Reviewerly/exHarmony huggingface dataset:
Streaming Time Series Forecasting using Multi-Target Regression with Dynamic Ensemble Selection

In mining temporal data streams, Dynamic Ensemble Selection (DES) has emerged as one of the most promising approaches of ensemble methods based on the assumption that each member of the ensemble is an expert in some local area of the stream. The aim is to select, on the fly, according to a given test instance x, a subset of experts from a pool of various models. To this end, meta-learning has been widely studied to predict the performance of each base-model and accordingly select the best ones and combine their outputs to compute the final prediction. However, most of the existing selection methods for time series forecasting on data streams do not handle model's dependencies, and therefore maybe missing useful insights. In this paper, we propose a novel approach to harness the potential dependencies within base-models' behavior based on Incremental Multi-Target Regression (MTR) to achieve Dynamic Ensemble Selection (DES). We show that explicitly considering models' dependencies improves overall performance. This work is the first to use Incremental MTR for learning the behavior of each component in an ensemble of forecasters on data streams. Finally, we conduct an extensive experimental study to compare the performance of the proposed methods against state-of-the-art approaches.
Machine-Learning directed Article Detection on the Web using DOM and text-based features

Burgeoning trend of digital advertising have led websites to embody massive irrelevant content such as page navigation, ads, distractions, promotional videos etc‥. on their pages. In contrast to desktop, limited screen space of mobile carrying variety of ineffectual content magnifies the visual complexity of webpage by disrupting focus of user to read useful content. To solve this issue, Reader mode functionality renders a clutter-free version of web page by stripping out all insignificant elements and letting user to read the content they are actually interested in. Existing implementation in our browser involves 2 modules - Article Detection module that runs heuristics to detect whether a web page is article or not, proceeded by Article Extraction module that extracts the main content of the web page by removing all unwanted elements. Our paper focusses on improving the accuracy of article detection logic by proposing a machine-learning based solution that significantly surpasses the present heuristic-based model in terms of accuracy. In order to get rid of false positives on webpages with meaningless content like product descriptions, we extended our previous solution [1] by incorporating backward-elimination heuristics for web page title and extracting strong Boolean predictors. Our approach also solves the problem of detecting reader mode on Google's AMP HTML web pages where main-content is rendered in an iframe. With this model, we were able to achieve a precision of 0.99 and a recall of 0.94, which outperforms the state-of-the-art techniques which are being used by the major Android based web browsers in the market today.
Knowledge Graph Reasoning Based on Tensor Decomposition and MHRP-Learning

In the process of learning and reasoning knowledge graph, the existing tensor decomposition technology only considers the direct relationship between entities in knowledge graph. However, it ignores the characteristics of the graph structure of knowledge graph. To solve this problem, a knowledge graph reasoning algorithm based on multihop relational paths learning (MHRP-learning) and tensor decomposition is proposed in this paper. Firstly, MHRP-learning is adopted to obtain the relationship path between entity pairs in the knowledge graph. Then, the tensor decomposition is performed to get a novel learning framework. Finally, experiments show that the proposed method achieves advanced results, and it is applicable to knowledge graph reasoning.
Restaurant Recommendation System Based on User Ratings with Collaborative Filtering

Abstract Recommendation systems are widely used as a reference in marketing products or businesses. The number of choices given sometimes makes a person confused in making choices. The recommendation system provides a solution in overcoming this. Collaborative filtering is a fairly popular algorithm used in recommendation systems that are based on references and information obtained from users. User rating is a variable that is widely used as a reference in establishing a recommendation system. User rating can influence other users in making their choice of the same product. The purpose of this study is to conduct an experiment in the application of a collaborative filtering algorithm and use the Pearson correlation function as a method used to see the level of user similarity in making a restaurant recommendation system. The methods used in this research are: (1) datasets preparation, (2) datasets filtering, (3) collaborative filtering, (4) pearson correlation, (5) recommendations, and (6) evaluation. Collaborative filtering combined with the Pearson correlation produces recommendations that are suitable for users based on characteristics or the same level of interest between users, making it easier for users to make choices. This is also indicated by a small error rate based on the RMSE results.
Developing an Autonomous Valet Parking System in Simulated Environment

