Reviewerly/exHarmony huggingface dataset:
Comparison of Style Features for the Authorship Verification of Literary Texts

The article compares character-level, word-level, and rhythm features for the authorship verification of literary texts of the 19th-21st centuries. Text corpora contains fragments of novels, each fragment has a size of about 50 000 characters. There are 40 fragments for each author. 20 authors who wrote in English, Russian, French, and 8 Spanish-language authors are considered. The authors of this paper use existing algorithms for calculation of low-level features, popular in the computer linguistics, and rhythm features, common for the literary texts. Low-level features include n-grams of words, frequencies of letters and punctuation marks, average word and sentence lengths, etc. Rhythm features are based on lexico-grammatical figures: anaphora, epiphora, symploce, aposiopesis, epanalepsis, anadiplosis, diacope, epizeuxis, chiasmus, polysyndeton, repetitive exclamatory and interrogative sentences. These features include the frequency of occurrence of particular rhythm figures per 100 sentences, the number of unique words in the aspects of rhythm, the percentage of nouns, adjectives, adverbs and verbs in the aspects of rhythm. Authorship verification is considered as a binary classification problem: whether the text belongs to a particular author or not. AdaBoost and a neural network with an LSTM layer are considered as classification algorithms. The experiments demonstrate the effectiveness of rhythm features in verification of particular authors, and superiority of feature types combinations over single feature types on average. The best value for precision, recall, and F-measure for the AdaBoost classifier exceeds 90% when all three types of features are combined.
Improved Image Captioning via Semantic Feature Update

Image captioning is one of the main visual tasks to achieve scene understanding, involving detecting and recognizing objects and their relationships, and describing the image content with natural language. In order to improve the accuracy of the spatial attention mechanism to obtain image features to improve the performance of image captioning, we propose an image captioning model based on semantic feature update, to extract the features of different semantic layers in the scene image and then iteratively update these features to achieve accurate description of the scene image. Firstly, we use Faster R-CNN to extract the object features, visual relationship features between the objects in the image, and the global features of the image. Secondly, we use a feature refining network that takes the object features and relationship features as input to update the object features and relationship features. Finally, the updated features as the input of the spatial attention mechanism are integrated into the description framework to improve the accuracy of image captioning. Experiments on the COCO dataset demonstrate the superiority of our proposed captioning model over other captioning models.
Attention Mechanism for Improving Facial Landmark Semantic Segmentation

Various services are being embodied for smart city using IT technologies. For instance, face recognition technology can be used for effectively search for missing persons or track criminals. The key to face recognition is to detect landmarks that are the main features of faces. Many studies have been done to detect them accurately and quickly, but there is still much to be done. Deep learning methods for facial landmark detection mainly have used convolutional neural networks. Recently, a new attempt to go beyond CNNs is being tried by considering relationships among all pixels in the images to improve missing long-range context problem in CNNs. In this chapter, we propose a scheme for improving the performance of facial landmark detection based on attention and show its performance through various experiments.
GAN-aided Serial Dependence Study in Medical Image Perception

Medical imaging has been critically important for the health and well-being of millions of patients. Although deep learning has been widely studied in medical imaging area and the performance of deep learning has exceeded human's performance in certain medical diagnostic tasks, detecting and diagnosing lesions still depends on the visual system of human observers (radiologists), who completed years of training to scrutinize anomalies. Routinely, radiologists sequentially read batches of medical images one after the other. A basic underlying assumption of radiologists' precise diagnosis is that their perceptions and decisions on a current medical image are completely independent from the previous reading history of medical images. However, recent research proposed that the human visual system has visual serial dependencies (VSDs) at many levels. VSD means that what was seen in the past influences (and captures) what is seen and reported at this moment. Our pilot data via naive artificial stimuli has shown that VSD has a disruptive effect in radiologic searches that impairs accurate detection and recognition of tumors or other structures. However, the naive artificial stimuli have been noted by both untrained observers and expert radiologists to be less authentic. In this project, we will generate authentic medical images via Generative Adversarial Networks (GANs) in order to replace the simple stimuli in future experiments. The rationale for the proposed research project is that once it is known how serial dependence arises and how it impacts visual search, we can understand how to control for it. Hence, the accuracy of diagnosis via medical imaging can significantly improve. The specific goals of this project are to establish, identify and mitigate the impact of VSD on visual search tasks in clinical settings.
An Improved Monocular-Vision-Based Method for the Pose Measurement of the Disc Cutter Holder of Shield Machine

