Reviewerly/exHarmony huggingface dataset:
Effects of Augmented Content’s Placement and Size on User’s Search Experience in Extended Displays

Using an augmented reality head-mounted display(HMD) to extend the display of smartphone could open up new user interface solutions, benefiting users with an increased screen real-estate and visualizations that leverage each device's capabilities. Some previous works have explored the viability of extended displays, but knowledge regarding considerable design factors and constraints for such displays is still very limited. In our work, we conducted an exploratory study to investigate how different properties of augmented content in extended displays affect the user's task performance and subjective workload in an image comparison task. Through an experiment with 24 participants, we compared four augmented content placements (top, right, left, and bottom) and two augmented content sizes (small and large). The study results demonstrated that there are significant effects of both placement and size of augmented content on task performance and subjective workload. Based on the findings of our study, we propose some recommendations for the future design of extended displays.
SORN-based Cascade Support Vector Machine

This paper presents a SORN-based cascade support vector machine (SVM). For the first time a SVM is implemented by the usage of SORNs (Set Of Real Numbers). The SORN representation is a dual number representation that uses a fixed set of exact values and open intervals. Arithmetic operations in SORN representation can be realized by lookup tables which allows fast and low-complexity computing. This arithmetic is used for the non-support vector filtering in the early stages of a cascade SVM. Once the training subsets passed the bottom layer of the cascade, the optimization on the remaining support vectors can be done in classic representations like floating point or fixed point.
HoloScale: horizontal and vertical scaling of cloud resources

Elastic and scalable compute resources are a fundamental part of cloud computing. Efficient management of cloud resources is crucial in order to provide high quality services and applications. In this work we present a novel method for scaling cloud resources and provide stability guarantees. We do this by leveraging ideas and concepts from classic control theory, namely mid-range control and combine horizontal scaling and vertical scaling in a novel way. Horizontal scaling is typically when one adds/removes whole unites of resources (e.g., virtual machines or containers), while vertical scaling is when one grows/shrinks already allocated resources (e.g., making a deployed virtual machine larger/smaller). Each methods has their own trade-offs: i) horizontal scaling is often slow and coarse-grained, but can scale over a large range, and ii) vertical scaling is often quick and smooth, but has limited range.The proposed algorithm is called HoloScale, which leverages the strengths of both scaling mechanisms, without the drawbacks. The method is capable of scaling smoothly, quickly, and over a large range. By using core concepts from control theory, we show that systems managed by the HoloScale algorithm are stable in the presence of time-varying scaling delays.
Optimizing Deep Neural Networks Through Neuroevolution With Stochastic Gradient Descent

Deep neural networks (DNNs) have achieved remarkable success in computer vision; however, training DNNs for satisfactory performance remains challenging and suffers from sensitivity to empirical selections of an optimization algorithm for training. Stochastic gradient descent (SGD) is dominant in training a DNN by adjusting neural network weights to minimize the DNN’s loss function. As an alternative approach, neuroevolution is more in line with an evolutionary process and provides some key capabilities that are often unavailable in SGD, such as the heuristic black-box search strategy based on individual collaboration in neuroevolution. This article proposes a novel approach that combines the merits of both neuroevolution and SGD, enabling evolutionary search, parallel exploration, and an effective probe for optimal DNNs. A hierarchical cluster-based suppression algorithm is also developed to overcome similar weight updates among individuals for improving population diversity. We implement the proposed approach in four representative DNNs based on four publicly available data sets. The experimental results demonstrate that the four DNNs optimized by the proposed approach all outperform corresponding ones optimized by only SGD on all data sets. The performance of DNNs optimized by the proposed approach also outperforms state-of-the-art deep networks. This work also presents a meaningful attempt for pursuing artificial general intelligence.
Using Close Reading as a Method for Evaluating Visualizations

Visualization research and practice that incorporates the arts make claims to being more effective in connecting with users on a human level. However, these claims are difficult to measure quantitatively. In this paper, we present a follow-on study to use close reading, a humanities method from literary studies, to evaluate visualizations created using artistic processes [Bares 2020]. Close reading is a method in literary studies that we've previously explored as a method for evaluating visualizations. To use close reading as an evaluation method, we guide participants through a series of steps designed to prompt them to interpret the visualization's formal, informational, and contextual features. Here we elaborate on our motivations for using close reading as a method to evaluate visualizations, and enumerate the procedures we used in the study to evaluate a 2D visualization, including modifications made because of the COVID-19 pandemic. Key findings of this study include that close reading is an effective formative method to elicit information related to interpretation and critique; user subject position; and suspicion or skepticism. Information gained through close reading is valuable in the visualization design and iteration processes, both related to designing features and other formal elements more effectively, as well as in considering larger questions of context and framing.
Gaussian Bayesian network comparisons with graph ordering unknown

