Reviewerly/exHarmony huggingface dataset:
Attend and Segment: Attention Guided Active Semantic Segmentation

In a dynamic environment, an agent with a limited field of view/resource cannot fully observe the scene before attempting to parse it. The deployment of common semantic segmentation architectures is not feasible in such settings. In this paper we propose a method to gradually segment a scene given a sequence of partial observations. The main idea is to refine an agent’s understanding of the environment by attending the areas it is most uncertain about. Our method includes a self-supervised attention mechanism and a specialized architecture to maintain and exploit spatial memory maps for filling-in the unseen areas in the environment. The agent can select and attend an area while relying on the cues coming from the visited areas to hallucinate the other parts. We reach a mean pixel-wise accuracy of $$78.1\%$$ , $$80.9\%$$ and $$76.5\%$$ on CityScapes, CamVid, and Kitti datasets by processing only $$18\%$$ of the image pixels (10 retina-like glimpses). We perform an ablation study on the number of glimpses, input image size and effectiveness of retina-like glimpses. We compare our method to several baselines and show that the optimal results are achieved by having access to a very low resolution view of the scene at the first timestep.
A Supervised Trajectory Anomaly Detection Using Velocity and Path Deviation

Nowadays, CCTV Surveillance applications have been significantly grown all over the world. Many methods have been implemented for detecting anomalies of moving objects in video. Implementation of fuzziness on features is one of the most robust detection techniques. The proposed model illustrates a novel detection technique with fuzziness on velocity and path deviation. This paper also covers a small survey on different techniques of detection of abnormalities and also shows the result on Queen Mary University of London junction dataset (QMUL).
TECHNOLOGY FOR DISTANT EXAMINATIONS IN A FOREIGN LANGUAGE AT A UNIVERSITY

T. Asten1, A. Rynkevich2, A. Karpova3 1Don State Technical University (RUSSIAN FEDERATION) 2Leonov Moscow Region University of Technology (RUSSIAN FEDERATION) 3Financial University under the Government of the Russian Federation (RUSSIAN FEDERATION)
Deep-Learning-Based Block Similarity Evaluation for Image Forensics

Identifying the type of a camera used to capture an investigated image is a useful image forensic tool, which usually employs machine learning or deep learning techniques to train the source camera models. In this research, we propose a forensic scheme to detect and even locate image manipulations based on deep-learning-based camera model identification. Because of the diversity of image tampering, it's difficult to collect a sufficient amount of tampered images for supervised learning. The proposed method avoids preparing tampered images as the training data but chooses to examine the information of original pictures only. We first train a convolutional neural network to acquire generic features for identifying camera models. Next, the similarity measurement using the Siamese network to evaluate the consistency of image block pairs is used to locate tampered areas. Finally, we refine more accurate tampered areas through a refined segmentation network. The contributions of this research include: (1) extending the study of determining image region consistency to forensics applications, (2) designing a better block comparison algorithm, and (3) improving the accuracy of tampered regions. The proposed scheme is tested by public-available tempered image datasets and our own data to verify the feasibility.
What's The Big Idea? Ridge Regression and Regularisation

Abstract H. D. Vinod marks the 50th anniversary of an idea that has become “an essential concept in data science”
Blockchains: A Conceptual Assessment from a Product Lifecycle Implementation Perspective

PLM systems are heavily distributed collaborative platforms resulting in multiple challenges related to the integrity and reliability of the exchanged information. Such issues are usually difficult to address using traditional software solutions. However, since the introduction of blockchains, such challenges are currently being effectively addressed. On the one hand, traditional PLM systems design philosophy was mainly about centralized proprietary systems that have difficulty to cope with new distributed and open environments imposed by the extensive use of IoT platforms and other Industry 4.0 tools. On the other hand, blockchains address data integrity issues by design, and mitigate some of the trust concerns found in collaborative platforms. Through this paper, we study and evaluate the integration of blockchain technology with PLM systems highlighting the different advantages, challenges, and issues that this integration might induce.
Design and Implementation of an Embedded Cardiorespiratory Monitoring System for Wheelchair Users

A novel design of an embedded cardiorespiratory monitoring system for wheelchair users is proposed. The entire system is composed of a sensor node, a smartphone, and a cloud server. The sensor node contains two parts of an ultra-wideband pulse radar and the data processing module; the former is used to obtain continuous vital-sign signals, and the latter processes the sampled signals to estimate the heart rate and the respiration rate, implemented in an embedded system to achieve a fully integrated radar system. The smartphone functions as the data bridge between the sensor node and the cloud server, which is responsible for sending emergent messages. Experiment results show that the proposed system could work reliably in static and dynamic cases of wheelchairs.
An Efficient Skewed Line Segmentation Technique for Cursive Script OCR

