Reviewerly/exHarmony huggingface dataset:
COMPARISON OF PRONUCLEAR (PN) NUMBER OBSERVATIONS BASED ON EMBRYOLOGIST’S EXPERIENCE AND DETECTION BY ARTIFICAL INTELLIGENCE (AI) TRAINED WITH DEEP LEARNING TECHNOLOGY

Gamete and embryo evaluation in human IVF programs are critical skills that are developed through formal followed by actual experience over many years in a laboratory. Whilst the aim is to ensure consistency in evaluation between embryologists in any one laboratory, it is a major challenge to ensure and maintain high accuracy across operators. Therefore, we have developed an automatic PN number detection system utilizing deep learning technology to provide an effective system for clinical use. In this study, the accuracy of PN number detection assessed by AI was compared with that assessed by junior, intermediate, and senior embryologists in order to indicate the effectiveness of the AI generated system vs. human assessment. Training data, for constructing the AI for automatic PN number detection, consisted of 70-80 images before and after pronucleus formation of one fertilized embryo were captured by a time-lapse incubator. Using these 70-80 images as one set, 300 sets of each 0PN, 1PN, 2PN, 3PN embryos were inputted into the AI algorithm. For comparison of data from embryologists, we selected three junior embryologists on the first day of work, three intermediate embryologists with three years of experience, and three senior embryologists with more than six years of experience. For testing data to evaluate accuracy of the AI algorithm, we used a total of 40 time-lapse videos including 0,1,2,3 PN embryos in which the PN numbers were decided by different embryologists. For the test data set, the PN number of each embryo was judged by each junior, intermediate, senior embryologist, and the AI. The correct answer rates were then compared. The correct answer rates for 0PN were 70.0% (junior), 96.7% (intermediate), 100% (senior) and 100% by AI. The correct answer rates for 1PN were 66.7% (junior), 85.2% (intermediate), 88.9% (senior) and 66.7% by AI. The correct answer rates for 2PN were 81.8% (junior), 97.0% (intermediate), 100% (senior) and 81.8% by AI. The correct answer rates for 3PN were 63.3% (junior), 96.7% (intermediate), 90.0% (senior) and 70.0% by AI. Whilst the accuracy of the AI was higher in some of the assessments (e.g. junior on 1 and 3 PNs and that of intermediate on 0 PN), it was not consistent with assessments by seniors. For the AI to be clinically useful it should have not only higher accuracy across all users but also be stable across different PN states. We are therefore continuing to improve the AI to have a higher accuracy for PN number detection by introducing into the algorithm, Z-axis multi-sliced images.
Childhood core binding factor (CBF) acute myeloid leukemia and its association with French American British (FAB) classification

Abstract&#x0D; Objective: To find the frequency of core binding factor acute myeloid leukaemia in our population, and to determine its association with morphological subtypes.&#x0D; Methods: The retrospective study was conducted at The Indus Hospital, Karachi, and comprised data of patients aged 1-17 years who were diagnosed with acute myeloid leukaemia from July 2013 to June 2017. Data was analysed using SPSS 21.&#x0D; Results: Of the 237 patients, 137(58%) were males and 100(42%) were females. The overall mean age was 8±4.34 years. Cytogenetic testing had been performed in 212(89.45%) cases, and core binding factor was detected in 72(34%) cases. There was significant difference between the mean values of white cell count and the subtypes (p=0.000). Also the difference between core binding factor and the subtypes was significant (p=0.000).&#x0D; Conclusion: There was found to be a significant association of core binging factor with specific subgroups of acute myeloid leukaemia.&#x0D; Key Words: Acute myeloid leukaemia, Core binding factor, Cytogenetic abnormalities, Prognosis. Continuous....
Cognitive Modeling of a Conceptual Domain Sociocultural Threats

The paper discusses ways of modeling the conceptual domain “sociocultural threats”, represented in the texts, included in purposely elaborated THREATCorpus. The results of the research present the process of cognitive categorization of the domain, determination of the levels of the cognitive category and rationale for its borders. The thesaurus, elaborated on the basis of the THREATCorpus, is regarded as a knowledge representation structure for sociocultural threats. It is proved that both the category “sociocultural threats” and the thesaurus are mental and language representations of the domain.
METHODOLOGICAL ASPECTS OF TEACHING PYTHON PROGRAMMING IN THE SCHOOL INFORMATICS COURSE

The article discusses the features of teaching programming in Python of high school students, taking into account the experience of programming in the Pascal language obtained by students in the main school. Based on the analysis of the content of textbooks for in-depth study of informatics and their own experience of teaching informatics, the authors show the advantages of the Python language for motivating students to study it and use specific examples to reveal certain methodological aspects of teaching programming in the Python language. In particular, using a comparative analysis of program codes for implementing data input and output procedures, conditional algorithmic construction, as well as for describing data types in two programming languages, the authors clearly show the advantages of developing programs in Python. More specific details of programming are considered on the example of the theme "Arrays".
Mutual Friendly Force Identification Protocol based on Hash-Chain for Personal Combat Systems

