Reviewerly/exHarmony huggingface dataset:
Time Series Prediction on College Graduation Using KNN Algorithm

The KNN algorithm is an algorithm for classifying data based on learning data taken from k of its closest neighbors. Classification using the K-Nearest Neighbor (KNN) algorithm can be used to predict whether a student will graduate on time or even be at risk of dropping out. This research implemented KNN algorithm because of its effectiveness in training large and robustness on noisy data. The input used is in the form of student academic data and produces output, namely the accuracy of the KNN algorithm. Data will be divided into time series into four parts, namely 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> year, 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> year, 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> year, and 4th year. The time series prediction aims to find out the exact time to make predictions. Testing was conducted using K-Fold Cross Validation by dividing the set of data into several folds, one-fold as test data and the other fold as training data. The results of this test are the accuracy of the predictions of each year experiencing increase and prediction in time series can be used for early detection.
appRaiseVR – An Evaluation Framework for Immersive Experiences

Abstract Objective . VR is evolving into everyday technology. For all diverse application areas, it is essential to understand the user’s condition to ensure a safe, pleasant, and meaningful VR experience. However, VR experience evaluation is still in its infancy. The present paper takes up this research desideratum by conflating diverse expertise and learnings about experience evaluation in general and VR experiences in particular into a systematic evaluation framework ( appRaiseVR ). Method . To capture diverse expertise, we conducted two focus groups (bottom-up approach) with experts working in different fields of experience evaluation (e. g., Movie Experience, Theatre Experiences). First, we clustered the results of both focus groups. Then, we conflated those results and the learnings about experience evaluation stemming from the field of user experience into the final framework (top-down approach). Results . The framework includes five steps providing high-level guidance through the VR evaluation process. The first three steps support the definition of the experience and evaluation conditions ( setting, level, plausibility ). The last two steps guide the selection to find an appropriate time course and tools of measure . Conclusion . appRaiseVR offers high-level guidance for evaluators with different expertise and contexts. Finally, establishing similar evaluation procedures might contribute to safe, pleasant, and meaningful VR experiences.
Resource Model Updating For Compositional Geometallurgical Variables

Abstract In the field of mineral resources extraction, one main challenge is to meet production targets in terms of geometallurgical properties. These properties influence the processing of the ore and are often represented in resource modeling by coregionalized variables with a complex relationship between them. Valuable data are available about geometalurgical properties and their interaction with the beneficiation process given sensor technologies during production monitoring. The aim of this research is to update resource models as new observations become available. A popular method for updating is the ensemble Kalman filter. This method relies on Gaussian assumptions and uses a set of realizations of the simulated models to derive sample covariances that can propagate the uncertainty between real observations and simulated ones. Hence, the relationship among variables has a compositional nature, such that updating these models while keeping the compositional constraints is a practical requirement in order to improve the accuracy of the updated models. This paper presents an updating framework for compositional data based on ensemble Kalman filter which allows us to work with compositions that are transformed into a multivariate Gaussian space by log-ratio transformation and flow anamorphosis. This flow anamorphosis, transforms the distribution of the variables to joint normality while reasonably keeping the dependencies between components. Furthermore, the positiveness of those variables, after updating the simulated models, is satisfied. The method is implemented in a bauxite deposit, demonstrating the performance of the proposed approach.
Evaluating Validity of Synthetic Data in Perception Tasks for Autonomous Vehicles

Autonomous vehicles have the potential to completely upend the way we transport today, however deploying them safely at scale is not an easy task. Any autonomous driving system relies on multiple layers of software to function safely. Among these layers, the Perception layer is the most data intensive and also the most complex layer to get right. Companies need to collect and annotate lots of data to properly train deep learning perception models. Simulation systems have come up as an alternative to the expensive task of data collection and annotation. However, whether simulated data can be used as a proxy for real-world data is an ongoing debate. In this work, we attempt to address the question of whether models trained on simulated data can generalize well to the real-world. We collect datasets based on two different simulators with varying levels of graphics fidelity and use the KITTI dataset as an example of real- world data. We train three separate deep learning based object detection models on each of these datasets, and compare their performance on test sets collected from the same sources. We also add the recently released Waymo Open Dataset as a challenging test set. Performance is evaluated based on the mean average precision (mAP) metric for object detection. We find that training on simulation in general does not translate to generalizability on real-world data and that diversity in the training set is much more important than visual graphics' fidelity.
Producing Digital Learning Resources (DLR) for Teacher Training

