Reviewerly/exHarmony huggingface dataset:
More Than Just an Auxiliary Loss: Anti-spoofing Backbone Training via Adversarial Pseudo-depth Generation

In this paper, a new method of training pipeline is discussed to achieve significant performance on the task of anti-spoofing with RGB image. We explore and highlight the impact of using pseudo-depth to pre-train a network that will be used as the backbone to the final classifier. While the usage of pseudo-depth for anti-spoofing task is not a new idea on its own, previous endeavours utilize pseudo-depth simply as another medium to extract features for performing prediction, or as part of many auxiliary losses in aiding the training of the main classifier, normalizing the importance of pseudo-depth as just another semantic information. Through this work, we argue that there exists a significant advantage in training the final classifier can be gained by the pre-trained generator learning to predict the corresponding pseudo-depth of a given facial image, from a Generative Adversarial Network framework. Our experimental results indicate that our method results in a much more adaptable system that can generalize beyond intra-dataset samples, but to inter-dataset samples, which it has never seen before during training. Quantitatively, our method approaches the baseline performance of the current state of the art anti-spoofing models with 15.8x less parameters used. Moreover, experiments showed that the introduced methodology performs well only using basic binary label without additional semantic information which indicates potential benefits of this work in industrial and application based environment where trade-off between additional labelling and resources are considered.
Traffic Mishap Injury Severity: An Unsupervised Approach

Predicting the injury severity of traffic accidents is of great significance in identifying the consequences on the victims of the accidents which may lead to physical or mental disturbance, financial losses, or the consequent health risks globally. Traffic mishaps are a number of the most essential troubles that the world is facing as they cause many deaths, injuries, and fatalities as nicely as financial losses each year. Identification of injuries severity is a key factor for the proper treatment as number of traffic accidents are increasing day by day and the victims are suffering from many major injuries even after many years of accidents. Unsupervised learning techniques are used in which association rule mining is applied on the Traffic Accident dataset which contains 660 records to generate frequent item sets. The strong rules contained in these regular item sets also show the relationship between influencing accident factors which can be used by breaking them to reduce the incidence of accidents. The rules can also be used to check the mishap scenes and some necessary safety measures can be made to avoid accidents and eventually improve the traffic safety of the city and providing assistance to doctors in treating patients. Further, a descriptive model is utilized for undertakings that would profit by the knowledge picked up from summing up information in new and fascinating manners. Therefore the Unsupervised learning techniques i.e Apriori Algorithm, Apriori- TID Algorithm, Eclat Algorithm revealed interesting patterns and knowledge that can be used in finding the association between accidents and related injuries.
Unsupervised Anomaly Detection for Communication Networks: An Autoencoder Approach

Communication networks are complex systems consisting of many components each producing a multitude of system metrics that can be monitored in real-time. Anomaly Detection (AD) allows to detect deviant behavior in these system metrics. However, in communication networks, large amounts of domain knowledge and huge manual efforts are required to efficiently monitor these complex systems. In this paper, we describe how AutoEncoders (AE) can elevate the manual effort for unsupervised AD in communication networks. We show that AE can be applied, without domain knowledge or manual effort and evaluate different types of AE architectures and how they perform on a variety of anomaly types found in communication networks.
On Comparison of Some Representations for the Evolution of Quantum Operators

This paper presents a comparative study regarding the evolutionary design of quantum operators in the form of unitary matrices. Three different techniques allowing the generation of unitary matrices are investigated whose parameters are tuned by means of evolutionary algorithms. Such problem may be considered as a numerical optimisation task because the unitary matrices are represented by complex numbers with real-valued coefficients. Specifically, Genetic Algorithm and Evolution Strategy are applied, each in four different setups, and evaluated on three case studies: the 2-qubit Controlled-NOT gate, 3-qubit entanglement operator and 4-qubit detector of an element with the maximum amplitude. The evolutionary algorithms in combination with the given representation techniques are evaluated in order to determine the abilities of various experimental setups to solve the given tasks. The main contribution is the utilisation of QR decomposition, a technique for generating unitary matrices from theoretically any sequence of complex numbers, that has been applied for the first time in this paper for the evolution of quantum operators. It will be demonstrated that the QR decomposition exhibits a good performance especially in solving more complex quantum operators in comparison with other representations.
An ANN Based Human Walking Distance Estimation with an Inertial Measurement Unit

