Reviewerly/exHarmony huggingface dataset:
Features of Perception of Pre-revolutionary Non-fiction Films

The purpose of this work is to reveal the peculiarities of perception of historical archival newsreels on the example of pre-revolutionary non-fiction films. The objective of the study is to identify the qualities of pre-revolutionary newsreels that influenced the mass audience before the revolution and compare them with those qualities that come out on top when perceived by modern viewers. In the work, an analytical approach is used as a method, which is based on theoretical provisions arising from the consideration of the specifics of newsreels as a special type of cinematography. The formation of Russian non-fiction cinema at an early stage is also considered, the techniques used in its development are revealed, and the extant evidence of the perception of newsreels in the first cinemas is studied. Films considered are : "The Sovereign Emperor, the Empress Empress and the Heir Tsarevich are pleased to taste sailor food on the imperial yacht "Standard" during their stay in the Skerries in 1908" (1908), "Asbestos mining in the Urals" (1911) and "The opening of the bridge across the Volga in the center of Rzhev" (1911). The results of the study confirm the influence of the so-called visual turn that occurred and indicate a fundamental difference in the perception of visual information compared to verbal. When analyzing the preserved film materials, including documentaries, it is noticeable that now these ephemeral screen images convey the breath of authentic life, and we look at the screen not to learn any story, but to feel the atmosphere of being of those people. The novelty of the undertaken research lies in the development of a model of perception of pre-revolutionary newsreels both in general terms and from the point of view of a specific audience before and now.
Experimental characterization of entanglement characteristics in free-space quantum communication links

The primary focus of this paper is high-performance quantum communication systems that facilitate secure data transfer via free-space links. We consider an approach that uses correlated photon pairs generated in such a way that their polarizations are entangled and can be used to support quantum encryption protocols. However, when deployed in free space, these links can be affected by channel distortion, primarily via the spatial and temporal fields of the refractive index along the propagation path. In classical links, these fields alter the optical wave front characteristics; however, this mechanism does not directly apply to the quantum states utilized in single-photon or entangled photon protocols. Transmitting signals with quantum-based encryption creates a realm of problems, not related to wave front distortions, but rather to integrity of the quantum states after the signals propagate over free-space channels. We study these phenomena by implementing a laboratory testbed capable of creating a turbulent environment using atmospheric chambers developed by the AFRL. It is then used for experimental investigation of quantum entanglement after photon pairs are propagated both collinearly and via separate paths.
Sentic-Emotion Classifier on eWallet Reviews

Emotion classification using hybrid framework using lexicon and machine learning algorithms have been proven to be more accurate. This research analyses emotions from reviews of a popular eWallet mobile application in Malaysia. The proposed Sentic-Emotion Classifier is evaluated on its performance as it analyses the code-switched reviews crawled that contain formal and informal or out-of-vocab words. The code-switched reviews are mainly made up of words and expressions in English and Malay language models. This research designs, implements, and investigates several novel techniques that have been shown to have reliable and consistent predictive outcomes, and these outcomes are validated with manually annotated reviews so that the proposed classifier can be evaluated objectively. The novel contributions of the Sentic-Emotion Classifier consist of 2-tier sentiment classification, extended emolex framework, and multi-layer discrete emotion hierarchical classes which is hypothesized to be able to yield better accuracy for emotion and intensity prediction for the proposed framework.
A METHOD FOR ENCRYPTION AND DECRYPTION OF LARGE MESSAGES BY USING NON-SINGULAR MATRIX

The objective of this paper is security for large messages helps to maintain secret by using 3 x 3 non-singular matrix modulo27 as key to transfer plain text to cipher text and invertible matrix modulo 27 of order 3 x 3 as key to transfer cipher text to plain text.
Threshold secret sharing with geometric algebras