The purpose of this article is to describe the development and design process of an educational app for teacher training. Online teaching and learning resources are used more than ever in teacher training. However, the utilization of digital technology in higher education shows unambiguously that very little has been transformed or improved so far (Selwyn, 2014: 109). Regarding teacher education specifically; the slow uptake of ICT is also documented (Tømte, 2015: 151). Using a design-based research methodology, the researchers were involved in planning, designing and developing new resources for new practices in flexible teacher-training studies. Through three iterative cycles, the main aim in this article is to describe aspects in the process of designing DLR for the hybridization of teacher training. The findings suggest several aspects of the design process that we believe can enhance teacher trainers and development of digital learning content in higher education.
DICE harder

The specification of the Device Identifier Composition Engine (DICE) has been established as a minimal solution for Trusted Computing on microcontrollers. It allows for a wide range of possible implementations. Currently, most implementations use hardware that was not specifically designed for this purpose. These implementations are reliant on black box MPUs and the implementation process has certain pitfalls due to the use of hardware that was not originally designed for the use in DICE.
Comparison of Autonomy and Study of Deep Learning Tools for Object Detection in Autonomous Self Driving Vehicles

Computer vision is a field of computer systems that can comprehend images and scenes and also identify them. Computer vision technology is very popular in recent times and it has numerous applications namely smart monitoring and surveillance, drones, medicine, sports, entertainment, industrial robotics and self driving cars. Image localization, classification and detection are the basis of the above technology applications. Current improvements in Convolutional Neural Networks i.e. CNNs also lead to exceptional feat in tasks related to visual identification. CNNs are largely applicable for image classification tasks, particularly, looking at an image and classifying various features in it. In this paper study is carried out on various computer vision algorithms and deep learning techniques and corresponding tools for object detection in autonomous self driving cars. Deep learning has revolutionized computer vision techniques namely for object detection. CNN for object detection include three main deep learning techniques; SSD (Single shot multibox detector), R-CNN i.e. Region based CNN, R-FCN i.e. Region based fully convolutional networks. In this paper, an effort is made to show how above deep learning techniques are effective in ameliorating pre-trained networks' performance using existing datasets under different weather and terrestrial conditions. A brief comparison is also done for human centered autonomy and full autonomy for self driving cars.
A new clustering and sorting algorithm for radar emitter signals

Abstract The traditional k-means clustering algorithm needs to set parameters manually in radar signal sorting application, which is sensitive to isolated points and prone to “batch” phenomenon, and the selection of initial clustering center has a direct impact on clustering effect. In order to solve the above problems, a radar signal sorting algorithm based on data field and improved k-means clustering is proposed. Firstly, the potential values of all data samples are calculated according to the theory of data field. After the isolated points are removed, the maximum local potential values are found. The nearest sample data is selected as the initial clustering center, and the maximum number of local potential values is selected as the clustering number. Finally, the improved k-means clustering algorithm is used to complete the radar signal sorting. The algorithm can automatically obtain the initial clustering center and the number of clusters, and the clustering results have larger inter cluster distance and smaller intra cluster distance, so the clustering results are better. Simulation results verify the feasibility of the algorithm.
PStream: A Popularity-Aware Differentiated Distributed Stream Processing System

Real-world stream data with skewed distributions raises unique challenges to distributed stream processing systems. Existing stream workload partitioning schemes usually use a “one size fits all” design, which leverages either a shuffle grouping or a key grouping strategy for partitioning the stream workloads among multiple processing units, leading to notable problems of unsatisfied system throughput and processing latency. In this article, we show that the key grouping based schemes result in serious load imbalance and low computation efficiency in the presence of data skewness while the shuffle grouping schemes are not scalable in terms of memory space. We argue that the key to efficient stream scheduling is the popularity of the stream data. We propose PStream, a popularity-aware differentiated distributed stream processing system which assigns the hot keys using shuffle grouping while assigns rare ones using key grouping. PStream leverages a novel light-weighted probabilistic counting scheme for identifying the currently hot keys in dynamic real-time streams. The scheme is extremely efficient in computation and memory consumption, so that the predictor based on it can be well integrated into processing instances in the system. We further design an adaptive threshold configuration scheme, which can quickly adapt to the dynamical popularity changes in highly dynamical real-time streams. We implement PStream on top of Apache Storm and conduct comprehensive experiments using large-scale traces from real-world systems to evaluate the performance of this design. Results show that PStream achieves a 2.3× improvement in terms of processing throughput and reduces the processing latency by 64 percent compared to state-of-the-art designs.
Entanglement-Assisted Data Transmission as an Enabling Technology: A Link-Layer Perspective