In this paper, we propose a hash-chain based friendly force identification protocol for personal combatants equipped with a personal combat system in a tactical wireless network.It is imperative in military operations to effectively and quickly identify friendly forces.If the identification of friendly forces is not correct, this can cause friendly fire.In current ground operations, the identification of friendly forces by personal combatants is neither secure nor safe.To address this issue, the proposed protocol uses a hash-chain to determine if a detected person is friendly.Only friendly forces with the same materials that are assigned before they deploy can construct an initial hash-chain.Moreover, the hash-chain is changed at specific times.The performance of the proposed protocol is evaluated on the assumption that the secret key is leaked, which is the worst scenario in the security research field.We verify that the proposed protocol is secure for the various attack scenarios, such as message replay attack, fabrication attack, and Denial of Service attack.
Blockchain-based e-Voting Application

Voting has been a fundamental part of the democratic system as it allows individuals to voice their opinions.Over the past few years, voting turnout has diminished as concerns regarding security, privacy, accessibility and integrity have been escalated.In order to address these issues, e-voting was introduced; however, only a few countries managed to use the application due to cost and central authority approvals.Hence, blockchain technology is an emerging platform as it allows decentralization through the use of distributed technology, expanding industries and processes.In this chapter, the researchers will discuss the significance of blockchain e-voting applications as well as provide details of the issues faced by current blockchain technologies.A comparative analysis of existing mechanisms is also provided in order to understand and mitigate the gaps before fully adopting blockchain technologies for e-voting applications.Thus, this chapter will be a kind of roadmap for blockchain-based e-voting applications to improve the current voting practices and processes.
Unsupervised Learning of Depth and Ego-Motion from Cylindrical Panoramic Video with Applications for Virtual Reality

We introduce a convolutional neural network model for unsupervised learning of depth and ego-motion from cylindrical panoramic video. Panoramic depth estimation is an important technology for applications such as virtual reality, 3D modeling, and autonomous robotic navigation. In contrast to previous approaches for applying convolutional neural networks to panoramic imagery, we use the cylindrical panoramic projection which allows for the use of the traditional CNN layers such as convolutional filters and max pooling without modification. Our evaluation of synthetic and real data shows that unsupervised learning of depth and ego-motion on cylindrical panoramic images can produce high-quality depth maps and that an increased field-of-view improves ego-motion estimation accuracy. We create two new datasets to evaluate our approach: a synthetic dataset created using the CARLA simulator, and Headcam, a novel dataset of panoramic video collected from a helmet-mounted camera while biking in an urban setting. We also apply our network to the problem of converting monocular panoramas to stereo panoramas.
Your Buddy, the Grandmaster: Repurposing the Game-Playing AI Surplus for Inclusivity

Advances in artificial intelligence (AI) techniques have resulted in immense breakthroughs in how well we can algorithmically play videogames. Yet, this increased investment in game-playing AI (GPAI) techniques has not translated into a tangible improvement in the game-playing experience for our players. This paper is inspired by the positive impact of accessibility modes in recent games as well as previous calls in games research literature to focus on player experience. Responding to these calls, we propose utilizing GPAI techniques not to beat the player, as is traditionally done, but to support them in fully experiencing the game. We claim that utilizing GPAI agents to help players overcome barriers is a productive way of repurposing the capabilities of these agents. We further contribute a design exercise to help developers explore the space of possible GPAI-driven assistance methods. This exercise helps developers discover types of challenges and ideate methods that vary in magnitude and assistance type. We first apply this design exercise to explore the design space of possible assistance methods for the action platformer game Celeste. We then implement two of the discovered methods that target different challenge types in a Unity clone of Celeste. Through this implementation, we discover several additional research questions we must answer before GPAI-driven assistance methods can be truly effective. We believe this research direction furthers the discussion on how to utilize GPAI in service of the player experience and also contributes to the creation of more inclusive games.
Functional Fixedness and Its Role in Reducing Productivity of Creative Thinking

