Reviewerly/exHarmony huggingface dataset:
On the Emergence of Autonomous Systems towards Deep Thinking Machines and General AI Driven by Abstract Intelligence Theories and Intelligent Mathematics

Thinking and inference are key essences of human intelligence as Ren&#x00E9; Descartes stated that "I think therefore I am (1637)." The ultimate goal of General AI (GAI) [1], [2], [3], [4] is to enable machine thinking and inference [5], [6], [7], [8], [9] beyond data-based learning [10], [11] towards run-time intelligence generation [9], [12] driven by Autonomous Systems (AS) [13], [14] and cognitive robots [15]. Basic research on machine thinking is powered by Abstract Intelligence theories [16], [17], [18], [19], [20], [21] and Intelligent Mathematics (IM) [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32] towards enabling symbiotic and collective intelligence [13], [33] and cognitive systems [34], [35], [36], [37], [38], [39].This keynote lecture presents theoretical foundations of and latest advances in machine thinking and inference driven by AS, GAI, and cognitive robots. Latest breakthroughs in IMs and run-time behavior generation by cognitive systems and AS are elaborated. Emerging paradigms of machine thinking autonomies are introduced to demonstrate the next generation of general AI and symbiotic intelligent systems. The advances in AS are expected to lead to a wide range of novel applications in deep machine thinking, machine knowledge learning, cognitive robots, inference computers, brain-inspired systems, and mission-critical intelligent systems.
Mode Design and Experiment of Unmanned Aerial Vehicle Search and Rescue in Inland Waters

The occurrence of accidents such as personnel falling into water requires higher rescue capability of search and rescue equipment. Traditional search and rescue methods based on ships and speedboats are difficult to find because their speed and visual field limit them. With the development of unmanned aerial vehicle (UAV) technology, UAV is gradually used in search and rescue work in inland rivers. This paper presents a method for U A V searching and rescuing on the inland rivers. First of all, Predicting the range of U A V search and rescue of inland river based on a prediction mode considering the influence of transverse and longitudinal slope of riverbank, wind, current, and wave on the drifting of the inland drowning person establishing a search and rescue range prediction model. Then design the U A V inland river and water search and rescue method according to the predicted range. According to the characteristics of the continuous expansion of the UAV search and rescue range, a mobile rectangular search method suitable for inland rivers is designed to determine the U A V&#x2019; s search distance. Finally, the proposed method is verified through experimental. By comparing the experimental results of UAV-based search and rescue and ship-based search and rescue, it is proved that the designed UAV -based search and rescue program can save time and improve efficiency.
Object-Based Synthesis of Scraping and Rolling Sounds Based on Non-Linear Physical Constraints

Sustained contact interactions like scraping and rolling produce a wide variety of sounds. Previous studies have explored ways to synthesize these sounds efficiently and intuitively but could not fully mimic the rich structure of real instances of these sounds. We present a novel source-filter model for realistic synthesis of scraping and rolling sounds with physically and perceptually relevant controllable parameters constrained by principles of mechanics. Key features of our model include non-linearities to constrain the contact force, naturalistic normal force variation for different motions, and a method for morphing impulse responses within a material to achieve location-dependence. Perceptual experiments show that the presented model is able to synthesize realistic scraping and rolling sounds while conveying physical information similar to that in recorded sounds.
Post-matching quantum conference key agreement

Twin-field interference-based quantum conference key agreement protocols have been proposed and have achieved good performance in terms of the key rate and transmission distance in the finite-key regime. However, its performance significantly decreases when the strict constraint is broken regarding the optical pulse intensity and probability. Here, we propose a post-matching QCKA protocol to remove this constraint while obtaining a higher key rate. Numerical results in the symmetric case show that our protocol can obtain a transmission distance 25% more than the previous asymmetric QCKA protocol when the decoy state optical pulse intensity is 1% higher than the ideal value of the constraint, and can obtain a transmission distance 100% higher when the decoy state optical pulse intensity is 10% higher than the ideal value of the constraint.
Incorporation of Data-Mined Knowledge into Black-Box SVM for Interpretability