Quantum entanglement as a resource has repeatedly proven to add performance improvements for various tasks in communication and computing, yet no current application justifies a wide spread use of entanglement as a commodity in communication systems. In this work, we detail how the addition of an entanglement storage system at the end-points of a communication link integrated seamlessly into the current Internet can benefit that link's capabilities via a protocol implementing the simple rule to "create entanglement when idle", and use entanglement-assisted communication whenever possible. The benefits are shown with regards to throughput, packet drop-rate, and average packet processing time. The modelling is done in an information-theoretic style, thereby establishing a connecting from information-theoretic capacities to statistical network analysis.
Comparing Gamepad and Naturally-mapped Controller Effects on Perceived Virtual Reality Experiences

A virtual reality (VR) system’s interface determines its corresponding interaction fidelity. While there exist several naturally-mapped interfaces, these have not been directly compared – in terms of presence, engagement, perceived performance, and real performance – to a more commonly-used interface for interacting with virtual reality: the gamepad. Because gamepads have evolved over several decades and have widespread adoption, it is pragmatic to ask whether it is viable to import them into the design of VR experiences. To study this, we developed a first-person shooter (FPS) Virtual environment and contrasted the VR experience using the Microsoft Xbox 360 controller against the HTC VIVE wand. We assessed the effect of the input device on self-reported presence, engagement, and performance as well as real performance in terms of accuracy and speed, two metrics relevant for our particular environment. A within-subjects FPS shooting task under accuracy and time constraints confirmed our hypotheses in favor of the VIVE wand: presence, engagement, perceived performance, and real performance were deemed significantly higher than the Xbox controller condition. These results are contextualized with participants‘ comments in a post-experiment debrief, in which (paradoxically) several participants reported preferring the gamepad. We discuss the implications of our findings and how what we call genre fidelity can impact the design of VR experiences.
Prioritized Restreaming Algorithms for Balanced Graph Partitioning

Balanced graph partitioning is a critical step for many large-scale distributed computations with relational data. As graph datasets have grown in size and density, a range of highly-scalable balanced partitioning algorithms have appeared to meet varied demands across different domains. As the starting point for the present work, we observe that two recently introduced families of iterative partitioners---those based on restreaming and those based on balanced label propagation (including Facebook's Social Hash Partitioner)---can be viewed through a common modular framework of design decisions. With the help of this modular perspective, we find that a key combination of design decisions leads to a novel family of algorithms with notably better empirical performance than any existing highly-scalable algorithm on a broad range of real-world graphs. The resulting prioritized restreaming algorithms employ a constraint management strategy based on multiplicative weights, borrowed from the restreaming literature, while adopting notions of priority from balanced label propagation to optimize the ordering of the streaming process. Our experimental results consider a range of stream orders, where a dynamic ordering based on what we call ambivalence is broadly the most performative in terms of the cut quality of the resulting balanced partitions, with a static ordering based on degree being nearly as good.
Hiding Process Memory Via Anti-Forensic Techniques

Nowadays, security practitioners typically use memory acquisition or live forensics to detect and analyze sophisticated malware samples. Subsequently, malware authors began to incorporate anti-forensic techniques that subvert the analysis process by hiding malicious memory areas. Those techniques typically modify characteristics, such as access permissions, or place malicious data near legitimate one, in order to prevent the memory from being identified by analysis tools while still remaining accessible. With this paper, we present three novel methods that prevent malicious user space memory from appearing in analysis tools and additionally making the memory inaccessible from a security analysts perspective. Two of these techniques manipulate kernel structures, namely Page Table Entries and the structures responsible for managing user space memory regions, while the third one utilizes shared memory and hence does not require elevated privileges. As a proof of concept, we implemented all techniques for the Windows and Linux operating systems, and subsequently evaluated these with both, memory forensics and live analysis techniques. Furthermore, we discuss and evaluate several approaches to detect our subversion techniques and introduce two Rekall plugins that automate the detection of hidden memory for the shared memory scenario.
Human Activity Recognition System using Smart Phone based Accelerometer and Machine Learning

