Reviewerly/exHarmony huggingface dataset:
A novel cell structure‐based disparity estimation for unsupervised stereo matching

It is well known that preserving depth edges is an effective solution for achieving the accurate disparity map in stereo matching, but many state-of-the-art methods do not preserve depth edges well. In order to solve it efficiently, the cell structure containing irregular and regular shape regions is designed to preserve depth edges. Based on the well-designed cell structure, a novel disparity estimation method for stereo matching is proposed, in which a two-layer disparity optimization method is proposed to refine the disparity plane; it includes the front-parallel disparities computation and slanted-surfaces disparity plane refinement. In the framework of front-parallel disparities computation, a tree-based cost aggregation method is presented to make full use of the segmentation information of cells and then performing semi-global cost aggregation. In the framework of slanted-surfaces disparity plane refinement, a new probability model is proposed that employs Bayesian inference for refining disparities in textureless, weak texture and occluded regions. Experimental results show that higher accuracy could be achieved via the proposed method compared with some known state-of-the-art stereo methods on KITTI 2015 and Middlebury dataset, which are the standard benchmarks for testing the stereo matching methods. It can also be indicated that the proposed method can produce accurate disparity map and have good generalization performance.
Organ Donation Application Using Blockchain Security

This article has developed an application for tracking the use of donor organs, which assists the individuals to find different donors and analyze the needs of different people. The proposed system is implemented by using blockchain and ethereum security mechanism. Maintaining healthy practices is critical for app security. To identify system vulnerabilities, the donor application must undertake application security testing. Security plan rules must fulfill fundamental criteria in line with company requirements and legislation. It facilitates the search process for necessitous people and it will be effortless as well as time saving. With the development in healthcare services, organ transplantation is increasing at an unprecedented rate by making it a significant research domain. All this information needs to be available whenever it is required by the hospital and patients. There is a need for developing a uniform tool to maintain the significant medical-related information like organ transplantation requirement of organs in a different hospital. The manual organ donation method has a number of drawbacks, including the fact that it consumes more time, frequently results in unreliable results, needs a lot of manpower, lacks donor knowledge, data retrieval takes a long time, and the percentage of accuracy is also poor. It may be difficult to approach the correct donor in an emergency situation. Rare blood groups and organs are not available all the time at all blood banks and recipients find difficulties to track the right donor. Henceforth, the proposed application provides elegant management of blood and organs, a list of hospitals, blood banks, and donors online.
Quantum entanglement in a family of Heisenberg models with the multiple components of Dzyaloshinsky-Moriya and Kaplan-Shekhtman-Entin-Wohlman-Aharony interactions

Quantum entanglement is one of the most important measures of quantum correlations. Using group-theoretical approach we found a family of four nine-parameter quantum states for the two-spin-1/2 Heisenberg system in an external magnetic field and with multiple components of Dzyaloshinsky-Moriya (DM) and Kaplan-Shekhtman- Entin-Wohlman-Aharony (KSEA) interactions. Exact analytical formulas are derived for the entanglement of formation for the quantum states found. In the proceeding, the family of found states and the structure of interactions in them are discussed.
PROFCAD: An Algorithm to Detect Anomalies in Cloud Applications for KPI Monitoring Systems

Cloud based applications have a lot of advantages over traditional applications in terms of usability and cost, and have become the norm in today’s technological world. With the emergence of such services, it has become critical to make sure that they guarantee high availability. Analysing these applications and their performance indicators is one way to make sure that they are running smoothly. Machine learning techniques such as anomaly detection can be used to make sure that the Key Performance Indicators are behaving normally. In this paper, we propose a novel algorithm based on supervised learning to detect and identify anomalies in KPI data. PROphet Forest Combination for Anomaly Detection is a three stage model, based on forecasting, feature engineering and classification. We evaluate the performance of the model with two time series datasets which capture real traffic communications. Our model has been able to detect anomalies accurately and has performed well in comparison with the other state of the art anomaly detection algorithms.
Face Recognition Based on Panoramic Video

Recently, face recognition has been widely used in lives, but it can still only maintain good results in specific scenarios. In different tasks, changes in the external environment will cause diversified changes in face images, such as panoramic video. The barrel distortion of the panoramic images generated by the fisheye lens will cause the face images to be deformed to varying degrees around the fisheye image. A new model DCMNet is proposed in this paper and a panoramic face dataset PIFI is created based on the panoramic video system. After experiments are conducted on different datasets, the results show that our model has better results on popular datasets and panoramic dataset.
A Noisy Image Segmentation Method Based on Improved Active Contour Model

