Miracle-dz/UniFAR-FTUs huggingface dataset:
Zero-shot Learning for Visual Recognition Problems

In this thesis we discuss different aspects of zero-shot learning and propose solutions for three challenging visual recognition problems: 1) unknown object recognition from images 2) novel action recognition from videos and 3) unseen object segmentation. In all of these three problems, we have two different sets of classes, the “known classes”, which are used in the training phase and the “unknown classes” for which there is no training instance. Our proposed approach exploits the available semantic relationships between known and unknown object classes and use them to transfer the appearance models from known object classes to unknown object classes to recognize unknown objects. We also propose an approach to recognize novel actions from videos by learning a joint model that links videos and text. Finally, we present a ranking based approach for zero-shot object segmentation. We represent each unknown object class as a semantic ranking of all the known classes and use this semantic relationship to extend the segmentation model of known classes to segment unknown class objects.
Clustering Theory and Data Driven Health Care Strategies

DoD health care requires reform with growing costs causing concerns of decreased military capability. Porter in [1] proposes a radical strategy to fix current health care delivery systems via organizing medical teams around patients with similar treatment requirements. This is a clustering problem; how do you partition the set of patients so that each group has similar treatment needs? We provide advances in clustering theory relevant to this new health care strategy. In particular, we create fast certifiably optimal k-means clustering using what is known as Probably Certifiably Correct (PCC) algorithms which achieves state-of-the-art performance under certain models. Inspired by the health-care clustering problem, we pay particular attention to a Bipartite Stochastic Block Model and produce an alternative PCC algorithm specific to this model. We conclude by demonstrating the potential utility of applying these clustering methods in health-care. Using conditional entropy as a metric, clusters obtained from our methods vastly outperform partitions prescribed by subject matter experts.
Deep learning with synthetic, temporal, and adversarial supervision

In this thesis we explore alternatives to manually annotated training examples for supervising the training of deep learning models. Specifically, we develop methods for learning under three different supervision paradigms, namely — (1) synthetic data, (2) temporal data, and (3) adversarial supervision for learning from unaligned examples. The dominant application domain of our work is text spotting, i.e. detection and recognition of text instances in images. We learn text localisation networks on synthetic data, and harness an adversarial discriminator for training text recognition networks using no paired training examples. Further, we exploit the changing pose of objects in temporal sequences (videos) to learn object landmark detectors. The unifying objective is to scale deep learning methods beyond manually annotated training data. We develop a large-scale, realistic synthetic scene text dataset. Armed with this large annotated dataset of scene images, we train a novel, fast fully-convolutional text detection network, and show excellent performance on real images. This generalisation from synthetic to real images, confirms the verisimilitude of our rendering process. The dataset, SynthText in the Wild, has been widely adapted by the research community, and has enabled the development of end-to-end text spotting models. While synthetic text can be readily generated, it needs to be adapted for the specific application domain. However, unaligned examples of text-images, and valid language sentences are abundant. With this in mind, we develop a method for text recognition which learns from such unaligned data. We cast the text recognition problem as one of aligning the conditional distribution of strings predicted from given text images, with lexically valid strings. This alignment is induced through an adversarial discriminator which tries to distinguish the predicted and real text strings apart. Our method achieves excellent text recognition accuracy, using no labelled training examples. Temporal sequences (videos) of objects encode changes in their pose. We develop a method to harness this, and learn object landmark detectors, which consistently track object parts across different poses and instances. We achieve this by conditionally generating a future frame given a past frame, and a sparse keypoint like (learnt) representation extracted from the future frame. We demonstrate generality of our method by learning landmarks for human faces (where we outperform existing landmark detectors), articulated human body, and rigid 3D objects, with no modification to the method. Finally, we propose one-step inductive training for improving generalisation in recurrent neural networks to longer sequences. We restrict the recurrent state to a spatial memory map which tracks the regions of the image which have been accounted for, and train the network for valid evolution of this map. We show excellent generalisation to much longer sequences on two sequential visual recognition tasks — joint localisation and recognition of multiple lines of text, and counting objects in aerial images.
INTEGRATING CODE REVIEW INTO HIGH SCHOOL PROGRAMMING PROJECTS