There is a tendency in contemporary psychology to intensify research on creativity. Numerous theories and models examine this phenomenon. An important direction in creativity research is the identification and analysis of psychological factors, which inhibit creative thinking. One of them is functional fixedness. This phenomenon has attracted researchers’ attention for a long time. On the other hand, it should be examined in more depth because human life is changing intensively in contemporary world. Functional fixedness restricts person's creative abilities and supports the stereotypical way of thinking. This concept was introduced by K. Duncker (1945), who considered functional fixedness as one of the most powerful antagonists of creative thinking. In academic literature, numerous synonyms of this term have been introduced, such as “problem-solving set” (Kearsley, 1975), rigidity (Leach, 1967), difficulty in re-structuring (Wertheimer, 1959). At the same time, there is a lack of research systematizing results of the classic theoretical and empirical studies on functional fixedness. The present study seems to partially fill this gap. The authors reveal and analyze the most important aspects and factors of creative thinking, which can help to understand the nature and negative role of functional fixedness in creative thinking. Different definitions of functional fixedness, most influential theories and models addressing this phenomenon, as well as forms of manifestation of functional fixedness in various spheres of individual productive activity are considered. This work is continued by the analysis of psychological methods to loosen functional fixedness.
Text-driven Speech Animation with Emotion Control

In this paper, we present a new approach to creating speech animation with emotional expressions using a small set of example models.To generate realistic facial animation, two example models called key visemes and expressions are used for lip-synchronization and facial expressions, respectively.The key visemes represent lip shapes of phonemes such as vowels and consonants while the key expressions represent basic emotions of a face.Our approach utilizes a text-to-speech (TTS) system to create a phonetic transcript for the speech animation.Based on a phonetic transcript, a sequence of speech animation is synthesized by interpolating the corresponding sequence of key visemes.Using an input parameter vector, the key expressions are blended by a method of scattered data interpolation.During the synthesizing process, an importance-based scheme is introduced to combine both lip-synchronization and facial expressions into one animation sequence in real time (over 120Hz).The proposed approach can be applied to diverse types of digital content and applications that use facial animation with high accuracy (over 90%) in speech recognition.
MOEA-RS: A Content-Based Recommendation System Supported by a Multi-objective Evolutionary Algorithm

Most recommendation systems use only one recommendation criterion at the time, often the accuracy. However, this approach is limited and can generate biased recommendations. In this work, a content-based recommendation system for movie recommendation supported by a Multi-objective Evolutionary Algorithm is proposed. The proposed system takes advantage of Multi-objective optimization and uses three criteria: accuracy, diversity, and novelty of the recommendation. The system uses a multi-objective algorithm to solve the recommendation problem. It will be evaluated using Movielens and IMDB movie datasets and its results will be compared with the state of the art recommendation systems.
A Self-Healing Model for QoS-aware Web Service Composition

In the Web Service Composition (WSC) domain, Web Services (WSs) execute in a highly dynamic environment, as a result, the Quality of Service (QoS) of a WS is constantly evolving, and this requires tracking of the global optimization overtime to satisfy the users’ requirements. In order to make a WSC adapt to such QoS changes of WSs, we propose a self-healing model for WSC. Self-healing is the automatic discovery, and healing of the failure of a composite WS by itself due to QoS changes without interruption in the WSC and any human intervention. To the best of our knowledge, almost all the existing self-healing models in this domain substitute the faulty WS with an equivalent one without paying attention to the WS selection processes to achieve global optimization. They focus only on the WS substitution strategy. In this paper, we propose a self-healing model where we use our hybrid approach to find the optimal WSC by using Parallel Genetic Algorithm based on Q-learning, which we integrate with K-means clustering (PGAQK). The components of this model are organized according to IBM’s Monitor, Analyse, Plan, Execute, and Knowledge (MAPE-K) reference model. The PGAQK approach considers as a module in the Execute component. WS substitution strategy has also been applied in this model that substitutes the faulty WS with another equivalent one from a list of candidate WSs by using the K-means clustering technique. K-means clustering is used to prune the WSs in the search space to find the best WSs for the environment changes. We implemented this model over the NET Framework using C# programming language. A series of comparable experiments showed that the proposed model outperforms improved GA to achieve global optimization. Our proposed model also can dynamically substitute the faulty WSs with other equivalent ones in a time-efficient manner
Cyber Security Measures for Internet of Things Devices

The Internet of Things (IoT) is a new paradigm that integrates the Internet and physical objects belonging to different domains such as home automation, industrial process, human health and environmental monitoring.It is intended for ubiquitous connectivity among different entities or "things".It can be seen as a pervasive network of networks: numerous heterogeneous entities both physical and virtual interconnected with any other entity or entities through unique addressing schemes, interacting with each other to provide/request all kinds of services.It deepens the presence of Internet-connected devices in our daily activities, bringing, in addition to many benefits, challenges related to security issues.The number of devices connected along with the ad-hoc nature of the system further exacerbates the situation.Therefore, security and privacy has emerged as a significant challenge for the IoT.This research work focuses on the security aspects of IoT and proposes security solutions for mitigation of the concerns.
Hardware Utilization of Models of Genetic Algorithms

The paper compares the models of Genetic Algorithms (GA). It is focused on parallelized genetic algorithms, namely on the models of Master-Slave GA, Coarse-Grained GA, and Fine-Grained GA. The obtained results were compared with a serial model. A main memory usage comparison and a Central Processor Unit (CPU) comparison were performed. Furthermore, the parallelization module was verified on several workstations and the testing was evaluated. As expected, the serial model had the lowest main memory requirements. For parallelized models with increasing population size, main memory usage also increased.
Do Alzheimer’s Disease Patients Appear Younger than Their Real Age?

