Reviewerly/exHarmony huggingface dataset:
Learning multi-scale deep features for person re-identification

Person re-identification (Re-ID) aims at retrieving a person of interest across multiple cameras. With the advancement of deep network and increasing demand of intelligent video surveillance, it has gained significantly increased interest in the computer vision community. In this paper, we propose a simple yet effective Multi-Scale Horizontal (MSH) model for person Re-ID task. Firstly, the model consists of a novel Multi-branch network which adopted residual network ResNet50. There are two branches in our network: global branch and local branch. In local branch, the model slice a person into different parts in multi-scales. Secondly, we present a mix pooling method which considering both average and maximum pooling method. Finally, we employ triple loss and softmax loss as the loss function of the network. Experiments on two datasets (Market1501 and DukeMTMC-reID) demonstrate the advantage of the proposed model.
Freedom of speech in liberal and non-liberal traditions

The article presents different theories and comparative analyses of freedom of speech in both liberal and non-liberal traditions. Whereas freedom of speech is not an absolute right, the question is if this right should depend wholly on the truth of the respective opinion or statement. Theories that justify free speech on the grounds of autonomy, tend to make truth a moral requirement of speech. Theories based on civility and public reason do restrict freedom of speech even further, often making a form of recognition a precondition of free speech. This reveals to be particularly relevant in multicultural contexts and discussions about blasphemy. From this overview of contemporary, global comparative debates on free speech, the article draws some conclusions. First, there should be the absolute primacy of free speech regarding governments and the powerful: Speak truth to power. Second, free speech should underlie no constraints where important individual rights are at stake. Third, academic freedom has a special status and should not be subject to the same limits as freedom of expression more generally. Forth, in civil society and the public sphere a more moderate approach to free speech should be adopted based on civility, public reason and recognition. Yet, any limits should be of moral and not legal nature.
An Empirical Review of Blockchain Models for Supply Chain Management from an Analytical Perspective

Supply chain management (SCM) involves design of multiple processes which includes procurement of goods, manufacturing, warehousing, transportation, distribution and selling to customer. Each of these steps requires design &#x0026; development of secure product tracking, privacy preservation, transparent transaction handling, trust modelling and distributed processing operations. These operational characteristics can be directly mapped with blockchains, because they provide transparency, traceability, immutability, and distributed mining operations. Thus, blockchains are quickly gaining popularity as one of the most favorable solutions for SCM modelling. In order to model SCM, a wide variety of blockchain-based solutions are proposed by researchers over the years, and each of these solutions vary in terms performance parameters. These parameters include, response time, scalability, security level, delay to add a new block, cost of deployment, and computational complexity. Due to such a wide variety of algorithmic availability, it becomes ambiguous for SCM designers to select the best blockchain models for a given application. In order to reduce this ambiguity, a survey of recently proposed blockchain models for SCM application are discussed in this text. Most of these models are based on machine learning, and are characterized on the basis of their nuances, advantages, limitations, and possible future research scopes. Upon referring this descriptive review, readers would be able to summarize characteristics for each SCM implementation, and identify the best suited models for their deployments. This text also compares the reviewed models in terms of statistical parameters like delay of block mining, searching time for SCM transaction, security level, computational complexity, and probable use cases. These performance metrics are compared across different models in order to estimate the best performing models for a given blockchain application. After referring this comparison, researchers will be able identify most optimum model &#x0026; their configuration for application-specific SCM deployments. Furthermore, this text also recommends various methods to improve performance of the reviewed models in order to apply them for real-time deployments.
Unmanned Aerial Vehicle and Vertical Takeoff and Landing Technologies

Unmanned aerial vehicles (UAVs), or drones, are aircraft which do not require an onboard human pilot during flight. However, rotary-wing UAVs are more popular, owing to vertical takeoff and landing (VTOL) capability, and they can hover at a point for extended periods. This chapter explores UAV technologies and their possible impact on transportation. An overview is included of various research methodologies that have a vital role in the safe operations of aerial vehicles for urban air mobility. The integration of UAVs in the National Airspace System (NAS) is examined, along with emerging concepts on Urban Air Mobility that focus on applications of drones, including package delivery and freight transportation, air metro, and air taxis. UAV traffic management and Federal Aviation Administration (FAA) regulations for UAV integration in national airspace are also described.
Sentiment Analysis on Cryptocurrency using Youtube Comments

