Reviewerly/exHarmony huggingface dataset:
The use of deep learning in image analysis for the study of oncology

Deep learning and the field of artificial intelligence have been buzzwords for years with their popularity continually growing. Deep learning is an area of machine learning that was inspired by the way our brains work. Thus, by using a mesh of layers that we refer to as neural networks, step by step each layer of the process takes in different information which will then output results. Deep learning has slowly shown itself to be a real powerhouse in the machine learning industry, particularly in its use of “Big Data.” The healthcare industry is primed for this sort of advanced machine learning application. The vast amount of data that could be collected paired with the importance of getting efficient and correct results shows a great path for deep learning to be introduced into the healthcare sector. Of course, there are many ways in which deep learning could be applied to the healthcare industry. However, one that is quite fascinating, is the use of Deep learning for image analysis, particularly in the field of oncology. Deep learning could be employed from diagnosis and prognosis to real-time surveillance of the analysis to the creation of treatment plans designed specifically for each of the individual patients. All of this would be made possible from the data collected and processed through deep learning image analysis models. In this chapter, we will analyze some of the most common deep learning algorithms used for this process, some of the ground-breaking work that has already been used in this field and discusses what next steps will lead to a future with deep learning image analysis in the study of oncology.
Predicting Visual Differentiability for Unmanned Aerial Vehicle Gestures

Unmanned Aerial Vehicles (UAVs) are increasingly integrated into diverse human interaction domains that require robust human-robot communication systems. Visual communication techniques have shown promise in their ability to communicate concrete information to observers. Such techniques, often described as a UAV ‘gesture,’ may be especially useful in the domain of unmanned aerial flight as they can be integrated as a stand-alone software solution in contrast to light or sound-based systems that often require additional hardware and add weight to a vehicle. Gestures may also be useful in contexts where long distance operation reduces the effectiveness of sound-based communication strategies. As gesture is a visual communication technique, it is critical that gestures are designed to optimize an observer’s ability to visually perceive the shape of a gesture’s motion. Factors such as low visual differentiability between gestures within a set may reduce an observer’s ability to classify the shape of a gestural motion. In this letter, we discuss the results from multiple gesture perception surveys. We also develop and evaluate techniques to predict, in advance, how participants may perceive a UAV gesture. We demonstrate that participant gesture classification accuracy correlates to trajectory distance measures and present a method for evaluating high-differentiabilty gesture sets. This letter will enable gesture designers to create gesture sets that are differentiable with high-confidence.
Depth-Aware Multi-object Tracking in Spherical Videos

This paper deals with the multi-object tracking (MOT) problem in videos acquired by 360-degree cameras. Targets are tracked by a frame-by-frame association strategy. At each frame, candidate targets are detected by a pre-trained state-of-the-art deep model. Associations to the targets known till the previous frame are found by solving a data association problem considering the locations of the targets in the scene. In case of a missing detection, a Kalman filter is used to track the target. Differently than works at the state-of-the-art, the proposed tracker considers the depth of the targets in the scene. The distance of the targets from the camera can be estimated by geometrical facts peculiar to the adopted 360-degree camera and by assuming targets move on the ground-plane. Distance estimates are used to model the location of the targets in the scene, solve the data association problem, and handle missing detection. Experimental results on publicly available data demonstrate the effectiveness of the adopted approach.
Student or Human Operator? Objective Results and Subjective Perceptions of the Process and Results of Offline and Online Learning

The article analyzes the different levels of students perceptions and attitudes and training areas to different learning formats: online and offline. The paper is the research a continuation conducted by the author since the pandemic beginning, reflecting the learners’ attitudes dynamics towards learning different aspects and distance learning in general. The study objects were a technical university different training areas students, have got learning experience of technical and humanities disciplines in different formats. The study subject were both objective indicators of different formats (grades comparison received during offline and online learning, and health status). The process subjective characteristics evaluation and learning results in different formats were also compared: satisfaction with the obtained educational results, the monotony, emotions state manifestation experienced when learning in different formats. In other words, the study compares objective and subjective characteristics of different formats: learning achievements and health preservation, satisfaction with the educational process and experienced emotional results. The empirical part is built on the empirical data analysis obtained through the questionnaire designed by the author. The hypotheses regarding the more positive attitude of students towards offline learning and the differences’ presence in attitude towards formats depending on which disciplines are emitted - technical or humanities are tested. The study results showed that the learners’ marks in the transition from offline to online format often increase or remain at the same level. However, subjectively, learners assess the learning process in the online format rather negatively, regardless of whether technical or humanities subjects are studied: the learning process pleasure decreases, monotony increases and satisfaction with the learning outcomes decreases. And this is despite the deteriorating health noted by the respondent’s majority. The conclusion is made about the learning process formalization in the transition to online, the transferring and changing personal meanings’ difficulty, the personal knowledge formation, which are the learning main criteria. It is noted that an important factor in the negative assessment of online learning are the differences in instrumental and technological readiness of participants in the educational process in the use of information and communication digital technologies. Lack of consideration of the impact of the design of the used learning tools on the process and the result of training also negatively affect the evaluation of online learning. It is pointed out the need to find a “formula” for blended learning and the learning technologies development - ICT and digital - up to the immersive reality creation, which will make the departure from offline to online learning the basis for effective learning, bringing satisfaction to all participants in the educational process. #CSOC1120.
Autonomous Knowledge Representation for Efficient Skill Learning in Cognitive Robots