One of the many scenarios associated with self-driving cars is related to automatic parking assistance systems. In our research, we implemented an Autonomous Valet Parking System and built a simulated, dynamically changing environment to test the method. This paper presents the three main parts of the system namely Parking Space Detection using ultrasonic sensors and LIDARs, Path Planning, which is achieved with Hybrid A * and RTR+CCRS planners, and Dynamic Object Detection employing neural networks and image segmentation on the output of an RGB-D camera.
TSARM-UDP: An Efficient Time Series Association Rules Mining Algorithm Based on Up-to-Date Patterns

In many industrial domains, there is a significant interest in obtaining temporal relationships among multiple variables in time-series data, given that such relationships play an auxiliary role in decision making. However, when transactions occur frequently only for a period of time, it is difficult for a traditional time-series association rules mining algorithm (TSARM) to identify this kind of relationship. In this paper, we propose a new TSARM framework and a novel algorithm named TSARM-UDP. A TSARM mining framework is used to mine time-series association rules (TSARs) and an up-to-date pattern (UDP) is applied to discover rare patterns that only appear in a period of time. Based on the up-to-date pattern mining, the proposed TSAR-UDP method could extract temporal relationship rules with better generality. The rules can be widely used in the process industry, the stock market, etc. Experiments are then performed on the public stock data and real blast furnace data to verify the effectiveness of the proposed algorithm. We compare our algorithm with three state-of-the-art algorithms, and the experimental results show that our algorithm can provide greater efficiency and interpretability in TSARs and that it has good prospects.
Design and analysis of multi-valued auto-associative quaternion-valued recurrent neural networks based on external inputs

This paper presents a design procedure for synthesizing auto-associative memories of quaternion-valued recurrent neural networks (QVRNNs) based on external inputs. By virtue of the geometrical properties of the activation function and the fixed point theorem, several inequalities are given to guarantee the global exponential stability for the QVRNNs. The proposed QVRNNs are robust in terms of the design parameter selection and neurons are reduced. Several illustrative examples applied to true color images are given to guarantee the validity of the results.
Bringing order to approximate matching: Classification and attacks on similarity digest algorithms

Fuzzy hashing or similarity hashing (a.k.a. bytewise approximate matching) converts digital artifacts into an intermediate representation to allow an efficient (fast) identification of similar objects, e.g., for blacklisting. They gained a lot of popularity over the past decade with new algorithms being developed and released to the digital forensics community. When releasing algorithms (e.g., as part of a scientific article), they are frequently compared with other algorithms to outline the benefits and sometimes also the weaknesses of the proposed approach. However, given the wide variety of algorithms and approaches, it is impossible to provide direct comparisons with all existing algorithms. In this paper, we present the first classification of approximate matching algorithms which allows an easier description and comparisons. Therefore, we first reviewed existing literature to understand the techniques various algorithms use and to familiarize ourselves with the common terminology. Our findings allowed us to develop a categorization relying heavily on the terminology proposed by NIST SP 800-168. In addition to the categorization, this article presents an abstract set of attacks against algorithms and why they are feasible. Lastly, we detail the characteristics needed to build robust algorithms to prevent attacks. We believe that this article helps newcomers, practitioners, and experts alike to better compare algorithms, understand their potential, as well as characteristics and implications they may have on forensic investigations.
One key to rule them all: Recovering the master key from RAM to break Android's file-based encryption

As known for a decade, cold boot attacks can break software-based disk encryption when an attacker has physical access to a powered-on device, including Android smartphones. Raw memory images can be obtained by resetting a device and rebooting it with a malicious boot loader, or—on systems where this is not possible due to secure boot or restrictive BIOS settings—by a physical transplantation of RAM modules into a system under the control of the attacker. Based on the memory images of a device, different key recovery algorithms have been proposed in the past to break Full Disk Encryption (FDE), including BitLocker, dm-crypt, and also Android's FDE. With Google's switch from FDE to File-based Encryption (FBE) as the standard encryption method for recent Android devices, however, existing tools have been rendered ineffective. To close this gap, and to re-enable the forensic analysis of encrypted Android disks, given a raw memory image, we present a new key recovery method tailored for FBE. Furthermore, we extend The Sleuth Kit (TSK) to automatically decrypt file names and file contents when working on FBE-enabled EXT4 images, as well as the Plaso framework to extract events from encrypted EXT4 partitions. Last but not least, we argue that the recovery of master keys from FBE partitions was particularly easy due to a flaw in the key derivation method by Google.
Who Owns Your Health Data? Two Interventions Addressing Data of Wearable Health Devices among Young Adults and Future Health Clinicians

