Reviewerly/exHarmony huggingface dataset:
A Case for Generalizable DNN Cost Models for Mobile Devices

Accurate workload characterization of Deep Neural Networks (DNNs) is challenged by both network and hardware diversity. Networks are being designed with newer motifs such as depthwise separable convolutions, bottleneck layers, etc., which have widely varying performance characteristics. Further, the adoption of Neural Architecture Search (NAS) is creating a Cambrian explosion of networks, greatly expanding the space of networks that must be modeled. On the hardware front, myriad accelerators are being built for DNNs, while compiler improvements are enabling more efficient execution of DNNs on a wide range of CPUs and GPUs. Clearly, characterizing each DNN on each hardware system is infeasible. We thus need cost models to estimate performance that generalize across both devices and networks. In this work, we address this challenge by building a cost model of DNNs on mobile devices. The modeling and evaluation are based on latency measurements of 118 networks on 105 mobile System-on-Chips (SoCs). As a key contribution, we propose that a hardware platform can be represented by its measured latencies on a judiciously chosen, small set of networks, which we call the signature set. We also design a machine learning model that takes as inputs (i) the target hardware representation (measured latencies of the signature set on the hardware) and (ii) a representation of the structure of the DNN to be evaluated, and predicts the latency of the DNN on the target hardware. We propose and evaluate different algorithms to select the signature set. Our results show that by carefully choosing the signature set, the network representation, and the machine learning algorithm, we can train accurate cost models that generalize well. We demonstrate the value of such a cost model in a collaborative workload characterization setup, wherein every mobile device contributes a small set of latency measurements to a centralized repository. With even a small number of measurements per new device, we show that the proposed cost model matches the accuracy of device-specific models trained on an order-of-magnitude larger number of measurements. The entire codebase is released at https://github.com/iitm-sysdl/Generalizable-DNN-cost-models.
Optimal Threshold Changeable Secret Sharing with New Threshold Change Range

Motivated by the need of catering for changes of security policy during the deployment of distribution of trust, threshold changeable secret sharing studies the construction of secret sharing schemes that have a built-in mechanism that, when activated, transforms the scheme into one with different access structures. By combining the two main techniques frequently used in previous constructions: packing and folding, we construct optimal threshold changeable ramp schemes that cover the full threshold change range, while known constructions either achieve only reconstruction threshold change or change both privacy and reconstruction thresholds but require the two thresholds to change proportionally. We justify the claim that the full threshold change range for which optimal schemes are possible is completely covered by proving a completeness result information-theoretically. The share size of these threshold changeable ramp schemes are much bigger than the lower bounds for plain ramp schemes (without requiring threshold changeability). This suggests the natural open question of understanding the share size lower and upper bounds for ramp schemes with built-in structures.
High Payload RDH Through Directional PVO Exploiting Center-Folding Strategy

In this paper, a high payload reversible data hiding scheme (HPRDH) has been designed using Directional Pixel Value Ordering (DPVO) Exploiting Center-Folding Strategy. At first, Center-Folding Strategy has been applied on a cover image to embed secret information which generates Dual Marked Images (DMI). After that, Directional Pixel Value Ordering (DPVO) has been utilized to embed more secret data on interpolated Dual Marked Images (DMI). Here, the advantage of the center-folding method has been employed, which compresses the valuable hidden information through averaging. On the other hand, DPVO provides repeated embedding on overlapped pixels of an image block in different directions like horizontal, vertical, and diagonal one after another. The proposed method is highly adaptive in nature because of its variable-size secret data along with different image block sizes for successful embedding. The experimental outcomes show that the proposed method is very suitable to embed more hidden data without compromising the visual quality of the image compared to other state-of-the-art methods. The intended outcome brought into the limelight some remarkable sublime characteristics in the field of hidden data communication, tamper detection, and digital forgery detection without which the technological life is stunted. Innumerable government and private sector facets including health care, commercial security, defense, and intellectual property rights get immensely benefited from this scheme.
DeepSnow

The falling cost of solar energy deployments has resulted in ever-increasing growth in solar capacity worldwide. The primary challenge posed by increasing grid-tied solar capacity is handling its variability due to continuously changing conditions. Thus, prior work has developed highly sophisticated models to estimate and forecast solar power output based on many characteristics, including location, elevation, time, weather, shading, module type, wiring, etc. These models are highly accurate for estimating solar power, especially over long periods, for sites at low latitudes, i.e., closer to the equator. However, models for sites at higher latitudes are less accurate due to the effect of snow on solar output, since even a small amount of snow can cover panels and reduce power to zero. Improving the accuracy of these models for annual solar output by even 2--3% is significant, as power translates directly into revenue, which compounds over the system's lifetime. Thus, if a site produces 2--3% less power on average per year due to snow than a model predicts, it can mean the difference between a positive or negative return-on-investment.
Research and Design of CAS Protocol Identity Authentication

