Reviewerly/exHarmony huggingface dataset:
Augmented reality for orthopedic and maxillofacial oncological surgery: a systematic review focusing on both clinical and technical aspects

This systematic review offers an overview on clinical and technical aspects of augmented reality (AR) applications in orthopedic and maxillofacial oncological surgery. The review also provides a summary of the included articles with objectives and major findings for both specialties. The search was conducted on PubMed/Medline and Scopus databases and returned on 31 May 2023. All articles of the last 10 years found by keywords augmented reality, mixed reality, maxillofacial oncology and orthopedic oncology were considered in this study. For orthopedic oncology, a total of 93 articles were found and only 9 articles were selected following the defined inclusion criteria. These articles were subclassified further based on study type, AR display type, registration/tracking modality and involved anatomical region. Similarly, out of 958 articles on maxillofacial oncology, 27 articles were selected for this review and categorized further in the same manner. The main outcomes reported for both specialties are related to registration error (i.e., how the virtual objects displayed in AR appear in the wrong position relative to the real environment) and surgical accuracy (i.e., resection error) obtained under AR navigation. However, meta-analysis on these outcomes was not possible due to data heterogenicity. Despite having certain limitations related to the still immature technology, we believe that AR is a viable tool to be used in oncological surgeries of orthopedic and maxillofacial field, especially if it is integrated with an external navigation system to improve accuracy. It is emphasized further to conduct more research and pre-clinical testing before the wide adoption of AR in clinical settings.
Resolve integer ambiguity based on the global deep grid-based algorithms

Abstract Grid theory is rather commonly-used through out the research of integer ambiguity. In order to promote the efficiency of computation, it is of great necessity to reduce the correlations of the grid basis through the reduction. The classical reduction algorithm is known as the LLL (Lenstra–Lenstra–Lovász) algorithm. So as to further enhance the reduction effect, the deep-insertion LLL algorithm can be utilized as an alternative to the basis vector exchange algorithm. In practice, the deep-insertion LLL algorithm can achieve a better reduction effect, but it requires more time for reduction. The PotLLL algorithm replaces the basis vector exchange condition of deep-insertion LLL with an improving in the basis quality, and it can run in polynomial time, but with certain limitations. Therefore, this article proposes a global deep-insertion PLLL algorithm (GS-PLLL) to address the issue of integer ambiguity. GS-PLLL adopts a global strategy for deep-insertion processing, and introduces a rotation sorting method for preconditioning the grid basis. Comparative evaluations were conducted using simulation experiments and real-world measurements on the LLL, DeepLLL, PotLLL, and GS-PLLL algorithms. The experimental results indicate that the GS-PLLL algorithm achieves a better reduction effect than the PotLLL algorithm while improving the efficiency of reduction.
Traffic prediction in SDN for explainable QoS using deep learning approach

Abstract The radical increase of multimedia applications such as voice over Internet protocol (VOIP), image processing, and video-based applications require better quality of service (QoS). Therefore, traffic Predicting and explaining the prediction models is essential. However, elephant flows from those applications still needs to be improved to satisfy Internet users. Elephant flows lead to network congestion, resulting in packet loss, delay and inadequate QoS delivery. Recently, deep learning models become a good alternative for real-time traffic management. This research aims to design a traffic predicting model that can identify elephant flows to prevent network congestion in advance. Thus, we are motivated to develop elephant flow prediction models and explain those models explicitly for network administrators’ use in the SDN network. H2O, Deep Autoencoder, and autoML predicting algorithms, including XGBoost, GBM and GDF, were employed to develop the proposed model. The performance of Elephant flow prediction models scored 99.97%, 99.99%, and 100% in validation accuracy of under construction error of 0.0003952, 0.001697, and 0.00000408 using XGBoost, GBM, and GDF algorithms respectively. The models were also explicitly explained using Explainable Artificial Intelligence. Accordingly, packet size and byte size attributes need much attention to detect elephant flows.
Soft law governance in the field of AI: a European perspective

Recent years have seen the emergence of a multitude of soft law initiatives aimed at governing artificial intelligence (AI) technologies and their perceived societal risks. This chapter engages a Foucauldian governmentality perspective to provide an analysis of the regulatory logics at play in particularly European attempts to (1) conceptualize AI as an object of governance, and (2) propose concrete mechanisms for its effective governance. Acknowledging the evolving state of the field, we illustrate and problematize increasingly institutionalized approaches to AI governance, including AI risk management, principle-based governance, and AI certification and labelling. While the Foucauldian approach is helpful in constructing an initial outline of this emergent governmental field, the chapter also highlights the significant ambiguities and uncertainties surrounding AI governance. We therefore think it important for regulators, scholars, and other interested or affected parties to prioritize the question of AI governance in years to come.
Decentralized Proof-of-Burn Auction for Secure Cryptocurrency Upgrade

