Reviewerly/exHarmony huggingface dataset:
Towards Flexible and Compiler-Friendly Layer Fusion for CNNs on Multicore CPUs

In deep learning pipelines, we demonstrate the performance benefits and tradeoffs of combining two convolution layers into a single layer on multicore CPUs. We analyze when and why fusion may result in runtime speedups, and study three types of layer fusion: (a) 3-by-3 depthwise convolution with 1-by-1 convolution, (b) 3-by-3 convolution with 1-by-1 convolution, and (c) two 3-by-3 convolutions. We show that whether fusion is beneficial is dependent on numerous factors, including arithmetic intensity, machine balance, memory footprints, memory access pattern, and the way the output tensor is tiled. We devise a schedule for all these fusion types to automatically generate fused kernels for multicore CPUs through auto-tuning. With more than 30 layers extracted from five CNNs, we achieve a 1.04x geomean with 1.44x max speedup against separate kernels from MKLDNN, and a 1.24x geomean with 2.73x max speed up against AutoTVM-tuned separate kernels in standalone kernel benchmarks. We also show a 1.09x geomean with 1.29x max speedup against TVM, and a 2.09x geomean with 3.35x max speedup against MKLDNN-backed PyTorch, in end-to-end inference tests.
DBSCAN-Based Tracklet Association Annealer for Advanced Multi-Object Tracking

Recently, as the demand for technological advancement in the field of autonomous driving and smart video surveillance is gradually increasing, considerable progress in multi-object tracking using deep neural networks has been achieved, and its application field is also expanding. However, various problems have not been fully addressed owing to the inherent limitations in video cameras, such as the tracking of objects in an occluded environment. Therefore, in this study, we propose a density-based object tracking technique redesigned based on DBSCAN, which has high robustness against noise and is excellent for nonlinear clustering. Moreover, it improves the noise vulnerability inherent to multi-object tracking, reduces the difficulty of trajectory separation, and facilitates real-time processing through simple structural expansion. Through performance test evaluation, it was confirmed that by using the proposed technique, several performance indices were improved compared to the existing tracking technique. In particular, when added as a post processor to the existing tracker, the tracking performance owing to noise suppression was considerably improved by more than 10%. Thus, the proposed method can be applied in industrial environments, such as real pedestrian analysis and surveillance security systems.
Automatic detection of cyberbullying and threatening in Saudi tweets using machine learning

Social media has become a major factor in people's lives, which affects their communication and psychological state. The widespread use of social media has formed new types of violence, such as cyberbullying. Manual detection and reporting of violent texts in social media applications are challenging due to the increasing number of social media users and the huge amounts of generated data. Automatic detection of violent texts is language-dependent, and it requires an efficient detection approach, which considers the unique features and structures of a specific language or dialect. Only a few studies have focused on the automatic detection and classification of violent texts in the Arabic Language. This paper aims to build a two-level classifier model for classifying Arabic violent texts. The first level classifies text into violent and non-violent. The second level classifies violent text into either cyberbullying or threatening. The dataset used to build the classifier models is collected from Twitter, using specific keywords and trending hashtags in Saudi Arabia. Supervised machine learning is used to build two classifier models, using two different algorithms, which are Support Vector Machine (SVM), and Naive Bayes (NB). Both models are trained in different experimental settings of varying the feature extraction method and whether stop-word removal is applied or not. The performances of the proposed SVM-based and NB-based models have been compared. The SVM-based model outperforms the NB-based model with F1 scores of 76.06%, and 89.18%, and accuracy scores of 73.35% and 87.79% for the first and second levels of classification, respectively.
State Estimation of Axisymmetric Target Based on Beacon Linear Features and View Relation

In order to realize state estimation for axisymmetric targets and improve the accuracy and robustness of state estimation, a state estimation method for axisymmetric targets based on beacon linear features and view relation is proposed in this paper. The depth camera is used to collect the image and depth information of the object, and the features of the beacon line are extracted by the thinning process and Hough transform. Then, the rotation matrix model based on view relation is constructed to solve the target state. Finally, an axisymmetric shore power plug is taken as the experimental object and the L–V (linear features and view relation) state estimation method is compared with the C–H and C–IPPE state estimation methods. The experimental results show that the L–V state estimation method has higher accuracy and robustness.
Guessing Outputs of Dynamically Pruned CNNs Using Memory Access Patterns