This work explores the effects of the introduction of variational autoencoder based representation learning, and of its resulting latent spaces, within a robotic cognitive architecture to be able to efficiently learn models and policies when raw perceptual dimensionality is very high. The main focus of the paper is on the decision processes of the robots used for action selection. To this end we propose a procedure to obtain from autonomously produced latent state spaces the world and utility models necessary for deliberative operation as a first type of decision process. Additionally, we present a neuroevolutionary based approach to generate policies, for reactive operation, based on the information of the latent state space and using the previously obtained world and utility models to permit offline learning. A set of experiments over a real robot using vision, with the consequent high dimensional raw perceptual space, are carried out in order to validate the proposal.
Digital Transformation in Higher Education: A Case Study in Vietnam from Human Rights-Based Approach

Taking into account the findings on the digital divide in Vietnam (Do et al. [1]), as well as the methodology of digital transformation in higher education in Germany (Bond et al. [2]) and the direction of a human rights-based approach (Peeraer and Van [3], Kristin and Ingunn [4]), we carried out this research on the association between digital transformation in education and human rights. The research will first analyze a university’s digital transformation status in a mountainous region of Vietnam, where the majority and ethnic minorities are evenly distributed. The research also specifies the 4A requirements of educational access, which include availability, accessibility, acceptability, and adaptation based on the existing state of institutions, infrastructure, and lecturer and student individual characteristics. The findings of the research are intended to draw practical lessons, offer recommendations, and equip instructors and students with skills as digital citizens in the modern age, as well as policy proposals in the sectors of education and human rights. This research also addresses some of the shortcomings of earlier research on identifying behavioral trends in the usage of digital technologies on a limited basis.
The Implication Problem for Functional Dependencies and Variants of Marginal Distribution Equivalences

We study functional dependencies together with two different probabilistic dependency notions: unary marginal identity and unary marginal distribution equivalence. A unary marginal identity states that two variables x and y are identically distributed. A unary marginal distribution equivalence states that the multiset consisting of the marginal probabilities of all the values for variable x is the same as the corresponding multiset for y. We present a sound and complete axiomatization and a polynomial-time algorithm for the implication problem for the class of these dependencies, and show that this class has Armstrong relations.
Identification of MEEK-Based TOR Hidden Service Access Using the Key Packet Sequence

Tor enables end user the desirable cyber anonymity with obfuscation technologies like MEEK. However, it has also manifested itself a wide shield for various illegal hidden services involved cyber criminals, motivating the urgent need of deanonymization technologies. In this paper, we propose a novel communication fingerprint abstracted from key packet sequences, and attempt to efficiently identify end users MEEK-based access to Tor hidden services. Specifically, we investigate the communication fingerprint during the early connection stage of MEEK-based Tor rendezvous establishment, and make use of deep neural network to automatically learn and form a key packet sequence. Unlike most of existing approaches that rely on the entire long communication packet sequence, experiments demonstrate that our key packet sequence enabled scheme can significantly reduce both the time and hardware resource consumption for the identification task by 23%–37% and 80%–86%, respectively, while being able to keep a slightly better accuracy.
Developing Personas in UX Process: A Case Study for a Web-Documentary to Increase Empathy Among Social Groups

