system to mine teaching resources and uses spectral clustering methods to integrate multiple teaching resources to improve the practicability of data mining algorithms. In addition, this paper combines digital technology to deal with teaching resources. Finally, after building the system, this paper designs experiments to verify the performance of the system. From the research results, it can be seen that the system constructed in this paper has certain teaching and practical effects, and it can be applied to a larger teaching scope in subsequent research.
Investigation of Heterogeneity Sources for Occupational Task Recognition via Transfer Learning

Human activity recognition has been extensively used for the classification of occupational tasks. Existing activity recognition approaches perform well when training and testing data follow an identical distribution. However, in the real world, this condition may be violated due to existing heterogeneities among training and testing data, which results in degradation of classification performance. This study aims to investigate the impact of four heterogeneity sources, cross-sensor, cross-subject, joint cross-sensor and cross-subject, and cross-scenario heterogeneities, on classification performance. To that end, two experiments called separate task scenario and mixed task scenario were conducted to simulate tasks of electrical line workers under various heterogeneity sources. Furthermore, a support vector machine classifier equipped with domain adaptation was used to classify the tasks and benchmarked against a standard support vector machine baseline. Our results demonstrated that the support vector machine equipped with domain adaptation outperformed the baseline for cross-sensor, joint cross-subject and cross-sensor, and cross-subject cases, while the performance of support vector machine equipped with domain adaptation was not better than that of the baseline for cross-scenario case. Therefore, it is of great importance to investigate the impact of heterogeneity sources on classification performance and if needed, leverage domain adaptation methods to improve the performance.
l <sub>1</sub> norm of coherence is not equal to its convex roof quantifier

Since a rigorous framework for quantifying quantum coherence was established by Baumgratz et al. [T. Baumgratz, M. Cramer, and M. B. Plenio, Phys. Rev. Lett. 113, 140401 (2014)], many coherence measures have been found. For a given coherence measure $C$, extending the values of $C$ on pure states to mixed states by the convex roof construction, we will get a valid coherence measure $\overline{C}$, we call $\overline{C}$ the corresponding convex roof quantifier of $C$. Whether $C=\overline{C}$ for a given coherence measure is an important question. In this work, we show that for the widely used coherence measure, $l_{1}$ norm of coherence $C_{l_{1}}$, it holds that $C_{l_{1}}\neq \overline{C_{l_{1}}}$.
Detection of Anonymising Proxies Using Machine Learning

Network Proxies and Virtual Private Networks (VPN) are tools that are used every day to facilitate various business functions. However, they have gained popularity amongst unintended userbases as tools that can be used to hide mask identities while using websites and web-services. Anonymising Proxies and/or VPNs act as an intermediary between a user and a web server with a Proxy and/or VPN IP address taking the place of the user’s IP address that is forwarded to the web server. This paper presents computational models based on intelligent machine learning techniques to address the limitations currently experienced by unauthorised user detection systems. A model to detect usage of anonymising proxies was developed using a Multi-layered perceptron neural network that was trained using data found in the Transmission Control Protocol (TCP) header of captured network packets
What data do we need for training an AV motion planner?

We investigate what grade of sensor data is required for training an imitation-learning-based AV planner on human expert demonstration. Machine-learned planners [1] are very hungry for training data, which is usually collected using vehicles equipped with the same sensors used for autonomous operation [1]. This is costly and non-scalable. If cheaper sensors could be used for collection instead, data availability would go up, which is crucial in a field where data volume requirements are large and availability is small. We present experiments using up to 1000 hours worth of expert demonstration and find that training with 10x lower-quality data outperforms 1x AV-grade data in terms of planner performance (see Fig. 1). The important implication of this is that cheaper sensors can indeed be used. This serves to improve data access and democratize the field of imitation-based motion planning. Alongside this, we perform a sensitivity analysis of planner performance as a function of perception range, field-of-view, accuracy, and data volume, and reason about why lower-quality data still provide good planning results.
STAR: A Scalable Self-taught Learning Framework for Older Adults’ Activity Recognition