In this work, we propose a geometric algebra‐based variation of a well‐known threshold secret‐sharing scheme introduced by Adi Shamir in 1979. Secret sharing is a cryptographic primitive which allows a secret input to be divided into multiple shares which are then sent to a collection of parties. The shares are generated so that only “authorized” sets of shares can reconstruct the secret. In Shamir's scheme, any sufficiently large set of shares can reconstruct the secret. The minimum number of shares which can obtain the secret is called the threshold , and any number of shares smaller than the threshold reveals nothing about the secret. The shares are generated such that each party can perform computations, generating a new set of shares that, when reconstructed, are equivalent to performing those exact computations directly on the secret input data. Our variant changes the domain from which secrets are taken: A finite field with prime order is replaced by a geometric algebra over a finite field of prime order. This change preserves the important security properties of Shamir's scheme, namely, idealness (secrets and shares are chosen from the same space) and perfectness (“unauthorized” sets of shares learn nothing about the secret). Our scheme allows secret sharing to be seamlessly added to the arsenal of GA‐based applications. Our extension of Shamir's secret scheme was first worked out for geometric algebras. It appears, however, that in fact it works for other algebras, a situation worthy to be explored in future work. For definiteness, in this paper, we restrict the analysis to the case of geometric algebras.
Real-time Background Music System for Immersive Dialogue in Metaverse based on Dialogue Emotion

To enhance immersive experiences for metaverse environements, background music is often used. However, the background music is mostly pre-matched and repeated which might occur a distractive experience to users as it does not align well with rapidly changing user-interactive contents. Thus, we implemented a system to provide a more immersive metaverse conversation experience by 1) developing a regression neural network that extracts emotions from an utterance using KEMDy20, the Korean multimodal emotion dataset 2) selecting music corresponding to the extracted emotions from an utterance by the DEAM dataset where music is tagged with arousal-valence levels 3) combining it with a virtual space where users can have a real-time conversation with avatars.
Design and evaluation of AMAZE: A multi-finger approach to select small and distant objects in dense virtual environments

Object selection is vital in VR applications, as it allows users to interact with and manipulate virtual objects. In specialized contexts like molecular visualizations, VR objects may be small, distant, and situated in dense virtual environments, posing challenges such as reduced accuracy, increased error rates, and diminished user experience. This research explores the design and evaluation of a multi-finger continuous zoom-based technique for selecting targets that are small, distant and located in dense virtual environments. We design, evaluate and present two techniques, AMAZE and AMAZE-X via two studies. In our first study, we compared AMAZE to existing methods of Expand and Pinch-to-Select and found that it outperformed them in accurately selecting objects. AMAZE also ranked as the most natural and enjoyable technique. However, AMAZE had the highest task completion time, prompting us to refine it into AMAZE-X. We made two crucial adjustments: increasing the CD ratio and adding user-to-object distance visualization. We assessed AMAZE-X’s performance against the techniques from the first study. AMAZE-X demonstrated superior performance in terms of task completion time without errors, as well as being easy to use, learn, and the most favored technique among participants. Based on our findings, we offer four design recommendations for developing interaction techniques that cater to small, distant object selection in dense 3D virtual environments.
Dynamic scene deblurring via receptive field attention generative adversarial network

Dynamic scene deblurring has important practical significance, and some deep learning deblurring methods have outstanding performance in this domain. However, simple network structures cannot effectively extract feature information to remove serious motion blur, whereas complex network structures tend to ignore the correlation between features in different areas. To solve this problem, we propose a receptive field attention generative adversarial network. In the generator, a receptive field attention block is proposed, which can extract multi-scale feature information and adaptively adjust the feature response between channels through the attention mechanism. In this way, it is possible to expand the network’s receptive field while preserving the correlation between feature information. In addition, a local–global dual-scale discriminator is adopted to evaluate the output at different scales respectively and improve the perceptual quality of the generated results. In order to generate more realistic sharp images, multiple loss functions are adopted to constrain the network training. We compare our method with the state-of-the-art image deblurring methods. The experimental results demonstrate that our method achieves excellent deblurring performance.
On Reducing Reasoning and Querying in Natural Logic to Database Querying

This paper outlines a systems architecture for deductive querying of knowledge bases in a natural logic. The natural logic may be conceived as an extension of formal ontology languages featuring multiple quantified relationships between classes in addition to the subclass relation. It is proposed to obtain deductive querying by computation and storing of relevant logical consequences in a preliminary step. The paper explains how then subsequently query answers within the natural logic can be computed through plain relational database querying using ordinary query languages.
A First Evolutionary Fuzzy Approach for Change Mining with Smart Bands