Human Activity Recognition (HAR) has gained significance importance due to its wide range of applications in security, healthcare, surveillance, virtual reality, control systems and automation. Sensors embedded in modern mobile phones enable unobtrusive detection of Activities of Daily Living (ADL). Various statistical and deep learning techniques for the automated detection of human activity have been presented recently. In this study, we have collected accelerometry data through a mobile phone carried by a user for number of days to classify ADL on the basis of exhibited movement into stationary, light ambulatory, intense ambulatory and abnormal classes. ADL such as walking, sitting and jogging etc. are performed and classified simultaneously by mobile phone application and users for comparative analysis. Collected data is given as an input to the trained model and analyzed by implementing the J48 classifier. Results reveal an accuracy score of around 70% for each activity class and it is noted that the classification was performed with an accuracy of above 80% for stationary activity. It is shown that ADL can be recognized with high accuracy using accelerometry data collected in a constrained environment and a single sensor. J48 classifier also correctly classified activities that have a strong correlation between them such as sitting on a chair and standing in stationary position. This work is significant for utilization in long term health monitoring systems that are capable of ensuring neurological health for masses through HAR and mobile phones embedded with accelerometers.
SeGMA: Semi-Supervised Gaussian Mixture Autoencoder

We propose a semi-supervised generative model, SeGMA, which learns a joint probability distribution of data and their classes and is implemented in a typical Wasserstein autoencoder framework. We choose a mixture of Gaussians as a target distribution in latent space, which provides a natural splitting of data into clusters. To connect Gaussian components with correct classes, we use a small amount of labeled data and a Gaussian classifier induced by the target distribution. SeGMA is optimized efficiently due to the use of the Cramer-Wold distance as a maximum mean discrepancy penalty, which yields a closed-form expression for a mixture of spherical Gaussian components and, thus, obviates the need of sampling. While SeGMA preserves all properties of its semi-supervised predecessors and achieves at least as good generative performance on standard benchmark data sets, it presents additional features: 1) interpolation between any pair of points in the latent space produces realistically looking samples; 2) combining the interpolation property with disentangling of class and style information, SeGMA is able to perform continuous style transfer from one class to another; and 3) it is possible to change the intensity of class characteristics in a data point by moving the latent representation of the data point away from specific Gaussian components.
Intelligent optimization for automated video surveillance at the edge: A cross-layer approach

The interest in large scale automated video surveillance systems and the interest in using cloud in supporting such systems has increased dramatically. Unfortunately, building such a large system requires huge resources (for processing and storage) and very high network bandwidth. This paper, proposes a framework for resources efficient intelligent automated surveillance framework that utilizes edge servers. In this framework, multiple video sources capture and send videos to a an edge server which performs an intelligent computer vision based cross-layer optimization of the video sources hardware resources and the network bandwidth. The network can be a wireless network and the sources can be mobile. The proposed solution changes the application rates and the required link layer parameters (the transmission opportunities in our case study) of the sending video sources according to the dynamic network conditions to maximize the overall accuracy of the computer vision algorithm(s) of interest in the system. The proposed framework utilizes an enhanced effective airtime estimation algorithm utilizing a Proportional Integral Differential (PID) controller that measure the available useful bandwidth in the network. Furthermore, we propose a bandwidth pruning mechanism to reach any desired tradeoff between the computer vision algorithm accuracy and the energy consumption of the video sources. We evaluate and present the effectiveness of the proposed framework, the effective airtime estimation algorithm, and the proposed bandwidth pruning mechanism, through extensive experiments using OPNET.
Ultrafast critical ground state preparation via bang–bang protocols

Abstract The fast and faithful preparation of the ground state of quantum systems is a challenging but crucial task for several applications in the realm of quantum-based technologies. Decoherence limits the maximum time-window allowed to an experiment to faithfully achieve such desired states. This is of particular significance in systems featuring a quantum phase transition, where the vanishing energy gap challenges an adiabatic ground state preparation. We show that a bang–bang protocol, consisting of a time evolution under two different values of an externally tunable parameter, allows for a high-fidelity ground state preparation in evolution times no longer than those required by the application of standard optimal control techniques, such as the chopped-random basis quantum optimization. In addition, owing to their reduced number of variables, such bang–bang protocols are very well suited to optimization tasks, reducing the high computational cost of other optimal control protocols. We benchmark the performance of such approach through two paradigmatic models, namely the Landau–Zener and the Lipkin–Meshkov–Glick model. Remarkably, we find that the critical ground state of the latter model, i.e. its ground state at the critical point, can be prepared with a high fidelity in a total evolution time that scales slower than the inverse of the vanishing energy gap.