We propose a new approach for a secure decentralized and censorless upgrade of existing cryptocurrencies to newly created tokens without interaction from any external information sources (oracles). The proposed scheme is based on burning of existing cryptocurrencies tokens and implemented via the multi-currency auction. The auction is carried out on the blockchain of the new token and implemented using a smart-contract that processes participants' bids of burnt tokens of other cryptocurrencies and supports a new token price discovery algorithm for each cryptocurrency with no oracles or any other trusted source of information. Contrary to traditional ways of getting the new asset, like centralized and decentralized exchanges, etc., our method requires no user registration (as well as no KYC – “know your customer” procedure that requires obligatory client identification) and provides a predicted supply level of the new asset for an adequate price within a model with economically rational participants. We provide the results of decentralized auction simulations implemented for several strategies of user behavior (based on bid prices with normal and log-normal distribution laws), both under the normal operation and in the presence of adversary who follows specific strategies.
An Analysis of an Open Source Binomial Random Variate Generation Algorithm

The binomial distribution is the probability distribution of the number of successes for a sequence of n independent trials with success probability p. Efficiently generating binomial random variates is important in many modeling and simulation applications, such as in medicine, risk management, and fraud and anomaly detection, among others. A variety of algorithms exist for generating binomial random variates. This paper concerns the algorithm chosen for ρμ, an open source Java library for efficient randomization, which uses a hybrid of two existing binomial random variate algorithms: the BTPE Algorithm (Binomial, Triangle, Parallelogram, Exponential) and the inverse transform for cases that BTPE cannot handle. BTPE uses rejection sampling, and BTPE’s authors originally provided an analytical formula for the expected number of iterations in terms of n and p. That expression is complicated to interpret in practical contexts. I explore BTPE by instrumenting ρμ’s implementation to empirically analyze its acceptance/rejection behavior to gain further insights into its runtime performance. Although the number of iterations depends upon n and p, my experiments show that the average number of iterations is always under two, and that the average number of random uniform variates required to generate a single random binomial is under four (two per iteration). Thus, when analyzing the runtime of a simulation algorithm that includes steps generating random binomials, one can consider such steps to have a constant runtime.
Keynote Speech 3: Beyond securing networks and storage: emerging attacks and defences to machine intelligence

Over the past three decades most research efforts in security and privacy have focused on network and storage security. Recently, Deep Neural Network (DNN) classifiers gain wide adoption in different complex tasks, including natural language processing, computer vision and cyber security. However, the underlying assumption of attack free operating environment has been defied by the introduction of several attacks such as adversarial examples and Trojan backdoor attacks. In Adversarial attacks the adversary perturbs the input examples during inference to force the DNN to misclassify while the adversary in the Trojan Backdoor operates in both training and inference phases. In the training phase the adversary trains the DNN in a way such that it behaves normally when the Trojan trigger does not exist, and it misclassifies if the trigger exists. Given that only the adversary knows the trigger, the users of the DNN will be fooled to trust the DNN model. The adversary can now attach the Trigger to the input examples during inference causing the DNN model to misclassify. In this talk we will discuss our development of several computationally efficient defense approaches for the Adversarial attacks enabling real-time detection of the attack for the first time. We will also discuss our development of an adaptive black-box defense approach for the Trojan Backdoor attack that outperforms the state-of-the-art by studying the relationships among the prediction logits of the DNN. After that we will discuss our recent follow up work in which we show how to jointly combine the above two adversaries to practically launch a new stealthy attack, dubbed AdvTrojan. AdvTrojan is stealthy because it can be activated only when: 1) a carefully crafted adversarial perturbation is injected into the input examples during inference, and 2) a Trojan backdoor is implanted during the training process of the model. We leverage adversarial noise in the input space to move Trojan-infected examples across the model decision boundary, making it difficult to detect. The stealthiness behavior of AdvTrojan fools the users into accidentally trusting the infected model as a robust classifier against adversarial examples. We will also discuss our future research that is focused on expanding the attack and defense mechanisms to new areas such as malicious domain detection, federated learning setting, personalized federated learning, and Graph Neural Networks. We will also discuss several application domains of adversarial as well as Trojan Backdoor attacks.
Nilpotent groups in cryptographic key exchange protocol for N≥ 1

