Miracle-dz/UniFAR-FTUs huggingface dataset:
Demo: OneOS - Middleware for Running Edge Computing Applications as Distributed POSIX Pipelines

While many IoT platforms provide high-level programming environments to hide the complexities of heterogeneity and network dynamism, they come with the cost of adopting a framework-specific API. In this demo paper, we present a demo of OneOS, a middleware providing a distributed computing environment through the standard POSIX API. We demonstrate running a video stream processing application as a distributed POSIX pipeline on the OneOS network.
Ensuring Accountability and Outsourced Decryption in IoT Systems using Ciphertext-Policy Attribute-Based Encryption

Attribute based cryptography enhances the chances of secure communication on large scale. There are several features of attribute based encryption which have been proposed as different protocols. Most of these are suitable for access control in large systems like cloud services. Very few protocols focus on reducing the computational overhead for lower end devices like Internet of Things sensors and actuators. Hence, it is desirable to have a mix of features in protocols for IoT architecture. Our protocol enforces accountability of different parties involved while reducing the computational overhead during decryption on miniature devices. We prove that our protocol is RCCA-secure in selective security model and achieve accountability and unlinkability.
Flexible Job Shop Scheduling for Decreasing Production Costs

Considering the inventory cost of workpieces in process,machining time cost,direct labor cost,inventory cost of early finished products and cost increment due to late finished products,a method calculating production cost of dual-resource(machine tool plus labor) flexible job shop scheduling is proposed.A new hybrid genetic algorithm is designed by embedding simulated annealing algorithm(SA) into genetic algorithm(GA),where GA is used to search for a group of better solutions to the problem of minimizing production cost and then SA is applied to searching them for the best one.Introducing an operation-based encoding and a new decoding method,several kinds of crossover operations are used to enable the algorithm to search the optimal solution in solvable space as far as possible.To avoid missing the optimal solution during the course of evolution,the optimized solution in every generation is kept then updated uninterruptedly.An example is given to prove that the scheduling method is feasible and efficient.
VQuAD: Video Question Answering Diagnostic Dataset

In this paper, we investigate the task of Video based Question Answering. We provide a diagnostic dataset that can be used to evaluate the extent of reasoning abilities of various methods for solving this task. Previous datasets proposed for this task do not have this ability. Our dataset is large scale (around 1.3 million questions jointly for train and test) and evaluates both the spatial and temporal properties and the relationship between various objects for these properties. We evaluate state of the art language model (BERT) as a baseline to understand the extent of correlation based on language features alone. Other existing networks are then used to combine video features along with language features for solving this task. Unfortunately, we observe that the currently prevalent systems do not perform significantly better than the language baseline. We hypothesise that this is due to our efforts in ensuring that no obvious biases exist in this dataset and the dataset is balanced. To make progress, the learning techniques needs to obtain an ability to reason, going beyond basic correlation of biases. This is an interesting and significant challenge provided through our work. We release our dataset and source code for our baseline modules in the following webpage https://delta-lab-iitk.github. io/vquad/.
Outsourced CP-ABE with Whitebox Accountability in IoT Systems

Cryptography based on identity and attributes enhances the chance of secure communication on a large scale. Several attribute-based encryption schemes achieve diﬀerent objectives when used in various protocols. Most of these are suitable for large systems like cloud services. There are a few protocols which focus on reducing the computational overhead for lower end devices like Internet of Things sensors and actuators. It is desirable to have a mix of features in protocols for IoT security architecture. We ﬁrst propose a scheme to ensure accountability in CPABE scheme FAME. The protocol is proven CPA-secure with full security in random oracle model. We also prove its accountability. We also propose a hybrid protocol that enforces user accountability and outsourced decryption in IoT systems and achieve full security in replayable chosen ciphertext attack (RCCA) under random oracle model.
A Data-Driven Optimal Control Decision-Making System for Multiple Autonomous Vehicles

With the fast development and rising popularity of autonomous vehicle (AV) technology, multiple AVs may soon be driving on the same road simultaneously. Such multi-AV coexistence driving situations will lead to new and persistent challenges. Therefore, improvements on making control decisions for multiple AVs becomes necessary for continued driving safety. In this paper, we propose a multi-AV decision making system (MADM), which considers multi-AV coexistence driving situations during the decision-making process. In MADM, we first build a policy formation method to generate policies that learn the driving behaviors of an expert based on the expert's driving trajectory data. We then develop a multi-AV decision-making method, which adjusts the formed policies through multi-agent reinforcement learning. The adjusted policies make control decisions for multiple AVs with safety guarantee. We used a real-world traffic dataset to evaluate the decision making performance of MADM in comparison with several state-of-the-art methods. Experimental results show that MADM reduces emergency rate by as high as 51% when compared with existing methods.
Generalized Predictive Direct Power Control With Constant Switching Frequency for Multilevel Four-Leg Grid Connected Converter