Using active contour model to segment image is a classical image segmentation method. But the effect of this method is not good. The current active contour model is sensitive to noise, and it is difficult to achieve accurate segmentation of weak boundary image. This paper proposes an image segmentation algorithm based on gradient vector flow active contour model. Firstly, the wavelet transform is used to process the segmented image to solve the interference of noise on image segmentation. Then, the gradient vector flow active contour model is used to segment the denoised image to fit the contour curve evolution process of different regions in the image, so as to realize the segmentation of different regions. Compared with other current image segmentation algorithms, the simulation results show that the gradient vector flow active contour model can segment the image with high accuracy, and the segmentation time is greatly reduced, and the anti-noise ability is improved. The overall performance of the algorithm is obviously better than other image segmentation algorithms.
Layerweaver+: A QoS-Aware Layer-Wise DNN Scheduler for Multi-Tenant Neural Processing Units

Many cloud service providers employ specialized hardware accelerators, called neural processing units (NPUs), to accelerate deep neural networks (DNNs). An NPU scheduler is responsible for scheduling incoming user requests and required to satisfy the two, often conflicting, optimization goals: maximizing system throughput and satisfying quality-of-service (QoS) constraints (e.g., deadlines) of individual requests. We propose Layerweaver+, a low-cost layer-wise DNN scheduler for NPUs, which provides both high system throughput and minimal QoS violations. For a serving scenario based on the industry-standard MLPerf inference benchmark, Layerweaver+ significantly improves the system throughput by up to 266.7% over the baseline scheduler serving one DNN at a time.
Manifest Information System at PT ASDP Indonesia Ferry (Persero) Ketapang Branch Web-Based

PT ASDP Indonesia Ferry (Persero) is one of the state-owned companies in Indonesia that plays a role in connecting the islands in Indonesia. PT ASDP Indonesia Ferry (Persero) itself, apart from being a liaison between islands, has other duties, namely serving service users and ensuring the safety of service users including vehicles and passengers, but PT ASDP Indonesia Ferry (Persero) has a system that can record the number of service users, both vehicles and passengers. Manually. With these problems, a web-based manifest information system was created that can record and collect information on service users, both vehicles and passengers, quickly. So that in service, service users fill out the manifest and get the manifest code before going to the tollgate, then the manifest code is validated by the field officer. The result of this study is an information system that can record service users both vehicles and passengers with a web-based basis so as to assist field officers in recording and making manifest reports.
Real Time Laser Tracking

Tracking algorithms are a vast, vibrant and complex area of research in computer vision and due to their increased utilization in the commercial application they have propelled researchers to continuously devise more efficient and competitive algorithms. Laser tracking, by definition, is to track a blob of light over a sequence of images. Most of the laser-tracking algorithms in literature deal with pre-recorded videos, however, most of the scenarios in which it is used demand real- time response on a relative memory and compute constrained environment with a very low margin of error. It becomes pivotal to analyse the real-time performance of any algorithm before it is used in these cases. Hence the extensive study under this topic is taken. We develop an algorithm to satisfy this criterion (real-time) while still consuming 52 percent less memory compared to widely used algorithm background subtraction.
Challenges of Agile Adoption in Banking Industry: A Systematic Literature Review

Bank is a highly regulated institution, therefore, should comply with the regulation set by the authorities. Executing a project in a bank will need persistent examination and highly anticipated documentation. The bank industry faces pressure because of arising new and nontraditional competitors who use the agile approach. However, there may appear challenges during the adoption because the bank industry usually uses a traditional approach. Hence, a study based on a systematic literature review was conducted to understand the possibility of agile adoption in the banking industry and to explore the challenges concerning agile adoption in the banking industry across the globe. From each challenge will be discussed the solution to deal with it. Knowledge areas from PMBOK (Project Management Body of Knowledge) were used in this study to map those challenges. The most mentioned challenges come from project integration management and project resource management. The challenges also mapped into project scope management, project cost management, project risk management, project schedule management, project communications management, project quality management, and project stakeholder management. The banking industry used SATP and SAFe to ease agile adoption if it complies with the regulation. The result provided in this can help academicians to better understand the challenges of agile adoption and potential solutions in the banking industry. As the practitioners can get insight and lessons learned provided in this paper to solve the challenges.
Automatic Baggage Threat Detection Using Deep Attention Networks