Mobile health monitoring devices (MHMDs) have become widely integrated into our personal lives as well as healthcare delivery. However, less attention is paid to individual consumer and clinician awareness, knowledge, and agency, in regards to the privacy and security of data from wearable devices. Two cross sectional intervention studies assessed cybersecurity attitudes, risky cyber behaviors, and mobile health monitoring device dependence among undergraduate students and future health practitioners. The video based intervention used in study two was effective in changing attitudes toward cybersecurity (n =165, t-test, p ≤ 0.001) where study one’s paper-based intervention was not (n = 200, t-test, p = 0.347). In both studies, improved attitude toward cybersecurity was associated with increased risky cyber behavior (p ≤ 0.001, rs=0.334 and p = 007, rs=0.228, respectively). Attitude was associated with device dependence in study one, but not study two (p ≤ 0.001, rs=0.263 and p = 0.432 respectively). Attitude toward cybersecurity varied by self-identified social class (ANOVA p = 0.005), with those in the upper/upper middle class having a more positive attitude than those in the middle class (p = 0.009) and the working/lower class (p = 0.048). Both studies indicated participants felt the need for more awareness and knowledge of risks, suggested personal experiences were their leading factor in changing cyber behaviors and attitudes, and that consumer knowledge, industry standards, as well as policy change are needed to increase the security of data being generated by wearable health technologies. Health practitioner and consumer awareness and knowledge of data security is a critical issue as personal and identifiable data continues to be commodified.
RETRACTED ARTICLE: Identification of lesion using an efficient hybrid algorithm for MRI brain image segmentation

Splitting the image into mutually exclusive regions is referred to as image segmentation. The demarcation, portrayal, and cognitive imaging of expanses of preference in any MR images is segmentation. Identifying the expanses of preference is considered the most complicated task since there are great diversities identified in the MRI images. Diversities in the image content, huddled objects, constriction, image noise, non-uniform object quality, and other factors are responsible for complications identified in the segmentation process. Various techniques and algorithms are being designed and made available for image segmentation. But still, there is a good scope for developing a resourceful, dissolute procedure for curative image segmentation. The proposed work aims to spot the disbelieving region from the related region in the MRI brain Image using Fractional Order BAT Algorithm Fuzzy C. In this paper, Delaunay triangulation (DT) recompenses and fractional order nature-inspired algorithms are used to segment the lesion's assembly from brain tissue meritoriously. The approach is corroborated with 450 MR images gained from different sources or subjects. The relevant features are extracted and based on the results of Fuzzy C Means, and the image is segmented as normal or abnormal. The method provided a considerable improvement over the existing mechanisms adopted for segmentation based on evaluation parameters like sensitivity, accuracy, Jaccard Index. The mechanism helps radiologists in validating their screening activities effectively.
Differences in African banking systems: causes and consequences

Abstract This paper links banking system development to the colonial and legal history of African countries. Based on a sample of 40 African countries from 2000 to 2018, our empirical findings show a significant dependence of current financial institutions on the inherited legal origin and the colonization type. Findings also reveal that current financial legal institutions are not major determinants of banking system development, and that institutional development and governance quality are more important. A high share of government spending relative to GDP also positively affects banking system development in African countries.
Comparing Performance of Evolutionary Algorithms - A Travelling Salesman Perspective

The recent advancements in the fields of computer science and engineering have led to strides in several multi-disciplinary and trans-disciplinary scientific research areas. The Travelling Salesman Problem is one such classical optimization problem that is widely used in various fields including Social Net-work analysis, traffic flow control, human resource management, logistic planning, etc. Attempts have been made in the past to solve this problem with different computational techniques including basic dynamic programming, neural networks to several others. This paper presents a comparison of four optimization algorithms in solving the Travelling Salesman Problem. The chosen approaches are the classical pure Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant-Colony Optimization (ACO) and the Simulated Annealing algorithm (SA). The paper compares and contrasts the performance based on the problem-solving exercises.
Silhouettes from Real Objects Enable Realistic Interactions with a Virtual Human in Mobile Augmented Reality

