Reviewerly/exHarmony huggingface dataset:
TempDiff: Feature Map-Level CNN Sparsity Enhancement at Near-Zero Memory Overhead via Temporal Difference

The demand for distributed vision systems at an unceasingly larger scale requires the availability of sensor nodes able to execute high-level visual tasks (e.g., object detection for visual monitoring). Such tasks routinely entail the high computational cost of Convolutional Neural Networks (CNN), which conflicts with the tight power budget and memory capacity of sensor nodes at the edge. This paper introduces an approach to reduce the computational cost of object detection in CNN accelerators available in edge devices. The proposed approach induces additional feature map-level sparsity (i.e., computation skipping) at inference time by exploiting temporal correlation among frames. The proposed approach has an uncommonly favorable computation-memory tradeoff, as significant computation reduction is achieved at the cost of very small additional memory for the storage of intermediate features. As further benefits, no architectural changes or retraining are required, allowing immediate deployment in existing vision frameworks and suppressing the need for storing multiple models. Results show that the proposed TempDiff method achieves up to 37% computation reduction with 1.1% accuracy drop based on the SSD(VGG16) object detection network, under both VIRAT and ImageNet-VID datasets. Similarly, 18.3% (35.8%) computation reduction at 3.3% (3.2%) memory overhead, and 3.8% (6.8%) accuracy drop in YOLOv1 (VGG16) (SSD (VGG16)) is achieved under the CAMEL dataset. Furthermore up to 58% computation reduction with 2% accuracy drop and 3.7% memory overhead were achieved for YOLOv3-Tiny network under the ImageNet-VID dataset.
Private Hierarchical Clustering in Federated Networks

Analyzing structural properties of social networks, such as identifying their clusters or finding their central nodes, has many applications. However, these applications are not supported by federated social networks that allow users to store their social contacts locally on their end devices. In the federated regime, users want access to personalized services while also keeping their social contacts private. In this paper, we take a step towards enabling analytics on federated networks with differential privacy guarantees about protecting the user's social contacts. Specifically, we present the first work to compute hierarchical cluster trees using local differential privacy. Our algorithms for computing them are novel and come with theoretical bounds on the quality of the trees learned. Empirically, our differentially private algorithms learn trees that are of comparable quality (with at most about 10% utility loss) to the trees obtained from the non-private algorithms, while having reasonable privacy (0.5 łeq ε łeq 2). Private hierarchical cluster trees enable new application setups where a service provider can query the community structure around a target user without having their social contacts. We show the utility of such queries by redesigning two state-of-the-art social recommendation algorithms for the federated social network setup. Our recommendation algorithms significantly outperform the baselines that do not use social contacts.
Design and Simulation of AES S-Box Towards Data Security in Video Surveillance Using IP Core Generator

Broadcasting applications such as video surveillance systems are using High Definition (HD) videos. The use of high-resolution videos increases significantly the data volume of video coding standards such as High-Efficiency Video Coding (HEVC) and Advanced Video Coding (AVC), which increases the challenge for storing, processing, encrypting, and transmitting these data over different communication channels. Video compression standards use state-of-the-art techniques to compress raw video sequences more efficiently, such techniques require high computational complexity and memory utilization. With the emergent of using HEVC and video surveillance systems, many security risks arise such as man-in-the-middle attacks, and unauthorized disclosure. Such risks can be mitigated by encrypting the traffic of HEVC. The most widely used encryption algorithm is the Advanced Encryption Standard (AES). Most of the computational complexity in AES hardware-implemented is due to S-box or sub-byte operation and that because it needs many resources and it is a non-linear structure. The proposed AES S-box ROM design considers the latest HEVC used for homeland security video surveillance systems. This paper presents different designs for VHDL efficient ROM implementation of AES S-box using IP core generator, ROM components, and using Functions, which are all supported by Xilinx. IP core generator has Block Memory Generator (BMG) component in its library. S-box IP core ROM is implemented using Single port block memory. The S-box lookup table has been used to fill the ROM using the .coe file format provided during the initialization of the IP core ROM. The width is set to 8-bit to address the 256 values while the depth is set to 8-bit which represents the data filed in the ROM. The whole design is synthesized using Xilinx ISE Design Suite 14.7 software, while Modelism (version10.4a) is used for the simulation process. The proposed IP core ROM design has shown better memory utilization compared to non-IP core ROM design, which is more suitable for memory-intensive applications. The proposed design is suitable for implementation using the FPGA ROM design. Hardware complexity, frequency, memory utilization, and delay are presented in this paper.
A digital driving method using pulse‐density modulation with a random dither matrix for higher motion image quality