Because of the rising popularity of cryptocurrency in the world, it is essential in these times to understand the market sentiment to make predictions of price and make investment related decisions. Therefore, a model is designed to classify YouTube comments based on cryptocurrency. The proposed model consists of a stacked ensemble consisting of Decision Tree, K Nearest Neighbors, Random Forest Classifier and XGBoost and a meta/base classifier &#x2013; Logistic Regression. The proposed model achieves an accuracy of 94.2%. In addition, based on our research, we've come to several important findings and takeaways about the current state of cryptocurrencies around the world.
A Comparative Performance Analysis of Various Pre-Trained Convolutional Neural Network Models

The forms of the data are changing with technological advancement. With this revolution, the text data sets were replaced by images and today technological advancement has resulted in large datasets in the form of videos. However, there arises the need of technology to deal with images and develop intelligent systems that can identify required information correctly from the images. Such task can be accomplished by the pre-trained models of convolutional neural network. In this paper, we come up with a comparison of performance of various pre-trained cnn models like alexnet, googlenet, squeezenet etc. that are used for image classification. Here, we compare and display the accuracy level of these cnn models of detecting an object from a huge dataset. The performance has been measured on a single dataset of flowers available in the online repository of Kaggle.
Secure Metadata Curating the Agile Software Development Process and its Need for Blockchain Storage

The paper presents the use of Blockchain technology to record the sub-path way of Agile team using Scrum or Lean processes. The agile software development team shares a number of sequential transactions that affect the developmental flow of the software. The key to any agile team’s success is the flow of information and coordination with collective responsibilities of the team. The main factors like the corporate culture, user involvement, team capabilities, project management process, project definition process, testing, objectives and goals, scheduling, budgeting, unambiguous requirements specification, selection of agile method and technology. The paper indicates that any manipulation of these main factors would lead to indifferent results and hence a proper documental control of the development process, which is accessible selectively but understood by all stake holders. The paper brings in Blockchain with smart contracts for successfully implementing and increasing profitability from agile software development in an organization along with effective grants, penalties and all stake holders’ duties regulation.
Optimizing the range of a bilateral filter to enhance defects and threads visibility in x-ray radiographic testing

Radiographic testing is extensively used in non-destructive evaluation. However, the contrast of object defects in X-Ray images is generally weak compared to the image dynamic range, which limits defects visibility and makes the image inspection time consuming and tedious. To circumvent this issue, our work builds on a bilateral filter to extract and amplify the low-contrast local variations of the image, supposed to represent the object defects. A critical step of this approach lies in the selection of the bilateral filter range parameter, which sets a contrast upper-bound to the signals that are filtered out, to be scaled to a constant fraction of the rendering range. As an original contribution, we propose to set this parameter to maximize the tone-mapped quality index (TMQI) of the reconstructed image. Since the TMQI combines a multi-scale structural similarity criterion with a measure of image naturalness, its maximization both discourages the selection of a (too large) range parameter that penalizes structural similarity, and prevents the lack of naturalness associated to an excessive noise amplification caused by a too small range parameter. Image subjective quality assessment experiments not only demonstrate that our approach largely enhances defect visibility on real use cases, but also reveal that our recommended parameter is preferred by users to any reasonable alternative in a large majority of cases ( 90%). An objective evaluation based on the Image Discrete Entropy shows that the enhanced images compare favorably to the ones obtained with previous works devoted to High Dynamic Range image visualization
Further Development of Blockchain Techniques for Security and Expenses

The Ethereum blockchain is intended to move from a Proof-of-Work agreement convention to a Proof-of-Stake one. Right by Construction Casper conveys a theoretical group of numerical Proof-of-Stake agreement conventions however it doesn't give block creation methodologies to hubs which this the undertaking plan to take care of them. A testing system in the view of a current Casper library has been carried out to reproduce blockchains managed by the proposed fundamental techniques and idleness, and hub count and upward are estimated during the reproductions. The carried-out structure has been approved utilizing the essential methodologies and grants the execution of more intricate ones through a conventional code design. The proposed model offers a decent method for contrasting inactivity and upward for techniques yet isn't proficient to assess the manner in which a procedure scales with the quantity of hubs in the organization
Blockchain and Industry 4.0 – A study