Realistic interactions with real objects (e.g., animals, toys, robots) in an augmented reality (AR) environment enhances the user experience. The common AR apps on the market achieve realistic interactions by superimposing pre-modeled virtual proxies on the real objects in the AR environment. This way user perceives the interaction with virtual proxies as interaction with real objects. However, catering to environment change, shape deformation, and view update is not a trivial task. Our proposed method uses the dynamic silhouette of a real object to enable realistic interactions. Our approach is practical, lightweight, and requires no additional hardware besides the device camera. For a case study, we designed a mobile AR application to interact with real animal dolls. Our scenario included a virtual human performing four types of realistic interactions. Results demonstrated our method’s stability that does not require pre-modeled virtual proxies in case of shape deformation and view update. We also conducted a pilot study using our approach and reported significant improvements in user perception of spatial awareness and presence for realistic interactions with a virtual human.
Identifying User Authentication and Most Frequently Used Region Based on Mouse Movement Data: A Machine Learning Approach

User authentication in computer demands a reliable, highly secure and fast system so that it can address many severe attacks, which are unable to handle by the conventional password system. Several behavioral biometrics has already progressed to overcome these problems. Mouse-operating behavior of a user is prevalent as powerful behavioral biometrics for access control. It requires detecting authorized and unauthorized users within a very short period of time while a user interacts initially with the computer. In this correspondence paper, we present the mouse dynamics biometric authentication approach that shows the performance from five and ten second's data of mouse movements. The features are extracted from the given dataset to find out the most frequently used regions. In order to identify the accuracy, the extracted features are evaluated and accomplish accuracy using 5 machine learning classification algorithms. The extensive experimental result proves, random forest algorithm outperforms than others and the efficacy of the proposed method is evaluated by False Positive Rate achieves 1.74%, False Negative Rate of 29.23% and 87.23% classification accuracy. An additional experiment is carried to compare the notable improvement of the proposed approach with other methods in the literature.
Teacher-Student Interaction Patterns Change During an Early Science Teaching Intervention

Abstract This study aimed to explore the interaction between teachers and young students in terms of their question and answer patterns during science lessons and to investigate whether this changes over the course of an intervention called ‘Language as a Tool for learning science’ (LaT). It also compared experienced teachers with novices. A total of 16 teachers—of which 8 experienced and 8 novice teachers—and their students participated in this study. The teachers’ utterances were coded with regard to the use of questioning strategies, and the students’ reasoning skills were categorized by the use of three types of scientific reasoning skills (observations, predictions, explanations). Consistent with a complex dynamic systems orientation, we analyzed the interactions among these dimensions by means of state space grids (SSG). The results showed that before the intervention, the teacher-student interaction often took the form of a relatively rigid pattern in which teachers did not ask questions and students did not respond with reasoning expressions. In the course of the LaT intervention, a richer repertoire and a greater amount of interactions emerged in which knowledge was “co-constructed” by means of open-ended questions of the teacher and reasoning by the students. The results also suggest that the patterns of experienced and novice teachers were quite similar to each other.
Absolutely maximally entangled states in tripartite heterogeneous systems

Absolutely maximally entangled (AME) states are closely related to quantum error correction codes. They are typically defined in homogeneous systems. However, the heterogeneous system is very common in a practical setup. In this work, we focus on the AME states in tripartite heterogeneous systems. We first introduce irreducible AME states as the basic elements to construct AME states in the systems with large local dimensions. Then, we introduce an array called magic solution array and show it is related to the AME states in the systems whose local dimensions are l, m, n with $$3\le l<m<n\le m+l-1$$ . Furthermore, we identify in which kinds of heterogeneous systems the AME states are irreducible. We also indicate some applications of our results. First, we propose a protocol to prepare a kind of tripartite heterogeneous AME states. Second, we present a method to construct k-uniform states of more parties from two AME states. In addition, we establish the connection between heterogeneous AME states and multi-isometry matrices, and apply heterogeneous AME states to realize quantum steering.
There and then: interacting with spatio-temporal visualization

Interactivity plays a crucial role in Information Visualization (InfoVis). Together with the visual representation, interaction allows the user to implicitly form mental models of the relationships within the data, identify patterns, and pursue analysis hypotheses. To make the most of it, it is vital to fully understand what opportunities and limitations interactivity arise in different contexts. Spatio-temporal (ST) data sets are often extensive and challenging to analyze and display. Although inter-action is essential to mitigate the inherent challenges concerning ST visualization, there has been little research on its specificities. In this paper, we scrutinize these specificities by conducting a case study: we applied a generic interaction taxonomy for InfoVis to 25 examples of ST visualization of reference and performed the necessary adjustments towards a new specialized taxonomy, according to the triad Theme, Time and Space.