Segmentation of cursive text remains the challenging phase in the recognition of text. In OCR systems, the recognition accuracy of text is directly dependent on the quality of segmentation. In cursive text OCR systems, the segmentation of handwritten Urdu language text is a complex task because of the context sensitivity and diagonality of the text. This paper presents a line segmentation algorithm for Urdu handwritten and printed text and subsequently to ligatures. In the proposed technique, the counting pixel approach is employed for modified header and baseline detection, in which the system first removes the skewness of the text page, and then the page is converted into lines and ligatures. The algorithm is evaluated on manually generated Urdu printed and handwritten dataset. The proposed algorithm is tested separately on handwritten and printed text, showing 96.7% and 98.3% line accuracy, respectively. Furthermore, the proposed line segmentation algorithm correctly extracts the lines when tested on Arabic text.
How to Design a National Aggregated Ranking?

The article presents a new method of designing a national ranking of higher education institutions on the basis of aggregating all existing public national rankings. The new aggregating method MetALeague (Method of Analysis of Leagues) can help to do away with monopoly in evaluation, thus, providing room for independent evaluation of HEIs. The analysis of the current state of procedures and methods of independent evaluation of Russian HEIs in the form of rankings and databases was carried out. The suggested MetALeague method is based on the mathematical tool of the voting theory in small groups and helps to combine different independent evaluations in a single format. A possible approach to creating such a unified national ranking in the form of 10 leagues is considered and the calculation results are presented. The developed ranking methods and the obtained results can be useful for self-diagnosis and promotion of research, organizational and methodological activities of HEIs, and also when introducing a risk-oriented approach in oversight activities.
A multitask joint framework for real-time person search

Person searches generally involve three important parts: person detection, feature extraction and identity comparison. However, a person search integrating detection, extraction and comparison has the two following drawbacks. First, the accuracy of detection will affect the accuracy of comparison. Second, it is difficult to achieve real-time results in real-world applications. To solve these problems, we propose a multitask joint framework for real-time person search (MJF) that optimizes person detection, feature extraction and identity comparison. For the person detection module, we propose the YOLOv5-GS model, which is trained with a person dataset. YOLOv5-GS combines the advantages of the Ghostnet and the squeeze-and-excitation block and improves the speed of person detection. For the feature extraction module, we design a model adaptation architecture, which can select different networks according to the number of people. It can balance the relationship between accuracy and speed. For identity comparison, we propose a 3D pooled table and a matching strategy to improve identification accuracy. On the condition of 1920 $$\times$$ 1080-resolution video and a 200-ID table, the IR and the FPS achieved by our method reach 82.69% and 25.14, respectively. Therefore, the MJF can achieve real-time person search.
Bayes-TrEx: a Bayesian Sampling Approach to Model Transparency by Example

Post-hoc explanation methods are gaining popularity for interpreting, understanding, and debugging neural networks. Most analyses using such methods explain decisions in response to inputs drawn from the test set. However, the test set may have few examples that trigger some model behaviors, such as high-confidence failures or ambiguous classifications. To address these challenges, we introduce a flexible model inspection framework: Bayes-TrEx. Given a data distribution, Bayes-TrEx finds in-distribution examples which trigger a specified prediction confidence. We demonstrate several use cases of Bayes-TrEx, including revealing highly confident (mis)classifications, visualizing class boundaries via ambiguous examples, understanding novel-class extrapolation behavior, and exposing neural network overconfidence. We use Bayes-TrEx to study classifiers trained on CLEVR, MNIST, and Fashion-MNIST, and we show that this framework enables more flexible holistic model analysis than just inspecting the test set. Code and supplemental material are available at https://github.com/serenabooth/Bayes-TrEx.
Estimation of the time for calculating the attributes of browser fingerprints in the user authentication task

This paper presents an overview of the essence of web space which may contain information required to identify and authenticate users on the Internet in order to prevent attempts at malicious acts. Essences with a common content or the same detection method are structured into groups. The greatest attention is paid to one of these groups of entities: «the browser fingerprint» group. An approach and software have been proposed that allow for automated search of the values of entities belonging to this group and for estimating the time of this search for a specific infrastructure in order to develop adaptive authentication mechanisms. Moreover, an analysis of the average time to calculate the values of attributes has been carried out for the most informative browser fingerprints.
Learning to Localize Actions from Moments

