Reviewerly/exHarmony huggingface dataset:
BSO-ES: A Hybrid Direct Policy Search Algorithm for Reinforcement Learning

Recently, it has been demonstrated that evolution strategies (ES), a class of blackbox optimization algorithms, can achieve competitive performance on many reinformance learning (RL) tasks, compared to deep RL algorithms. However, ES can be seen as a randomized local search algorithm based on stochastic finite difference and it is often hard to escape from deep or deceptive local optimum, which means that its exploration ability can be further improved. Brain storm optimization (BSO) is a swarm intelligence algorithm with tradeoff between exploration and exploitation via clustering. To enjoy best of both worlds, this paper proposes a hybrid direct policy search algorithm (BSO-ES) for RL tasks within a distributed computing framework. Specifically, we use BSO as a global searcher to explore the parameter space and ES as a local searcher to further fine-tune policies. We maintain multiple parallel policies at the same time and exchange preferable policies periodically. Experimental results on six challenging continuous control tasks from MuJoCo show that our proposed algorithm is superior to or competitive with OpenAI-ES. In particular, our algorithm has less failures to reach a fixed reward threshold.
Interference Management for Over-the-Air Computation and Cellular Coexistence Systems

Over-the-air computation has recently been developed to achieve rapid wireless data aggregation. Since wireless data aggregation has pervaded a wide range of applications such as massive machine type communication and on-device federated machine learning, the co-existence of communication and artificial intelligence computing is inevitable in future communication networks. In this paper, we study the interference management for the co-existence of AirComp and cellular communication to minimize the mean square error of AirComp. In the AirComp and cellular co-existence communication system, the signals transmitted by cellular devices are considered as interference for AirComp. We formulate a joint design problem for transceivers of AirComp system and cellular system. Then we propose an efficient algorithm to jointly optimize both AirComp and cellular systems. Furthermore, our numerical results show that our proposed algorithm achieves a good convergence performance and there exists a trade-off between the aggregation accuracy of AirComp and the target signal-to-interference-plus-noise-ratio of cellular communication.
Investigating Normalized Conformal Regressors

Conformal prediction can be applied on top of any machine learning predictive regression model, thus turning it into a conformal regressor. Given a significance level <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\epsilon$</tex> , conformal regressors output valid prediction intervals, i.e., the probability that the interval covers the true value is exactly <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1-\epsilon$</tex> . To obtain validity, a calibration set that is not used for training the model must be set aside. In standard inductive conformal regression, the size of the prediction intervals is then determined by the absolute error made by the predictive model on a specific instance in the calibration set, where different significance levels correspond to different instances. In this setting, all prediction intervals will have the same size, making the resulting models very unspecific. When adding a technique called normalization, however, the difficulty of each instance is estimated, and the interval sizes are adjusted accordingly. An integral part of normalized conformal regressors is a parameter called <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\beta$</tex> , which determines the relative importance of the difficulty estimation and the error of the model. In this study, the effects of different underlying models, difficulty estimation functions and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\beta$</tex> -values are investigated. The results from a large empirical study, using twenty publicly available data sets, show that better difficulty estimation functions will lead to both tighter and more specific prediction intervals. Furthermore, it is found that the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\beta$</tex> -values used strongly affect the conformal regressor. While there is no specific <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\beta$</tex> -value that will always minimize the interval sizes, lower <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\beta$</tex> -values lead to more variation in the interval sizes, i.e., more specific models. In addition, the analysis also identifies that the normalization procedure introduces a small but unfortunate bias in the models. More specifically, normalization using low <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\beta$</tex> -values means that smaller intervals are more likely to be erroneous, while the opposite is true for higher <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\beta$</tex> -values.
Investigation of Maximization Bias in Sarsa Variants

The overestimation of action values caused by randomness in rewards can harm the ability to learn and the performance of reinforcement learning agents. This maximization bias has been well established and studied in the off-policy Q-learning algorithm. However, less study has been done for on-policy algorithms such as Sarsa and its variants. We conduct a thorough empirical analysis on Sarsa, Expected Sarsa, and n-step Sarsa. We find that the on-policy Sarsa variants suffer from less maximization bias than off-policy Q-learning in several test environments. We show how the choice of hyper-parameters impacts the severity of the bias. A decaying learning rate schedule results in more maximization bias than a fixed learning rate. Larger learning rates lead to larger overestimation. A larger exploration parameter leads to worse bias in Q-learning but less bias in the on-policy algorithms. We also show that a larger variance in rewards leads to more bias in both Q-Learning and Sarsa., but Sarsa is less affected than Q-learning.
An Analysis of Various Text Segmentation Approaches

