Reviewerly/exHarmony huggingface dataset:
FLSNet: Robust Facial Landmark Semantic Segmentation

The human face is one of the most viewed visual objects in a person's life and is used for identifying a person through facial landmarks, which includes the eyes, nose, mouth, and ears that make up a face. It is also possible to communicate nonverbally through the movements of facial landmarks; that is, change of facial expression. Thus, facial landmarks play a crucial role in human-related image analysis. Automatic facial landmark detection is a challenging problem in the field of computer vision, and various studies are underway. The emergence of Deep Neural Networks has played an important role in solving difficult problems in computer vision. Semantic segmentation is a field in which images are classified into pixel units and has also developed rapidly by incorporating deep learning. In this paper, we propose a method for accurately extracting facial landmarks using semantic segmentation. First, we introduce a semantic segmentation architecture for sophisticated landmark detection, and datasets composed of facial images and ground truth pairs. Then, we suggest how improve the performance of pixel classification by adjusting the imbalance of the number of pixels according to the face landmark. Through extensive experiments, we evaluated our approach using the metrics pixel accuracy and intersection over union.
Interactive Narrative Intervention Alibis through Domain Revision

Interactive narrative systems produce branching story experiences for a human user using an interactive world. A class of interactive narrative systems, called strong story systems, manage a user's experience by manipulating the interactive world and its characters according to a formal story model. In these systems, a human user may place the world into a state such that the formal story model can no longer control interaction. One solution to this problem, called intervention, is to exchange the undesirable outcomes of a player's action for a set that do not violate the story model. However, the player may become aware that their intended action is being intervened against by a context-sensitive, meta-narrative process. In this paper we describe a method of ensuring game world alibis for interventions through domain modification of world mechanics.
Video Person Re-ID: Fantastic Techniques and Where to Find Them (Student Abstract)

The ability to identify the same person from multiple camera views without the explicit use of facial recognition is receiving commercial and academic interest. The current status-quo solutions are based on attention neural models. In this paper, we propose Attention and CL loss, which is a hybrid of center and Online Soft Mining (OSM) loss added to the attention loss on top of a temporal attention-based neural network. The proposed loss function applied with bag-of-tricks for training surpasses the state of the art on the common person Re-ID datasets, MARS and PRID 2011. Our source code is publicly available on github1.
End-to-End Offline Speech Translation System for IWSLT 2020 using Modality Agnostic Meta-Learning

In this paper, we describe the system submitted to the IWSLT 2020 Offline Speech Translation Task.We adopt the Transformer architecture coupled with the meta-learning approach to build our end-to-end Speechto-Text Translation (ST) system.Our meta-learning approach tackles the data scarcity of the ST task by leveraging the data available from Automatic Speech Recognition (ASR) and Machine Translation (MT) tasks.The meta-learning approach combined with synthetic data augmentation techniques improves the model performance significantly and achieves BLEU scores of 24.58, 27.51, and 27.61 on IWSLT test 2015, MuST-C test, and Europarl-ST test sets respectively.
ELITR Non-Native Speech Translation at IWSLT 2020

This paper is an ELITR system submission for the non-native speech translation task at IWSLT 2020.We describe systems for offline ASR, real-time ASR, and our cascaded approach to offline SLT and real-time SLT.We select our primary candidates from a pool of pre-existing systems, develop a new end-toend general ASR system, and a hybrid ASR trained on non-native speech.The provided small validation set prevents us from carrying out a complex validation, but we submit all the unselected candidates for contrastive evaluation on the test set.
Uniformity Attentive Learning-Based Siamese Network for Person Re-Identification

Person re-identification (Re-ID) has a problem that makes learning difficult such as misalignment and occlusion. To solve these problems, it is important to focus on robust features in intra-class variation. Existing attention-based Re-ID methods focus only on common features without considering distinctive features. In this paper, we present a novel attentive learning-based Siamese network for person Re-ID. Unlike existing methods, we designed an attention module and attention loss using the properties of the Siamese network to concentrate attention on common and distinctive features. The attention module consists of channel attention to select important channels and encoder-decoder attention to observe the whole body shape. We modified the triplet loss into an attention loss, called uniformity loss. The uniformity loss generates a unique attention map, which focuses on both common and discriminative features. Extensive experiments show that the proposed network compares favorably to the state-of-the-art methods on three large-scale benchmarks including Market-1501, CUHK03 and DukeMTMC-ReID datasets.
Nodule Localization in Thyroid Ultrasound Images with a Joint-Training Convolutional Neural Network

