Reviewerly/exHarmony huggingface dataset:
One-shot style transfer using Wasserstein Autoencoder

We propose an image style transfer method based on disentangled representation obtained with Wasser-stein Autoencoder. Style transfer is an area of image generation technique that generates an image that shows content taken from one image with a style taken from another image. While there are extensive researches in this area, most of them require some ‘training’ time to generate images with a specific style. The proposed method does not require training time since we train a versatile network that can be used for any style and content image. The network encodes images into disentangled latent variables that represent content and style. We can transfer image style by simply replacing style latent variables. We tested the proposed method with images from CelebA and confirmed that it can generate style transferred images.
Modal Uncertainty Estimation for Medical Imaging Based Diagnosis

Medical image based diagnosis is constantly faced with uncertainties. In an ambiguous scenario, different experts will reach different conclusions from their initial assumptions. It is thus important for machine learning models to be capable of proposing different plausible predictions, along with meaningful uncertainty measures. In this work we propose such a novel learning-based framework, named modal uncertainty estimation (MUE), to learn such one-to-many relationship with faithful uncertainty estimation in the medical image understanding tasks. Technically, MUE is based on conditional generative models, but it crucially uses a set of discrete latent variables, each representing a latent mode hypothesis that explains one type of input-output relationship. We justify the use of discrete latent variables by the multi-modal posterior collapse problem in the common conditional generative models. Consequently, MUE can estimate the uncertainty effectively. MUE demonstrates significantly more accurate uncertainty estimation for one-to-many relationship than the current state-of-the-art, and is more informative for practical use. We validate these points on both real and synthetic tasks.
Extreme Learning Machine Design for Dealing with Unrepresentative Features

Extreme Learning Machines (ELMs) have become a popular tool for the classification of electroencephalography (EEG) signals for Brain Computer Interfaces. This is so mainly due to their very high training speed and generalization capabilities. Another important advantage is that they have only one hyperparameter that must be calibrated: the number of hidden nodes. While most traditional approaches dictate that this parameter should be chosen smaller than the number of available training examples, in this article we argue that, in the case of problems in which the data contain unrepresentative features, such as in EEG classification problems, it is beneficial to choose a much larger number of hidden nodes. We characterize this phenomenon, explain why this happens and exhibit several concrete examples to illustrate how ELMs behave. Furthermore, as searching for the optimal number of hidden nodes could be time consuming in enlarged ELMs, we propose a new training scheme, including a novel pruning method. This scheme provides an efficient way of finding the optimal number of nodes, making ELMs more suitable for dealing with real time EEG classification problems. Experimental results using synthetic data and real EEG data show a major improvement in the training time with respect to most traditional and state of the art ELM approaches, without jeopardising classification performance and resulting in more compact networks.
Pattern Identification of Bot Messages for Media Literacy

The massive use of online social media is a reality nowadays. Such an increasing usage also raises growth in malicious activities in social media, one of which is the use of automated users (bots) that disseminate false information and can insert bias in analyses done on gathered social media data. Based on the concept of media literacy, this research presents a method to teach the human user to identify a pattern of a text produced by a bot, providing a tool (guide) to analyze social media text. Users who learned to identify a bot user with the guide had an average of 90% accuracy in the classification of new messages, against 57% of the participants who had no contact with the guide. The produced guide received a usefulness rating between 4 and 5 by the participants (scale from 1 to 5, with 5 being the highest value).
Optimal Feature Set for Smartphone-based Activity Recognition

Human activity recognition using wearable and mobile devices is used for decades to monitor humans’ daily behaviours. In recent years as smartphones being widely integrated into our daily lives, the use of smartphone’s built-in sensors in human activity recognition has been receiving more attention, in which smartphone accelerometer plays the main role. However, in comparison to the standard machine, when developing human activity recognition using a smartphone, the limitations such as processing capability and energy consumption should be taken into consideration, and therefore, a trade-off between performance and computational complexity should be considered. In this paper, we shed light on the importance of feature selection and its impact on simplifying the activity classification process, which enhances the computational complexity of the system. The novelty of this work is related to identifying the most efficient features for the detection of each individual activity uniquely. In an experimental study with human users and using different smartphones, we investigated how to achieve an optimal feature set, using which the system complexity can be decreased while the activity recognition accuracy remains high. For that, in the considered scenario, we instructed the participants to perform different activities, including static, dynamic, going up and down the stairs, and walking fast and slow while freely holding a smartphone in their hands. To evaluate the obtained optimal feature set implementing two major classification algorithms, the decision tree and the Bayesian network, we investigated activity recognition accuracy for different activities. We further evaluated the optimal feature set by comparing the performance of the activity recognition system using the optimal feature set and three feature sets taken from the state-of-the-art. The experimental results demonstrated that replacing a large number of conventional features with an optimal feature set has only a negligible impact on the overall activity recognition system performance while it can significantly decrease the system’s complexity, which is essential for smartphone-based systems.
Semi-supervised learning for raindrop removal on a single image