The area of science that seeks to grasp natural language naturally and computationally is natural language processing, or NLP. It attempts to interpret, evaluate, understand, and produce languages that humans normally use to engage with each other. Text segmentation is one of the fundamental issues of NLP that finds its use in many tasks such as description, extraction of texts, interpretation of meaning, extraction of emotions, etc. Text segmentation is the way toward separating composed content into parts. We can separate the content into words, sentences, and themes. Each section has its important significance. If we partition the content into portion, then we can undoubtedly recover the data from the content. There are numerous strategies accessible for text segmentation, both supervised and unsupervised, in this paper, we are going to discuss the various techniques available for text segmentation and discover which method gives best result for text segmentation.
Gist Trace-based Learning: Efficient Convention Emergence from Multilateral Interactions

The concept of conventions has attracted much attention in the multi-agent system research. In this article, we study the emergence of conventions from repeated n -player coordination games. Distributed agents learn their policies independently and are capable of observing their neighbours in a network topology. We distinguish two types of information representation about the observations: gist trace and verbatim trace. We conjecture that learning based on the gist trace, which overlooks the details and focuses only on the general choice of action of a neighbourhood, should achieve efficient convention emergence. To this end, a novel learning method that makes use of the gist trace is proposed. The experimental results confirm that the proposed method establishes conventions much faster than the state-of-the-art learning methods across diverse settings of multi-agent systems. In particular, the use of gist trace derived at a low level of abstraction further improves the efficiency of convention emergence.
Pedagogical component of educational and scientific programs aimed at training a Doctor of Philosophy

The urgency of the study is due to the need to provide graduates of the Doctor of Philosophy educational and scientific programs with the mastery in methodology of pedagogical activities. The purpose of the article is to analyze the content, scope, components of the pedagogical component of educational and scientific programs aimed at training a Doctor of Philosophy. To achieve the purpose of the article we used these general scientific methods: analysis, synthesis, comparison, specification, generalization, content analysis to identify and characterize the peculiarities of developing the ability to teach students of the third (educational-scientific) level within the educational process of modern higher education institutions. The importance of the formation of teaching competence in the context of compliance with the provisions of legal documents related to the training of Doctors of Philosophy, descriptors of the National Qualifications Framework, criteria for assessing the quality of the educational program and professional standards is substantiated. It is emphasized that relevant competences, programme learning outcomes and educational components providing student’s development as a representative of academic staff are normative provisions of the programme under focus. It is emphasized that the formation of the pedagogical component of educational and scientific programs aimed at training a Doctor of Philosophy depends on the policy of an institution of higher education, which determines the scope of pedagogical (teaching) practice, sets disciplines prerequisites. Some possibilities of non-formal education for the development of the future PhD’s teaching competence are outlined. The conclusion about the role and importance of the pedagogical component of educational and scientific programs aimed at training a Doctor of Philosophy is made.
A contemporary framework for probing in social design: on continuous dialogues and community building

Feelings of loneliness and social exclusion are a widespread phenomenon of digital modernity occurring across nations and cohorts, and it is therefore a significant area of interest to be addressed by the policies of welfare states and by designers. Existing research emphasises qualities in probes and probing such as providing inspiration and informing designers as well as enabling dialogue and collaboration. However, limited attention has been paid to the fact that probing and toolkits may require specific qualities where sentiments of exclusion are at stake. By examining a series of design interventions tackling loneliness, we analyse the type of methods that have been designed and used, their role and effect on relations between designers and participants as well as the design team itself. We also assess which tools failed to sustain their focus on initiating and deepening the dialogue among design participants. We conclude that dialogue and community building are essential goals in many areas of social design and suggest a framework for working with probing in this context. Probing is positioned as a way of deepening processes of interaction and inclusion with emphasis on the design proposal as a starting point for creating a community.
An Exploratory Study on Leadership and Liberal Arts Education using “Followership” Programs －Case of Approaching a Non-subject Educational Program