In our study we explored the potential of peer evaluation of students' work in upper secondary education. Mutual evaluation technique was employed in informatics teaching in the form of code review – the reciprocal commenting on classmates' programming projects. Although at the beginning, the students perceived code review as an extra work, many of them later recognized its usefulness and appreciated its benefits. We believe that also thanks to mutual commenting on program codes the students developed really good projects and achieved very good grading from the teacher. Moreover, after completing this activity, the teacher observed higher self-reliance of students during development of further program codes.
Application of Boolean Logic to Natural Language Complexity in Political Discourse

OF THESIS Application of Boolean Logic to Natural Language Complexity in Political Discourse Press releases serve as a major influence on public opinion of a politician, since they are a primary means of communicating with the public and directing discussion. Thus, the public’s ability to digest them is an important factor for politicians to consider. This study employs several well-studied measures of linguistic complexity and proposes a new one to examine whether politicians change their language to become more or less difficult to parse in different situations. This study uses 27,500 press releases from the US Senate between 2004–2008 and examines election cycles and natural disasters, namely hurricanes, as situations where politicians’ language may change. We calculate the syntactic complexity measures clauses per sentence, T-unit length, and complex-T ratio, as well as the Automated Readability Index and Flesch Reading Ease of each press release. We also propose a proof-of-concept measure called logical complexity to find if classical Boolean logic can be applied as a practical linguistic complexity measure. We find that language becomes more complex in coastal senators’ press releases concerning hurricanes, but see no significant change for those in election cycles. Our measure shows similar results to the well-established ones, showing that logical complexity is a useful lens for measuring linguistic complexity.
Development of SCADA System based on Web Technologies

— The article covers methodology for construction of technological processes management systems using web technologies. The structure of modern management systems was analyzed, the emphasis was made on the higher management layer – SCADA-systems. The main advantages of distributed Web SCADA were presented in comparison to the desktop systems. Review of modern Web SCADA systems was made, their architecture, key components and interactions were investigated. Limitations in regard to application of existing Web SCADA systems to objects that demand constant optimization of management settings were displayed. Implementation of Web SCADA system using Arduino controller, based on the example of the absorption technological process was proposed. Algorithms for collection, storage, processing and visualization of process data were provided, as well as a mathematical model of absorber to calculate control actions. The interaction of SCADA with low-level controller was presented. S oftware components for SCADA were developed, based on open-source web-technologies such as: user interface (html + js), backend (php), database (mysql). Advantages of both the developed software and SCADA-systems in general were described. Recommendations and possible ways of implementation for Web-SCADA were provided.
An Improved Anonymous Authentication Protocol for Location-Based Service

The authentication protocols are implemented to provide identity authentication for communication entities. With the increasing demand of network security and the development of technology, the anonymous authentication protocol for LBS (location based service) has gradually become a research hotspot and attracted extensive attentions, which provides anonymity of the user, confidentiality of location information and identity authentication simultaneously. An efficient and secure anonymous authentication protocol with enhanced privacy for location based service using asymmetric cryptography was proposed by Memon et al. in 2015. However, some scholars pointed out some weaknesses of Memon et al.’s protocol, such as the lack of resistance to password change attack, privileged insider attack and offline password guessing attack. They also pointed out the protocol failed to prevent adversaries from getting the session-specific temporary information. Besides, the mutual authentication was not perfect. Therefore, in this paper, we further analyze the security of Memon et al.’s protocol and propose an improved anonymous authentication protocol for location based service. We use the timestamp mechanism and rely on CDH (Computational Diffie-Hellman) problem to improve security primarily. The security of the proposed protocol is verified using BAN logic and the performance comparison and efficiency analysis are carried out. The results show that our improved protocol has higher security with little more computation cost.
P4DB: On-the-Fly Debugging for Programmable Data Planes