The lack of interpretability often makes black-box models challenging to be applied in many practical domains. For this reason, the current work, from the black-box model input port, proposes to incorporate data-mined knowledge into the black-box soft-margin SVM model to enhance accuracy and interpretability. The concept and incorporation mechanism of data-mined knowledge are successively developed, based on which a partially interpretable soft-margin SVM ( pTsm -SVM) optimization model is designed and then solved through reformulating the optimization problem as standard quadratic programming. An algorithm for mining linear positive (negative) class knowledge from general data sets is also proposed, which generates a linear two-dimensional discriminative rule with specificity (sensitivity) equal to 1 and the highest possible sensitivity (specificity) among all two-dimensional feature spaces. The knowledge-integrated pTsm -SVM works by achieving a good trade-off among the “large margin”, “high specificity”, and “high sensitivity”. Our experimental results on eight UCI datasets demonstrate the superiority of the proposed pTsm -SVM over the standard soft-margin SVM both in terms of accuracy and interpretability.
Construction and Application of College English Blended Teaching System Based on Multidata Fusion

With the rapid development of educational information technology, “online and offline” hybrid teaching has become the main trend of foreign language teaching reform in colleges and universities. The blended teaching mode integrates the traditional teaching mode with modern educational technology and reconstructs the traditional college English teaching mode in the aspects of teaching object, teaching content, teaching method, teaching environment, and teaching evaluation. Based on the multisource data such as students’ learning situation, learning duration, and academic performance, this paper builds a student English learning analysis system that realizes the integration of multisource data, completes the unified processing and analysis of multisource data, and then displays it through the interface. The system helps English teachers to guide students in different degrees according to the students’ English learning situation and academic performance, so that English teachers have a clearer and more comprehensive understanding of students’ learning and living conditions and timely guide students with incorrect learning attitudes, so as to avoid students’ detours.
Age Differences in the Interpretation of Facial Emojis: Classification on the Arousal-Valence Space

Emojis are universal tools that are frequently used to express people's emotional states throughout daily communications. They are often applied in various fields of research, such as consumer surveys, as indicators of users' emotional states. Further analyses of emoji interpretation among people with age are required to ensure the validity of emojis as a metric in such fields of research, thereby reducing misunderstandings. However, details regarding the effect of age on both arousal and valence, as they pertain to the interpretation of emojis, remain unclear. Therefore, in this study, we investigate the effects of the interpretation of facial emojis on the arousal-valence space among people of varying age groups. We conducted an online survey involving 2,000 participants, whereby we employed a nine-point scale to evaluate the valence and arousal levels associated with 74 facial emojis. Based on the two axes of valence and arousal among the age groups involved in this study, emojis are categorized into six similar clusters. For the two negative clusters, i.e., strongly negative and moderately negative sentiments, the group involving middle-aged participants showed significantly higher levels of arousal compared to the group involving young participants. Additionally, not all emojis classified into the aforementioned negative clusters indicate age difference. Based on these results, this study recommends using emojis with no age-related effects on the negative clusters as indices for evaluating human emotions.
Impact of COVID-19 Pandemic on the Educational-Instructional Process of the Students from Technical Faculties

Even though we live in a period when the word digitization is prevalent in many social areas, the COVID-19 pandemic has divided mankind into two main categories: some people have seen this crisis as an opportunity to move the activities online and, furthermore, to accelerate digitization in as many areas as possible, while others have been reluctant, keeping their preferences for face-to-face activities. The current work presents the results of an analysis on 249 students from 11 engineering faculties. The study aims to identify the impact of the COVID-19 pandemic on students’ educational experiences when switching from face-to-face to online education during a public health emergency or COVID-19-related state of alert. The overall conclusion was that, although the pandemic has brought adverse consequences on the health and life quality of many people, the challenges that humankind has been subjected to have led to personal and professional development and have opened up new perspectives for carrying out the everyday activities.
MASS: Multi-Agent Scheduling System for Intelligent Surveillance

With the recent development of autonomous driving technology, attempts to apply autonomous mobile robots to security and surveillance have been continued. Algorithms such as localization, obstacle avoidance, and path planning are essential for indoor autonomous driving of mobile robots. Among them, in this study, we deal with the path planning algorithm of mobile robots. In particular, the goal is to generate a path that covers the entire area of the given map, focusing on patrolling and guarding. We propose an algorithm that divides the generated path into multiple paths and allocates it to multi-robots by clustering. In addition, we propose a path planning algorithm that considers weights assigned on the probability map. We evaluated the performance of robot path generation with a real-world map from a testbed at the Korea Institute of Robotics and Technology Convergence (KIRO) in Pohang, Korea. This study also presents the results of cases with and without importance weights.
QMEDNet: A quaternion-based multi-order differential encoder–decoder model for 3D human motion prediction