This paper provides a thorough exploration of the applications of nilpotent groups for some n ≥ 1 in cryptography, focusing on their unique algebraic properties and their role in designing secure cryptographic systems. Through an in-depth analysis of cryptographic protocols utilizing nilpotent groups, this paper contributes to a deeper understanding of the potential and computational integration of nilpotent groups which isa key to quantum era cryptography.
Intermediate English Conversation Training for Indonesian Migrant Workers

This program aims to improve Indonesian migrant workersâ€™ English conversation proficiency, run by Indonesian Diaspora Network Global. The program helped 35 workers from Hong Kong, Taiwan, Singapore, and Malaysia. Universitas Atma Jaya Yogyakarta assisted IDN Global with teaching materials and facilitators composed of three lecturers and three students. Understanding the workersâ€™ need to master English skills, an intermediate English course program was designed with food as its theme. The program was conducted via Zoom every Wednesday from 21.00-23.00 Indonesian Western Time. Each meeting consisted of a lead-in, vocabulary activities, group discussion, class discussion, and reflection. Based on the reflection, interview, and the participantsâ€™ presentation activity, this program had been more conducted with a better structure in developing the participantsâ€™ involvement, confidence, and ability to communicate in English.
Strategic Thinking Based on the Creation Dilemma of Hong Kong TVB Criminal Investigation Drama

Hong Kong TVB criminal investigation drama is the golden representative drama genre of TVB TV dramas in the 1990s. It has formed a consistent genre paradigm in the process of gradual development. By watching TVB criminal investigation dramas, mainland audiences learn about the lives and culture of Hong Kong people, creating a common cultural memory for the entire Chinese-speaking community. Since the new century, TVB's criminal investigation dramas have experienced a problem of declining drama quality, which has not been resolved even after the co-production of "Go North". This article aims to provide narrative text strategies and ideas for TVB criminal investigation dramas facing the creative difficulties of the new era. The article first finds the crux of the gradual decline of TVB criminal investigation dramas, explores the reasons behind the decline, and analyzes the improvement methods in the text creation of the dramas based on the current cultural context.
Augmented Reality and Interactive Devices: Exploring New Dimensions of Public Art

Public art plays a vital role in urban development, which can improve the quality and charm of the city and promote its prosperity and development. Augmented reality and interactive devices are commonly used in the development of urban public art. Interactive device art is an art form that requires the audience's personal participation and interaction. It combines technology and art to bring immersive sensory experience and emotional resonance to the audience. Starting from related concepts, this study explores the application of public art in augmented reality and interactive devices, providing theoretical basis and reference for related development and research.
Research on the Dilemmas and Promotion Strategies of Smart Campus Construction in Universities from the Perspective of Education Informatization 2.0

With the deepening of educational informatization, smart campuses have become an important direction for the development of current universities. In this study, the main challenges faced in the current construction of smart campuses were first analyzed. On this basis, strategies for universities to promote smart campuses are proposed, aiming to establish the concept of intelligent teaching and reconstruct the intelligent teaching environment; Adopting a multi-level architecture model to build an intelligent management system based on data centers; Adopting a "people-oriented" service concept to meet the personalized needs of teachers and students. The ultimate goal is to achieve a comprehensive transformation of the construction of smart campuses in universities from traditional informatization to "digital campuses", and to enhance the school's governance capacity and level.
User-centric evaluation of recommender systems: a literature review

Recommender systems have seen a rapid rise of application in various industries, with several services now being implemented online. Over the years, various authors have been researching on how to improve the recommender system's performance and outcome with various algorithms and evaluation criteria. This research considers users as the end consumer of the recommender system and develops a systematic literature review for its evaluation from a user-centric perspective. It first presents the evaluation categories from a system testing view and then presents a novel framework of recommender system evaluation from a user feedback perspective. The study then presents a useful timeline for researchers who intend to use mathematical evaluation metrics in recommender system research. The three-step approach to this literature review will assist future research in getting a focused user-centric view on the recommender system's evaluation.
Games of Existence: The Digital Transformation of Fictionality