This paper aims to present the process of personas development for a digital solution to increase empathy among social groups. It is a part of a research focusing on promoting empathy in university students related to external groups through a web-based platform, with the broader concern of contributing to the fight against prejudice and discrimination with immigrants in European countries. Research has demonstrated that empathy can have an important role in decreasing bias related to external groups. Also, developing personas is an important part of the UX process; however, its use is often misunderstood, and developers’ interests bias its development. A reliable and well-prepared data collection comprising tools development – such as interviews and questionnaires- is crucial for personas creation. In this sense, this paper presents a case study of personas creation, from tools development and application to data analysis and personas definition. – As a result of data collection, three personas were developed based on 83 European university students’ respondents.
Acute Lymphocytic Leukemia Classification Using Color and Geometry Based Features

Acute Lymphocytic Leukemia (ALL) is a malignant hematological disease. It is also known as acute lymphoblastic leukemia. Being the most common type of childhood cancer, it requires prompt treatment to increase the chances of recovery. There are variant forms or ALL types in the diagnosis procedure, referred to as L1, L2, and L3. Hence, it is possible to apply effective treatment if leukemic cells are identified correctly, i.e., if their proper types are known. Clinical observations say that these subtypes have distinct geometric and color features. This work is mainly focused on classifying ALL type cells. We have used a novel combination of geometric and color-based features for efficient classification. Improved image enhancement and noise removal procedures are also employed as preprocessing. The proposed method has been tested on ALL-IDB data sets. The results corroborate the method’s usefulness in identifying ALL subtypes.
Spirituality as a Socially Valued Aspect of Upbringing

Purpose: A great interest toward the problems of a personality and human beings in general determines the topical questions of a personal spirituality formation in modern sociocultural surrounding. A personal spirituality is his moral essence, inspiring him above his material benefits, and reveals the manifestation of being truly human. The identification of a person embodies his individual uniqueness and complex feelings. Design/methodology/approach: Modern generation needs spirituality and strengthening of its moral orientation. The sources of these changes are based on the root of spirituality and legacy of the past including the legacy of such outstanding philosophers as F. M. Dostoevsky and L. N. Tolstoy. Findings: As the spiritual life of a society is based on moral values and personal spirituality formation ideals, the development of a child depends on universal human values of the formation of spiritual individuality in the educational system. The idea of the universal goodness as the highest estimation of the society development proves the child’s right for self-realization and individual abilities manifestation. The attitude to the childhood as an important period of life forms the methods of the spiritual and ethic system of upbringing. Originality/value: The idea of the formation of the spiritual and ethic personality attracts attention because firstly, it declares a man as the highest value of the society and creates the attitude to the child as the inherent worth to the childhood as an important responsible period in the man’s life; secondly, it suggests principles and parenting practices of the spiritual and ethic personality, capable for active creative activity, self-development, self-improvement; thirdly, it explains the appearance of humanistic tendencies in child’s development and acknowledgment of his right to individuality.
NI-UDA: Graph Contrastive Domain Adaptation for Nonshared-and-Imbalanced Unsupervised Domain Adaptation

With the technology development, information networks continuously generate a large amount of integrated labeled Big Data. Some types of labeled data in real scenes are scarce and difficult to obtain, such as some aerospace data. It is important to address the problem of nonshared and imbalanced unsupervised domain adaptation (NI-UDA) from the labeled Big Data with nonshared and long-tail distribution to unlabeled specified small and imbalanced space applications, where nonshared classes mean the label space out of the target domain. Previous methods proposed to integrate the semantic knowledge of Big Data to help the unsupervised domain adaptation for sparse data. However, they have the challenges of limited effect of knowledge sharing for long-tail Big Data and the imbalanced domain adaptation. To solve them, our goal is to leverage priori hierarchy knowledge to enhance domain contrastive aligned feature representation with graph reasoning. Our method consists of hierarchy graph reasoning (HGR) layer and K-positive contrastive domain adaptation (K-CDA). Our HGR contributes to learn direct semantic patterns for sparse classes by hierarchy attention in self-attention, nonlinear mapping, and graph normalization. For alleviating imbalanced domain adaptation, we proposed K-CDA, which explores k-positive instances for each class to every mini-batch with contrastive learning to align imbalanced feature representations. Compared with the previous contrastive UDA, our K-CDA alleviates the problems of large memory consumption and high computational cost. Experiments on three benchmark datasets shows our methods consistently improve the state-of-the-art contrastive UDA algorithms.
Robot Path Planning Using β Hill Climbing Grey Wolf Optimizer