Estimation of the walking distance including stride length of a pedestrian is an essential part of a motion tracking and monitoring devices used in applications such as health care, search and rescue and games. One popular approach is to derive it through processing the acceleration signals output from the inertia measurement unit (IMU) attached to a foot or another part of the body of the pedestrian. This approach is well known for its large accumulative errors in integration of the signals with noises of a sensor and the difficult to accurately determine the time duration of a stride. This paper presents a novel approach to estimate walking distance from IMU sensor readings based on an artificial neural network (ANN). Experiments show that the proposed approach is more accurate and effective than traditional methods relying on double integral method.
Image inpainting using scene constraints

Recent deep learning-based inpainting methods have shown significant improvements and generate plausible images. However, most of these methods may either synthesis unrealistic and blurry texture details or fail to capture object semantics. Furthermore, they employ huge models with inefficient mechanisms such as attention. Motivated by these observations, we propose a new end-to-end generative-based multi-stage architecture for image inpainting. Specifically, our model exploits the segmentation labels predictions to robustly reconstruct the object boundaries and avoid blurry or semantically incorrect images. Meanwhile, it employs edges predictions to recover the image structure. Different than previous approaches, we do not predict the segmentation labels/edges from the corrupted image. Instead, we employ a coarse image that contains more valuable global structure data. We conduct a set of extensive experiments to investigate the impact of merging these auxiliary pieces of information. Experiments show that our computationally efficient model achieves competitive qualitative and quantitative results compared to the state-of-the-art methods on multiple datasets.
Uncertainty Driven Workflow Scheduling Using Unreliable Cloud Resources

The Cloud infrastructure offers to end users a broad set of heterogenous computational resources using the pay-as-you -go model. These virtualized resources can be provisioned using different pricing models like the unreliable model where resources are provided at a fraction of the cost but with no guarantee for an uninterrupted processing. However, the enormous gamut of opportunities comes with a great caveat as resource management and scheduling decisions are increasingly complicated. Moreover, the presented uncertainty in optimally selecting resources has also a negatively impact on the quality of solutions delivered by scheduling algorithms. In this paper, we present a dynamic scheduling algorithm (i.e., the Uncertainty-Driven Scheduling - UDS algorithm) for the management of scientific workflows in Cloud. Our model minimizes both the makespan and the monetary cost by dynamically selecting reliable or unreliable virtualized resources. For covering the uncertainty in decision making, we adopt a Fuzzy Logic Controller (FLC) to derive the pricing model of the resources that will host every task. We evaluate the performance of the proposed algorithm using real workflow applications being tested under the assumption of different probabilities regarding the revocation of unreliable resources. Numerical results depict the performance of the proposed approach and a comparative assessment reveals the position of the paper in the relevant literature.
An Analysis of Reinforcement Learning Applied to Coach Task in IEEE Very Small Size Soccer

The IEEE Very Small Size Soccer (VSSS) is a robot soccer competition in which two teams of three small robots play against each other. Traditionally, a deterministic coach agent will choose the most suitable strategy and formation for each adversary's strategy. Therefore, the role of a coach is of great importance to the game. In this sense, this paper proposes an end-to-end approach for the coaching task based on Reinforcement Learning (RL). The proposed system processes the information during the simulated matches to learn an optimal policy that chooses the current formation, depending on the opponent and game conditions. We trained two RL policies against three different teams (balanced, offensive, and heavily offensive) in a simulated environment. Our results were assessed against one of the top teams of the VSSS league, showing promising results after achieving a win/loss ratio of approximately 2.0.
Analyzing Deep Neural Network’s Transferability via Fréchet Distance

Transfer learning has become the de facto practice to reuse a deep neural network (DNN) that is pre-trained with abundant training data in a source task to improve the model training on target tasks with smaller-scale training data. In this paper, we first investigate the correlation between the DNN's pre-training performance in the source task and their transfer results in the downstream tasks. We find that high performance of a pre-trained model does not necessarily imply high transferability. We then propose a metric, named Fréchet Pre-train Distance, to estimate the transferability of a deep neural network. By applying the proposed Fréchet Pre-train Distance, we are able to identify the optimal pre-trained checkpoint, and then achieve high transferability on downstream tasks. Finally, we investigate several factors impacting DNN's transferability including normalization, different networks and learning rates. The results consistently support our conclusions.
Solar Over-Irradiance Events: Preliminary Results from a Global Study