This article propose s a network that is mainly used to deal with a single image polluted by raindrops in rainy weather to get a clean image without raindrops. In the existing solutions, most of the methods rely on paired images, that is, the rain image and the real image without rain in the same scene. However, in many cases, the paired images are difficult to obtain, which makes it impossible to apply the raindrop removal network in many scenarios. Therefore this article proposes a semi-supervised rain-removing network apply to unpaired images. The model contains two parts: a supervised network and an unsupervised network. After the model is trained, the unsupervised network does not require paired images and it can get a clean image without raindrops. In particular, our network can perform training on paired and unpaired samples. The experimental results show that the best results are achieved not only on the supervised rain-removing network, but also on the unsupervised rain-removing network.
Semantic and Optical Flow Guided Self-supervised Monocular Depth and Ego-Motion Estimation

The self-supervised depth and camera pose estimation methods are proposed to address the difficulty of acquiring the densely labeled ground-truth data and have achieved a great advance. As the stereo vision could constrain the predicted depth to a real-world scale, in this paper, we study the use of both left-right pairs and adjacent frames of stereo sequences for self-supervised semantic and optical flow guided monocular depth and camera pose estimation without real pose information. In particular, we explore (i) to construct a cascaded structure of the depth-pose and optical flow for well-initializing the optical flow, (ii) a cycle learning strategy to further constrain the depth-pose learning by the cross-task consistency, and (iii) a weighted semantic guided smoothness loss to match the real nature of a depth map. Our method produces favorable results against the state-of-the-art methods on several benchmarks. And we also demonstrate the generalization ability of our method on the cross dataset.
A Search Engine for Scientific Publications: A Cybersecurity Case Study

Cybersecurity is a very challenging topic of research nowadays, as digitalization increases the interaction of people, software and services on the Internet by means of technology devices and networks connected to it. The field is broad and has a lot of unexplored ground under numerous disciplines such as management, psychology, and data science. Its large disciplinary spectrum and many significant research topics generate a considerable amount of information, making it hard for us to find what we are looking for when researching a particular subject. This work proposes a new search engine for scientific publications which combines both information retrieval and reading comprehension algorithms to extract answers from a collection of domain-specific documents. The proposed solution although being applied to the context of cybersecurity exhibited great generalization capabilities and can be easily adapted to perform under other distinct knowledge domains.
Contrastive Learning Framework by Maximizing Mutual Information for Visual Question Answering

Visual Question Answering (VQA) is an extremely stimulating and challenging research area which requires joint image content and language understanding to answer questions about a given image. The existing VQA models have made many efforts in the direction of improving the ability to understand images and achieved good results. However, these models ignore the relationship between the image and the corresponding question, which is strongly correlated when the data is collected (i.e., the question asked by the staff should be consistent with the current image). To capture the essential features between the image and the question, we propose a new VQA model based on contrastive learning by maximizing mutual information. The core idea of our model is to maximize the mutual information between question features and corresponding image features, and minimize the mutual information between question features and their irrelevant image features, so as to improve the ability of image understanding and question understanding of the model simultaneously. The experimental results indicate that the feature representation learned by our model is more representative, and the performance of our model on VQA v1.0 and VQA v2.0 datasets is better than the baseline model.
Coordination and Cooperation in Robot Soccer