The manual disc cutter changing of the shield machine has greater security hidden danger, which leads to the urgent need to develop a disc cutter changing robot to replace the over-worn disc cutters. The rotation positioning error of the large diameter cutter head, the motion control error of the disc cutter changing robot, and the complexity of the disc cutter changing bin environment lead to the inaccuracy of the actual pose of the disc cutter. Therefore, it is necessary to increase the machine vision measurement system to accurately measure the pose of the disc cutter or its holder. This paper proposes a cutter holder positioning method, which uses the parallel line features and actual contour area of the cutter holder to determine its initial pose, and then uses the initial pose to calculates its accurate pose based on the distance matching method. The innovation of the method is that the pose measurement is carried out through the unique characteristics of the cutter holder, and it combines the initial pose estimation method based on the geometric characteristics of the cutter holder with the distance matching method which depends on the initial pose to solve the cutter holder pose. Simulation analysis shows that the accuracy of the proposed method is close to that of the most advanced PnP algorithms, and its robustness is better than others. Experimental results also verify the high accuracy of the proposed method.
Creation of Personas by End-Users in Alcohol and Drugs Abuse Rehabilitation

Personas is a consolidated technique to support design solutions and designers have successfully used the technique to build digital solutions in the health domain. There are several studies in the literature that examine the creation of Personas with the participation of health professionals. However, since patients are generally only information providers, there have been few studies of patients being involved by acting as creators of Personas for therapeutic reasons. This paper investigates the co-creation of Personas by patients undergoing rehabilitation for alcohol and drugs abuse. The activity was part of a larger project in which patients adopted the SemTh approach to co-create therapeutic digital games. Twenty-one patients, one healthcare professional and four computing professionals took part in the Personas creation workshop and seven personas were created. By conducting a qualitative analysis, in this paper, we were able to determine eleven lessons learned from the process.
Video Abnormal Behavior Detection Based on Optical Flow Method and Convolutional Neural Network

This paper proposes a new algorithm for abnormal behavior detection in surveillance video. Firstly, the motion information image of each frame is constructed by calculating the optical flow size and the angle difference between the optical flow vectors between consecutive frames, and then the obtained motion image information is input into the convolutional neural network (CNN) for training, and used for video abnormal behavior detection. In the algorithm, the motion information image generated based on optical flow information can provide the motion information features in the video image more accurately, which makes it easier to distinguish the normal behavior and abnormal behavior of the video. The experiment of this algorithm is carried out on the commonly used data set PETS 2009. Experimental results show that the proposed method and other algorithms have a significant improvement in the accuracy of abnormal behavior detection.
E-Biometric Voting Machine

The voting process plays a key role in electing the decision-makers in democratic countries like India. Nowadays the electronic voting machine is widely used, which has extensively reduced counting times as compared to the older election process that used ballots. This is the motivation to propose a work that automates the process of voting using biometrics, the Internet of Things (IoT), and cloud storage. This would reduce the time, expenses, and human effort. The proposed system prevents fake votes using biometric validation, making it more credible. This mechanism is more secure as it would use a two-step verification process implemented using OTP sent through SMS. The proposed system is to be made on Raspberry Pi and contains an LCD touch screen, enabling a smooth interface for voting and a fingerprint scanner for biometric verification. Results will be instantly available on storage which reduce counting time. We are confident that introducing this system in the general elections would bring a remarkable change in the election process.
SoloDB for Social Media’s Big Data Using Deep Natural Language with AI Applications and Industry 5.0

Deep natural language processing is an algorithmic approach that enables computers to understand language using patterns, purpose, adequate experience, and a natural human data extraction context. It goes beyond a strategy for syntax and depends on a semantic strategy. Industry 5.0 democratizes the co-production of information from Big Data, building on the current symmetrical innovation concept. The Industrial Revolution 5.0 is transforming companies into working through human and computer cooperation with the massive amount of data. Industry 5.0 develops human expertise and accuracy of the computer and will be creative and satisfy consumer needs with the final product. Big data produces usable data and analyzes the best data suited for the good of the industry. The industry has no benefit from NLP converted knowledge without Big Data. Currently, users can get information from several Web sites and do not have enough time to scan all Web sites. Data is distributed with various forms of data such as education, cinema, and politics. Numerous Web sites and social media (WhatsApp, Twitter, etc.) data are distributed in the world. These different data are collected via social media and stored in one SoloDB database that allows the user to access it quickly and easily. With the authorization of the administrative process, the database information can be accessed. Deep natural language, Big Data, and artificial intelligence will be discussed, and the results will be evaluated using the Industry 5.0 private database. The combination of computer and person would make it easy to access information from the database in a customized manner.
Towards Stereoscopic Video Deblurring Using Deep Convolutional Networks