The article is devoted to the digital transformation of fictionality in the context of philosophical ontology. The used methodology is semiotic modeling. The traditional ontological question “what does it mean to exist?” is considered from the position of the general semiotics. We define existence through the notion of sign. We interpret the classical criteria of existence - observability, calculability, consistency, accountability - through semantic, syntactic, and pragmatic rules of the sign process. We introduce the criteria of existence for the object and for the subject, revealed as fulfillment of semiosis these or those rules in these or those layers of reality. We use Kant’s transcendental scheme to distinguish between the layers of existence: sensus, ratio, intellectus, which can also be expressed in other terminological ways, such as perception, language and reference. We show the difference in the definition of existence for natural and artificial objects. We define the role of interpretation as a procedure for identifying types of existing and their corresponding specific semantic rules of communication language. We understand fictionality as double signification or auto-referential negation - the designation in the act of communication of incalculable and unobservable entities as existing, that is, as calculable and observable. Thus, the game of existence is played by shifting the boundaries of the conceivable and the inconceivable, the possible and the impossible. The subject part of the work is constructed as an application of philosophical ontology theoretical constructs for the analysis of fictional subjects communicated by the science fiction. Examples of the subject possible digital transformation modeled by the science fiction are considered: the mind upload to a non-biological medium, distributed consciousness, artificial additions or transformations of corporeality, “intelligent environment” - situations when the functions of reflexion are transferred from the subject to the environment.
Investigation and Research on the Satisfaction of Undergraduate Liberal Arts Major Students in China with Their Major Courses and Its Influencing Factors under the Background of the "New Liberal Arts"

Major course satisfaction is not only one of the significant sources of motivation for university students' professional learning, but also a crucial factor in improving teaching quality and student learning outcomes. From the perspective of "New Liberal Arts" education background, this study investigated the satisfaction degree of liberal arts undergraduates and its influencing factors, examined the influence of curriculum, teacher teaching and interdisciplinary nature of curriculum on the satisfaction degree of liberal arts teaching, and explored the influence degree of different factors on satisfaction degree through quantitative analysis, and found out the main influencing factors. Finally, the paper puts forward some measures to improve the quality of higher education. The survey shows that under the background of "New Liberal Arts", liberal arts undergraduates' satisfaction with professional courses is generally high, but schools and teachers still need to take different measures to improve teaching quality and continue to improve students' satisfaction with professional courses.
Edutainment as a New Educational Technology: A Comparative Analysis

This paper is devoted to the study of edutainment as a new educational technology. The purpose of this study was to identify the most effective ratio of gamified forms of work at a foreign language lesson in a technical university. During the theoretical study, significant criteria were distinguished, namely personal involvement in the lesson, level of satisfaction, information and emotional overload, content value and academic performance. The interrelation of the criteria was exposed by using Likert scale, the Spearman coefficient and consecutive comparative analysis of traditional and gamified lessons. Conclusions were drawn about the benefits and drawbacks of the edutainment at a foreign language lesson. One the one hand, it improves the level of motivation, self-confidence, academic performance, facilitates and diversifies the learning process. On the other hand, the integration of edutainment unavoidably compromises the content value of education. The form of edutainment with 75% of gamified tasks suggests the most positive student response but decreases objective educational efficiency. Based on the average ratio and objective assessment of academic performance, it can be concluded that the 25% and 50% forms of edutainment provide the most efficient learning. The revealed patterns might be considered when planning classes in a foreign language, and in accordance with the objectives of the lesson, the most appropriate form of edutainment might be chosen.
Intelligent agents path planning in wireless sensor networks based on Vor-PSO algorithm

Wireless sensor networks (WSN) have wide applications in various fields, and intelligent agents have been applied to different tasks in the area of WSN because of their robust monitoring and exploration ability. This work proposes a path planning scheme in WSN based on computational geometry and particle swarm optimisation (PSO), which aims to minimise the cost of intelligent agents. First, we reconstruct the WSN topology according to the Voronoi diagram, which can convert the uncertain path cost into a deterministic expression. Then, using the discrete variational method, we construct an optimal path cost function considering exposure and length. Next, we develop a path planning algorithm based on Voronoi topology and PSO (Vor-PSO). Furthermore, we design a fitness function that considers the angle between the Voronoi edge and the optimal extreme point to update the particle position. The proposed heuristic algorithm effectively solves the problem of finding a feasible path in a high-coverage WSN and can be applied to different types of WSNs and multi-agent cluster task planning. Finally, simulation results are given to prove the effectiveness and computational performance of the proposed Vor-PSO algorithm.
Brief Analysis of the New Challenge in Teaching Management in Colleges and Universities under Internet Education

The development of Internet education marks the beginning of the era of MOOC, and the process of education informatization in China has been advancing with each passing day. The combination of online and offline teaching has become the new normal of teaching mode. The construction of online education resources is not only the result of teaching reform in colleges and universities changing with the times but also the new challenge facing teaching management in colleges and universities. Colleges and universities must scientifically study and analyze the key issues in the construction of online education resources and the organization and management of online teaching, so as to ensure the high-quality talent cultivation and its development.