This paper presents the methodology and preliminary results from a global study on solar over-irradiance events, which are more frequent than previously believed and can negatively impact utility-scale PV operations. Data from five test sites in Florianópolis and Brotas de Macaúbas in Brazil, Bernburg in Germany, Albuquerque, in the USA and Loughborough, in the United Kingdom are presented and analyzed.
Composing Algorithm Portfolio with Problem Set of Unknown Distribution

Portfolio approaches aim to combine different algorithms and take advantages of their strengths since it is challenging for a single algorithm to solve a problem set with all optimization problems. When there are many algorithms to choose from, there are various algorithm combinations of possibilities. Selecting a well-composed algorithm portfolio becomes essential to solve a problem set efficiently. In this paper, we propose a problem set dependent method to automatically and generically accomplish portfolio construction. The problem set can be composed of any problem, and come from any distribution or any benchmark. To decide which algorithm should be added in the portfolio, we utilize the average rank of results from solving problems to find the best-performing algorithm. Then we select complementary algorithms for the portfolio by applying Pearson correlation coefficient of fitness values. The method then iterates to compose more and more complex portfolios until there is no more improvement. This method is tested under three different problem sets. The experimental result shows the good ability of this approach to detect well-cooperated algorithms; and the composed portfolio is proved to have good adaptability to the problem set.
6D Pose Estimation for Texture-Less Industrial Parts in the Crowd

Recovering the 6D object pose of industrial part is a common but challenging problem in many robotic applications. In this paper, an accurate 6D pose estimation approach is proposed for texture-less industrial part in the crowd. The proposed method consists of three stages: object detection, pose hypotheses generation, and pose refinement. Firstly, the bounding boxes of object instances in an RGB image are detected by a convolution neural network. The training dataset is automatically synthesized using an efficient image rendering method. Then, highlight detection and removal are employed to eliminate noise edges. The coarse pose hypotheses are generated using an edge-based fast directional chamfer matching algorithm. After that, the accurate 6D poses are obtained by applying a non-linear optimization to these pose hypotheses. A re-weighted least-squares loss function is utilized to suppress outlier noise in optimization. Finally, an edge direction consistency score is used to evaluate these obtained poses and eliminate outliers. The proposed method only relies on single RGB image to recover the 6D object pose in the crowd. Experimental results of texture-less industrial parts show the accuracy and robustness of the proposed method.
Fuzzy Output Support Vector Machine Based Incident Ticket Classification

Incident ticket classification plays an important role in the complex system maintenance. However, low classification accuracy will result in high maintenance costs. To solve this issue, this paper proposes a fuzzy output support vector machine (FOSVM) based incident ticket classification approach, which can be implemented in the context of both two-class SVMs and multi-class SVMs such as one-versus-one and one-versus-rest. Our purpose is to solve the unclassifiable regions of multi-class SVMs to output reliable and robust results by more fine-grained analysis. Experiments on both benchmark data sets and real-world ticket data demonstrate that our method has better performance than commonly used multi-class SVM and fuzzy SVM methods.
Fingerprint Based Authentication Architecture for Accessing Multiple Cloud Computing Services using Single User Credential in IOT Environments

Abstract Cloud computing technology provides access to the pool of configurable resources including storage space, application, services and on demand network. Involvement of Cloud with the organization minimizes organization efforts toward fulfilling its customer’s needs. One of the major advantages of cloud computing is the Single Sign On (SSO) technique that allows the user to access multiple application services using a single user credential. In cloud computing, there are many issues, and challenges to be discussed. However, prevention from security attacks is much more difficult in preserving privacy of user agents. This paper has proposed SSO-based bio-metric authentication architecture for cloud computing services to overcome the security and privacy attacks. Bio-metric authentication is effective for resources controlled by end devices at the time of accessing the cloud services since these devices are computationally inefficient for user information processing during authentication. Accordingly, security attack in the cloud computing gets minimized using the proposed architecture. The proposed architecture also includes a novel approach in which there exist one to one relationship between user agent and the service provider. In this user agents can use their fingerprint while requesting for registration and accessing different cloud application services at cloud. Based on comparative study with several existing architectures, the highlights of the proposed architecture have been presented.
Tsuga Convictio: Visualizing for the ecological, feminine, and embodied