Abstract We propose a new digital driving technique using pulse‐density modulation (PDM) with a random dither matrix for evenness of luminance uniformity and higher motion image quality. As a digital driving technique, pulse‐width modulation (PWM) is conventionally used for active‐matrix organic light‐emitting diode (AMOLED) displays in order to improve luminance uniformity. However, in PWM driving, it is difficult to realize high‐frame‐rate driving for improving motion image quality. On the other hand, it is considered that PDM driving method is effective to obtain high motion image quality because of non‐frame‐refreshing property. In the present paper, we proposed a PDM driving technique using a random dither matrix and evaluated the relationship between the display image quality and the driving frequency in the proposed method through simulations. As a result, it is confirmed that good image quality can be obtained by using a blue‐noise mask as a random dither matrix at a practical driving frequency. Moreover, we demonstrated the proposed technique using an actual AMOLED panel.
An Efficient Missing Data Imputation Model on Numerical Data

With the rising spotlight on Data Science and Big data analytics in both private and public sectors, enormous amount of domain-specific information has been collected. Missing information is a typical issue and consequently has pulled in significant consideration. Incomplete data analysis is a practical problem, and an intriguing issue. This study presents a methodology and framework for imputing missing value and at different missing rate of variables. An effective missing data imputation model based using Long Short-Term Memory (LSTM) on numerical dataset is proposed. For the hidden layers in a deep neural network, Rectified Linear Units (ReLU) is conventionally used as an activation function. However, there are situations where progressively more complicated functions are required to tackle vanishing gradient problem. The proposed LSTM with ReLU activation function is advantageous in terms of mapping features, weight distribution and loss function. We estimate LSTM-ReLU’s hyper-parameter values and compared its average RMSE performance with another neural network LSTM model LSTM-Softmax and machine learning models like Naïve Bayes and KNN. Experiments were carried out on the various datasets. Results obtained using LSTM-ReLU model outperforms LSTM-Softmax, Naïve Bayes and KNN method. LSTM-ReLU achieves an average performance gain of 40% – 46% over LSTM-Softmax imputation with varied missingness value and 40% - 42% on varied hyper-parameter size. Accuracy is much greater and mean square error less than 0.02% with Naïve Bayes and KNN model.
Workload Prediction of Serverless Computing

research-article Share on Workload Prediction of Serverless Computing Authors: Jinyue Wei Dongbei University of Finance and Economics, China Dongbei University of Finance and Economics, ChinaView Profile , Ming Gao Dongbei University of Finance and Economics; Northeastern University, China Dongbei University of Finance and Economics; Northeastern University, ChinaView Profile Authors Info & Claims ICDLT 2021: 2021 5th International Conference on Deep Learning Technologies (ICDLT)July 2021 Pages 93–99https://doi.org/10.1145/3480001.3480016Online:12 November 2021Publication History 0citation65DownloadsMetricsTotal Citations0Total Downloads65Last 12 Months65Last 6 weeks6 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
MultiTempGAN: Multitemporal multispectral image compression framework using generative adversarial networks

Multispectral satellites that measure the reflected energy from the different regions on the Earth generate the multispectral (MS) images continuously. The following MS image for the same region can be acquired with respect to the satellite revisit period. The images captured at different times over the same region are called multitemporal images. Traditional compression methods generally benefit from spectral and spatial correlation within the MS image. However, there is also a temporal correlation between multitemporal images. To this end, we propose a novel generative adversarial network (GAN) based prediction method called MultiTempGAN for compression of multitemporal MS images. The proposed method defines a lightweight GAN-based model that learns to transform the reference image to the target image. Here, the generator parameters of MultiTempGAN are saved for the reconstruction purpose in the receiver system. Due to MultiTempGAN has a low number of parameters, it provides efficiency in multitemporal MS image compression. Experiments were carried out on three Sentinel-2 MS image pairs belonging to different geographical regions. We compared the proposed method with JPEG2000-based conventional compression methods and three deep learning methods in terms of signal-to-noise ratio, mean spectral angle, mean spectral correlation, and laplacian mean square error metrics. Additionally, we have also evaluated the change detection performances and visual maps of the methods. Experimental results demonstrate that MultiTempGAN not only achieves the best metric values among the other methods at high compression ratios but also presents convincing performances in change detection applications.
Deploying Blockchain based Security Solutions for Vehicular Edge Computing Networks

The technological advancements in the automobile industry with support from Internet of things (IoT) have gained a lot of popularity in this developing era. However, these developments come up with various security challenges in vehicular networks and have attracted many researchers to work in this domain. The road safety and security can be better managed by embedding blockchain in vehicular networks. The efficient security technique makes use of edge computing for significant utilization of network resources. This paper proposes two different deployment techniques of blockchain in edge-based vehicular networks: i) blockchain based solution for edge devices, ii)blockchain based solution for RSUs. Along with these security solutions, various challenges related to the computation of data generated by different vehicular devices have also been addressed.
Load Balancing in Cloud Computing Using Support Vector Machine and Optimized Dynamic Task Scheduling