While extending network programmability to a more considerable extent, P4 raises the difficulty of detecting and locating bugs, e.g., P4 program bugs and missed table rules, in runtime. These runtime bugs, without prompt disposal, can ruin the functionality and performance of networks. Unfortunately, the absence of efficient debugging tools makes runtime bug troubleshooting intricate for operators. This paper is devoted to on-the-fly debugging of runtime bugs for programmable data planes. We propose P4DB, a general debugging platform that empowers operators to debug P4 programs in three levels of visibility with rich primitives. By P4DB, operators can use the watch primitive to quickly narrow the debugging scope from the network level or the device level to the table level, then use the break and next primitives to decompose match-action tables and finely locate bugs. We implement a prototype of P4DB and evaluate the prototype on two widely-used P4 targets. On the software target, P4DB merely introduces a small throughput penalty (1.3% to 13.8%) and a little delay increase (0.6% to 11.9%). Notably, P4DB almost introduces no performance overhead on Tofino, the hardware P4 target.
Control-Oriented Modeling of Wireless Power Transfer Systems With Phase-Shift Control

This paper is concerned with control-oriented modeling of a class of wireless power transfer systems with a phase-shift-controlled inverter. Two methods are investigated: the analytical modeling method, which is based on physical laws, and the refined instrumental variable method, which is based on sampled data. It shows that the former provides physical insights into the system, and that the resulting models will be accurate if the true circuit component parameters are a priori known. Otherwise, the model accuracy may decline. By contrast, the latter provides a cheap solution for accurate modeling, based only on a set of sampled input–output data, and the resulting model can capture very well the dynamic behavior of the system within a defined operating range. In real applications, it is always hard to know the actual circuit component parameters, since they may drift from the nominal values, due to some reasons such as the aging of circuit components and the variation of coil placement, making the analytical modeling method not very accurate. Therefore, to preserve the virtues of both methods, it is suggested to use them as a combination, that is, using the analytical modeling method to determine the model structure while using the data-driven modeling method to estimate the model parameters. All results derived in this paper are verified by means of both numerical simulation and experimental validation.
Integration of Accelerated Deep Neural Network Into Power Transformer Differential Protection

Differential protection scheme is the main protection scheme of power transformers, which still holds the risk of sending false trips subject to inrush currents. This article aims to develop a differential protection scheme to discriminate power transformer magnetizing current from internal faults to decrease the risk of false trips. In this article, an accelerated convolutional neural network (CNN) based approach is designed for the discrimination between internal faults and inrush current. The main competitive advantage of the proposed algorithm is its capability in fusing the feature extraction and fault detection blocks into a single deep neural network block by enabling the network to discover important features automatically. The result of this point is that the algorithm is more efficient in terms of speed, hardware usage, and accuracy. The proposed method is applied to a simulated 230-kV network and an experimental prototype. Different cases with various external factors are simulated to calculate reliability indexes. The comparison between the accelerated CNN, conventional CNN, and nine widely used methods demonstrates the faster and more reliable performance of the proposed algorithm.
Automating system generation in Clash

There is an increased interest in graduation assignments that involve the usage of the Clash-language. To prevent that obtained knowledge in these projects is neglected, the CAES chair started a project where multiple students will work on a framework in the Clash-language that can be utilized for real-time systems. This thesis is written in the early stages of the project. The ability to enable the communication of the (to be created) functional blocks is considered important and should be defined in an early stage of the project. The research question was therefore defined as: How can the unique features of the Clash language be utilized to define a deterministic System on Chip interconnection standard? This Master thesis proposes a methodology and presents a tool that generates hardware from data flow.
Software Driven Networks: Use Cases and Framework

This document presents an application framework and associated use cases to help define the scope of the SDNP working group. The use cases start with an abstract representation of a multi-tier application and illustrate the composition of a network service that can meet the requirements of the particular application. The service composition process is split into several steps, including application requirement specification, network mapping, service binding, and policy control. We also provide examples of interactions between an SDNP controller and L2 and L3 control layer protocols in order to deliver the end-to-end service. Finally, as a derivate of that, an architecture framework for SDNP that meets these requirements is proposed.
Proximity-Aware IaaS in an Edge Computing Environment With User Dynamics