A good data visualization opens up space for good conversations. That is, a data visualization is a starting point in a discussion of a shared understanding of the world, a way for us to know together. Consequently, our practices as data visualization designers-through collecting data and portraying them visually-create (or reinforce) ways we can understand the world, and thus move through it-it's ontological. Alarmingly, there is an emerging sense among new media artists and scholars that our practices of collecting and visualizing data can create rigid, harmful understandings of the world; ontologies of control. In particular, D'Ignazio and Klein argue in their book, Data Feminism, that our current practices of data science can lead to the “silencing, extraction, monetization, or invisibility” of people (or other living things) that the data represent [9]. In response to this violence, they assert that we need to make visible those who are creating the data, and those who are represented by the data. In other words, we need to “bring back the bodies.” This research interrogates how we might recenter the embodied, situated nature of data through the design of Tsuga Convictio, an experimental data collection process and a data visualization to support the conversations critical to our future-the reflective community conversations that help us belong to our human and ecological communities. By designing for these conversations, we discovered fluid, feminine, and embodied ways to create-and (re)enchant data visualizations. Along the way, we begin to answer some of the fundamental questions designers implicitly (and explicitly) answer when they make data visualizations. Most profoundly, we (re)imagine what visualizations can do-data visualizations are more than just tools of control; they are tools for imagination and transformation.
Modeling the Electrical Parameters of a Multi-Stage Thermoelectric Module by a Neural Network

This work presents the results of modeling the electrical parameters - current I and voltage E - of a multistage Peltier module working in generator mode using a neural network. A preliminary experiment was performed to accurately determine the current and voltage generated, depending on the applied temperature difference ΔT and load resistance R. The values obtained were used to train the neural network.
Video Monitoring Queries

Recent advances in video processing utilizing deep learning primitives achieved breakthroughs in fundamental problems in video analysis such as frame classification and object detection enabling an array of new applications. In this paper we study the problem of interactive declarative query processing on video streams. In particular we introduce a set of approximate filters to speed up queries that involve objects of specific type (e.g., cars, trucks, etc.) on video frames with associated spatial relationships among them (e.g., car left of truck). The resulting filters are able to assess quickly if the query predicates are true to proceed with further analysis of the frame or otherwise not consider the frame further avoiding costly object detection operations. We propose two classes of filters <inline-formula><tex-math notation="LaTeX">$IC$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$OD$</tex-math></inline-formula> , that adapt principles from deep image classification and object detection. The filters utilize extensible deep neural architectures and are easy to deploy and utilize. In addition, we propose statistical query processing techniques to process aggregate queries involving objects with spatial constraints on video streams and demonstrate experimentally the resulting increased accuracy on the resulting aggregate estimation. Finally, we introduce a framework based on extreme value theory to detect unexpected objects on video streams and experimentally demonstrate its utility. Combined these techniques constitute a robust set of video monitoring query processing techniques. We demonstrate that the application of the techniques proposed in conjunction with declarative queries on video streams can dramatically increase the frame processing rate and speed up query processing by at least two orders of magnitude. We present the results of a thorough experimental study utilizing benchmark video data sets at scale demonstrating the performance benefits and the practical relevance of our proposals.
Methods to Generate Multiple-valued Bent Functions of an odd Number of Variables

Methods to generate p-valued bent functions of an odd number of variables are addressed. An extension of the Maiorana method originally developed to generate bent functions of an even number of variables is presented. It is shown that the simplest Maiorana function may be used to generate a large number of bent functions of an odd number of variables using as parameters a 1-place bent function, a random permutation matrix and the value vector of a random p-valued function.
MART: Motion-Aware Recurrent Neural Network for Robust Visual Tracking

We introduce MART, Motion-Aware Recurrent neural network (MA-RNN) for Tracking, by modeling robust long-term spatial-temporal representation. In particular, we propose a simple, yet effective context-aware displacement attention (CADA) module to capture target motion in videos. By seamlessly integrating CADA into RNN, the proposed MA-RNN can spatially align and aggregate temporal information guided by motion from frame to frame, leading to more effective representation that benefits a tracker from motion when handling occlusion, deformation, viewpoint change etc. Moreover, to deal with scale change, we present a monotonic bounding box regression (mBBR) approach that iteratively predicts regression offsets for target object under the guidance of intersection-over-union (IoU) score, guaranteeing non-decreasing accuracy. In extensive experiments on five benchmarks, including GOT-10k, LaSOT, TC-128, OTB-15 and VOT-19, our tracker MART consistently achieves state-of-the-art results and runs in real-time.