Data Science is one of the most prominent interdisciplinary fields of Artificial Intelligence at the moment. It aims to analyze large volumes of data (big data) in complex environments in order to extract knowledge from them. In this contribution a first approach to change mining is presented. The algorithm employs an evolutionary fuzzy system and it is developed within an Android application which will allow us the incorporation and comparison of new algorithms in the future. The accompanying study is based on the analysis of data generated by wearables, more specifically smart bands, in order to discover knowledge in sleep and activity data generated by the user in different time periods.
Efficient generator of mathematical expressions for symbolic regression

Abstract We propose an approach to symbolic regression based on a novel variational autoencoder for generating hierarchical structures, HVAE. It combines simple atomic units with shared weights to recursively encode and decode the individual nodes in the hierarchy. Encoding is performed bottom-up and decoding top-down. We empirically show that HVAE can be trained efficiently with small corpora of mathematical expressions and can accurately encode expressions into a smooth low-dimensional latent space. The latter can be efficiently explored with various optimization methods to address the task of symbolic regression. Indeed, random search through the latent space of HVAE performs better than random search through expressions generated by manually crafted probabilistic grammars for mathematical expressions. Finally, EDHiE system for symbolic regression, which applies an evolutionary algorithm to the latent space of HVAE, reconstructs equations from a standard symbolic regression benchmark better than a state-of-the-art system based on a similar combination of deep learning and evolutionary algorithms.
Real-Time High-Quality Visualization for Volumetric Contents Rendering: A Lyapunov Optimization Framework

Real-time volumetric contents streaming on augmented reality (AR) devices should necessitate a balance between end-users' quality of experience (QoE) and the latency requirements. Lowering the quality of the volumetric contents to diminish the latency hinders the user's QoE. Otherwise, setting the quality of volumetric contents relatively high to improve the users' QoE increases the latency, which can be challenging to meet user satisfaction in AR services. Based on this trade-off observation, our proposed method maximizes time-average AR quality under latency requirements, inspired by Lyapunov optimization framework. In order to control the AR quality depending on latency requirements, we control the point cloud rendering ratio in the volumetric contents under the concept of Lyapunov optimization. Our extensive evaluation demonstrates that our proposed method achieves desired performance improvements, i.e., avoiding latency growing while ensuring the high quality of the volumetric contents streaming in AR services.
Velocity range-based reward shaping technique for effective map-less navigation with LiDAR sensor and deep reinforcement learning

In recent years, sensor components similar to human sensory functions have been rapidly developed in the hardware field, enabling the acquisition of information at a level beyond that of humans, and in the software field, artificial intelligence technology has been utilized to enable cognitive abilities and decision-making such as prediction, analysis, and judgment. These changes are being utilized in various industries and fields. In particular, new hardware and software technologies are being rapidly applied to robotics products, showing a level of performance and completeness that was previously unimaginable. In this paper, we researched the topic of establishing an optimal path plan for autonomous driving using LiDAR sensors and deep reinforcement learning in a workplace without map and grid coordinates for mobile robots, which are widely used in logistics and manufacturing sites. For this purpose, we reviewed the hardware configuration of mobile robots capable of autonomous driving, checked the characteristics of the main core sensors, and investigated the core technologies of autonomous driving. In addition, we reviewed the appropriate deep reinforcement learning algorithm to realize the autonomous driving of mobile robots, defined a deep neural network for autonomous driving data conversion, and defined a reward function for path planning. The contents investigated in this paper were built into a simulation environment to verify the autonomous path planning through experiment, and an additional reward technique "Velocity Range-based Evaluation Method" was proposed for further improvement of performance indicators required in the real field, and the effectiveness was verified. The simulation environment and detailed results of experiments are described in this paper, and it is expected as guidance and reference research for applying these technologies in the field.
Transparency Enhances Positive Perceptions of Social Artificial Intelligence