Edge computing enables application services to leverage low-latency responses. This paper describes the design of infrastructure-as-a-service for edge computing (EC-IaaS) to realize multiple application services on the same edge servers. In the provision of application services, it is important for application service providers (ASPs) to satisfy proximity constraints regarding the edge servers hosting virtual machines (VMs). State-of-the-art methods to provide VMs satisfying these proximity constraints require ASPs to have a large amount of information from an EC-IaaS provider, which incurs large computational and communication overheads. Otherwise, the edge servers will incur the large power draw. In this paper, we propose a virtual region model. The virtual region model abstracts the details of an edge computing infrastructure. We extend the VM request and placement processes based on the virtual region model, to support dynamics of end-user devices. We confirm that the amount of information, which ASPs receive from an EC-IaaS provider, is the same level of the least amount required by the state-of-the-art methods. Additionally, the power draw of edge servers is the same as the minimum in the state-of-the-art methods, and this level is even sustained when the number of end-user devices changes in a city.
A review on object detection from Unmanned Aerial Vehicle using CNN

UAV stands for unmanned Aerial Vehicle, which can be as small as birds, regular drones or as big as private aircraft with no pilot on board. Since there is no one onboard UAV is remotely controlled. UAV is currently being used for a different purpose, examples are spy footages, sky view footage, reconnaissance, attacking roles, aerial surveillance, motion picture filmmaking, disaster rescue, parcel delivery, warehouse management, and other uses. Due to UAV multi-functionality and portability especially drones demand is growing faster, therefore, people need systems that work with the UAV (drones) to detect objects in real-time for military, safety reconnaissance, and surveillance. This paper review approaches to detect objects from camera view and from UAV scenes using machine learning algorithms. The growth of computer vision systems initiated better algorithms using huge training and testing datasets, faster GPU and CPU so that systems can achieve state of the art object detection system by training and classifying the data using machine learning approach. Since there are different orientation, background, occlusion in an image, object detection is not an easy task. The goal of object detection is to categorize images and video feeds from UAV into common categories.
Exploiting the Dynamic Mutual Influence for Predicting Social Event Participation

It is commonly seen that social events are organized through online social network services (SNSs), and thus there are vested interests in studying event-oriented social gathering through SNSs. The focus of existing studies has been put on the analysis of event profiles or individual participation records. While there is significant dynamic mutual influence among target users through their social connections, the impact of dynamic mutual influence on the people's social gathering remains unknown. To that end, in this paper, we develop a discriminant framework, which allows to integrate the dynamic mutual dependence of potential event participants into the discrimination process. Specifically, we formulate the group-oriented event participation problem as a two-stage variant discriminant framework to capture the users’ profiles as well as their latent social connections. The validation on real-world data sets show that our method can effectively predict the event participation with a significant margin compared with several state-of-the-art baselines. This validates the hypothesis that dynamic mutual influence could play an important role in the decision-making process of social event participation. Moreover, we propose the network pruning method to further improve the efficiency of our technical framework. Finally, we provide a case study to illustrate the application of our framework for event plan design task.
Outlier Detection By Clustering-Based Ensemble Model Construction

Outlier detection means to detect data samples that deviate significantly from the distribution of normal data. Most outlier detection methods calculate an outlier score that indicates the extent to which a data sample is out of normal state and determine it to be an outlier when its outlier score is above a given threshold. However, since the range of an outlier score is different for each data and the outliers exist at a smaller ratio than the normal data, it is very difficult to determine the threshold value for an outlier score. Further, in an actual situation, it is not easy to acquire data including a sufficient amount of outliers available for learning. In this paper, we propose a clustering-based outlier detection method by constructing a model representing a normal data region using only normal data and performing binary classification of outliers and normal data for new data samples. Then, by dividing the given normal data into chunks, and constructing a clustering model for each chunk, we expand it to the ensemble method combining the decision by the models and apply it to the streaming data with dynamic changes. Experimental results using real data and artificial data show high performance of the proposed method.
Anti-jamming mobile control using QoS-based reinforcement learning

: In cyber-physical systems, the reliable data communication between the physical systems and computer systems are inevitable, and it is an essential issue to protect the systems from jamming attacks that obstruct wireless communication. Moreover, for practical uses of the systems, Quality of Service (QoS) is also important. In this paper, we consider a mobile robot and propose an anti-jamming mobile control system that uses QoS-based reinforcement learning. We show that the control system can ﬁ nd a policy that provides planning with a short path while avoiding jamming attacks.