With the rapid development of science and technology today, in the face of a variety of different systems, the use of a secure, unified and efficient authentication system can not only simplify the workload of administrators, but also simplify the memory of users and ensure the security of user data. We studied the advantages and disadvantages of several existing authentication methods. In order to facilitate multiple systems to achieve unified authentication login, we choose to use CAS to realize SSO's single sign-on function. It enables multiple subsystems to share a safe and reliable login method, and users can switch access freely among multiple subsystems. At the same time, in order to avoid the disadvantages caused by user's simple password, we improved the CAS encryption method with a small part of the custom encryption method. Some opinions and suggestions are put forward on the problem that single point of logout does not destroy session and users can log in after logout.
Proof-Theoretic Conservative Extension of HOL with Ad-hoc Overloading

Logical frameworks are often equipped with an extensional mechanism to define new symbols. The definitional mechanism is expected to be conservative, i.e. it shall not introduce new theorems of the original language. The theorem proving framework Isabelle implements a variant of higher-order logic where constants may be ad-hoc overloaded, allowing a constant to have different definitions for non-overlapping types. In this paper we prove soundness and completeness for the logic of Isabelle/HOL with general (Henkin-style) semantics, and we prove model-theoretic and proof-theoretic conservativity for theories of definitions.
Development of a novel real-time simulation of human skeleton/muscles

The objective of this research work was to develop a model of human skeleton with the capability of real-time simulation of the physical movements of a person in front of the motion capture hardware (Kinect) in order to analyze the motion data and measure the changes of joint torques. Mevea simulation software has been utilized for this purpose, which is a novel application of this software in the field of biomechanics. The model of the human skeleton was created in Mevea using the graphics built in 3ds Max. Simulink external interface for Mevea was established. Simulink acts as a connection between the Mevea software and Kinect for controlling the model. The developed model has been tested through three case studies involving the elbow joint, thoracic joint, and full body. Changes in torque and angular position of joints based on the input of joints are presented as graphs. The developed real-time model of the human skeleton in Mevea can execute the real-time simulation of a person's movements in front of a motion capture camera and provide the changes of torques, which are dependent on the angular positions of the body joints. This work provides the possibility to use the developed real-time model for physiotherapeutic rehabilitation to identify problematic muscles based on produced torque of the joints in order to specify the therapeutic options. The future research direction would be creating a reference databank by measuring healthy individuals' muscle forces for comparison purposes.
node2coords: Graph Representation Learning with Wasserstein Barycenters

In order to perform network analysis tasks, representations that capture the most relevant information in the graph structure are needed. However, existing methods learn representations that cannot be interpreted in a straightforward way and that are relatively unstable to perturbations of the graph structure. We address these two limitations by proposing node2coords, a representation learning algorithm for graphs, which learns simultaneously a low-dimensional space and coordinates for the nodes in that space. The patterns that span the low dimensional space reveal the graph's most important structural information. The coordinates of the nodes reveal the proximity of their local structure to the graph structural patterns. We measure this proximity with Wasserstein distances that permit to take into account the properties of the underlying graph. Therefore, we introduce an autoencoder that employs a linear layer in the encoder and a novel Wasserstein barycentric layer at the decoder. Node connectivity descriptors, which capture the local structure of the nodes, are passed through the encoder to learn a small set of graph structural patterns. In the decoder, the node connectivity descriptors are reconstructed as Wasserstein barycenters of the graph structural patterns. The optimal weights for the barycenter representation of a node's connectivity descriptor correspond to the coordinates of that node in the low-dimensional space. Experimental results demonstrate that the representations learned with node2coords are interpretable, lead to node embeddings that are stable to perturbations of the graph structure and achieve competitive or superior results compared to state-of-the-art unsupervised methods in node classification.
Attention-Aware Feature Aggregation for Real-Time Stereo Matching on Edge Devices