In order to deal with the sequence information in the task of 3D human motion prediction effectively, many previous methods seek to predict the motion state of the next moment using the traditional recurrent neural network in Euclidean space. However human motion representation in Euclidean space has high distortion and shows a weak semantic expression when using deep learning models. In this work, we try to process human motion by mapping Euclidean space into a Hypercomplex vector space. We propose a novel model based on quaternion to predict the three-dimensional motion of a human body. The core idea of this study is to use the fusion information to understand and process the human motion state in quaternion space. The multi-order differential information is fused both in the encoder and decoder of feature extraction and mapped to the quaternion space, respectively. The encoder takes graph convolution as the basic unit and the decoder adopts gated recurrent units. Numerous experiments have been carried out to prove that the multi-order information in quaternion space can help build a more reasonable description for 3D human motion. The performance of the proposed QMEDNet is superior to most of the advanced short and long-term motion prediction methods in both public datasets, Human 3.6M and CMU Mocap.
A hybrid CBIR system using novel local tetra angle patterns and color moment features

The exponential growth of communal media platforms including Facebook and Twitter, and the accessibility of low-cost digital capturing devices have generated an enormous number of multimedia content including images. Effective handling of such massive image collection has boosted the development of content-based image retrieval (CBIR) approaches. Researchers have suggested both machine learning and non-learning-based techniques for CBIR. However, machine learning-based methods outperform the non-learning-based methods in the CBIR domain. The CBIR demands the development of reliable descriptors to attain the most appropriate images from the depository and better address the semantic gap problem. To counter these problems, we suggest a novel second-order Local Tetra Angle Patterns (LTAP) to better capture the texture features from the image. LTAPs are computed from adjacent pixels of 0°, 45°, 90°, and 135° using the second-order directional derivatives. Further, we propose a hybrid feature vector by concatenating LTAPs and RGB color features and using the genetic algorithm (GA) to select the finest appropriate features that enhance the image retrieval performance of our system. We employed our hybrid descriptor with the GA to optimize the support vector machine (SVM) for the image classification task and used the Chi-square quadratic distance measure to determine the resemblance between the query image and the images in the repository. Experimental results on three standard datasets including the Corel 1 k, Oxford flower, and CIFAR-10 indicate the effectiveness of the presented system over the contemporary CBIR methods.
Cross-lingual multi-speaker speech synthesis with limited bilingual training data

Modeling voices for multiple speakers and multiple languages with one speech synthesis system has been a challenge for a long time, especially in low-resource cases. This paper presents two approaches to achieve cross-lingual multi-speaker text-to-speech (TTS) and code-switching synthesis under two training scenarios: (1) cross-lingual synthesis with sufficient data, (2) cross-lingual synthesis with limited data per speaker. Accordingly, a novel TTS synthesis model and a non-autoregressive multi-speaker voice conversion model are proposed. The TTS model designed for sufficient-data cases has a Tacotron-based structure that uses shared phonemic representations associated with numeric language ID codes. As for the data-limited scenario, we adopt a framework cascading several speech modules to achieve our goal. In particular, we proposed a non-autoregressive many-to-many voice conversion module to address multi-speaker synthesis for data-insufficient cases. Experimental results on speaker similarity show that our proposed voice conversion module can maintain the voice characteristics well in data-limited cases. Both approaches use limited bilingual data and demonstrate impressive performance in cross-lingual synthesis, which can deliver fluent foreign speech and even code-switching speech for monolingual speakers.
Blockchain-based Value Creation for Supply Chain in COVID-19 Drug Distribution

This research explores how the supply chain application of blockchain impacts workflow across organizations to encourage value-creating opportunities by modeling the distribution of the COVID-19 vaccine. It focuses on 1) identifying the critical factors that could generate additional value by using blockchain in the supply chain workflow, 2) implementing a vaccine logistics scenario on a blockchain platform, and 3) examining the value creation and value appropriation opportunities resulting from embedding blockchain onto the supply chain. This study, which is currently in progress, demonstrates the practical benefits and business implications for applying blockchain in an urgent vaccine distribution environment.
The Blockchain Revolution: Decoding Digital Currencies

Cryptocurrencies and decentralized finance have grown considerably since the publication of the white paper on bitcoin in 2009. This article presents an overview of cryptocurrencies, blockchain technology, and their applications, explaining the spirit of the enterprise and how it compares with traditional operations.
Incentive Framework for Cross-Device Federated Learning and Analytics With Multiple Tasks Based on a Multi-Leader-Follower Game