Path planning is a computational problem called the navigation problem or the piano mover’s problem that requires establishing the sequence of viable designs that takes an object from its origin towards its destination. Computational modeling, computer animation, robotics, and computer gaming all use the phrase. A competent path planning technology of mobile robots can save a ton of time, yet diminish the wear and capital venture of mobile robots. Several approaches for mobile robot path planning have been explored and published in the literature. This study deployed a hybrid of the Gray Wolf Optimization (GWO) and β Hill Climbing method called β Hill Climbing Grey Wolf Optimizer (β-HCGWO) to tackle the problem of robot path planning. We utilized a map with three circular impediments in the robot path planning test trials. β-HCGWO algorithm was fitted to solve this problem. Its performance compared to other metaheuristic algorithms was evaluated for the robot path planning task. The results obtained showed a better optimal path found for the used test map.
Medical image denoising

In the past two decades, with the development of technology, especially the development of digital image technology, a large number of medical images have been used to help doctors diagnose patients' diseases in clinical practice. Medical images, like any other image form, are susceptible to noise and artifacts. Noise can be random or white noise with an even frequency distribution. It can also be frequency-dependent noise introduced by a device's mechanism or signal processing algorithms. These noises will make the image unclear and blurry, which will cause some unacceptable, potentially wrong or missed diagnosis. Therefore, denoising the image is a very important part for making an accurate diagnosis. Non-local self-similarity characteristics of images, and the block-matching and 3D filtering based methods have shown powerful denoising performance. Recently, many new approaches with advanced architectures of convolutional neural network have been proposed to enhance the quality of medical images. The main aim of this chapter is to demonstrate the methods for denoising medical images based on four modalities: ultrasound, magnetic resonance imaging, computed tomography, and positron emission tomography.
Denial-of-Service Vulnerability of Hash-based Transaction Sharding: Attack and Countermeasure

Since 2016, sharding has become an auspicious solution to tackle the scalability issue in legacy blockchain systems. Despite its potential to strongly boost the blockchain throughput, sharding comes with its own security issues. To ease the process of deciding which shard to place transactions, existing sharding protocols use a hash-based transaction sharding in which the hash value of a transaction determines its output shard. Unfortunately, we show that this mechanism opens up a loophole that could be exploited to conduct a single-shard flooding attack, a type of Denial-of-Service (DoS) attack, to overwhelm a single shard that ends up reducing the performance of the system as a whole. To counter the single-shard flooding attack, we propose a countermeasure that essentially eliminates the loophole by rejecting the use of hash-based transaction sharding. The countermeasure leverages the Trusted Execution Environment (TEE) to let blockchain's validators securely execute a transaction sharding algorithm with a negligible overhead. We provide a formal specification for the countermeasure and analyze its security properties in the Universal Composability (UC) framework. Finally, a proof-of-concept is developed to demonstrate the feasibility and practicality of our solution.
Retargetable Optimizing Compilers for Quantum Accelerators via a Multilevel Intermediate Representation

We present a multilevel quantum–classical intermediate representation (IR) that enables an optimizing, retargetable compiler for available quantum languages. Our work builds upon the multilevel intermediate representation (MLIR) framework and leverages its unique progressive lowering capabilities to map quantum languages to the low-level virtual machine (LLVM) machine-level IR. We provide both quantum and classical optimizations via the MLIR pattern rewriting subsystem and standard LLVM optimization passes, and demonstrate the programmability, compilation, and execution of our approach via standard benchmarks and test cases. In comparison to other standalone language and compiler efforts available today, our work results in compile times that are 1,000× faster than standard Pythonic approaches, and 5–10× faster than comparative standalone quantum language compilers. Our compiler provides quantum resource optimizations via standard programming patterns that result in a 10× reduction in entangling operations, a common source of program noise. We see this work as a vehicle for rapid quantum compiler prototyping.
Efficient Spatiotemporal Transformer for Robotic Reinforcement Learning

Intense spatiotemporal coupling states frequently appear in robotic tasks, and this coupling enriches the information encapsulated in each state. Taking advantage of historical observations can provide more information about the robot, especially for partially observable Markov decision processes. How to deal with this coupling remains a challenging issue in robotic reinforcement learning (RL), and we allege that the imbalanced processing capability of spatiotemporal details is one of the bottlenecks of the vanilla transformer model in learning robotic policies. To address this problem, we novelly propose an efficient spatiotemporal transformer structure. To our knowledge, this work is the first to improve the transformer with spatiotemporal information in RL. In each attention block, we sequentially execute attention computation twice: the first to process the temporal sequence of the input and the latter to manage the spatial state. This input reconstruction enables sufficient information extraction to promote data efficiency. We also add correlation encoding into the query and key computation of multi-head attention, providing the operability of associating states between and within time steps. We evaluate the proposed approach on several robot tasks, and it outperforms state-of-the-art transformer-based online RL.