Everything around us is transforming with advancement in time, as a result, new technologies are developing with upcoming industry revolution, which is also widely popular as Industry 4.0. It includes new trends related to Cyber Physical system, Augmented Reality, Artificial Intelligence, Internet of Things (IoT), Cloud Computing and many more. Many physical infrastructures are transformed to virtual ones, which allowed decision making and planning in decentralized form. It also enabled users to accomplish real time communications with help of integrated computing algorithms. Main challenge involves decentralization and heterogeneous nature of systems. Blockchain can be solution to this problem, which targets at integration and management of heterogeneous systems. This paper discusses various solutions related to the conjunction of blockchain and Industry 4.0. It also explores various pros and cons of traditional security solutions and provides evaluation of present blockchain-based security solutions. It also discusses blockchain based application areas of Industry 4.0.
Binary descriptors for Copy-Move Forgery Detection in Digital Photographs

Today, image forensic is an emerging area which aims at authenticating the credibility of an image. Sophisticating image editing tools make it easy to forge images in different ways and one amongst them is copy-move (CM) forgery which is considered in this paper. CM forgery modifies the content of an image by copying a portion of an image and pasting it in a distinct location in the similar image. Fraudsters, in order to conceal the fraud and to deceive the human eyes, sometimes do some post-processing operations such as rotation, scaling, multiple CM, etc. The widely used block-based methods for CM forgery detection are not robust enough to affine transformation and are not invariant to scaling, rotation, and noise. So, in this work, key-point-based CM forgery detection methods based on BRISK and ORB descriptors are proposed for detecting CM forgeries in digital images. The presented methods are dependent upon blobs, detecting using DoG operator, from which BRISK and ORB features are extracted. The extracted features are matched using Hamming distance metrics to find similar key points to identify the CM regions. The work was implemented in Python and synthesized images were used in this to analyze and compare the efficacy of the presented techniques. The experimental outcomes demonstrates that the presented technique was effectual for multi-CM attacks and geometric transformations namely rotation and scaling. Though both the methods were able to detect the CM forgeries efficiently, ORB executed faster compared to BRISK.
Drivable Area and Road Anomaly Segmentation using SSLG with V-Disparity Maps

Real world applications like robotic wheelchairs need the automatic detection of roads, potholes, and anomalies. Conventional image processing methods perform the improper recognition of anomalies and result in poor performance. Thus, this article mainly focuses on the implementation of Self-Supervised Label Generator (SSLG) based road anomaly detection system using vertical disparity maps. Initially, the disparity maps are used to identify the borders of the road and then anomalies are identified using filtered disparity maps. Further, the depth anomaly map is calculated using probabilistic approaches. Further, the implementations are performed on real world Red-Green-Blue-Depth (RGB-D) dataset. The simulation results show that the performance of proposed method results in superior performance as compared to the state-of-the-art approaches.
Analysis on Hidden Layer in Deep Artificial Neural Network for Classification of Sentiments

Data in online business contains opinion/comments from the user regarding the purchase that can be used for the benefit of businesses in various aspects. These comments are referred as sentiments on the particular purchase that is required to help taking decisions for further improvement in business. This kind of data produce massive stream, and it is almost impossible of manual tracking and extracting useful information. Hence, every online business has the process of extracting sentiments or opinions from reviews given by users on a particular subject, area or product with Natural Language Processing (NLP) that categorize the opinions of people about the products or the reviews. This research work focuses on hotel industry in which users post comments and views in online mode regarding the hotels they stayed, and NLP is used to label the sentiments on the posts already. Hence, to categorize the database on various sentiments this work implements deep learning architecture with multi feedforward artificial neural network. This neural network has three layers namely input layer, more than one hidden layer and output layer. Among them, fixing number of hidden layers, neurons in hidden layer and dropout ratio has a significant role in obtaining accuracy of the model. This research analyzes the fixation of hidden layers, neurons and dropout ratio by comparing variant sizes and ratios with ReLU activation function. The work is done and visualized in Rapidminer tool.
Text-To-Image Synthesis Using Modified GANs

Synthesis of high-resolution images for the input textual descriptions is a prominent field of research that needs to be looked into, in this world of computer vision and animations. The task of image generation has its own challenges such as, working on the main object and background elements simultaneously, considering the ambiguity of natural language and reducing the noise that may be collected along with the input. It is also equally important to determine the importance of words given in the input textual description and ranking them according to their degree of relevance to desired image. Generative Adversarial Networks (GANs) are one of the newest developments for synthesis of images from text. In our literature review article, we have provided a consolidated idea of GAN s and how their variants are helpful in producing high resolution images.
Detecting DoH tunnels with privacy protection using federated learning

