Reviewerly/exHarmony huggingface dataset:
DETECTren: Vehicle Object Detection Using Self-Supervised Learning based on Light-Weight Network for Low-Power Devices

Vehicle object detection is a fundamental task in computer vision. Most modern classifiers and trackers are built upon the object detectors. For example, self-driving cars use object detection on low-power devices to capture the information from the surrounding environment. Currently, object detection uses a huge amount of labelled data to train the detector. Moreover, these detectors are designed for high-end hardware (i.e., GPUs) and cannot be used on low-power devices. In this paper, we propose DETECTren, a novel object detector that uses self-supervised learning to leverage both the limited labelled data and the huge amount of unlabelled data. DETECTren learns to accurately detect the vehicle and its bounding box. DETECTren is divided into two tasks, (1) The pretext task and (2) The downstream task. In the pretext task, DETECTren uses an autoencoder with ResNet50 as a backbone sub-network to learn rotation-invariant features. The input image is rotated three times; a 90 degree rotation of the original, a 180 degree rotation of the original, and a 270 degree rotation of the original. These three images along with the original image are fed into the pretext sub-network to output a rotation invariant image. In the downstream task, a detector sub-network is used to detect and regress the bounding boxes coordinates. To match the output of the pretext task and the input of the downstream task, matching convolutional layers layers are used with trainable parameters. DETECTren is implemented using mixed-precision to be compatible with low-power devices. Experiments on the Kitti dataset show that DETECTren achieves high Average Precision (AP).
Performance Analysis of Software Enabled Accelerator Library for Intel Architecture

With the advancement of the General Purpose Graphics Processing Unit (GPGPU) for parallel processing, the Central Processing Unit (CPU) based architecture industry focused its development on software accelerator based performance maximizer so as to provide the parallel environment in a software way. This set of accelerator libraries support all stages of data science or data analysis pipelines viz. preprocessing, transformation, analysis, modelling, validation, and decision-making. The recent trends of large dataset based computation models are to use all the available features to create rule-bases for validation rather than feature summarization or reduction based on subset selection statistical techniques. Deep learning allows these computational models that are composed of multiple processing layers to learn and train from the large dataset with multiple levels of abstraction without reduction in features. Intel’s Data Analytics Acceleration Library (Intel DAAL) enables applications to process large datasets to make faster and better predictions with available Intel processors in a lab environment. In this paper, we will discuss the enabling factors of Intel’s DAAL and how it optimizes the performance in a Python computational environment with and without Anaconda frameworks.
A Multiobjective Discrete Grey Wolf Optimization Approach for Transactional and QoS-driven Web Services Composition

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Application of Data Mining Technology in Software Engineering

Abstract With the rapid development of informatization, computer database software systems have entered various fields of society, which has brought about the explosive growth of industry data. Faced with massive amounts of data, computers with limited storage capacity have to abandon some outdated data, and the application of various data mining technologies related to it has gradually matured. The purpose of this article is to discuss the application research of data mining technology in software engineering. This article analyzes the correlation analysis of a large number of bug repair source code update data and bug defect reports in the version control system SVN and the defect tracking system Bugzilla in the software engineering project development process, and tries to classify the bug report by data mining technology: defect changes and potential defects change. Starting from large-scale software engineering projects, apply data mining technology to the huge software engineeri ng knowledge base. Especially the software development and maintenance are explained, as well as the more challenging problems in the future. This paper uses data mining technology to study the dependency of the source code files of each module of the software system, and helps software developers quickly understand the software architecture by understanding the interrelationships between the modules, and provides suggestions for modification paths. Experimental research shows that this paper compares with F-measure and concludes that FL-M-GSpan algorithm is better than TS-M-GSpan algorithm. At the same time, it is found that the FL-M-GSpan algorithm always has a better accuracy rate close to 95%, while the TS-M-GSpan algorithm always has a better recall rate.
Shallow learning for MTL in end-to-end RNN for basic sequence tagging