The accurate localization of nodules in ultrasound images can convey crucial information to support a reliable diagnosis. However, this is usually challenging due to low contrast and image artifacts, especially in thyroid ultrasound images where nodules are relatively small in most cases. To address these problems, in this paper, we propose a joint-training convolutional neural network (CNN) for thyroid nodule localization in ultrasound images. Considering the advantage of the faster region-based CNN (Faster R-CNN) in detecting natural targets, we adopt it as the basic framework. To boost the representative power and noise suppression capability of the network, the attention mechanism module is embedded for adaptive feature refinement along the channel and spatial dimensions. Furthermore, in the training process, we annotate the training set in a novel way, called joint-training annotation, by exploiting the fake foreground (FFG) area around the nodule as a spatial prior constraint to improve the sensitivity to small nodules. Ablation experiments are conducted to verify the effectiveness of our proposed method. The experimental results show that our method outperforms others by a mean average precision (mAP) of 0.93 and achieves an intersection over union (IoU) of 0.9, indicating that the localization results agree well with the ground truth. Furthermore, extended experiments on breast nodule datasets are also conducted to verify the generalizability of the proposed approach. Above all, the proposed algorithm is of considerable significance for accurate thyroid nodule localization in ultrasound images and can be generalized to other types of nodules, thereby providing trustworthy assistance for clinical diagnosis.
Explaining Arguments with Background Knowledge

Abstract Most information we consume as a society is obtained over the Web. News – often from questionable sources – are spread online, as are election campaigns; calls for (collective) action spread with unforeseen speed and intensity. All such actions have argumentation at their core, and the conveyed content is often strategically selected or rhetorically framed. The responsibility of critical analysis of arguments is thus tacitly transferred to the content consumer who is often not prepared for the task, nor aware of the responsibility. The ExpLAIN project aims at making the structure and reasoning of arguments explicit – not only for humans, but for Robust Argumentation Machines that are endowed with language understanding capacity. Our vision is a system that is able to deeply analyze argumentative text: that identifies arguments and counter-arguments, and reveals their internal structure, conveyed content and reasoning. A particular challenge for such a system is to uncover implicit knowledge which many arguments rely on. This requires human background knowledge and reasoning capacity, in order to explicate the complete reasoning of an argument. This article presents ongoing research of the ExpLAIN project that aims to make the vision of such a system a tangible aim. We introduce the problems and challenges we need to address, and present the progress we achieved until now by applying advanced natural language and knowledge processing methods. Our approach puts particular focus on leveraging available sources of structured and unstructured background knowledge, the automatic extension of such knowledge, the uncovering of implicit content, and reasoning techniques suitable for informal, everyday argumentation.
Kripto Paraların Altın ve Dövizle İlişkisi: Bitcoin Örneği

Cryptocurrency can be defined as a digital asset and a virtual element designed to be an alternative exchange tool for cash in terms of how it works, securing transactions using encryption (cryptography).Looking across the world, there are Bitcoin, Ethereum, Bitcoin Cash, Ripple, Litecoin, Cardano, Nem, Iota, Stellar, Dash and many more cryptocurrencies.The most famous of the cryptocurrencies today is Bitcoin, which is the most preferred in terms of transaction volume and constitutes approximately 50% of the cryptocurrency volume.Bitcoin, created by a person or community named Satoshi Nakatomo in 2008 and the first transfer in 2009, is a digitally created cryptocurrency.The aim of this study is to investigate the relationship between Bitcoin, a cryptocurrency, and gold ounce prices and dollar index.In the study, 2012-2019 was determined as the term and the monthly data were examined.ARDL Boundary test approach was used as a method to determine the cointegration relationship between the examined variables.As a result of the study, a long-term co-integrated relationship between Bitcoin's gold and foreign exchange price was determined.With this result, a 1% increase in the gold ounce price will increase Bitcoin prices by about 15% in the long run; The 1-unit increase in the USD index indicates that it will increase Bitcoin prices by about 0.28%.However, it was concluded that there was no co-integrated relationship between the variables in the short term.
Human Activity Recognition with Deep Reinforcement Learning using the Camera of a Mobile Robot

This paper presents a new human activity recognition method that uses a camera mounted on a mobile robot. We assume that the robot's camera captures images of a person and recognizes his/her activities based on skeletal and visual features extracted from the images. A key issue encountered with this method for activity recognition is that it requires the robot to position itself so that it has an adequate field of view of the activities being conducted. For example, if the robot is directly behind a person while observing that person making tea, it will be difficult for the robot to distinguish that activity from other similar activities such as preparing a meal or washing dishes. Our method employs deep reinforcement learning to control the movements of the mobile robot that is observing the activities in order to maximize its recognition accuracy while minimizing its energy consumption related to its movement. We propose effective action- and state-space designs that can achieve early training convergence and highly accurate activity recognition by: (i) incorporating the confidence of the activity recognition output when evaluating the quality of the current state (position), (ii) incorporating the costs of subsequent actions when estimating values for those actions, and (iii) designing an effective action space that accelerates reinforcement learning by restricting the movement space of the robot to the circumference of a circle with a predefined radius centered on the person.
A Comparative Study of Pretrained Convolutional Neural Network Model to Identify Plant Diseases on Android Mobile Device