The purpose of this study is to confirm the necessity of linking “followership” research and educational trends with leadership education in liberal arts education, and to contribute to future education and research direction through followership programs and the operation of non-subject educational programs. In order to solve the research issue, first, previous studies that can confirm the connection between followership and leadership through literature research were examined. In addition, followership competency items verified by experts were collected and used as the basis for analysis, and in-depth interviews were additionally conducted with students participating in leadership and liberal arts classes to add potential necessary competencies. The research results are as follows: First, we confirmed that followership programs can serve to complement leadership education and are already sufficiently active in university education. Second, we were able to confirm five followership competencies that K university students thought were of primary necessity. Third, we found that followership-related programs can have a positive effect on enhancing the core competencies of leadership and liberal arts subjects. Fourth, we realized the keen necessity of adding additional non-subject educational program support in various fields that can incorporate followership programs. Lastly, we learned of the need to apply various research methods to graduates and enrolled students in order to spread the educational effectiveness of followership programs in liberal arts education, and suggested that followership-related non-subject educational programs should be fully considered and operated in the future.
Actionable Knowledge for Efficient Actions

This chapter looks at what facilitates the relevant decision-making and the modalities that can make the action (effect) more efficient. The aim is to provide a better understanding of the couple (knowledge, action)Couple (knowledge, action). There is a strong dependency between the notion of knowing about a given world and the decisions that can be made and consecutively the potential actions that can be undertaken. We bring the notion of “mastering knowledge” for efficient actions. Mastering knowledge amounts to having a coherent set of means to represent the most useful knowledge in the context of the action, to know how to resort as necessary to the appropriate formalizations to model the situations.
How Should I Do? Users’ Attitude Toward Information Security and Data Protection Using Smartphone

Information Security and Data Protection (ISDP) nowadays are growing threatening to users as spreading Information and Connection Technology (ICT) development and smartphone using. Many identified issues about information security and data protection emerged and call for public awareness and actions. However, how users understand and ready to adopt right actions for this consumption privacy and right still not clear? Thus, this study aims to investigate users’ attitude toward to ISDP, with the framing of users’ attitude by cognition, emotion, and actions. To investigate the connection between users’ attitude and the potential threats. This study reviews some cases, related issue which proposed that users are less, following which we suggest some potential strategies for advance ISDP.
Grid Row Parameter Identification Using Tensorflow Convolutional Neural Network

Text recognition is an important field of pattern recognition application. In this paper, a joint parameter recognition method based on convolutional neural network is proposed to solve the problems of small font, various cables, and traditional manual detection difficulty and low efficiency. Based on TensorFlow framework, this method builds a convolutional neural network model that can realize end-to-end recognition. First manually adjust the shade number serious cables, from different angles to adjust good cable collection into image preprocessing, and then use the contour detection and projection image segmentation algorithm on area of the wire number to find out and cut them into a single character, after normalization processing packaged into the sample set and test set in proportion to the convolutional neural network training, Finally, the obtained joint parameters are compared with the standard wiring information base, and the wrong or missed cables are screened out for manual adjustment. The convolutional neural network model is applied to power grid cable identification, and the results show that it can accurately locate and identify the routing parameters, effectively assist manual detection to identify the cable parameters, reduce the error rate of human eye detection, and greatly improve the efficiency of manual detection.
Image Encryption Algorithm based on Particle Swarm Optimization and Chaos Logistic Map

With the rapid growth of data transmission and sharing technology, image encryption is becoming a widely discussed topic in the field of information security. In this paper, Particle Swarm Optimization (PSO) algorithm with Chaos Logistic Map is applied to create an encryption algorithm. The proposed algorithm benefits from PSO's ability to be able to quickly search a large space, having a short computation time, and having a higher probability of finding the global optimum, and thus PSO produces a very good encryption image. Furthermore, the use of logistic map for confusion and diffusion operations is very beneficial. The algorithm starts by creating several encrypted images, which are the particles for PSO, and the logistics maps and the encryption key used is based on the plain image only. The optimization step is measuring the pixels correlation, where lower correlation values are better, and these serve as the fitness function. The simulation results of the proposed algorithm indicate an effective encryption process. In addition, the security analysis illustrates the ability of this algorithm to provide satisfying levels of security in comparison with other image encryption schemes.
Persona Identification based on Social Media Profile Images for Personification and Safety