Federated learning and analytics are proposed to collaboratively learn models or statistics from decentralized data. However, the accuracy of global models relies heavily on the participation of data owners. Furthermore, for a multi-task system, data owners need to allocate limited computation resources efficiently among different local tasks. In this paper, based on the federated optimization, we use a multi-leader-follower game to build an incentive framework for the federated learning and analytics system with multiple tasks. In the proposed framework, each task owner at the upper layer decides its reward rate to incentivize data owners to participate in the federated learning or analytics. Then, based on the reward rates of tasks, each data owner (e.g., mobile device or organization) at the lower layer determines its accuracy level of local tasks' solutions. We analyze the optimal computation strategies of data owners. Because the closed-form expression of the lower-layer solution cannot be obtained, we leverage the variational inequality (VI) theory to analyze the existence of the proposed game's solution. Moreover, we provide a block coordinate descent (BCD) algorithm to solve the proposed game. Simulation results show that our proposed framework can achieve the performance improvement of the whole system.
A Consideration of Scalability for Software Defined Perimeter Based on the Zero-trust Model

Software Defined Perimeter (SDP), a zero trust model developed by Cloud Security Alliance, has been attracted attention in the technological industry since its introduction to a world adapting to digital transformation. The SDP market is expected to grow from USD 3,141 million in 2019 to USD 10,613.87 million by 2025 globally at a Compound Annual Growth Rate (CAGR) of 22.49&#x0025; during the forecast period. Many trust models have been introduced since the realization for the need of cyber security, such as public key infrastructure, software defined network, and virtual private network. SDP gained importance as a zero trust model since in the digital world no one can be trusted. With the introduction of new models and technical devices, there arises the need to improve newly introduced technology on various grounds when customers adapt to devices. Hence, we discussed overcoming current issues of SDP of scalability, reliability, and usability, etc. With the number of organizations sharing information online expanding, there is need for scalable and reliable SDP that is easy to maintain and cost efficient for evolving organizations. Thus, we newly proposed several scalable SDP models that enable easier installation management of real networks of organizations with different organizational structures.
pHPA: A Proactive Autoscaling Framework for Microservice Chain

Microservice is an architectural style that breaks down monolithic applications into smaller microservices and has been widely adopted by a variety of enterprises. Like the monolith, autoscaling has attracted the attention of operators in scaling microservices. However, most existing approaches of autoscaling do not consider microservice chain and severely degrade the performance of microservices when traffic surges. In this paper, we present pHPA, an autoscaling framework for the microservice chain. pHPA proactively allocates resources to the microservice chains and effectively handles traffic surges. Our evaluation using various open-source benchmarks shows that pHPA reduces 99%-tile latency and resource usage by up to 70% and 58% respectively compared to the most widely used autoscaler when traffic surges.
Drivers of downloading and reasons for not downloading COVID-19 contact tracing and exposure notification apps: A national cross-sectional survey

Background Bluetooth-enabled smartphone apps have been developed and implemented in different sites globally to help overcome capacity limitations of traditional interview-based COVID-19 contact tracing. Two apps are currently available in Canada: ABTraceTogether exclusively in Alberta and COVID Alert in nine other provinces and territories. This study aims to examine factors associated with downloading of these apps to inform targeted promotion and marketing to increase app uptake. Methods We performed a cross-sectional survey with adult participants (≥18 years old) from an online national panel. Participants were asked if they had downloaded an app and, if applicable, reasons for not downloading. Logistic regression was used to identify sociodemographic factors and trusted information sources associated with downloading and reasons for not downloading. Results Of the included 4,503 respondents (36% response rate), 1,394 (31%) had downloaded an app. Demographic and socioeconomic factors positively associated with app download were: 1) being female, 2) higher household income, 3) higher education level attained, and 4) more liberal political views. The odds of downloading an app were higher for participants who trusted health-related information sources, and lower for those who trusted internet searches, family and friend, or Facebook. The most cited reasons for not downloading were related to data security concerns and perceived lack of benefit from the apps. Interpretation These findings identify sociodemographic segments with the lowest app uptake, reasons for not downloading and trusted information sources to inform targeted promotion and marketing strategies to improve uptake of apps to facilitate contact tracing.
Swarm Intelligence Algorithms for Optimal Scheduling for Cloud-Based Fuzzy Systems

A fuzzy cloud resource scheduling model with time-cost constraints is built using fuzzy triangular numbers to represent uncertain task execution time. Task scheduling reduces total time and cost spent on a project. It connects virtual machines and functions. Particle swarm optimization (HPO) is used to plan cloud resources (HSOA). The approach uses orthogonal particle swarm initialization to increase the quality of the initial particle exploration, rerandomization to regulate the particle search range, and real-time updating of inertia weights to control particle speed. The suggested problem model and optimization approach are evaluated using random simulation data provided by the CloudSim simulation platform. Less overall execution time and a lower cost are shown to have fast convergence and solution capabilities in experiments.