Activity Recognition (AR) in older adults living with Neurocognitive disorders caused by diseases such as Alzheimer’s is still a challenging research problem. The inherent natural variation in performing an activity increases while repeating the same activity for an older adult, let alone the variation introduced when another older adult performs the same activity. Moreover, the challenges in acquiring the labeled data while preserving the privacy, availability of annotators with domain knowledge, aversion towards cameras even for a minimal amount of time for ground truth data collection, and psychological and mental health status make AR for older adults challenging. In this paper, we postulate a self-taught learning-based approach that helps recognize activities with variations that are not being directly seen during the training phase. We hypothesize that the features extracted using deep architectures from unlabeled data instances can learn general underlying representations of activities efficiently and help improve activity classification in a supervised setting, although the data instances in labeled data do not follow the generative distribution of that of unlabeled data. We posit real data from a retirement community center using our in-house SenseBox infrastructure and survey-based assessments concurrently done by a clinical evaluator to study the relationship between activities and functional/behavioral health of older adults. We evaluate our proposed self-taught learning-based approach, STAR, using the presented in-house Alzheimer’s Activity Recognition (AAR) dataset acquired in a real-world deployment in 25 homes which outperforms the state-of-the-art algorithm by about 20%.
Runtime Monitoring Processes Across Blockchains

Business processes have been long researched, with many tools, languages, and diagrammatic notations having been developed for automation. Recently, distributed ledger technology (of which Blockchain is one type) has been proposed for use in the monitoring of business process compliance. Such a set-up is attractive since it allows for immutability and thus a perfect record of the history of the business process regulated.As blockchain platforms mature and their applications increase, one can observe that instead of having one blockchain as a ‘one world computer’ multiple blockchains will co-exist while possibly interacting. Existing work for business processes within the blockchain domain have focused on single isolated blockchain implementations. In this paper, we do away with this severely limiting assumption and propose a method to monitor business processes spanning different blockchains and other off-chain servers. We apply this work to business processes expressed in BPMN along with annotations proposed for a blockchain context. We further describe how we handle blockchain interoperability by synthesizing automatically off-chain monitors, acting as notaries, that handle message passing between blockchain systems, and how we employ hash-locking for cryptographically secure token swapping.
Harnessing the Power of Artificial Intelligence and Machine Learning in Mineral Exploration—Opportunities and Cautionary Notes

Editor’s note: The aim of the Geology and Mining series is to introduce early-career professionals and students to various aspects of mineral exploration, development, and mining in order to share the experiences and insight of each author on the myriad of topics involved with the mineral industry and the ways in which geoscientists contribute to each. Abstract Artificial intelligence (AI), and machine learning (ML) have emerged in the last few years from relative obscurity in the mineral exploration sector and they now attract significant attention from people in both industry and academia. However, due to the novelty of AI and ML applications, their practical use and potential remain enigmatic to many beyond a relatively few expert practitioners. We introduce this subject for the nonexpert and review some of the current applications and evolving uses. For the most traditionally minded geologist, we argue that ML can be an invaluable new tool, contributing to topics that range from exploratory data analysis to automated core logging and mineral prospectivity mapping, such that it will have a substantial impact on how exploration is conducted in the future. However, ML algorithms perform best with a large amount of homogeneously distributed clean data for a well-constrained objective. For this reason, the application to exploration strategy, especially for optimizing target selection, will be a challenge where data are heterogeneous, multiscale, amorphous, and discontinuous. For the more tech-savvy geologist and data scientist, we provide notes of caution regarding the limitations of ML applied to geoscience data, and reasons to temper expectations. Nonetheless, we project that such technologies, if used in an appropriate manner, will eventually be part of the full range of exploration tasks, allowing explorers to do more with their data in less time. However, whether this will tip the scales in favor of higher discovery rates remains to be demonstrated.