Cloud computing enables the internet based computing to everywhere. In cloud computing the task scheduling and load balancing is the important issue that must be considered in data centers. In cloud platform service providers allocate the resources based on the available resources this will increase the QoS. The process of distributing workloads is a load balancing Technique. In this paper focusing the load balancing between the virtual machine. Load balancing are different types that is network load, memory capacity, storage capacity and CPU load. Load balancing supports failover, enabling scalability and decreases costs and maximizes the resource availability. This paper presents the detailed review of load balancing with machine learning technique such as naïve bayes, random forest, Decision Tree and Support Vector Machine. By these machine learning model, SVM algorithm significantly to classify the incoming request to reduce the processing time (makespan). Also provide the moth flame optimization technique for dynamic task scheduling to make better scheduling under different load condition. We have experimented and evaluated this algorithm in a cloud simulation environment; we obtain better outcome than some other well-known algorithms. The Simulation results demonstrate that the proposed technique has a better development in terms of reducing the makespan time and load balancing.
On the Road to Large-Scale 3D Monocular Scene Reconstruction using Deep Implicit Functions

Autonomous driving relies on building detailed models of a vehicles surroundings, including all hazards, obstacles and other road users. At present, much of the autonomous driving literature reduces the world to a collection of parametric 3D boxes. While this framework is sufficient for many driving scenarios, other important scene details (e.g. overhanging structures, open car doors, debris, potholes etc.) are not modelled. Recently deep implicit functions have been shown to be suitable for representing fine grained details at arbitrarily high resolutions using images alone. However, they have predominantly been employed in constrained situations, such as reconstructing individual objects or small-scale scenes. In this work we explore the application of deep implicit functions to larger scenes in the context of real-world autonomous driving scenarios. In particular we focus on the challenging case where only monocular images are available at test time. While most implicit function networks rely on watertight meshes for training, these are not in general available for real world scenes. We therefore propose an alternative training scheme using Li-DAR to provide approximate ground truth occupancy supervision. We also show that incorporating priors such as pre-detected object bounding boxes can improve the quality of reconstruction. Our method is evaluated on a real-world autonomous driving dataset.
Printed Arabic Characters Recognition Using Combined Features and CNN classifier

In this paper we investigate the optical characters recognition for Arabic language (AOCR). This system is considered as a challenging research topic due to richness and difficulties of Arabic writing. The OCR system incorporates three main stages that are segmentation, feature extraction and recognition. In this work, we have proposed a new method to recognize printed Arabic characters. This method is based on the combined features extraction, which are the densities of black pixels, invariant moments of Hu and Gabor features and the Convolution Neural Network CNN classifier. Experiments are conducted on the Printed Arabic Text set PAT-A01.The result show that the features combination enhances the recognition accuracy rate.
High utility itemset mining using genetic algorithm assimilated with off policy reinforcement learning to adaptively calibrate crossover operation

Abstract Mining high utility itemsets (HUI) is a current thrust field in data mining that has received numerous methodologies for addressing it effectively. The difficulty with HUI is to locate a number of items that have a high degree of utility in comparison to other different sets in a transaction database. Traditional accurate HUIM algorithms usually have to solve the exponential problem of big search spaces when the size or number of different items in the database is quite vast. Evolutionary computation (EC)‐based algorithms have been offered as an alternate and successful technique to solving HUIM issues since they may generate a collection of approximately optimum solutions in a short amount of time. Many genetic algorithm (GA)‐based approaches have been developed in recent years to efficiently mine HUI from transaction databases. The selection technique, crossover probability, mutation probability, and finishing criteria of the genetic algorithm have an greater impact on generating a reasonably decent solution and the processing time. Particularly crossover is a convergence operation which is intended to pull the population toward a local minimum/maximum. During HUIM using GA both low and high crossover rate will have the problem of decreasing the quality of intermediate itemset and take longer time to converge to some optima and vice versa. This problem can be solved by adjusting the crossover rate adaptively depending on environmental inputs. The proposed approach describe a hybrid system that employs a reinforcement learning (RL) agent to adaptively calibrate the crossover operation to increase the performance of a genetic algorithm. To estimate state‐action utility values, the RL agent employs Q‐learning, which it then employs to execute high‐level adaptive control over the crossover operation in the genetic algorithm. To evaluate the performance of the proposed methodology, extensive experiments were conducted on a four benchmark datasets and compared with three state‐of‐art EC approaches HUPE UMU ‐GRAM, Bio‐HUIF‐GA, HUIM‐BPSO, and one exact approach HUP‐Miner . The result analysis witnessed that proposed approach outperforms EC approaches in terms of execution time, discovered HUIs and convergence.
OmniOpt – A Tool for Hyperparameter Optimization on HPC