This article proposes a novel generalized predictive direct power control (PDPC) with a constant switching frequency for a multilevel four-leg (4L) grid connected converter (GCC) diode clamped converter (DCC). A new predictive power model for 4L-GCC is developed in which the proposed PDPC can operate with a constant switching frequency without any modulation stage. During each switching period, four appropriate switching vectors are selected and applied using precalculated switching times. Moreover, these appropriate switching vectors are selected only from one sector, and a new proposed approach to calculate the switching times is proposed and generalized for any level of 4L-DCC. The proposed PDPC for the multilevel 4L-GCC can ensure better active and reactive powers control, lower grid current harmonics, better compensation of grid neutral current and reactive power, and accurate balancing of the dc capacitor voltages. The presented results demonstrate the effectiveness of the proposed control strategy.
PlonKup: Reconciling PlonK with plookup

In 2019, Gabizon, Williamson, and Ciobotaru introduced P lon K – a fast and ﬂexible ZK-SNARK with an updatable and universal structured reference string. P lon K uses a grand product argument to check permutations of wire values, and exploits convenient interactions between multiplicative subgroups and Lagrange bases. The following year, Gabizon and Williamson used similar techniques to develop plookup – a ZK-SNARK that can verify that each element from a list of queries can be found in a public lookup table. In this paper, we present P lon K up , a fully succinct ZK-SNARK that integrates the ideas from plookup into P lon K in an eﬃcient way
On the Role of Engagement in Human Information Interaction: From Research to Implementation

This workshop will provide a forum for researchers, practitioners and developers interested in user engagement and emotion in the context of human information interaction. Specifically, the workshop aims to address the following questions: “How do we ensure that the measurement of subjective information experience is robust and scalable? ”, “How do we design interfaces for engaging and emotionally compelling experiences? ”, and “How do we prevent disengagement? ” The ability to answer these questions relies upon: a solid conceptual understanding of subjective experiences; robust, scalable approaches to measuring engagement; and the ability to utilize this knowledge in information systems and interface design. This three-part half-day workshop will include: talks by the organizers to ground the workshop’s themes; position paper presentations and design exemplars from attendees, and an interactive session focused on design scenarios and prototyping. The intersection of emotion and engagement with measurement and design in information interaction contexts is a timely issue for the iSchool community.
Multi -Classification based Alzheimer's Disease Detection with Comparative Analysis from Brain MRI Scans using Deep Learning

The neurodegenerative Alzheimer's Disease is the most widely recognized cause of ‘Dementia’ and was allegedly the 7th highest cause of death globally. Yet, there is still no conclusive test for distinguishing Alzheimer's disease. Our proposed model eliminates these challenges in a significant manner. The technique is fit for investigating and analyzing different classes in a single setting and requires significantly less previous apprehension. Several handcrafted or predefined machine learning and deep learning models have been imple-mented in this field of study. Our proposed multi-classification model is primarily implemented based on the Open Access Series of Imaging Studies (OASIS) data and suggests an 18-layer architecture. We have implemented a unique preprocessing approach using all three anatomical planes of the MRI scans in a single sequential model, which was also evaluated afterwards. The research also explores a comparative study among multiple and binary classes in terms of performance and efficiency. Pre-defined models such as Inception V3and VGG19 have also been brought to comparison to measure the model's reliability. Our multiclass setting shows an accuracy of over 80%, which is higher than most of the existing multi-classification models in this dataset. Moreover, the in-depth comparative study using binary classification shows a significant accuracy of over 92%, which ensures the all-around efficacy of the model.
A Deep Learning Technique for Electricity Price Forecasting in Consideration of Spikes

This paper presents a Deep Neural Network (DNN) method for electricity price forecasting in power markets. They are inclined to generate spikes that are dozens to hundred times as large as the normal prices so that the prediction is hard to handle. This paper focuses attention on the prediction of spikes to suppress the forecasting errors. This paper deals with the pretraining technique of Autoencoder (AE) in Deep Learning. To enhance the performance of AE, this paper presents a Denoising-Autoencoder (DAE)-based method that consists of DAE and Multilayer Perceptron (MLP) of ANN with the clustering technique. DAE is an extension of AE in a sense that noisy learning data is used with random numbers. The use of clustering enhances the model accuracy due to data similarity. The effectiveness of the proposed method is tested for data of New England ISO, USA.
Real-Time CSI-Based Wireless Imaging for Human-Machine Interaction