Personification is important to ensure safety in social media. Images embedded in social media data is an important source intelligently ensure the personal safety. Paper proposes an applied machine learning on embedded image media to extract important feature traits and understand the semantics of correlation of feature space to understand the personification of given user. A strong belief is calculated using cascaded method and this inference is applied to different ml algorithms to derive the overlap of feature space. This algorithm validates the proposed objective that the user with more overlap and correlation among feature space are highly personified with each other. Using this personification-based method, better safety is ensured to mitigate possible social media attacks.
Remix Viralia. For a Reality of the Imaginary Re-imagined. Art and Education in the Era of Covid-19

There is a before and there is an after. Let's start with the after. The outbreak of Covid-19 forced cultural institutions to explore alternative digital spaces with online exhibits and the use of virtual, augmented, and mixed reality. The educational world, such as colleges and universities, has also had to rethink a new way of communicating and collaborating. New technologies have re-mediated creating a different degree of participation and connection. In this essay I will argue, on one side, about the art system some concrete examples of extended reality. On the other hand, through the educational system, I will analyze how the interface between teachers and students has radically changed the relationship within an artificial and immersive environment. During the pandemia we have witnessed both the hybrid nature of the spect-actor-in the fruition of the work of art in the museum system, and a convergence towards distance learning where the relationship between teacher and student has been rethought with the help of some technological tools (Oculus, Gear VR) and distance education services (#iorestoacasa). But it is not the tool that is the main factor of this essay, but its instrumental proxy, that is, the quality (affordance) of rethinking it in an ecosystemic direction, giving us a different methodology, a choral art (#restacorale) to capture this variation of the Real, re-imagine the imaginary and remix every degree of reality.
MetaGB: A Gradient Boosting Framework for Efficient Task Adaptive Meta Learning

Deep learning frameworks generally require sufficient training data to generalize well while fail to adapt on small or few-shot datasets. Meta-learning offers an effective means of tackling few-shot scenarios and has drawn increasing attention in recent years. Meta-optimization aims to learn a shared set of parameters across tasks for meta-learning while facing challenges in determining whether an initialization condition can be generalized to tasks with diverse distributions. In this regard, we propose a meta-gradient boosting framework that can fit diverse distributions based on a base learner (which learns shared information across tasks) and a series of gradient-boosted modules (which capture task-specific information). We evaluate the model on several few-shot learning benchmarks and demonstrate the effectiveness of our model in modulating task-specific meta-learned priors and handling diverse distributions.
WRITING RESPONSE TO OPEN-ENDED TASKS IN THE EXTERNAL INDEPENDENT EVALUATION TEST IN THE UKRAINIAN LANGUAGE AND LITERATURE: A LESSON IN THE 11TH GRADE OF UPPER SECONDARY SCHOOL

The article presents a lesson plan on coherent speech development in 11th- grade upper secondary school students. The study focuses on revealing the students’ understanding of the concept of problem question as it is essential for enhancing the learners’ skills to express their own ideas on the issues offered for discussion in the certification paper by providing well-grounded arguments and relevant examples. The emphasis is laid on structural components, compositional features, and language of the text as a result of performing the open-ended tasks with a detailed written response at the external independent evaluation test in the Ukrainian language and literature.
Enhancement of Daytime Crescent Image Using Wiener Filter Based De-Blurring Technique

The aim of this research is to enhance the astronomical image, specifically the crescent image. Millions of Muslims care of monitoring the new crescent to determine the beginning of the fasting month (Ramadan), Eid Al-Fitr(Islamic Eid), and the beginning of the Hijri calendar. These timings are based on the appearance of the crescent in the sky. Because it is very difficult to detect the new crescent during day time, it is important to develop a technique to help detecting it. In the proposed method, the image restoration technique has been used to extract the crescent image from the telescope. The captured image is very noisy due to blurring and weather factors. This noise reduces the quality of the image. In this study, some of crescent images have been captured during the day time, where the crescent age was less than 50 hours before or after the new moon by using a portable digital camera installed on the telescope. Both, the deconvolution and De-Blurring technique with Hanning filter have been used to enhance the crescent's image. This indicates that the Wiener filter is a versatile and powerful technique for de-blurring and noise removal which can be successfully used in taking the crescent's images. In fact, our algorithm's performance was compared with that of the Wiener filter, Blind deconvolution, and Lucy Richardson algorithm. The resulted enhanced images show the crescent's image as can be seen visibly and can be recognized by the human eye. We discovered that and that Wiener filter deconvolution method is better than Lucy Richardson algorithm.