Hyperparameter optimization is a crucial task in numerous applications of numerical modelling techniques. Methods as diverse as classical simulations and the great variety of machine learning techniques used nowadays, require an appropriate choice of their hyperparameters (HPs). While for classical simulations, calibration to measured data by numerical optimization techniques has a long tradition, the HPs of neural networks are often chosen by a mixture of grid search, random search and manual tuning.In the present study the expert tool “OmniOpt” is introduced, which allows to optimize the HPs of a wide range of problems, ranging from classical simulations to different kinds of neural networks. Thereby, the emphasis is on versatility and flexibility for the user in terms of the applications and the choice of its HPs to be optimized. Moreover, the optimization procedure – which is usually a very time-consuming task – should be performed in a highly parallel way on the HPC system Taurus at TU Dresden. To this end, a Bayesian stochastic optimization algorithm (TPE) has been implemented on the Taurus system and connected to a user-friendly graphical user interface (GUI). In addition to the automatic optimization service, there is a variety of tools for analyzing and graphically displaying the results of the optimization.The application of OmniOpt to a practical problem from material science is presented as an example.
The Behavior Change Design Cards: A Design Support Tool for Theoretically-Grounded Design of Behavior Change Technologies

Despite a wealth of behavior change theories and techniques available, designers often struggle to apply theory in the design of behavior change technologies.We present the Behavior Change Design (BCD) cards, a design support tool that makes behavioral science theory accessible to interaction designers during design meetings.Grounded on two theoretical frameworks of behavior change, the BCD cards attempt to map 34 behavior change techniques to five stages of behavior change, thus assisting designers in selecting appropriate techniques for given behavioral objectives.We present the design of the BCD cards along with the results of two formative and one summative study that aimed at informing the design of the cards and assessing their impact on the design process.
American Sign Language Static Gesture Recognition using Deep Learning and Computer Vision

Specially-abled people (speech and hearing impaired) rely on hand-gestures for communication on a daily basis. Majority of the people aren’t aware of the universally accepted hand-gestures alphabet, making communication difficult between the two groups of people. In an attempt to fill this void, this research work proposes a real time hand-gesture based recognition system based on the American Sign Language (ASL) dataset and capturing data through a BGR webcam and processing it using Computer Vision (OpenCV). The 29 static gestures (the alphabet) from the official, standard ASL dataset were trained with the help of Vision Transformer Model (ViT). The model showed an accuracy rate of 99.99% after being trained with 87,000 RGB samples for 1 epoch (2719 batches of 32 images each).
Distributed Merkle’s Puzzles

Merkle’s puzzles were proposed in 1974 by Ralph Merkle as a key agreement protocol between two players based on symmetric-key primitives. In order to agree on a secret key, each player makes T queries to a random function (oracle), while any eavesdropping adversary has to make \(\varOmega (T^2)\) queries to the random oracle in order to recover the key with high probability. The quadratic gap between the query complexity of the honest players and the eavesdropper was shown to be optimal by Barak and Mahmoody [CRYPTO’09].We consider Merkle’s puzzles in a distributed setting, where the goal is to allow all pairs among M honest players with access to a random oracle to agree on secret keys. We devise a protocol in this setting, where each player makes T queries to the random oracle and communicates at most T bits, while any adversary has to make \(\varOmega (M \cdot T^2)\) queries to the random oracle (up to logarithmic factors) in order to recover any one of the keys with high probability. Therefore, the amortized (per-player) complexity of achieving secure communication (for a fixed security level) decreases with the size of the network.Finally, we prove that the gap of \(T \cdot M\) between the query complexity of each honest player and the eavesdropper is optimal.
Video Face Recognition System: RetinaFace-Mnet-Faster and Secondary Search

Face recognition is widely used in the scene. However, different visual environments require different methods, and face recognition has a difficulty in complex environments. Therefore, this paper mainly experiments complex faces in the video. First, we design an image pre-processing module for fuzzy scene or under-exposed faces to enhance images. Our experimental results demonstrate that effective images pre-processing improves the accuracy of 0.11%, 0.2% and 1.4% on LFW, WIDER FACE and our datasets, respectively. Second, we propose RetinacFace-mnet-faster for detection and a confidence threshold specification for face recognition, reducing the lost rate. Our experimental results show that our RetinaFace-mnet-faster for 640\(\,\times \,\)480 resolution on the Tesla P40 and single-thread improve speed of 16.7% and 70.2%, respectively. Finally, we design secondary search mechanism with HNSW to improve performance. Ours is suitable for large-scale datasets, and experimental results show that our method is 82% faster than the violent retrieval for the single-frame detection.