Multitask learning (MTL) has been successfully applied for sequence tagging tasks in many machine learning approaches. The idea of MTL is employed to obtain better performance by sharing information among the tasks compared to a single task. However, most of the MTL in end-to-end sequence tagging models perform by sharing the entire set of parameters resulting in overwriting of parameters and need retraining of all the parameters. In the paper, a novel pipeline architecture has been proposed that effectively combines two RNN based sub-networks for sequence tagging tasks with minimal training overhead and truly acts as an end-to-end system. The proposed architecture performs Named entity recognition (NER) tagging as the primary sequence tagging task along with phrase tagging that works as the assistance sequence tagging task. To utilize the learning from the assisted network, we modify the base network by adding fully connected (FC) layers for NER. Our MTL approach tries to retain the learning parameters from individual tasks and, retraining is done only to the FC(shallow) layers of the modified base network. To validate the proposed MTL settings, we train on CoNLL 2003 corpus and compare the result with previously well established end-to-end based NER tagging models.
Sal-HMAX: An Enhanced HMAX Model in Conjunction With a Visual Attention Mechanism to Improve Object Recognition Task

The Hierarchical Max-pooling models (HMAX) have demonstrated excellent outperformance when integrated with various computer vision algorithms for the purpose of recognizing objects in images. However, the conventional HMAX model has two main problems: 1) it is computationally expensive to learn base matrixes, especially at layer S2 (matching layer), and 2) the patch selection in the standard HMAX model is randomly selected resulting in generating redundant and uninformed extracted patches. In this paper, a combination of the HMAX model and a selective attention mechanism is proposed to address the aforementioned drawbacks of HMAX models. Applying a selective mechanism of attention filters out unnecessary information and highlights more important and significant parts of a given image., An attention function is used to increase the matching velocity at the S2 layer, since through attention we only consider patches with more details. On the other hand, high operational precision is expected due to the extraction of distinct patches in the training phase of the S2 layer. The results of experiments show that the proposed model outperforms the conventional HMAX model. The proposed model establishes a mean accuracy of 93.7% on the first ten best-classified categories using the Caltech-101 dataset.
Revisiting Machine Learning Training Process for Enhanced Data Privacy

The increasing use of machine learning algorithms for nearly every aspect of our lives has brought a new challenge to the forefront, one of user-privacy. Once the data has been shared by the user online, it is difficult to revoke the access of that data if it has already been used to train the model. For any personal data, every user should reserve the right for the data to be forgotten. To solve the above-mentioned problem, a few frameworks have been introduced recently to achieve machine unlearning or inverse learning. Although there is no specific definition of forgetting in DNNs (deep neural networks) yet, our focus will be on selectively forgetting a subset of data belonging to a class, which was initially used to train the model, without the need of re-training from scratch, nor using the initial training data. This method scrubs the weights clean of the data that needs to be forgotten. Concepts for the stability of stochastic gradient descent and differential privacy are exploited in this approach to address the problem of selective forgetting in DNNs.
POSTER: An Open-Source Framework for Developing Heterogeneous Distributed Enclave Applications

We present an integrated open-source framework to develop, deploy, and use event-driven distributed enclaved applications across heterogeneous Trusted Execution Environments (TEEs). Our framework strives for strong application authenticity and integrity guarantees, and optionally confidentiality and availability, while minimizing the run-time Trusted Computing Base (TCB). For software developers, our framework provides a high level of abstraction over the platform-specific TEE layer that provides isolation, attestation and secure communication amongst distributed application components, allowing developers to focus of application logic. We provide a notion of event-driven programming to develop distributed enclave applications in Rust and C for heterogeneous TEEs, including Intel SGX, ARM TrustZone and the open-source Sancus. This heterogeneity makes our framework uniquely suitable for a broad range of use cases which combine cloud processing, mobile and edge devices, and lightweight sensing and actuation.
Cryptocurrency and ETF's - Regulatory Challenges - A Case for Progressive Regulators

Ever since their introduction in 1989 in the USA, Exchange Traded Funds (ETF's) have gained wide popularity and Asset Management Companies (AMCs) have been using innovative nomenclature for them to entice potential investors. Among a host of theme-based ETFs which offer varied investment strategies and returns, Blockchain ETF's are of relatively recent origin and have caught investors' attention as a substitute for investment in blockchain technology directly or through listed companies. Since 2018, blockchain ETFs have appeared on the horizon designing products backed by companies investing in or developing blockchain based solutions. Parallel to this, there is a growing class of unregulated Bitcoin ETF, Trusts and other financial products that track the value of Bitcoin and trade on traditional market exchanges rather than cryptocurrency exchanges. The instant paper attempts to examine the fundamentals of blockchain technology, the factors throwing up block chain and Bitcoin ETFs in to prominence and their legal recognition by the regulators. It also attempts to study whether the blockchain ETF is just a proxy for Bitcoin/Bitcoin ETFs or a radically different product requiring fresh regulatory oversight. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> The contents of this research are personal views of the authors.
Using digital teaching materials in university education