With the knowledge of action moments (i.e., trimmed video clips that each contains an action instance), humans could routinely localize an action temporally in an untrimmed video. Nevertheless, most practical methods still require all training videos to be labeled with temporal annotations (action category and temporal boundary) and develop the models in a fully-supervised manner, despite expensive labeling efforts and inapplicable to new categories. In this paper, we introduce a new design of transfer learning type to learn action localization for a large set of action categories, but only on action moments from the categories of interest and temporal annotations of untrimmed videos from a small set of action classes. Specifically, we present Action Herald Networks (AherNet) that integrate such design into an one-stage action localization framework. Technically, a weight transfer function is uniquely devised to build the transformation between classification of action moments or foreground video segments and action localization in synthetic contextual moments or untrimmed videos. The context of each moment is learnt through the adversarial mechanism to differentiate the generated features from those of background in untrimmed videos. Extensive experiments are conducted on the learning both across the splits of ActivityNet v1.3 and from THUMOS14 to ActivityNet v1.3. Our AherNet demonstrates the superiority even comparing to most fully-supervised action localization methods. More remarkably, we train AherNet to localize actions from 600 categories on the leverage of action moments in Kinetics-600 and temporal annotations from 200 classes in ActivityNet v1.3.
Extracting triples from Vietnamese text to create knowledge graph

Knowledge graph (KG) plays an increasingly important role in the current technology era. It is very useful in many fields such as searching for information, supporting question answering systems and in other AI applications, etc. Besides the private Knowledge Graphs like Google's "Knowledge graph", we also have Open Knowledge graphs as DBpedia, YAGO, ... But generally, these Open Knowledge graphs contain very little data in Vietnamese. Due to this practice, our team proposed a way to create Vietnamese Knowledge graph by automatically scratching the Vietnamese text on the website as input, then using Named-entity recognition (NER) to recognize entities as nouns and combined with POS tag identifies words as verbs to extract triple in the simple sentences of the paragraph. The triple was then loaded into Neo4j to visualize the Knowledge graph.
Hybrid recommendation model based on deep learning and Stacking integration strategy

In the traditional recommendation algorithms, due to the rapid development of deep learning and Internet technology, user-item rating data is becoming increasingly sparse. The simple inner product interaction mode adopted by the collaborative filtering method has a cold start problem and cannot learn the complex nonlinear structural features between users and items, while the content-based algorithm encounters the difficulty of effective feature extraction. In response to this problem, a hybrid model is proposed based on deep learning and Stacking integration strategy. The traditional recommendation algorithm is first fused by using the Stacking integration strategy to make up for the shortcomings of the single recommendation algorithm to achieve better recommendation performance. The fusion-based model learns the more abstract and deeper nonlinear interaction features by deep learning technology, which makes the model performance gain further. The experiment comparison on the MovieLens-1m dataset shows that the proposed hybrid recommendation model can significantly improve the accuracy of rating prediction.
A Robot Obstacle Avoidance Method based on Random Forest HTM Cortical Learning Algorithm

Robotics mainly concern with the movement of robot with improvement obstacle avoidance, this issue is handed. It contains of a Microcontroller to process the data, and Ultrasonic sensors to detect the obstacles on its path. Artificial intelligence is used to predict the presence of obstacle in the path. In this research random forest algorithm is used and it is improved by RFHTMC algorithm. Deep learning mainly compromises of reducing the mean absolute error of forecasting. Problem with random forest is time complexity, as it involves formation of many classification trees. The proposed algorithm reduces the set of rules which is used for classification model, to improve time complexity. Performance analysis shows an significant improvement in results as compare to other deep learning algorithm as well as random forest. Forecasting accuracy shows 8% improvement as compare to random forest with 26% reduced operation time.
Detection and recognition of LPI radar signals using visibility graphs

The detection and recognition of radar signals play a critical role in the maintenance of future electronic warfare (EW). So far, however, there are still problems with signal detection and recognition, especially in the low probability of intercept (LPI) radar. This paper explores the usefulness of such an algorithm in the scenario of LPI radar signal detection and recognition based on visibility graphs (VG). More network and feature information can be extracted in the VG two-dimensional space, this algorithm can solve the problem of signal recognition using the autocorrelation function. Wavelet denoising processing is introduced into the signal to be tested, and the denoised signal is converted to the VG domain. Then, the signal detection is performed by using the constant false alarm of the VG average degree. Next, weight the converted graph. Finally, perform feature extraction on the weighted image, and use the feature to complete the recognition. It is testified that the proposed algorithm offers significant improvements, such as robustness to noise, and the detection and recognition accuracy, over the recent researches.
All in the Exponential Family: Bregman Duality in Thermodynamic Variational Inference.