Detecting threats in densely packed luggage is challenging for aviation security due to the partial occlusion or self-occlusion of prohibited items. Computer-aided systems have assisted security personnel to an extent. However, they require in the loop manipulation of X-ray baggage images to improve visibility of concealed prohibited items. Researchers have proposed several methods to detect threats automatically, but the occlusion problem is still prevalent. This paper proposes a novel attention mechanism that leverages spatial and channel-wise information of a given intermediate feature map. The mechanism can be seamlessly placed into existing Deep Convolutional Neural Network (DCNN) architectures. It sequentially infers the channel and spatial attention that recalibrates feature responses of the network by highlighting visual cues and dulling cues that do not contribute to the semantics of an image. In our experimentation, the proposed attention mechanism is implemented into Faster Region-based Convolutional Neural Network (Faster-RCNN) and thoroughly validated on publicly available datasets such as OPXray, SIXray and HIXray. It outperforms prior methods on the OPIXray, achieving a mean average precision (mAP) of 91.20%. For completeness, we also validate the proposed approach on ImageNet and MS-COCO datasets; it achieves an accuracy of 77.12 top-1 and 93.46 top-5 on ImageNet; and 39.7 mAP on MS-COCO.
Detection and Analysis of P2P Malware Detection in IoT Smart Home Applications

The Internet of Things is a trending technology with the objective of connecting billions of devices to the Internet and offering automation like never. The devices can make their own decisions based on the input data. With all the advantages and features of the IoT platform, the only challenge that seems to be complicated is security. IoT security is a vast topic because of the numerous layers in architecture and the complexity involved in the data processing operations. The research study considered statistical analysis using Python programming language for analyzing the importance of security framework and how the security frameworks can excel against P2P botnet type of malware. A public data set was part of the solution design on the Google Colab platform. The data set consisted of many entries and variables, which revealed the data packets in P2P attacks. This attack is relevant to the discussion of the security framework because all the layers of the architecture are affected by a malware attack. The peer-to-peer communication between two or more IoT devices requires support by the controller layer of the architecture. The details of botnet traffic and attack are self-explanatory. The objective is to detect the network packets with malicious codes include using the proposed method. Detecting SYN flood attacks, ICMP flood attacks, HTTP attacks, and DNS amplification attacks are part of the solution.
Detection of Spam Bots on Twitter using Machine Learning

Twitter is a popularly used microblogging website that is used to share views, opinions, and updates. However, in recent times, an epidemic of spammer accounts have spread across the website causing disorder and chaos among the normal users. These spammers either aim to promote some commercial agenda or disturb the peace in the online environment. Our project aims to analyze the tweets made by users and predict if they might be spammers so that appropriate action can be taken on them. This is done using machine learning. The random forest algorithm has been modified by giving weighted importance to certain variables assigned using domain knowledge that has been obtained from exploratory analysis of various twitter data sets and knowledge from scientific research papers. A bag of words has also been added to the algorithm, in order to quickly identify the key phrases used by spam bots. By identifying the spammers we can systematically report them and create a more peaceful online environment.
Interpretation of Adversarial Attack on Unsupervised Domain Adaptation

Recent advances in deep neural networks has accelerated the process of automation in several fields like image processing, object detection, segmentation tasks and many more. Though, it has been also proved that these deep neural networks or deep CNNs need large scale dataset to be trained on to produce desired output. Supplying huge dataset often becomes difficult for many fields. Domain adaptation is supposed to be a possible way to cope with this problem of large data requirement as it allows model to gain experience from one large source dataset during training and exploit that experience during working with any smaller, related but technically different dataset. But the threat remains when the concept of adversarial machine learning strikes. Like many other deep learning models, adaptive models seem to be vulnerable to adversarial attacks. We target to analysis how these attack techniques from adversarial machine learning affect unsupervised adaptive models’ performance for two related but structurally different dataset like MNIST and MNISTM. We used three different attack techniques called FGSM, MIFGSM and PGD for the experiment. Experiments show the deadly effect of these attack techniques on both of the baseline and adaptive models where adaptive model seem to be more vulnerable than the baseline non-adaptive model.
Oil Pipelines Detection Based on Unmanned Aerial Vehicle Pipeline Patrol Image

Target detection of oil pipeline for the perspective of Unmanned Aerial Vehicle (UAV) is one of the important topics of UAV oil pipeline patrol. The existing methods have limitations for the image with complex background, and can only identify the location of the pipeline, but cannot obtain the specific geometric information and width characteristics of the oil pipeline. In this paper, an oil pipeline detection and extraction algorithm based on UAV pipeline patrol is proposed. Wavelet multi-scale edge detection is used to improve the robustness of pipeline edge detection in complex background. Based on the stroke width transform, the specific geometric information of the oil pipeline is obtained. The pipeline position information can be obtained based on Hough linear detection. On this basis, the pipeline detection algorithm process is proposed and tested. The method can obtain more information about the pipeline more accurately, which is the necessary prerequisite for locating pipeline deformation in the process of UAV oil pipeline patrol.
Ensemble Kalman Filter for Continuous-Discrete State-Space Models