In this paper a quantitative research is conducted on the perception of 203 university professors from Chile (n=36), Mexico (n=85) and Argentina (n=82) on the effectiveness of digital teaching materials in the classroom. This paper analyzes the differences in perception according to the country and discusses the influence of the Global Innovation Index (GII) of each country on these differences.
Image Quality Assessment: Edge Based Entropy features estimation using Soft Computing Techniques

With the always expanding interest for image handling based applications, the requirement for Image Quality Assessment (IQA) techniques for proficient and dependable assessment of picture quality in concurrence with human quality decisions has similarly taken off at each phase of image processing. The limits of customary IQA strategies as far as having the option to work just under a pre-characterized set of filtering algorithms confines the effectiveness of methods in explicit degradations has roused the investigation of IQA dependent on Soft Computing metaheuristic techniques, which are equipped for choosing the features shrewdly. A swarm based meta heuristic technique known as Grey Wolf Optimization (GWO) has been utilized, which impersonates the conduct of wolf packs to encompass and chase their prey. This paper presents a basic and direct strategy for artificially assessing computerized images by a solitary parameter, entropy. The benchmark dataset Colourlab Image Database: Image Quality (CID:IQ) is employed for the extensive Matlab investigations. Outcomes are addressed graphically to approve the viability of GWO algorithm over others. GWO evidently surmounts Artificial Neural Networks (ANN) based optimization algorithm in terms of entropy. These meliorations are likely to propel the future researchers to prefer the soft computing techniques for use in different image handling areas including horticultural sciences, medication, cryptography, and so on, for evaluating their images in pre-processing phases for improved outcomes.
Complementary Fusion of Deep Network and Tree Model for ETA Prediction

Estimated time of arrival (ETA) is a very important factor in the transportation system. It has attracted increasing attentions and has been widely used as a basic service in navigation systems and intelligent transportation systems. In this paper, we propose a novel solution to the ETA estimation problem, which is an ensemble on tree models and neural networks. We proved the accuracy and robustness of the solution on the A/B list and finally won first place in the SIGSPATIAL 2021 GISCUP competition.
Image Mosaic Technology Based on Harris Corner Feature

Image mosaic technology can obtain images with ultra-wide viewing angles without reducing the resolution. With the increasing demand of panoramic images, image mosaic technology has become the focus of computer vision research. This paper studies the image mosaic technology based on Harris corner feature. The extraction and description process of corner features and the process of image mosaic by calculating homography matrix are introduced in detail. Finally, the experiment is carried out on the existing data set and the actual shot data, and the experimental results can directly observe the process of scene splicing under different perspectives. The experimental results show that it has a good application effect in actual images, but in the case of great changes in the scene perspective, that is, to complete the mosaic of more than ten images, it is necessary to introduce the technology of boundary fusion.
Recurrent Neural Network based Sensor Fusion Algorithm for Alternative Position, Navigation and Timing

A new sensor fusion algorithm for Alternative Position, Navigation and Timing (APNT) is designed with deep Recurrent Neural Network (RNN) using Long Short-Term Memory (LSTM). The proposed deep RNN for APNT estimates the position of the Unmanned Aerial Vehicle (UAV) using the UAV position measurements from the holographic radar and the Radio Positioning System (RPS). For the training dataset generation, flight simulations with multiple episodes are conducted with the measurement models of the holographic radar and the RPS. The testing results of the well-trained deep RNN are provided for verification and validation of the proposed deep RNN. The advantage of the proposed deep RNN over the Extended Kalman Filter (EKF) which is a conventional sensor fusion algorithm is demonstrated by comparing their testing results.
Evaluation of an AoI Mapping and Analysis Tool for the Identification of Visual Scan Pattern