In human-machine interaction systems, device-free and privacy-aware activity monitoring and gesture recognition is of great interest. In this framework, this work introduces a multi-step wireless imaging approach based on channel state information (CSI) and machine learning for the user's presence detection, localization, identification, and gesture recognition by using commodity Wi-Fi signals. The preliminary experimental validation in a real environment shows an excellent classification accuracy of 98%.
Object Classification in Underwater SONAR Images using Transfer Learning Based Ensemble Model

Advancements in machine learning and deep learning avails the opportunity to enhance our customisation to crucial problems widely in any domain. Object detection in underwater sonar is evolving and deep learning provides reliable techniques. In our experiments we approached the sonar object classification with transfer learning and ensemble approach which produced better results than single machine learning and deep learning algorithms for the task. The preliminary step of feature extraction preserves complex and significant structures from the image data and improves classification performance. Also experiment model overcomes the scarce training data with predefined model, ResNet50. Optimized classification results achieved with ensemble classifiers for the sonar objects.
A survey on Deep Learning Architectures for effective Crop Data Analytics

Deep learning has emerged as a precise tool for image and non-image-based data analytics in various domains. Smart farming is a major domain where deep learning approaches have been effectively applied for crop data analytics. Crop monitoring is a significant task in smart farming to enhance agricultural productivity. Accurate prediction of crop yield with limited availability of information related to environmental parameters is the main challenge. Crop diseases are often the major reason for yield loss. In developing countries, rural farmers are not equipped with modern techniques for real-time crop disease detection and thus fail to implement corrective actions. Recently, deep learning approaches have proven to be more effective in generating more accurate results in crop data analytics. In this study, research works pertaining to deep learning-based models applied in crop yield estimation and crop disease detection are reviewed.
Teaching Out-of-Order Processor Design with the RISC-V ISA

We describe our experience teaching an undergraduate capstone (and elective graduate course) in computer architecture with a semester-long project in which teams of five students design and implement an out-of-order (OoO) pipelined processor core using the open-source RISC-V instruction set. The course content includes OoO scheduling algorithms for instructions to exploit instruction-level parallelism (ILP), example designs, caching, prefetching, and virtual memory. The labs and projects help students gain proficiency with the SystemVerilog language.Students use the concepts learned in class to design processors with the goals of achieving correctness and high performance for a suite of representative test programs. Using RISC-V enables students to validate and benchmark their designs by compiling test programs using GCC with a custom linker. By collaborating as a team, students learn how to write and debug a large code base over the two-month project.For computer architecture educators, we describe technical aspects of the final project and common advanced features implemented by students. We hope describing our experience serves not only to demonstrate a method of teaching modern computer architecture, but also to inspire other course designs centered around other aspects of modern computer architecture (GPUs, FPGAs, hardware/software codesign, etc). We have open-sourced our lab and project materials to enable others to teach similar courses.CCS CONCEPTS• Social and professional topics → Computer engineering education.ACM Reference Format:Stephen A. Zekany, Jielun Tan, James A. Connolly, and Ronald G. Dreslinski. 2021. Teaching Out-of-Order Processor Design with the RISC-V ISA. In ISCA Workshop on Computer Architecture Education (WCAE ’21), June 17, 2021, Virtual Event, USA. ACM, New York, NY, USA, 8 pages. https://doi.org/10.1145/nnnnnnn.nnnnnnn
Automated Defect Inspection in Reverse Engineering of Integrated Circuits

In the semiconductor industry, reverse engineering is used to extract information from microchips. Circuit extraction is becoming increasingly difficult due to the continuous technology shrinking. A high quality reverse engineering process is challenged by various defects coming from chip preparation and imaging errors. Currently, no automated, technology-agnostic defect inspection framework is available. To meet the requirements of the mostly manual reverse engineering process, the proposed automated frame- work needs to handle highly imbalanced data, as well as unknown and multiple defect classes. We propose a network architecture that is composed of a shared Xception- based feature extractor and multiple, individually trainable binary classification heads: the HydREnet. We evaluated our defect classifier on three challenging industrial datasets and achieved accuracies of over 85 %, even for underrepresented classes. With this framework, the manual inspection effort can be reduced down to 5 %.
The Design Optimization for Hot Forging Process of Connecting-rod Based on Approximate Model and Numerical Simulation