Dynamic activation pruning of convolutional neural networks (CNNs) is a class of techniques that reduce both runtime and memory usage in CNN implementations by skipping unnecessary or low-impact computations in convolutional layers. However, since dynamic pruning results in different sequences of memory accesses depending on the input to the CNN, they potentially open the door to inference-phase side-channel attacks that may leak private data with each input. We demonstrate a memory-based attack inferring a dynamically-pruned CNN’s outputs for various victim CNN models and datasets. We find that an attacker can train their own machine learning model to learn to guess victim image classifications using the victim’s memory access patterns with significantly better than random chance. Moreover, unlike previous related work, our attack: 1) continually leaks user data for each input and 2) does not require adversarial presence during the victim training.
An Ultra-High Speed Gesture Recognition Algorithm Based on MobileNetV2

To solve the problem of interference in gesture detection and recognition under complex background, an ultra-high speed gesture recognition method based on a depth model is proposed. Generally, 12.5 frames per second (FPS) can achieve the requirements of real-time detection. This article reaches 2600FPS, which is defined as ultra-high-speed. The proposed algorithm makes up for the defects of the accuracy and robustness in traditional methods, as well as improves the low recognition speed of the general neural network under complex situations. Firstly, a neural network is designed and trained to accurately distinguish three different gestures. Secondly, a pruning and a merging operation is performed on the trained neural network, respectively. Without significantly affecting the detection and recognition results, the network is compressed and the efficiency is improved. Finally, the single-precision floating point data is quantized into integer data to further improve the detection and recognition speed. The experimental results show that the gesture recognition algorithm proposed in this paper can reach 2600FPS under the premise that the accuracy rate is 97.8%.
A Ship Radar Matching Method Based on Target Attributes and Point Pair Topological Characteristic

Abstract We introduce a new ship target association method called SRM-AT, which stands for a Ship Radar Matching Method Based on Target Attributes and Point Pair Topological Characteristics. Using the radar properties of electromagnetic signals, as well as the longitude and latitude coordinates of electromagnetic signals and remote sensing images, this method calculates the point pair properties probability and PPTC (Point Pair Topological Characteristic) probability of electromagnetic signals and remote sensing images, as well as the syntheses of BPAF (Basic Probability Assignment Function) of the two through D-S evidence theory, to update the probability matrix of target association between electromagnetic signals and remote sensing images by using probability relaxation labelling method iteratively. The main contributions of this paper are: firstly, using decision trees to classify electromagnetic signals; finally, at the same time applicable to electromagnetic signals and remote sensing image ship formation and non-ship formation form. Through comparative experiments, this paper verifies that the method in this paper is superior to the comparative method in terms of association accuracy.
Optimal K-Means Clustering Algorithm for Weblog Mining

World wide web (WWW) generates a huge number of unstructured data and information. The information is stored in weblog file. Weblogs information can be analyzed and visualized by various clustering algorithm. In this work, the k-means clustering algorithm is applied for grouping of the users with similar interest based on accessing of similer information. The optimal value of k is also determined by Elbow method to obtained optimal numbers of clusters. Clustering results are analyzed by various values of k. Comparative analysis ofvarious methods used for selecting the optimal number of clusters are also analyzed.
Black Dawet App Android Based

Restaurants with large crowds may face queues and large orders. One of them is the Dawet Jembut Kecabut Restaurant which sells dawet ireng. This restaurant has not implemented technology in terms of queuing and customer orders so that there is not a maximum queue of buyers and sellers have to ask every order to the buyer. From the above problems, the Black Dawet App was made. Android based queue and order tracking application. The waterfall method is applied in building this application. This application uses the Flutter SDK which uses the Dart programming language as an android display and CodeIgniter for the backend and MySQL as database management. With this application you can maximize the order queue at the Dawet Jembut Kecabut Restaurant, because the buyer must have a queue number and enter the order when going to buy.
Cloud Computing Applications: An Open Source Software (OSS) Approach

World is moving towards “Cloud Computing” as part of the advancement of Technology in general and Information and Communication Technology (ICT) in specific. In simple form Cloud computing can be referred to as the application of computer for storing and accessing of data through the web enabled devices, rather than your computer's hard drive. Cloud computing is mainly performed functions are managing ,storing and processing data with help of internet.The present study aimed to see you how making the best use of the cloud storage application. There are many cloud storage providers that not only provide you the free cloud storage space but also with many other features. Some cloud storages are there in the market that not only provides the best user experience and incredible features, but helps users to enjoy the free services after the SignUp. It is clear from the study that all the cloud applications are easy to use for storage their personal and business data. Cloud applications such as mega, Media fire, Adrive, Surdoc cloud storage are easy to use and provide free storage space up to 50GB Microsoft is the largest company as measured by market cap. Its market share is $889.286B.
Information Hiding Using a Coded Aperture as a Key