The analysis of operator’s work patterns in safety-critical domains is increasingly assisted by eye-tracking technologies. A reason is a growing demand for empirically justified assurance to ascertain an equal level of safety after the implementation of new techniques and higher levels of automation. In the field and simulation studies, head-mounted eye-tracking devices are a preferred choice because of the easy, reliable and fast startup. The downside is time-consuming work to map gaze points to the world/workplace coordinates which makes head-mounted devices applicable to a small number of episodes to analyze. This paper presents a solution called AoI Mapping and Analysis Tool (AMAT) that relies on visual features provided by the integrated scene video camera. AoI-templates can be defined from the video recording and are constantly matched for an AoI-analysis. AMAT was evaluated with a field study example from Air Traffic Control (ATC) in the tower at Linköping City airport and a training simulator study example from Vessel Traffic Service (VTS) in Gothenburg. Example situations demonstrate the capabilities of AMAT to define AoIs, evaluate the validity of the chosen set of AoIs and export to an example analysis program Eloquence for the visualization of results. The AoI-sequence of 4 vessels encounter in the Gothenburg archipelago was shown as well as the comparison of four tower controller’s sequences during the final approach. The final discussion highlights the capabilities and limitations of the implemented techniques that rely on the AKAZE point feature detection and linear homographic transformations. A major source of disturbance lies in the varying light conditions evoking under-exposure of the video camera and fast head movements.
Quantitative analysis of syllable properties in Croatian, Serbian, Russian, and Ukrainian

Ten chapters from a Russian novel and its translations into Croatian, Serbian, and Ukrainian are automatically syllabified following the same approach in all four languages. Syllable frequencies and syllable length are modelled by probability distributions which are commonly used for frequency and length of words (the Zipf-Mandelbrot distribution and the Dacey-Poisson distribution, respectively). We show that Zipf’s law of brevity, according to which the more frequent words tend to be shorter, can be extended to syllables. We suggest a generalization of the Menzerath-Altmann law, a relation between word length and the mean syllable length. The generalized version of the law is valid for both word types and word tokens.
Use of Clustering with CNN to Categorize Images inside different directories on OS

In this burgeoning area of Machine Learning and computer vision (CV), image classification plays an eminent role in various real-world problem statements. Advanced implementation of the technology can be used to minimize human interaction with the computers and perform efficiently to overcome the time constraints of the real-world problem. One such application can be used to classify the images stored inside different directories in the operating system and group them on different folders based on their categories. The proposed work focuses on the implementation of this application to overcome the time constraints while searching the images manually inside different directories by the human-computer interaction. This paper demonstrates the use of Keras and Tensorflow to implement CNN on image-based clustering. The convolution operation is used to extract the features from the images. These features are extracted using edge detection, object extraction, and image segmentation. After the features being extracted, the images are clustered (based on their Euclidean distance from other images depending on their features) using the K-means clustering algorithm. To enhance the result, pre-processing is employed on the images for the reduction of noise and elucidate the blurred pixels of the image.
Adaptive Catalyst for Smooth Convex Optimization

In this paper, we present a generic framework that allows accelerating almost arbitrary non-accelerated deterministic and randomized algorithms for smooth convex optimization problems. The major approach of our envelope is the same as in Catalyst []: an accelerated proximal outer gradient method, which is used as an envelope for a non-accelerated inner method for the \(\ell _2\) regularized auxiliary problem. Our algorithm has two key differences: 1) easily verifiable stopping condition for inner algorithm; 2) the regularization parameter can be tuned along the way. As a result, the main contribution of our work is a new framework that applies to adaptive inner algorithms: Steepest Descent, Adaptive Coordinate Descent, Alternating Minimization. Moreover, in the non-adaptive case, our approach allows obtaining Catalyst without a logarithmic factor, which appears in the standard Catalyst [, ].
Construction of hot spot tracking model of university network public opinion based on text clustering

From the perspective of big data, internet public opinions in higher education institutions are informative, arbitrary, invisible and abrupt. Taking consideration of these features, this paper constructs a model to tracking network public opinion hot spots in higher education institutions. Adopting data processing ways including Chinese words segmentation and text clustering based on Single-Pass, this model consists three parts: data collection, data processing and data analysis. Through this method, this paper figures out important contents among a bulk of online public opinion topics in colleges and universities and determinate hot spot topics focused by campus students.
Reflexivity and Artificial Intelligence in Control (Subjectness-oriented Approach)

The organization of control processes is based on specialized paradigms and ontologies (subjectness, cybernetic, etc.). Control paradigms and ontologies set requirements for AI, which determine the development of AI, as well as the creation of mechanisms for controlling and neutralizing negative consequences. Reflexivity in the control of social systems has become increasingly important in recent decades. The article examines the place and role of AI in ensuring reflexive activity in the subjectness paradigms of control. The evolution of subjectness paradigms is considered from the standpoint of the development of scientific rationality. The proposed approach can be used to solve urgent problems of IFAC and TECIS due to the increasing role of hybrid reality environments (subjectness, digital, physical), including the problems of convergence of natural and artificial intelligence.