These days stereoscopic cameras are commonly used in daily life, such as the new smartphones and emerging technologies. The quality of the stereo video can be affected by various factors (e.g., blur artifact due to camera/object motion). For solving this issue, several methods are proposed for monocular deblurring, and there are some limited proposed works for stereo content deblurring. This paper presents a novel stereoscopic video deblurring model considering the consecutive left and right video frames. To compensate for the motion in stereoscopic video, we feed consecutive frames from the previous and next frames to the 3D CNN networks, which can help for further deblurring. Also, our proposed model uses the stereoscopic other view information to help for deblurring. Specifically, to deblur the stereo frames, our model takes the left and right stereoscopic frames and some neighboring left and right frames as the inputs. Then, after compensation for the transformation between consecutive frames, a 3D Convolutional Neural Network (CNN) is applied to the left and right batches of frames to extract their features. This model consists of the modified 3D U-Net networks. To aggregate the left and right features, the Parallax Attention Module (PAM) is modified to fuse the left and right features and create the output deblurred frames. The experimental results on the recently proposed Stereo Blur dataset show that the proposed method can effectively deblur the blurry stereoscopic videos.
Cooperative Driven Algorithm for Sheep Herd Trajectory Tracking by Two Sheepdogs

In this paper, we consider the design of sheep herd driven algorithm by two sheepdogs. The sheep herd follow simple interaction rules among sheep, namely, separation and cohesion, which are treated as prior knowledge to the sheepdogs. The objective for the sheepdogs is to drive the sheep herd to follow a desired trajectory in a cooperative way. Inspired by the behaviors of real sheepdogs, for each sheepdog, we have developed a semi-circle reciprocation algorithm featuring dynamic turn-around point selection taken into consideration the ingredients of the positions of the sheep, the desired trajectory and the position of the other sheepdog. Numerical simulations are conducted to examine the performance of the proposed algorithm.
Improving End-to-End Contextual Speech Recognition via a Word-Matching Algorithm With Backward Search

End-to-end automatic speech recognition (E2E-ASR) prefers the common words during training rather than rare ones related to contextual information such as song names. Thus, recognizing contextual information correctly is a hurdle for E2E-ASR to reach the production-level. To overcome the limitations of E2E-ASR in recognizing contextual information, this work presents a post-processing followed by E2E-ASR in an algorithmic way, referred to as a word-matching algorithm with backward search (WMA-BS). At first, we allow E2E-ASR to roughly detect the position of target words that has similar pronunciation with desired contextual phrases. After that, given the hypothesis from E2E-ASR with the rough position of target words, WMA-BS estimates the correct target words and decides whether to replace the target words with the contextual phrase or not, according to their phonetic and literal similarity. Applying the proposed method to E2E-ASR achieved relative improvement up to 52.7% in word error rate across several harsh conditions.
Short‐term solar power forecasting based on convolutional neural network and analytical knowledge

With a high proportion of variable renewable energy integration, accurate forecasting approach is of vital importance in ensuring the reliable and economic operation of power system. Therefore, in this article, a novel method for predicting photovoltaic (PV) power generation based on convolutional neural network (CNN) is proposed. Analytical models of PV systems are formulated, thereby providing physical knowledge about the relationship between PV output and critical meteorological features. To explore the nonlinear and time-varying properties of PV output, CNN is adopted in this article, which matches the patterns of similar days. Case studies based on realistic datasets in Australia demonstrate that the forecasting performance for solar power can be effectively improved by taking advantage of the proposed CNN-based learning method.
SmartWave: a Smart Platform for Marine Environmental Monitoring