The ensemble Kalman filter (EnKF) is well-established for discrete state-space models. In this paper, we provide the methodology of applying the EnKF to continuous-discrete (CD) state-space models. The proposed CD EnKF algorithm is a bank of the CD extended Kalman filters for the time update. Then, the observation update is formulated using the Gaussian-sum distributed predicted state probability density function (PDF). We also provide the observation update based on the Dirac’s delta mixture predicted state PDF. The numerical simulation using a benchmark filtering problem called the satellite reentry is conducted to investigate the performance of the CD EnKFs. The performance comparison with the EnKF applied to the discretized model is also made.
Prelims

Citation (2021), "Prelims", Carbonaro, A. and Breen, J.M. (Ed.) Effective Leadership for Overcoming ICT Challenges in Higher Education: What Faculty, Staff and Administrators Can Do to Thrive Amidst the Chaos (Emerald Studies in Higher Education, Innovation and Technology), Emerald Publishing Limited, Bingley, pp. i-xi. https://doi.org/10.1108/978-1-83982-306-020211001
Frame-level Feature Tokenization Learning for Human Body Pose and Shape Estimation

In this paper, we propose a frame-level feature tokenization method for human body pose and shape es-timation(FTHE). Despite conventional 3D human pose and shape estimation methods have achieved success based on a single image, recovering accurate and smooth 3D human motion from a video is still challenging. Different from existing methods, our FTHE aims to pay attention to the meaningful detailed temporal feature between different granular tokens of video objects, and reduce the dominance of the current static frame. To this end, we carefully design an accurate and interpretable temporal encoding module for feature extraction and motion reconstruction. More specifically, our model captures temporal features and static features of different granular tokens, and simultaneously enhances their correlation and multi-granularity consistency. Extensive experimental results on large-scale publicly available datasets demonstrate that our FTHE achieves compelling performance compared to the state-of-the-art. Code has been made available at: https://githuh.com/chriful/FG_2020_FTHE.
Research on multi-source heterogeneous data fusion method based on cross-modal transformer

There are a large number of heterogeneous data in manufacturing industry, which are characterized by diversity of sources, low quality and complex types, and there are also some problems such as poor data consistency, weak availability and low sharing rate. Therefore, a multi-source heterogeneous information fusion method based on Transformer is proposed in this paper. The method can fuse multi-source heterogeneous data of multiple modes of enterprises, and make use of complementary information of cross-modal heterogeneous data to form the combination and unified expression of data features. The method focuses on the interaction between multi-mode sequences across different time steps and completes the fusion from one mode to another. The empirical analysis shows that the proposed multi-mode fusion method can well fuse different types of heterogeneous data, and a good fusion data set can effectively improve the model classification effect and provide data support for subsequent data analysis and decision-making.
Effects of Measurement Uncertainties on Multitarget Tracking

Target tracking is the process of estimating the positional attributes of unknown targets using the measurement data from sensors like radar, LiDAR, camera of air, space, ground, maritime, underwater and autonomous systems. Target tracking involves finding the exact number of targets at any given time and identifying the state (e.g., location and velocity) of the targets over time in the platform or earth coordinates using the measurement data. An accurate measurement data will produce good track estimates. However, in a real-world scenario, none of the measurements are perfect due to several factors, like the resolution of the sensor, noise and inaccuracies originating from external influences. Uncertainties in the measurements will degrade the performance of any trackers. Ignoring some of the errors in the measurements for simplicity may lead to wrong spatial perception.
Service Engineering in Next Generation Networks

Mobile Edge Computing (MEC) is defined by the European Telecommunications Standards Institute as a cloud server at the edge of a mobile cellular network.This edge is equipped with a virtualization platform which may be a Network Function Virtualization (NFV) platform.The server may be deployed at a macro base station site, multi technology aggregation site, and even Radio Network Controller (RNC) site.It can have access to all the information available at the edge (e.g.position of mobile users, radio conditions).This enables a plethora of applications in areas such as Content Delivery Networks (CDNs), Internet of Things (IoT), smart cities, and network optimization.This talk discusses our main research contributions in the field.From design point of view, it introduces innovative architectures and procedures that enable provisioning the previously listed applications in the so called next generation networks.These networks are known to be highly dynamic and might mobile entities such as smartphones in hybrid cloud/fog or 5G telco networks.From algorithmic point of view, it discusses the obtained results, as well as, our on-going work for service placement and dynamic Quality of Service (QoS) management.Several case studies are presented throughout the talk such as fire detection and fighting application and autonomous cars.