A Bayesian approach is proposed that unifies Gaussian Bayesian network constructions and comparisons between two networks (identical or differential) for data with graph ordering unknown. When sampling graph ordering, to escape from local maximums, an adjusted single queue equi-energy algorithm is applied. The conditional posterior probability mass function for network differentiation is derived and its asymptotic proposition is theoretically assessed. Simulations are used to demonstrate the approach and compare with existing methods. Based on epigenetic data at a set of DNA methylation sites (CpG sites), the proposed approach is further examined on its ability to detect network differentiations. Findings from theoretical assessment, simulations, and real data applications support the efficacy and efficiency of the proposed method for network comparisons.
AMC-IoT: Automatic Modulation Classification Using Efficient Convolutional Neural Networks for Low Powered IoT Devices

Automatic modulation classification (AMC) is used to identify the modulation for the received signal. IoT devices use modern communication methods which are based on multiple input multiple output (MIMO) in which the signals are received from various sources. The identification of modulation is vital. Feature based AMC methods combined with deep learning techniques has the potential to meet the latency requirement in the IoT applications. An efficient convolutional neural network based on depthwise separable convolution has been proposed to classify the modulation of the received signals. The proposed architecture has 58% less parameters than the conventional convolutional architecture and the performance is comparable.
Construction Safety Surveillance Using Machine Learning

Safety has always been a matter of concern in all industrial activities, especially construction. Hard hats or safety helmets act as the first line of protection against serious head injuries. Some workers don't quite follow the instructions and signs that require them to follow safety measures. And in case of any accidents, the workers will not be eligible for insurance, if they were not wearing the safety equipments. This applied research paper mainly focuses to detect persons with and without a helmet in the construction site. The accuracy and performance of Neural Networks were tested and compared with other hand crafted features like Haar and LBP classifiers, Histogram of Oriented Gradients and Sequential Classifiers. These hand crafted features gave more false detections than neural nets, when tested in real conditions. Different Neural Networks were tested on Edge Devices such as Nvidia Jetson TX2 and Jetson Nano, for commercial deployment. Compared to the other neural networks, the SSD MobileNet model showed better performance without considerable drop in accuracy, when tested on edge devices in real-time. This makes it a preferable solution for this application-oriented problem.
Phishing Website Detection Using Effective Classifiers and Feature Selection Techniques

Phishing is a relatively new form of network assault where a web page illegally invokes current users to request financial or personal data or passwords. This act jeopardizes the privacy of many users and consequently, ongoing research has been carried out to find detection tools and to develop existing solutions. Classifiers based on machine learning can be used to detect phishing websites effectively and therefore, various machine learning classification algorithms i.e. Naive Bayes, J48 and HNB are implemented and compared through this research. In addition, performance of a classifier combining HNB and J48 was also closely observed as a solution to the stated problem. The study proposes a novel manual feature selection approach and presents a comparative study with Filter method feature selection techniques. The dataset used in this research is collected from UCI machine learning repository, has 2670 instances and 30 attributes of website structure. The empirical result indicated that the Address bar based feature group achieved the highest accuracy in detecting phishing website. In addition, two top algorithms, HNB and J48, were developed for an integrated multi-classified process. The findings have shown that combining techniques results in 96.25% accuracy in the identification of phishing websites for all apps.
Error-correction and noise-decoherence thresholds for coherent errors in planar-graph surface codes

We numerically study coherent errors in surface codes on planar graphs, focusing on noise of the form of $Z$- or $X$-rotations of individual qubits. We find that, similarly to the case of incoherent bit- and phase-flips, a trade-off between resilience against coherent $X$- and $Z$-rotations can be made via the connectivity of the graph. However, our results indicate that, unlike in the incoherent case, the error-correction thresholds for the various graphs do not approach a universal bound. We also study the distribution of final states after error correction. We show that graphs fall into three distinct classes, each resulting in qualitatively distinct final-state distributions. In particular, we show that a graph class exists where the logical-level noise exhibits a decoherence threshold slightly above the error-correction threshold. In these classes, therefore, the logical level noise above the error-correction threshold can retain significant amount of coherence even for large-distance codes. To perform our analysis, we develop a Majorana-fermion representation of planar-graph surface codes and describe the characterization of logical-state storage using fermion-linear-optics-based simulations. We thereby generalize the approach introduced for the square lattice by Bravyi \textit{et al}. [npj Quantum Inf. 4, 55 (2018)] to surface codes on general planar graphs.
Redfish Green500 Benchmarker (RGB): Towards Automation of the Green500 Process for Data Centers