In recent years, the interest in the study of seas and oceans has dramatically increased as they are considered of primary importance for forecasting catastrophic events or for supporting blue economy, as well as the marine tourism, improving the tourist reception or enhancing any marine-related activity. This led to the development of IT platforms that allow to monitor the marine environment and provide a number of services to different kinds of final users, whether they are private individuals interested in the status of the seas, or companies whose business depends on the marine environmental monitoring. The main limitations of current platforms are due to such a difference between free trials, which often focus only on specific aspects of deep waters, and subscriptions, which provide analyzes whose reliability is generally not proportional to the costs. This paper presents SmartWave, a project funded by Regione Sicilia (European Regional Development Fund), that aims to develop a novel IT platform to observe and predict phenomena that characterize the marine environment, while also providing the consumer with a unified portal to collect, access and analyze marine-related information. To achieve this goal, one of the main challenges of this project is to aggregate and standardize heterogeneous data from multiple sources in order to offer very accurate information to private or business consumers.
Learning and Exploiting Partial Knowledge in Distributed Estimation

In distributed estimation, several sensor nodes provide estimates of the same underlying dynamic process. These estimates are correlated but due to local processing, the correlations are only partially known or even unknown. For a consistent fusion of the local estimates, the correlation needs to be properly treated. Many methods provide consistent but overly conservative fusion results. In this paper, we propose to learn partial knowledge about the correlation in the form of correlation sets and exploit this knowledge to provide less conservative estimates. We use a simple numerical example to demonstrate the advantages of the proposed approach in terms of quality and consistency and how the quality of the fused estimate increases with time.
Dual Attention Triplet Hashing Network for Image Retrieval

In recent years, learning-based hashing techniques have proven to be efficient for large-scale image retrieval. However, since most of the hash codes learned by deep hashing methods contain repetitive and correlated information, there are some limitations. In this paper, we propose a Dual Attention Triplet Hashing Network (DATH). DATH is implemented with two-stream ConvNet architecture. Specifically, the first neural network focuses on the spatial semantic relevance, and the second neural network focuses on the channel semantic correlation. These two neural networks are incorporated to create an end-to-end trainable framework. At the same time, in order to make better use of label information, DATH combines triplet likelihood loss and classification loss to optimize the network. Experimental results show that DATH has achieved the state-of-the-art performance on benchmark datasets.
iTOP: Automating Counterfeit Object-Oriented Programming Attacks

Exploiting a program requires a security analyst to manipulate data in program memory with the goal to obtain control over the program counter and to escalate privileges. However, this is a tedious and lengthy process as: (1) the analyst has to massage program data such that a logical reliable data passing chain can be established, and (2) depending on the attacker goal certain in-place fine-grained protection mechanisms need to be bypassed. Previous work has proposed various techniques to facilitate exploit development. Unfortunately, none of them can be easily used to address the given challenges. This is due to the fact that data in memory is difficult to be massaged by an analyst who does not know the peculiarities of the program as the attack specification is most of the time only textually available, and not automated at all.
Transfer Learning in Autonomous Driving Using Real-World Samples

The Sim2Real gap is a topic that has been receiving a great deal of attention lately. Many Artificial Intelligence techniques, for example Reinforcement Learning, require millions of iterations to achieve satisfactory performance. This requirement often forces these techniques to solely train in simulation. If the gap between the simulated environment and the target environment is too broad, however, the trained agents will lose out on performance when deployed. Bridging this gap lowers the performance loss during deployment, in turn improving the effectiveness of these agents. This paper proposes a new technique to tackle this issue. The technique focuses on the use of demonstration samples gathered in the target environment and is based on two transfer learning fundamentals. By combining the advantages of Domain Randomization and Domain Adaptation, agents are able to transfer training performance to the target environment more successfully. Experimental results show a strong decrease in performance loss during deployment when the agent is exposed to the demonstration samples during training. The proposed technique describes a methodology that we believe can be applied in fields other than autonomous driving in order to improve transfer learning performance.
Speech Synthesis Using Stressed Sample Labels for Languages with Higher Degree of Phonemic Orthography

Introducing neural networks into the field has improved the performance of speech synthesis systems significantly. Most research was done on the English language which has substantial speech data resources, however, a low degree of grapheme-phoneme correspondence. Other, low resource languages pose different challenges that may be overcome using different approaches to text embeddings. In the present paper we present the results of using stressed text labels in speech datasets to train a speech synthesis model for a low speech data resource language - Lithuanian. Nvidia’s implementation of the Tacotron 2 system and the Lithuanian language speech dataset (corpus) with stressed text labels were used to train speech synthesis models. By introducing accentuation into sample labels, we show a significant improvement in speech naturalness as measured by MOS.