Recent works have demonstrated superior results for depth estimation from a stereo pair of images using convolutional neural networks. However, these methods require large amounts of computational resources and are not suited to real-time applications on edge devices. In this work, we propose a novel method for real-time stereo matching on edge devices, which consists of an efficient backbone for feature extraction, an attention-aware feature aggregation, and a cascaded 3D CNN architecture for multi-scale disparity estimation. The efficient backbone is designed to generate multi-scale feature maps with constrained computational power. The multi-scale feature maps are further adaptively aggregated via the proposed attention-aware feature aggregation module to improve representational capacity of features. Multi-scale cost volumes are constructed using aggregated feature maps and regularized using a cascaded 3D CNN architecture to estimate disparity maps in anytime settings. The network infers a disparity map at low resolution and then progressively refines the disparity maps at higher resolutions by calculating the disparity residuals. Because of the efficient extraction and aggregation of informative features, the proposed method can achieve accurate depth estimation in real-time inference. Experimental results demonstrated that the proposed method processed stereo image pairs with resolution 1242 \(\times \) 375 at 12–33 fps on an NVIDIA Jetson TX2 module and achieved competitive accuracy in depth estimation. The code is available at https://github.com/JiaRenChang/RealtimeStereo.
Scale-Aware Multi-View Reconstruction Using an Active Triple-Camera System

This paper presents an active wide-baseline triple-camera measurement system designed especially for 3D modeling in general outdoor environments, as well as a novel parallel surface refinement algorithm within the multi-view stereo (MVS) framework. Firstly, the pre-processing module converts the synchronized raw triple images from one single-shot acquisition of our setup to aligned RGB-Depth frames, which are then used for camera pose estimation using iterative closest point (ICP) and RANSAC perspective-n-point (PnP) approaches. Afterwards, an efficient dense reconstruction method, mostly implemented on the GPU in a grid manner, takes the raw depth data as input and optimizes the per-pixel depth values based on the multi-view photographic evidence, surface curvature and depth priors. Through a basic fusion scheme, an accurate and complete 3D model can be obtained from these enhanced depth maps. For a comprehensive test, the proposed MVS implementation is evaluated on benchmark and synthetic datasets, and a real-world reconstruction experiment is also conducted using our measurement system in an outdoor scenario. The results demonstrate that (1) our MVS method achieves very competitive performance in terms of modeling accuracy, surface completeness and noise reduction, given an input coarse geometry; and (2) despite some limitations, our triple-camera setup in combination with the proposed reconstruction routine, can be applied to some practical 3D modeling tasks operated in outdoor environments where conventional stereo or depth senors would normally suffer.
Association Rule Implementation Using Algorithm Apriori To Analize Fishing Pattern In Indonesia

Abstract There are more than 80 species of fish caught by fishermen in the sea of Indonesia. To find out what kinds of fish mostly caught, it is necessary to analyse the data pattern of fish being caught. The activities of searching and associating the data pattern are closely related to data mining technique that being used to discover the rules of association of items. In this associative rule method, there are two process can be used: the process of generating frequent itemset and finding associative rules. The Frequent Itemset Generation is a process to get the connection of the itemset and the value of the association based on the value of support and confidence. The algorithm used to generate the frequent itemset is Apriori Algorithm. The Apriori Algorithm has a weakness in the extraction of the appropriate feature of the used attributes. This condition causes the rules formed in large number. This research applies Apriori Algorithm based on principal component analysis to obtain more optimal rules. After the experiments using the apriori algorithm applied with the magnitude ˚ = 30, minimum Support 80% and Confidence 80%, the result of the rule formed are totally 82 rules.
Image Classification Based on Approximate Wavelet Transform and Transfer Learning on Deep Convolutional Neural Networks

In this paper a novel method has been proposed based on a combination of approximate computing, Discrete Wavelet Transform and deep neural network for image classification. In the recent trends, image classification using deep learning network comes under the limelight of the world of artificial intelligence. In the paper we have applied the approximation through bit width reduction technique through discrete wavelet transform technique for the processing of the input database. For feature extraction and classification we have developed a deep convolution neural network that is trained with that preprocesses data. The results show that the proposed model reduces the elapsed time and achieve a good rate of accuracy as compare to the Alexnet and Resnet-50 CNN models.
“What about users?”: Development and validation of the mobile information security awareness scale (MISAS)

Purpose The current study aimed to develop and validate Mobile Information Security Awareness Scale (MISAS) based on the prototype model for measuring information security awareness and the relevant literature. Design/methodology/approach The scale was developed and validated with the participation of 562 students from four universities. The construct validity of the scale was tested through exploratory factor analysis and confirmatory factor analysis. Findings The reliability of the scale was tested through corrected item-total correlations and Cronbach alpha. The MISAS includes six factors and 17 items. The identified factors were labeled as backup, instant messaging and navigation, password protection, update, access permission and using others' devices. Research limitations/implications The scale included only the human aspects of mobile information security. The technical aspects are not within the scope of this study. For this reason, future studies might develop and validate a different scale focusing on the technical aspects of mobile information security. Originality/value The developed scale contributes to the literature on the human aspects of mobile information security.
Differentiation, Diversification and Acquisition from an Online Bookstore: Multidimensional Assessment of Amazon’s Strategic Management