The goal of this research study is to develop and demonstrate methods to automate benchmarking the energy efficiency of high performance computing systems based on the Green500 methodology via direct measurements obtained from the baseboard management controllers using Redfish standard, instead of via an external power meter and a manual process. To achieve this goal, we have designed and developed an automatic Green500 benchmark tool based on Redfish, called RGB (Redfish Green500 Benchmarker). This tool also evaluates implementations of the Redfish standard to determine their ability to meet the requirements of the Green500 benchmarking protocols. We further develop another tool, RGB Checker, which checks the level of the accuracy and the precision of the RGB tool based on the Redfish standard. We have performed validation tests using these tools on a production cluster, on several different testbeds with various types of servers, and on a simulated environment.
Eyelid Gesture Control using Wearable Tunnelling Magnetoresistance Sensors

Everyday technologies are more than ever digitized with internet of thing's systems and disabled individuals may feel excluded.Handsfree gesture approaches such as eye movements/blinking can enhance the interacting with modern technology.This work presents eye blinking for eyelid gesture control using a wearable magnetic system consisting of a flexible magnetic strip on the eyelid and spintronic magnetic sensors with its analogue front-end circuit.To detect eye blinking, tunnelling magnetoresistance (TMR) sensors with sensitivity of 11mV/V/Oe are embedded into an eyeglass frame.For successful detection of the small magnetic field generated by 6 mm diameter with 1 mm thickness magnetic strip on the eyelid, a sensor readout circuit is designed to amplify the collected signal and cancel the external noise and offset.The circuit is capable of filtering low frequencies <0.5 Hz and DC offsets.High frequencies above >28 Hz is filtered for both magnetic field and eyelid movement noise.Each TMR sensor circuit is equipped with a fixed-gain amplifier for detecting low-magnetic field from the mm-sized magnetic strips.The blinks can be repeated within a set time frame and since both eyelids will be detected, multiple command combinations are possible for classification.Based on magnetic field simulation results, the circuit was simulated and has shown high repeatability and stability that can classify eyeblinks based on amplitude threshold.As a result, the signal can be scaled and classified on a Bluetooth microcontroller capable of connecting to various Bluetooth enabled devices for the disabled individuals to communicate external technology with.
On equivalence between known polynomial APN functions and power APN functions

Constructions and equivalence of APN functions play a significant role in the research of cryptographic functions. On finite fields of characteristic 2, 6 families of power APN functions and 14 families of polynomial APN functions have been constructed in the literature. However, the study on the equivalence among the aforementioned APN functions is rather limited to the equivalence in the power APN functions. Meanwhile, the theoretical analysis on the equivalence between the polynomial APN functions and the power APN functions, as well as the equivalence in the polynomial APN functions themselves, is far less studied. In this paper, we give the theoretical analysis on the inequivalence in 8 known families of polynomial APN functions and power APN functions.
iBench: a Distributed Inference Simulation and Benchmark Suite

We present a novel distributed inference benchmarking system, called “iBench”, that provides relevant performance metrics for high-performance edge computing systems using trained deep learning models. The proposed benchmark is unique in that it includes data transfer performance through a distributed system, such as a supercomputer, using clients and servers to provide a system-level benchmark. iBench is flexible and robust enough to allow for the benchmarking of custom-built inference servers. This was demonstrated through the development of a custom Flask-based inference server to serve MLPerf's official ResNet50v1.5 model. In this paper, we compare iBench against MLPerf inference performance on an 8-V100 GPU node. iBench is shown to provide two primary advantages over MLPerf: (1) the ability to measure distributed inference performance, and (2) a more realistic measure of benchmark performance for inference servers on HPC by taking into account additional factors to inference time, such as HTTP request-response time, payload pre-processing and packing time, and invest time.
Research on Unbalanced Data Processing Algorithm Base Tomeklinks-Smote