This paper proposes a visual information hiding technique using coded apertures as a key. In general, a watermark embedded as high-frequency components is difficult to extract if it is captured outside of the focal rength and defocus blur occurs. Installation of a coded aperture (CA) into the camera is a simple solution to mitigate the difficulty and several attempts are conducted to make better design for stable extraction. To the contrary, our motivation is to design a specific CA as well as information hiding scheme, where secret information can be decoded only if an image with hidden information is captured with the key aperture whose characteristic is matched with the information hiding scheme. The proposed technique designs the key aperture patterns and information hiding scheme through evolutionary multi-objective optimization so as to minimize the decryption error of a hidden image when using the key aperture while minimizing the accuracy when using other apertures. Experimental results have shown that the proposed information hiding technique was more secure than a password-based system which uses case-insensitive eight alphanumeric characters.
Orthopantomographic X ray defocused image restoration with blind and nonblind deconvolution for diagnostic use in dental application

Quality enhanced digital images using deblurring and restoration techniques plays vital role in many fields of medical applications, security related surveillances, remote sensing and satellite communications and defense sector. Use of X ray images is done extensively in medical field for diagnostic purpose which has become a backbone for medical practitioners. Quality images without any blur or noise therefore becomes an important tool for diagnosis which leads to use of image processing for improvement of the captured X ray images but were defocused while actual capturing. This paper elaborates about the findings achieved after restoration of Orthopantomographic X ray defocused image used in dental application. This research work emphasizes two techniques as blind and non-blind deconvolution used for the restoration of Orthopantomographic X-ray which is captured for the diagnosis related to growth and development monitoring of pediatric teeth. The results after deconvolution are compared to understand one of the unaddressed problems normally present as no previous information of point spread function for blind technique as compared with non-blind technique. It was observed that deconvolution techniques applied to defocused image restoration are helpful in further usage in dental applications as well as in the field of machine learning and digital image processing. At the end, simulated results of this work are compared using different image parameters like structural similarity index (SSIM). Further the performance of these deconvolution methods is compared and results are represented graphically for better understanding and usage in the field of image processing.
Research on Statistical Downscaling Method of Meteorological Data Based on Photovoltaic Power Forecast

Aiming at the low accuracy of traditional photovoltaic power prediction, a statistical downscaling method embedded with high-precision meteorological data is proposed. In this method, It can effectively make up for the insufficiency of future meteorological changes in the forecast area and provide more accurate meteorological data for photovoltaic power forecasting. The basis of this approach is that regional meteorological changes are conditional on large-scale weather. The statistical downscaling method takes the large-scale numerical forecast product as the forecast factor, establishes the statistical model between the regional forecast quantity and the large-scale forecast factor by using the historical data, and realizes the downscaling of the numerical forecast product. The simulation experiment is carried out using the weather forecast data of a certain area. The experiment shows that the method can effectively improve the error influence caused by the lower resolution, and it has practical significance.
Temporal-Spatial Deformable Pose Network For Skeleton-Based Gesture Recognition

Gesture recognition is a challenging research topic, and also has a wide range of potential applications in our daily life. With the development of hardware and advanced algorithms, we can easily extract skeleton data from video sequences and apply them for the recognition task. In this paper, we propose a novel temporal-spatial deformable pose network to leverage space and time information together. Our proposed network can automatically locate the most correlated joints across multiple frames and extract features accordingly. Additionally, we introduce a parallel multi-scale convolutional layer with different dilation rates, which can capture multi-term temporal information efficiently. We have conducted experiments on MSRC-12, ChaLearn 2013, and ChaLearn 2016 datasets and our proposed method outperforms state-of-the-art methods. Moreover, Additional experiments showed that our proposed module is more robust to handle noise data and dynamic gestures with various temporal scales.
Knowledge Distillation for Human Action Anticipation

We consider the task of training a neural network to anticipate human actions in video. This task is challenging given the complexity of video data, the stochastic nature of the future, and the limited amount of annotated training data. In this paper, we propose a novel knowledge distillation framework that uses an action recognition network to supervise the training of an action anticipation network, guiding the latter to attend to the relevant information needed for correctly anticipating the future actions. This framework is possible thanks to a novel loss function to account for positional shifts of semantic concepts in a dynamic video. The knowledge distillation framework is a form of self-supervised learning, and it takes advantage of unlabeled data. Experimental results on JHMDB and EPIC-KITCHENS dataset show the effectiveness of our approach.
A New Algorithm For Robot Path Planning Based On Automatic Shunting Of Ants And Particle Swarms

This paper proposes a new robot path planning algorithm which is a fusion algorithm of automatic shunt ant and particle swarm algorithm. In the first step, the robot motion space model is described by the grid method, in the second step, the global navigation path is planned by the automatic shunting ant algorithm, and the third step is the particle swarm algorithm for local search and tuning to obtain the best path. Experiments show that the algorithm is very effective.
<i>Live 4 Life</i>: A spatial performance tool to play the ephemeral and improvise with space and playback speeds