Optimization of mobile devices to be used as early identification tools of plant diseases using an application based on Convolutional Neural Network (CNN), with a high degree of accuracy and low power consumption is the focus of this study.The study was conducted using a dataset consisting of 38 different classes of PlantVillage dataset, which were then expanded using 2 classes of coffee plants and 4 classes of rice plants.The models that are going to be tested and compared consists of MobileNet V2, NasNet Mobile, DenseNet 121 layer, and InceptionV3.In the experiments, it was found that there was a decrease in accuracy when the application was run on a mobile device when compared to when it was run on a PC.Experiments also show that InceptionV3 is the most stable model and reaches the highest level of accuracy, which is 98.45% on mobile devices.However, InceptionV3 consumes a lot of resources when used on mobile devices.Meanwhile, MobileNet V2, NasNet Mobile, dan DenseNet 121, do not consume a lot of resources when tested on mobile devices.In terms of accuracy, NasNet Mobile reached 97.31%, then MobileNet V2 reached 96.55%, and DenseNet 121 reached 96.21%.Based on the research criteria, it can be concluded that the CNN model that is most suitable to be used on mobile devices is NasNet Mobile.Because it has a high degree of accuracy with low resource consumption.
A study of solar panel efficiency in Latvian climate conditions

With solar panels increasingly used in nearly zero energy building solutions it is important to clarify if the panels can achieve the efficiency indicators specified by their producers in real operation conditions. To determine the efficiency of poly- and monocrystalline panels depending on their spatial orientation and the seasons a set of test panels was installed in 2018 in Riga, Latvia for long-term monitoring of the amount of generated power and produced energy. Here we summarize the results of the first year of monitoring. Data indicate that orientation has high significance, very small amounts of energy are generated in autumn/winter and that there is a good correspondence of the actual efficiency to the standardized estimated values. The energy production is significantly reduced by the cloudiness that is characteristic for Latvia’s climate from October till January. To make a generalization of the results possible it is planned to continue the monitoring for several years.
Collision-free Random Paths between Two Points

This paper proposes a collision-free path planning algorithm based on the generation of random paths between two points.The proposed work applies to many fields such as education, economics, computer science and AI, military, and other fields of applied sciences.Our work has spanned several phases, where in the first phase a novel computer algorithm to generate random paths between two points in space has been developed.The aim was to be able to generate paths between two points in real-time that cannot be predicted in advance.In the second phase, we have developed an ontology that describes the domain of discourse.The aim was two folds; firstly, to provide an optimized generation of best points that are closer to the target point.Secondly, to provide sharable, reusable ontological objects that can be deployed to other projects.We reinforced our solution by the initiation of several case studies that have been designed using and extending our work.One problem that we have faced in some cases is the existence of some obstacles between the starting and the ending point.For example, in our work towards the automation of a navigation system for drones, we faced some obstacles like trees, no flying zones, and buildings.This problem is also applicable to mobile robots and other unmanned vehicles, where fee-collision mobility is necessary.In this phase, we have reworked the algorithm to generate random paths between two points P0(x0, y0), Pn(xn, yn) with obstacles.Our generated random paths are placed within circles that are centered in Pn: c1, c2, …, cn-1, which passes thru the points P1, P2, …, Pn-1 respectively.Point Pi may approach Pn if it takes any position within circle c centered in Pn with radius PiPn and satisfies some constraints, discussed in detail in the paper, which insure that the selected paths do not fall within obstacles and reach the target point.we also classified the generated paths based on given properties such as the longest path, shortest path, and paths with some given costs.The resulted algorithms were very encouraging and leading to the applicability of real-life cases.
Performance Analysis of Blockchain Consensus System With Interference Factors and Sleep Stage

Blockchain system is affected by many factors with the rapid development of technology.In order to study the blockchain system more realistically, the processes of transaction consensus and transaction storage are simulated in this paper, and factors such as interference factors, impatience phenomena of transactions and fault repairable conditions of the system during the service process are considered.Vacation queueing model with negative customers, impatient customers, repairable fault and two-phase service that customers can select sever in the first service is established.Using the matrix-geometric solution method, the expressions of the average confirmation time of transactions and other performance measures are given.The parameters of the system are obtained based on the statistics of the blockchain transaction data.The influence of each parameter on the performance measures of blockchain system is analyzed by using MATLAB software.The equilibrium reception rate is discussed by constructing the revenue function of the system, and equilibrium numerical results are obtained.
“Healthy surveillance”: Designing a concept for privacy-preserving mask recognition AI in the age of pandemics