Traditional machine learning algorithms tend to bias "majority" for classification of unbalanced data, which makes the classification accuracy of less-class samples lower. In order to improve the classification accuracy of less-class samples in the data set, this paper proposes a method based on imbalance TLS algorithm for data processing. This method first deletes duplicate samples in the original data set, and then deletes the boundary samples and noise samples that are Tomek links pairs' in the majority class and the minority class in the data set through the Tomek links undersampling algorithm and then oversampling the minority class samples with the smoth algorithm. After the obtained large-class sample and the obtained small-class sample are approximately balanced. On several commonly used data sets in the UCI database, compare with the original data set, the tradition al SMOTE oversampling only for small samples, and the traditional Tomek link under- sampling method for large samples only, Use SVM, logistic regression, Multilayer Perceptron (Neural Network) and random forest for classification. The experiment proves that the sampling method adopted in this paper can indeed improve the recognition of small class samples.
Studying the Effects of Hashing of Sparse Deep Neural Networks on Data and Model Parallelisms

Deep Neural Network (DNN) training and inference are two resource-intensive tasks that are usually scaled out using data or model parallelism where data parallelism parallelizes over the input data and model parallelism parallelizes over the network. Also, dense matrix-matrix multiplication is the key primitive behind training/inference of dense DNNs. On the contrary, sparse DNNs are less resource-intensive compared to their dense counterparts while offering comparable accuracy. Similarly, they can be parallelized using data or model parallelism with Sparse Matrix-Matrix Multiplication (SpMM) as the key primitive. To scale out, both data and model parallelisms initially use data parallelism to partition the input data among multiple machines. This initial partitioning of the input makes data and model parallelisms performance prone to load imbalance as partitions may be imbalanced. As part of this paper, we take a deeper look into data and model parallelisms and closely study the mechanics of the SpMM used for each. Moreover, to intuitively remedy their load imbalance problem, we incorporate hashing as a simple yet powerful method to address load imabalance. Finally, we use the IEEE HPEC sparse DNN challenge dataset to evaluate the performance of data and model parallelisms at scale. We scaled up to 32 machines (896 cores) and inferred a large sparse DNN with 4B parameters in 51 seconds. Results suggest that with hashing, data and model parallelisms achieve super-linear speedup due to better load balance and cache utilization.
A New High Capacity Video Steganography Scheme Using Differential Directional Pixogram

A video signal is a collection of images (frames) presented successively at a constant rate. To have a meaningful video segment, the images that compose the video segment should be related to each other. Therefore, the time domain of a video segment is a great source of data redundancy. Utilizing data redundancy is the driving force for data hiding schemes. This paper proposes the Differential Directional Pixogram (DDP) scheme to optimally exploit the temporal redundancy in a video segment. The idea is to extract a vector of pixels in the direction of the motion in each scene. Thus, it is expected that this vector will be composed of homogeneous pixels. The secret pixels are then embedded in the Discrete Cosine Transform (DCT) coefficients since the DCT has a powerful ability to express a highly correlated signal within few significant DCT coefficients leaving a large amount of insignificant DCT coefficients. As a result, the proposed DDP scheme is able to hide a substantial amount of secret data while achieving outstanding stego quality compared to competitive video steganography schemes.
ND-HMDs: Non-Differentiable Hardware Malware Detectors against Evasive Transient Execution Attacks

Transient execution attacks exploit performance optimizations, built into modern CPU designs, to leak sensitive data through side channels. Preventing such attacks is limited: (1) Software solutions engender high performance overhead, and (2) Hardware solutions require new CPU designs and intensive formal analysis, which is impractical due to the tremendous complexity of modern CPUs and lack of public documentation. To address these challenges, Hardware-Malware Detectors (HMDs), which utilize Hardware Performance Counters (HPCs), have been proposed to detect transient execution attacks as a computational anomaly, with a low impact on performance. Unfortunately, some recent studies show that HMDs detection can be easily evaded by obfuscating the HPC traces. Upon observing that two main methods mostly generate evasive transient execution attacks, namely gradient-based and sleep based, in this paper, we propose nondifferentiable HMDs (ND-HMDs) to defend against evasive transient execution attacks. In particular, ND-HMDs use nondifferentiable, gradient-free classifiers, rendering the gradient computations on ND-HMDs less useful for generating evasion samples. Our extensive evaluation shows that ND-HMDs can successfully defend against the gradient-based attacks, and are quite resistant to sleep based attack; while at the same time, ND-HMDs achieve high detection accuracy on the non-evasive transient execution attacks.
Distributed Extended Object Tracking Based on Diffusion Strategy

In this work, we study the problem of ellipse extended object tracking with multiple measurements. We propose a distributed extended object tracking algorithm for heterogeneous networks based on the diffusion extended Kalman filter. We use a set of nodes with different parameters to estimate the kinematic state and extension of the extended object simultaneously. Simulation results verify that the proposed distributed approach could outperform the method without cooperation.