&lt;b&gt;&lt;i&gt;Introduction:&lt;/i&gt;&lt;/b&gt; The most prominent risk factor of Alzheimer’s disease (AD) is aging. Aging also influences the physical appearance. Our clinical experience suggests that patients with AD may appear younger than their actual age. Based on this empirical observation, we set forth to test the hypothesis with human and computer-based estimation systems. &lt;b&gt;&lt;i&gt;Method:&lt;/i&gt;&lt;/b&gt; We compared 50 early-stage AD patients with 50 age and sex-matched controls. Facial images of all subjects were recorded using a video camera with high resolution, frontal view, and clear lighting. Subjects were recorded during natural conversations while performing Mini-Mental State Examination, including spontaneous smiles in addition to static images. The images were used for age estimation by 2 methods: (1) computer-based age estimation; (2) human-based age estimation. Computer-based system used a state-of-the-art deep convolutional neural network classifier to process the facial images contained in a single-video session and performed frame-based age estimation. Individuals who estimated the age by visual inspection of video sequences were chosen following a pilot selection phase. The mean error (ME) of estimations was the main end point of this study. &lt;b&gt;&lt;i&gt;Results:&lt;/i&gt;&lt;/b&gt; There was no statistically significant difference between the ME scores for AD patients and healthy controls (&lt;i&gt;p&lt;/i&gt; = 0.33); however, the difference was in favor of younger estimation of the AD group. The average ME score for AD patients was lower than that for healthy controls in computer-based estimation system, indicating that AD patients were on average estimated to be younger than their actual age as compared to controls. This difference was statistically significant (&lt;i&gt;p&lt;/i&gt; = 0.007). &lt;b&gt;&lt;i&gt;Conclusion:&lt;/i&gt;&lt;/b&gt; There was a tendency for humans to estimate AD patients younger, and computer-based estimations showed that AD patients were estimated to be younger than their real age as compared to controls. The underlying mechanisms for this observation are unclear.
Random Projection in the Presence of Concept Drift in Supervised Environments

In static environments Random Projection (RP) is a popular and efficient technique to preprocess high-dimensional data and to reduce its dimensionality. While RP has been widely used and evaluated in stationary data analysis scenarios, non-stationary environments are not well analyzed. In this paper we provide an evaluation of RP on streaming data including a concept of altering dimensions. We discuss why RP can be used in this scenario and how it can handle stream specific situations like concept drift. We also provide experiments with RP on streaming data, using state-of-the-art streaming classifiers like Adaptive Hoeffding Tree and concept drift detectors on streams containing altering dimensions.
Taxonomy and Universal Success Parameters of Token Models in Distributed Ledger Systems

Token models introduced by Distributed Ledger Systems (DLS) such as Bitcoin or Ethereum enabled a fundamental shift in the economic structures of our society. This led not only to a large number of different token models but also to a multitude of development strategies for them. However, only a few of existing tokens proved a significant success on the market. The reason for that is a missing unified understanding of parameters required for success and the lack of standardization in token economics in general. This paper derives a unified token economics taxonomy considering regulatory authorities, practitioners, and academics. The taxonomy encompasses five dimensions which are essential for classifying token economics. Finally, this work derives eight universal success parameters of a token model and in a broader sense of DLS. The clear taxonomy and parameters will enable a more efficient and expedient approach for token development.
Predictive Performance of Logistic Regression for Imbalanced Data with Categorical Covariate

Logistic regression is often used for the classification of a binary categorical dependent variable using various types of covariates (continuous or categorical). Imbalanced data will lead to biased parameter estimates and classification performance of the logistic regression model. Imbalanced data occurs when the number of cases in one category of the binary dependent variable is very much smaller than the other category. This simulation study investigates the effect of imbalanced data measured by imbalanced ratio on the parameter estimate of the binary logistic regression with a categorical covariate. Datasets were simulated with controlled different percentages of imbalance ratio (IR), from 1% to 50%, and for various sample sizes. The simulated datasets were then modeled using binary logistic regression. The bias in the estimates was measured using Mean Square Error (MSE). The simulation results provided evidence that the effect of imbalance ratio on the parameter estimate of the covariate decreased as sample size increased. The bias of the estimated depends on sample size whereby for sample size 100, 500, 1000 - 2000 and 2500 - 3500, the estimated were biased for IR below 30%, 10%, 5% and 2% respectively. Results also showed that parameter estimates were all biased at IR 1% for all sample size. An application using a real dataset supported the simulation results.