Domain Name System (DNS) service is a core service on the Internet and a key link to ensure the normal operation of the Internet. Since DNS can often pass through firewalls without being intercepted, it creates favorable conditions for attackers to build a covert channel based on the DNS protocol. DNS over HTTPS (DoH) can encrypt DNS lookup and response data packets to ensure that data packets are not monitored and used, but it also makes the DNS tunnels more difficult to detect. Due to the security of DoH, researchers began trying to detect DoH tunnels by using machine or deep learning. The effect of the model is not good if the data quality is poor or the amount of data is insufficient. Due to the privacy of traffic data, it is usually difficult to collect and share private traffic data to a centralized server. We propose a federated-learning DoH traffic classification framework (FL_DoH_CF), which permits multiple institutions to detect DoH tunnels by using convolutional neural network (CNN) without sharing traffic data. The experiments demonstrate that FL_DoH_CF is competitive with centralized learning, and it is still robust for non-independent and identically distributed (No_IID) data, and even achieves an accuracy of 99.86% for extreme one-class No_IID data.
A Study of Real-time Information on User Behaviors during Search and Rescue (SAR) Training of Firefighters

Search and rescue (SAR) is an essential skill for many first responders including firefighters. With the new development of technologies on environmental sensing, we are now equipped with the new capability of utilizing real-time information from a fire scene to assist SAR tasks. This work is to explore the effects of immersive maps, that collect information automatically from the environment including human and building structures, on user behaviors during SAR operations. We have developed a VR prototype system for SAR training with controlled fire scenes and information collections. We also designed and performed a user study focusing on the factors of information amount in an immersive map and danger degree of SAR tasks. We have summarized a set of user behaviors from our study, and captured their features with statistical data analysis. Our results confirm the advantages of real-time information for SAR tasks and differences of user behaviors under dangerous situations. Our results also demonstrate the potential of studying user behaviors with virtual training and deriving insights to design effective training programs.
[DC] Designing Immersive Tools for Supporting Cognition in Remote Scientific Collaboration

Scientists collaborate remotely across institutions, countries and continents. However, collaborating remotely is challenging. Video-conferencing tools used for meetings limit the cognitive practices that collaborators can partake in. In virtual reality (VR) users can gain back spatial affordances present in collocated collaboration and we can design interactions that would not be possible in the real world. My research aims to investigate how VR can support cognition in remote scientific collaboration through the design, de-velopment, and study of Embodied Notes: a cognitive support tool designed to be used in a collaborative virtual environment.
Federated Multi-task Graph Learning

Distributed processing and analysis of large-scale graph data remain challenging because of the high-level discrepancy among graphs. This study investigates a novel subproblem: the distributed multi-task learning on the graph, which jointly learns multiple analysis tasks from decentralized graphs. We propose a federated multi-task graph learning (FMTGL) framework to solve the problem within a privacy-preserving and scalable scheme. Its core is an innovative data-fusion mechanism and a low-latency distributed optimization method. The former captures multi-source data relatedness and generates universal task representation for local task analysis. The latter enables the quick update of our framework with gradients sparsification and tree-based aggregation. As a theoretical result, the proposed optimization method has a convergence rate interpolates between \( \mathcal {O}(1/T) \) and \( \mathcal {O}(1/\sqrt {T}) \) , up to logarithmic terms. Unlike previous studies, our work analyzes the convergence behavior with adaptive stepsize selection and non-convex assumption. Experimental results on three graph datasets verify the effectiveness and scalability of FMTGL.
Cross-Temporal Snapshot Alignment for Dynamic Multi-Relational Networks

Abstract A dynamic network is often represented as a sequence of snapshots evolving over time. In certain real-world scenarios, the identities of nodes in snapshots of a dynamic network are unknown and need to be figured out. To deal with such a challenge, recently, the task of cross-temporal snapshot alignment for dynamic networks is proposed, which aims to match equivalent nodes across temporal snapshots of a dynamic network given a small set of identified nodes. However, in many dynamic multi-relational networks like temporal knowledge graphs, the relation type information of edges, which can be useful for the alignment task, is neglected by existing methods. In this paper, we focus on a similar task but pay special attention to dynamic multi-relational networks. We propose a Relation-Aware Cross-Temporal Snapshot Alignment model (RCTSA) that incorporates time-dependent topological structure information into temporal node embeddings and temporal relation embeddings. To disentangle time-dependent information and time-independent information of the dynamic multi-relational network, RCTSA maintains a time embedding for each snapshot to preserve the temporal information which is incorporated into entity embeddings and relation embeddings to get the temporal embeddings. After training, the learned entity embeddings of unidentified nodes together with time embeddings can be used for the alignment task. Experimental results on real-world dynamic multi-relational networks demonstrate the effectiveness of our model.