Originated from a miniature online bookseller to a massive superstore company raking in $75.5 billion in the first fiscal quarter of 2020 despite the surge in COVID-19, Amazon is arguably a frontrunner in e-commerce over the course of its spectacular 25-year history. This paper conducts a critical assessment of Amazon’s strategy in multi-dimensions, evaluating its initial tactics, the subsequent diversification, how the acquisition could fit into its strategy, and the strategic challenges facing the Amazon Empire in the midst of the pandemic.
Multi‐view hand gesture recognition via pareto optimal front

Computational cost-ineffectiveness and ambiguity due to self-occlusion are bottlenecks of vision-based hand gesture recognition. In this study, the authors address these issues by proposing a novel multi-view hand gesture recognition method based on Pareto optimal front. They first present an oriented gradient local binary pattern operator to generate a groupwise gesture feature data set for multi-view hand gesture images. Then they take hand gesture recognition over multi-view hand images as a multi-query image retrieval problem and Pareto optimal front is constructed based on the dissimilarities between the testing images and sample images. The gesture corresponding to the point with the shortest distance to the origin on the Pareto optimal front is the final recognised result. Extensive experiments verify the accuracy and efficiency of their Pareto optimal font-based method.
Data Augmentation Powered by Generative Adversarial Networks

Face identification projects must be based on a highquality database of the faces to be recognized. The accuracy of the identification depends on the illumination and the diversity of the subject's expression, among other things. Naturally, a more diverse training database and data augmentation can help in loss reduction. In this research, we attempt to increase the quality of few-shot learning face identification by using Generative Adversarial Network-based data augmentation techniques. This paper presents a novel method to embed images into GAN's latent space, and to use the augmented versions for few-shot learning.
From Word Embedding to Cyber-Phrase Embedding: Comparison of Processing Cybersecurity Texts

Much of the vital information about emerging threats and the corresponding defensive measures are contained in large volumes of natural language texts online. Capturing such actionable intelligence in real-time is critical to prevent large scale attacks automatically. The ATT&CK framework is a widely recognized standard to catalog technical details of cyber threats and deploy mitigating measures. A technique in ATT&CK specifies a set of adversary actions to achieve a particular goal, such as Exfiltration over Command and Control channel. Details of the technique include encrypted traffic and encoded data. A key challenge in identifying such cyber intelligence from natural language texts is that for a given action, such as encrypted traffic, many alternative expressions are possible (e.g., send using a self-signed certificate, send using HTTPS requests). It is not practical to manually provide an exhaustive list of all such variants. We demonstrate that using cyber-phrase embedding on a cybersecurity text corpus is a promising approach to overcome such difficulties. Our evaluation demonstrates that our model outperforms existing models. We have created an open-source project to make our tools and data available for the cybersecurity research community.
A System for Unstructured Data Mining using Dynamic Ensemble Selection

Unstructured data represent as much as 90% of all business-relevant information. In Brazil, the practice of printing official journals dates back to the 19th century. Today more than 200 official journals in circulation, which together accumulate around 1.4 billion publications without textual standard. This work proposes the development of a system for unstructured data mining using a Dynamic Ensemble Selection. JudEasy implements, added in addition to classic text pre-processing methods, a set of twelve DES and a static method for creating categorized textual models for Brazilian of official journals. As results the DES-KL model obtained the highest accuracy rate of 96.81% and exceptional precision of 0.99.
Camera System for Efficient non-contact Measurement in Distance Medicine

The posture of body segments can be negatively influenced by many diseases of the nervous, visual and musculoskeletal systems. This article outlines a newly designed system and related procedures to record and evaluate anatomical body angles. The system is equipped with two mutually calibrated cameras allowing the evaluation of body movement in two anatomical planes. The hardware part of the camera system and calibration method was designed for practical use in clinical practice. Moreover, the proposed camera system allows for the recording and evaluation of motion data in a home environment or at a safe distance. It enables non-invasive and non-contact measuring of body segments and, therefore, can be used in distance rehabilitation and distance diagnosing. The study also demonstrates the hardware's performance accelerator based on a human body tracking algorithm. The device utilizes the third party algorithms, such as OpenPose, for the extraction of major body points from selected video frames. Any missing data points are then interpolated through the proposed tracking algorithm. This procedure results in an acceleration of the overall hardware performance.