Aiming at improving our physical strength and expanding our knowledge, tournaments and competitions have always contributed to our personal growth. Robotics and AI are no exception, and since beginning, competitions have been exploited to improve our understanding of such research areas (e.g. Chess, VideoGames, DARPA). In fact, the research community has launched (and it is involved) in several robotics competitions that provide a two-fold benefit of (i) promoting novel approaches and (ii) valuate proposed solutions systematically and quantitatively. In this paper, we focus on a particular research area of Robotics and AI: we analyze multi-robot systems deployed in a cooperative-adversarial environment being tasked to collaborate to achieve a common goal, while competing against an opposing team. To this end, RoboCup provide the best benchmarking environment by implementing such a challenging problem in the game of soccer. Sports, in fact, represent extremely complex challenge that require a team of robots to show dexterous and fluid movements and to feature high-level cognitive capabilities. Here, we analyse methodologies and approaches to address the problem of coordination and cooperation and we discuss state-of-the-art solutions that achieve effective decision-making processes for multi-robot adversarial scenarios.
Short-Form video classification based on Gate shift module and Semantic embedding

Abstract Most of the existing video classification methods are based on large-scale data training, which can better realize the recognition and classification of known categories. However, data labelling is cumbersome and most things are unknown. Therefore, the existing video classification methods fall into a data bottleneck. This paper proposes a short video classification method based on GSM and semantic embedding. It uses super large-scale text information to assist the recognition process of the video classification model. This is an important development in the classification effect of knowledge categories. Specifically, this article expands the video classification method, adds category semantic embedding in the video feature extraction process, and trains to continuously fit the word vector of the corresponding category, and then uses semantic similarity to realize the classification of unknown categories. Multi-angle comparative experiments verify the effectiveness of this model, which can achieve good classification of unknown video categories.
Semantically Constrained Document-Level Chinese-Mongolian Neural Machine Translation

By using document-level contextual information, document-level neural machine translation can achieve better results than ordinary machine translation, but traditional document-level machine translation is difficult to focus on the contextual sentence articulation relations and deep positional relations within the discourse while utilizing document-level vocabulary, and the model can concentrate only on relatively shallow inter-sentential relations or positional information. In this paper, we consider that most adjacent sentences are connected in document translation, and such links help improve the quality of translation. We propose a document translation model that focuses more on inter-sentential relations based on the previous work, and propose two methods to strengthen the model's positional information input, and combine these two methods to enhance the traditional Transformer positional information input. This paper also proposes a method for inserting paragraph information to allow inter-sentential relations to be learned by the model, and uses the improved Transformer model for Chinese-Mongolian document translation. Experiments show that in the improved Transformer system, the BLEU scores are enhanced on the Chinese-Mongolian machine translation task after fusing positional information and inter-sentential relation information, and the translation achieves better performance.
Features of the application of the method of quantile regression in problems of hydroecology

Such perspective method of the analysis of the natural time series is considered in article as method of quantile regression. They are discussed imperfection existing methods of the analysis of the natural processes (least square method and the method of linear robust regression). It is shown that transition towards quantile regression allows greatly to raise efficiency of the investigations of the natural time series. Main gоal of the work consists in practical applications of the quantile regression method for decision of the different problemsof the hydroecology. Hydrochemical time series were considered in article. By means of quantile regressin method was investigated behavior of the dissolved oxygen in water depending on temperature of river water and depending on discharge of river water (for quantiles of order 0.05; 0,50 und 0,95). When performing the investigations more perfect method of quantile regression was designed such as method piecewisf quantile regression (with uce polynomial degree 1; 2; 3). Numenical experiments when use the natural time series have shown greater advantage of the designed method of piecewise quantile regression in contrast with classical method of quantile regression.Such perspective method of the analysis of the natural time series is considered in article as method of quantile regression. They are discussed imperfection existing methods of the analysis of the natural processes (least square method and the method of linear robust regression). It is shown that transition towards quantile regression allows greatly to raise efficiency of the investigations of the natural time series. Main gоal of the work consists in practical applications of the quantile regression method for decision of the different problemsof the hydroecology. Hydrochemical time series were considered in article. By means of quantile regressin method was investigated behavior of the dissolved oxygen in water depending on temperature of river water and depending on discharge of river water (for quantiles of order 0.05; 0,50 und 0,95). When performing the investigations more perfect method of quantile regression was designed such as method piecewisf quantile regression (with uce polynomial degree 1; 2; 3). Numenical experiments when use the natural time series have shown greater advantage of the designed method of piecewise quantile regression in contrast with classical method of quantile regression.
Performance Evaluation of The SIERA System at the Regional Assets Management Agency of the DKI Jakarta Provincial Government Using the COBIT 5 Framework