This article describes my own way to improvise with space using a computer-based tool implemented in SuperCollider. The objective of this spatial performance tool is to have an ergonomic spatio-temporal and spectral control over numerous sound objects in real time, in order to alternate between spatialised polyrhythms and textures. After a brief review of spatial audio context, the spatial performance tool is summarised and detailed here by focusing on one of the core parameters: the playback speeds, which can act both on rhythm and space and enable among others the spatio-temporal articulation of the performance. As well as discussing the word ‘comprovisation’ and my conception of human–computer improvisation, the possibilities and approach of the tool in terms of improvisation and controllerism are illustrated through the use and combination of different controllers (computer keyboard, tactile interfaces, force touch sensors). Whereas some controllers are more dedicated to the selection and triggering of streams of spatialised sound events, others have their own mappings and ways of acting on some parameters (depending on the temporality of the sounds: playing or future events).
Algorithm for initializing a generalized fermionic Gaussian state on a quantum computer

We present explicit expressions for the central piece of a variational method developed by Shi et al. which extends variational wave functions that are efficiently computable on classical computers beyond mean-field to generalized Gaussian states [1]. In particular, we derive iterative analytical expressions for the evaluation of expectation values of products of fermionic creation and annihilation operators in a Grassmann variable-free representation. Using this result we find a closed expression for the energy functional and its gradient of a general fermionic quantum many-body Hamiltonian. We present a simple gradient-descent-based algorithm that can be used as an optimization subroutine in combination with imaginary time evolution, which by construction guarantees a monotonic decrease of the energy in each iteration step. Due to the simplicity of the quantum circuit implementing the variational state Ansatz, the results of the algorithms discussed here and in [1] could serve as an improved, beyond mean-field initial state in quantum computation. [1] Tao Shi, Eugene Demler, and J. Ignacio Cirac. Variational study of fermionic and bosonic systems with non-gaussian states: Theory and applications. Annals of Physics, 390: 245-302, 2018.
Research on the Path of University Curriculum Resources Construction under the Background of Education Informationization

NOTICE OF RETRACTION: While investigating potential publication-related misconduct in connection with the ICIMTech 2021 Conference Proceedings, serious concerns were raised that cast doubt on the integrity of the peer-review process and all papers published in the Proceedings of this Conference. The integrity of the entire Conference has been called into question. As a result, of its investigation, ACM has decided to retract the Entire Conference Proceedings and all related papers from the ACM Digital Library.
A STUDY ON CUSTOMERS PERCEPTION ON FORENSIC ACCOUNTING AND RISK MANAGEMENT IN SELECTED BANKS OF MUMBAI

Forensic Accounting is an integration of accounting, auditing and investigative skills used by forensic accountant which is suitable for the court and will be a basis of discussion, debate and ultimately dispute resolution. Before the Enron Scam, Rajat Gupta case and Satyam Scam, there was a little knowledge about forensic accounting in India. Media highlighted the irregularities of the Satyam case and need for hiring forensic accountant arose but even today the number of forensic accountants in India is still very low. The forensic accounting industry has gained importance over the years but awareness about the forensic accounting is yet to be created amongst the can be prevented by banks. The banks need to educate their employees and customers. The study is majorly focusing on the perception of banks customers towards forensic accounting regulatory authorities in India. Banks cannot give a 100% security to the people but it can take steps towards preventing the frauds. Technology being the major cause, it is like a two sides of the coin. At one side it has brought digitalisation in the banking industry, which has made all banking transaction very easy. On the other hand technology has given arise to many online banking frauds which and risk management.
Image super-resolution method based on generative adversarial network

Although deep convolutional neural networks have made breakthroughs in the accuracy and speed of single-image super-resolution, there are still many unsolved problems: firstly, How to refine the texture problem when performing super-resolution processing at a larger magnification ratio. Secondly, the existing convolutional neural network image super-resolution algorithms are prone to overfitting and insufficient convergence of the loss function. Aiming at two problems, an image super-resolution method based on generative adversarial network is proposed. The feature map is spatially transformed on the network to solve the problem of refined texture, combined with CycleGAN and SRGAN, the network structure is improved and the loss function is optimized, and the SRCICGAN algorithm is proposed to restore the four times down-sampled image to solve the loss function problem. The experiment is compared with the latest six methods on three data sets. The PSNR and SSIM indicators are 1.92% and 5.49% higher in the Flickr2K data set, respectively, and better visual effects can be obtained in terms of detailed texture.