The recently proposed Thermodynamic Variational Objective (TVO) leverages thermodynamic integration to provide a family of variational inference objectives, which both tighten and generalize the ubiquitous Evidence Lower Bound (ELBO). However, the tightness of TVO bounds was not previously known, an expensive grid search was used to choose a of intermediate distributions, and model learning suffered with ostensibly tighter bounds. In this work, we propose an exponential family interpretation of the geometric mixture curve underlying the TVO and various path sampling methods, which allows us to characterize the gap in TVO likelihood bounds as a sum of KL divergences. We propose to choose intermediate distributions using equal spacing in the moment parameters of our exponential family, which matches grid search performance and allows the schedule to adaptively update over the course of training. Finally, we derive a doubly reparameterized gradient estimator which improves model learning and allows the TVO to benefit from more refined bounds. To further contextualize our contributions, we provide a unified framework for understanding thermodynamic integration and the TVO using Taylor series remainders.
The Role of Knowledge in Enhancing SME Innovation

This study examines the knowledge of entrepreneurs on the complex concept of innovation before assessing how they apply this knowledge to increase the propensity to develop new products and services. Within this context, considerable emphasis explores the assertions that knowledge management can enhance the adoption of new technology through collaborative networks. The aim of the study is to build a body of work in the area of innovation and entrepreneurship that spans the divide between theory and practice by empirically harnessing the perspectives of the case study companies on their innovation behavior. The design of this study adopts the characteristics of a case study research which was applied extensively to provide qualitative data. To be included in the study, firms had to be in existence for at least a year, and firms of the same age were avoided. The findings revealed that the concept of innovation is heterogeneous and elusive. Therefore, the study concluded that innovation is a global concept that requires knowledge acquisition through collaborative networks.
Symbolic Network: Generalized Neural Policies for Relational MDPs

A Relational Markov Decision Process (RMDP) is a first-order representation to express all instances of a single probabilistic planning domain with possibly unbounded number of objects. Early work in RMDPs outputs generalized (instance-independent) first-order policies or value functions as a means to solve all instances of a domain at once. Unfortunately, this line of work met with limited success due to inherent limitations of the representation space used in such policies or value functions. Can neural models provide the missing link by easily representing more complex generalized policies, thus making them effective on all instances of a given domain? We present SymNet, the first neural approach for solving RMDPs that are expressed in the probabilistic planning language of RDDL. SymNet trains a set of shared parameters for an RDDL domain using training instances from that domain. For each instance, SymNet first converts it to an instance graph and then uses relational neural models to compute node embeddings. It then scores each ground action as a function over the first-order action symbols and node embeddings related to the action. Given a new test instance from the same domain, SymNet architecture with pre-trained parameters scores each ground action and chooses the best action. This can be accomplished in a single forward pass without any retraining on the test instance, thus implicitly representing a neural generalized policy for the whole domain. Our experiments on nine RDDL domains from IPPC demonstrate that SymNet policies are significantly better than random and sometimes even more effective than training a state-of-the-art deep reactive policy from scratch.
Application of AR Technology in Aircraft Maintenance Manual

Abstract The readability, interactivity and generality of aircraft maintenance manual directly affect maintenance cycle and cost. This paper presents the application of augmented reality technology in the development process of aircraft maintenance manual. The overall framework and functional requirements of the development of augmented reality intelligent maintenance system are given. Taking the leakage inspection procedure of AMM manual as an example, the process of augmented reality technical manual aircraft maintenance publication is explored.
Transient Non-stationarity and Generalisation in Deep Reinforcement Learning

Non-stationarity can arise in Reinforcement Learning (RL) even in stationary environments. For example, most RL algorithms collect new data throughout training, using a non-stationary behaviour policy. Due to the transience of this non-stationarity, it is often not explicitly addressed in deep RL and a single neural network is continually updated. However, we find evidence that neural networks exhibit a memory effect, where these transient non-stationarities can permanently impact the latent representation and adversely affect generalisation performance. Consequently, to improve generalisation of deep RL agents, we propose Iterated Relearning (ITER). ITER augments standard RL training by repeated knowledge transfer of the current policy into a freshly initialised network, which thereby experiences less non-stationarity during training. Experimentally, we show that ITER improves performance on the challenging generalisation benchmarks ProcGen and Multiroom.
Implementing Dockerized Elastic Stack for Security Information and Event Management

In security information and event management (SIEM), real-time monitoring together with analysis of log data for correlation of events can provide anomaly detection and notification. A centralized log system with big-data databases is required for SIEM in order to store and manage the log data. In this paper, we present dockerized Elastic Stack for security information and event management. The main reasons are because of lightweight, simplicity and supporting features. One of important Elasticsearch features is security information. Elasticsearch can act as Intrusion Detection System (IDS) together with log/event management as SIEM. Our experiments show that dockerized elastic stack can be efficiently used for security information and event management.