Objective –To measuring and knowing the level of capability, the value of gaps, and recommendations forimprovement for SIERA information system performance using the framework COBIT 5 based on the processAPO08 Manage Relationship and BAI07 Manage Change Acceptance and Transitioning.Methodology –This research uses a qualitative method and a quantitative method called the mix method. Thequalitative method is used to analyze the level of capability expected from the application of the SIERA informationsystem based on the results of interview answers. In contrast, the quantitative method is used to analyze the level ofcapability that has been achieved so far based on the results of a questionnaire that has been filled out by fourrespondents determined by a convenient sampling technique.Findings – The research found the results that the APO08 domain stopped at level 2, and the domain BAI07 stoppedat desired level 1, so corrective actions need to be taken to reach the level expected level 4.Novelty – By measuring the performance evaluation of the SIERA system, the Regional Asset Management Agencyof DKI Jakarta Province can determine the level of capability that has been achieved and the value of the gap withthe expected level of capability, and assisting the Regional Asset Management Agency of the DKI Jakarta ProvincialGovernment to take corrective actions based on recommendations from the findings in SIERA.Keywords: APO08 Manage Relationship, BAI07 Manage Change Acceptance and Transitioning, COBIT 5, DKIJakarta Regional Asset Management Agency
Information and communication technology application development to increase the percentage of earthquake’s survivors

Indonesia is a country that has enormous geographical potential for earthquake disasters. This makes Indonesia must have a good disaster management system between the institutions involved in disaster response. The use of information and communication technology has been widely used for disaster management throughout the world, primarily mobile phone-based technology. Mobile phone applications during the disaster response process can significantly increase response times, search for victims, and the placement of heavy equipment and Search and Rescue (SAR) personnel during the emergency response process. This research aims to develop a mobile phone application that will increase the percentage of survivors with variables measured in real-time earthquake notifications, knowledge of dealing with earthquake disasters, and calls to emergency medical personnel. These variables are obtained from literature studies; then processed into two features added into the application. After that, a survey was conducted to the community to determine whether the two added features could affect the user’s security and safety in facing an earthquake. The two features affect the community’s awareness of earthquake disasters and the belief that the community has a greater chance of surviving an earthquake disaster.
PETA: Photo Albums Event Recognition using Transformers Attention

In recent years the amounts of personal photos captured increased significantly, giving rise to new challenges in high-level multi-image understanding. Event recognition in personal photo albums presents one challenging scenario where life events are recognized from a disordered collection of images, including both relevant and irrelevant images. Event recognition in images also presents the challenge of high-level image understanding, as opposed to low-level image object classification. In absence of methods to analyze multiple inputs, previous methods adopted temporal mechanisms, including various forms of recurrent neural networks. However, their effective temporal window is local. In addition, they are not a natural choice given the disordered characteristic of photo albums. We address this gap with a tailor-made solution, combining the power of CNNs for image representation and transformers for album representation to perform global reasoning on image collection, offering a practical and efficient solution for photo albums event recognition. Our solution reaches state-of-the-art results on three prominent benchmarks, achieving above 90% mAP on all datasets. We further explore the related image-importance task in event recognition, demonstrating how the learned attentions correlate with the human-annotated importance for this subjective task, thus opening the door for new applications. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Application of first-order logic to identify organizers and perpetrators of illegal actions in teams of a limited circle of people

There are three main families of inference algorithms in first-order logic: direct inference and its application to deductive databases and production systems; backward inference procedures and logic programming systems; theorem proving systems based on the resolution method. When solving specific problems, the most effective algorithms are those that allow you to cover all the facts and axioms and must be taken into account in the process of inference. An example is considered in which it is necessary to prove the guilt of a person in murder. On the basis of statements, a knowledge base is formed from expressions, with the help of which an expression of first-order logic is compiled and proved using direct logical inference. The proof of the reasoning obtained in direct inference using the proof tree is given. However, direct inference provides for the implementation of all admissible stages of logical inference based on all known facts. The article also considers a method based on the resolution when implementing the reverse inference, taking into account the expression obtained in the direct inference. This expression is converted into a conjunctive normal formula using the laws of Boolean algebra and is proved by the elimination of events using the conjunction operation.