A methodology of design optimization based on approximate modeling and numerical simulation was proposed.Based on the analysis of hot forging for connecting rod using FEM software,the initial approximate modeling between design variables and objective function was established.The objective is to reduce forming damage on the connecting-rod part and the design variables are the dimensions of flash.During the process of design optimization,the actual numerical simulation program is replaced by approximate modeling.The damage value of forging parts is calculated and the process parameter of hot forging for connecting rod is optimized with very good results.
Saliency-Guided Textured Contact Lens-Aware Iris Recognition

Iris recognition requires an adequate level of the iris texture being visible to perform a reliable matching. In case when a textured contact lens covers the iris, a false non-match is reported or a presentation attack is detected. There are, however, scenarios in which one wants to maximize the probability of a correct match despite the iris texture being being partially or mostly obscured, for instance when a non-cooperative subject conceals their identity by purposely wearing textured contact lenses. This paper proposes an iris recognition method designed to detect and match portions of live iris tissue still visible when a person wears textured contact lenses. The proposed method includes (a) a convolutional neural network-based segmenter detecting partial live iris patterns, and (b) a Siamese network-based feature extraction model, trained in a novel way with images having non-iris information removed by blurring, to guide the network towards salient live iris features. Experiments matching pairs of iris images in which the iris is not wearing a lens in one image and is wearing a textured contact lens in the other, show a lower EER=10.6% for the proposed algorithm, compared to state-of-the-art iris code-based iris recognition (EER=33.6%). The source codes of the method are offered along with the paper.
Virtual Reality Technology in Regeneration of Historic Cultural Landscape

From cultural meaning of virtual reality technology,the paper discusses that it will influence living style profoundly and puts forward the possibility of regeneration of historic cultural landscape based on it.The application of virtual reality technology in design and planning work focus on two aspects: VR modeling VR design.The paper reviews the research status of application of virtual reality technology in regeneration of historic cultural landscape,discusses methods and studies cases on the topic.
Assessment of Technologies for Reducing CO2 Emission

There are a wide variety of technologies for reducing CO2 emissions, of which a greater part are those of energy technologies. The paper aims at assessing these technologies with regional differences of technology characteristics taken into account. The first part examines merits and demerits of individual technology, and thus envisages its possible future. The second part describes a global energy model, which generates comprehensive long term future scenarios of energy and CO2 emission in various regions of the world.
Wireless channel prediction based on auxiliary environmental information

The prediction range for wireless channels is limited by the coherent time while the pilot symbols are limited by the Doppler frequency in channel prediction. The relationship between the channel state information (CSI) and the static channel parameters was analyzed to develop a parametric predictor on the basis of auxiliary environmental static parameters. When the static parameters and the state of the moving station are known, the algorithm can accurately predict the CSI variation. The algorithm performance is compared with auto regression predictors using real measurement data for narrow-band flat-fading channel model. The prediction length is not limited to 1/2 wavelength and the number of pilots is a function of only the static scatterers.
Dual-Head Contrastive Domain Adaptation for Video Action Recognition

Unsupervised domain adaptation (UDA) methods have become very popular in computer vision. However, while several techniques have been proposed for images, much less attention has been devoted to videos. This paper introduces a novel UDA approach for action recognition from videos, inspired by recent literature on contrastive learning. In particular, we propose a novel two-headed deep architecture that simultaneously adopts cross-entropy and contrastive losses from different network branches to robustly learn a target classifier. Moreover, this work introduces a novel large-scale UDA dataset, Mixamo→Kinetics, which, to the best of our knowledge, is the first dataset that considers the domain shift arising when transferring knowledge from synthetic to real video sequences. Our extensive experimental evaluation conducted on three publicly available benchmarks and on our new Mixamo→Kinetics dataset demonstrate the effectiveness of our approach, which outperforms the current state-of-the-art methods. Code is available at https://github.com/vturrisi/CO2A.
Analysis of airborne phased-array antennas using hybrid method of parallel FDTD and UTD

A scheme is presented by applying FDTD-UTD algorithm to analyze phased array antennas mounted on airborne platform.The FDTD-UTD hybrid algorithm is presented by using the field vector component as a combination point of the FDTD and the UTD method.Firstly,the parallel FDTD method is utilized to find the field vector component of the phased array antennas,and then these vector components are employed as the source of the UTD method to obtain the disturbed radiation pattern.Numerical results show that the hybrid method not only extends the use of UTD algorithm,but also effectively solves challenging problems of this type.