The obligation to wear masks in times of pandemics reduces the risk of spreading viruses.In case of the COVID-19 pandemic in 2020, many governments recommended or even obligated their citizens to wear masks as an effective countermeasure.In order to continuously monitor the compliance of this policy measure in public spaces like restaurants or tram stations by public authorities, one scalable and automatable option depicts the application of surveillance systems, i.e., CCTV.However, large-scale monitoring of mask recognition does not only require a well-performing Artificial Intelligence, but also ensure that no privacy issues are introduced, as surveillance is a deterrent for citizens and regulations like General Data Protection Regulation (GDPR) demand strict regulations of such personal data.In this work, we show how a privacy-preserving mask recognition artifact could look like, demonstrate different options for implementation and evaluate performances.Our conceptual deep-learning based Artificial Intelligence is able to achieve detection performances between 95% and 99% in a privacy-friendly setting.On that basis, we elaborate on the trade-off between the level of privacy preservation and Artificial Intelligence performance, i.e. the "price of privacy".1 This also includes where and how long personal data is stored, which we will discuss at a later point when we explore different implementation options in Section 4.
Application of Homomorphic Encryption on Neural Network in Prediction of Acute Lymphoid Leukemia

Machine learning is now becoming a widely used mechanism and applying it in certain sensitive fields like medical and financial data has only made things easier.Accurate Diagnosis of cancer is essential in treating it properly.Medical tests regarding cancer in recent times are quite expensive and not available in many parts of the world.CryptoNets, on the other hand, is an exhibit of the use of Neural-Networks over data encrypted with Homomorphic Encryption.This project demonstrates the use of Homomorphic Encryption for outsourcing neural-network predictions in case of Acute Lymphoid Leukemia (ALL).By using CryptoNets, the patients or doctors in need of the service can encrypt their data using Homomorphic Encryption and send only the encrypted message to the service provider (hospital or model owner).Since Homomorphic Encryptions allow the provider to operate on the data while it is encrypted, the provider can make predictions using a pre-trained Neural-Network while the data remains encrypted all throughout the process and finally sending the prediction to the user who can decrypt the results.During the process the service provider (hospital or the model owner) gains no knowledge about the data that was used or the result since everything is encrypted throughout the process.Our work proposes a Neural Network model which will be able to predict ALL-Acute Lymphoid Leukemia with approximate 80% accuracy using the C_NMC Challenge dataset.Prior to building our own model, we used the dataset and pre-process it using a different approach.We then ran on different machine learning and Neural Network models like VGG16, SVM, AlexNet, ResNet50 and compared the validation accuracies of these models with our own model which lastly gives better accuracy than the rest of the models used.We then use our own pretrained Neural Network to make predictions using CryptoNets.We were able to achieve an encrypted prediction of about 78% which is close to what we achieved when validating our own CNN model that has a validation accuracy of 80% for prediction of Acute Lymphoid Leukemia (ALL).
Deep Learning-Based Earlier Detection of Esophageal Cancer Using Improved Empirical Wavelet Transform From Endoscopic Image

In the current scenario, the research perspective on esophageal cancer becomes severe, high-prognosis malignancy; poor prognosis is primarily attributed to the fact that most tumors remain asymptomatic and unrelated before it grows through the esophagus. Significant decreases in mortality from esophageal cancer may require effective approaches to detect and nurse more patients at early, curable stages. A new Improved Empirical Wavelet Transform (IEWT) dependent on feature extraction approach and a consistent homology for the diagnosis of early esophageal endoscopic cancer have been proposed in this article. The approach is to convert an input endoscope image into CIE colored spaces L * x * y, and the x* and y* components to create a fusion image for analysis. Further, the two kinds of wavelets are obtained by adding the two forms to the fusion signal. Another is the lower-frequency component provided by the improved empirical wavelet transformation of the wave, and the other is the high- components generated from the Deep Learning-based Complex Empirical Wavelet Transformation (DL-CEWT). The fractal sizes are determined using the box interpolation method for each small block, and the abnormal regions are defined for the basis of their fractal sizes. Binary pictures are obtained by the complex threshold in each frequency variable and then divided into small blocks in every binary image. Using the homology of every block to obtain the new features in the entry image. The extraction strategies for this application are comprehensive and preliminary findings indicate that the method is effective for the early detection of esophageal cancer in an image.