Social chatbots are aimed at building emotional bonds with users, and thus it is particularly important to design these technologies so as to elicit positive perceptions from users. In the current study, we investigate the impacts that transparent explanations of chatbots’ mechanisms have on users’ perceptions of the chatbots. A total of 914 participants were recruited from Amazon Mechanical Turk. They were randomly assigned to observe conversations between a hypothetical chatbot and a user in one of the two-by-two experimental conditions: whether the participants received an explanation about how the chatbot was trained and whether the chatbot was framed as an intelligent entity or a machine. A fifth group, who believed they were observing interactions between two humans, served as a control. Analyses of participants’ responses to the postobservation survey indicated that transparency positively affected perceptions of social chatbots by leading users to (1) find the chatbot less creepy, (2) feel greater affinity to the chatbot, and (3) perceive the chatbot as more socially intelligent, though these effects were small. Moreover, transparency appeared to have a larger effect on increasing the perceived social intelligence among participants with lower prior AI knowledge. These findings have implications for the design of future social chatbots and support the addition of transparency and explanation for chatbot users.
Transient Execution Attacks

Transient execution attacks exploit performance optimization mechanisms in processor hardware to leak information from victim software that is executing on a vulnerable processor.To rigorously study these attacks, researchers have developed relatively simple processor models that nonetheless adequately model all relevant processor behavior.Based on these models, we provide an accessible explanation of the essence of this class of attacks.O ne of the fascinating aspects of the field of computer security is that, on a regular basis, attack research comes up with truly surprising and creative new techniques to break security.A relatively recent example of such a surprising new class of attack techniques are transient execution attacks like Spectre [9], Meltdown [10] and Foreshadow [2].Approximately five years after their public disclosure, these attacks have bootstrapped a very active research community 1 , and our understanding of attacks and possible defenses has increased significantly.In particular, to support rigorous analysis of defenses, several simple models [4], [7] have been proposed, and these models are also very well-suited to explain the basics of transient execution attacks in a very accessible way, perhaps even in an educational context.That is exactly what we do in this article.Transient execution attacks are mainly relevant for shared computation platforms, like cloud platforms.On such platforms, several parties can be running software on the same processor, and we will view one of these parties as the victim, and another as the attacker.Obviously, there will be mechanisms in place to isolate victim and attacker.For instance, victim and attacker might be executing within different virtual machines, with a hypervisor separating victim and attacker state.It is useful to distinguish between two types of state.Architectural state is directly visible to software.It typically consists of memory and registers.Microarchitectural state is state that the processor implementation maintains to improve performance, but that is otherwise transparent to software.Typical examples
IVAS: An Intelligent Video Analysis System based on Edge Computing

With the development of the Internet of Things (IoT), the amount of global data is increasing rapidly. However, due to the increased burden of the cloud network, it is difficult to implement low-latency and high-efficiency video analysis in the cloud computing mode. To address this issue, this paper proposes an Intelligent Video Analysis System (IVAS) that can execute deep learning algorithms on low-power edge IoT devices, such as face detection and face recognition algorithms. IVAS enables fast, accurate, and real-time inference calculations of intelligent video analysis algorithms, providing an evaluation platform for performance testing, key parameter selection, and result analysis. The experiments based on real-world data confirm that IVAS can achieve good performance in people counting under an edge computing environment.
Semantic Communication for Image Transmission in Intra-Datacenter Optical Interconnects

A semantic communication system is proposed for massive high-definition image transmission in optical intra-connection links within the datacenter. Contrastive analysis shows that the proposed model can improve transmission capacity by more than two times.
Outsourcing Privacy-Preserving Federated Learning on Malicious Networks through MPC

While Federated Learning (FL) enables training by only sharing model updates rather than data, FL can still be prone to privacy leaks. Therefore, many efforts have been made to adopt homomorphic encryption or differential privacy approaches to prevent this. However, these solutions come with several issues that may limit their widespread adoption in applications that involve sensitive data sitting in silos. Such issues include but are not limited to trust in the aggregation server, the accuracy of the model, potential collusion among clients, and limited aggregation function support. To address these issues, we advocate using secure Multiparty Computation (MPC) to offer privacy-preserving computation. Specifically, we propose an FL framework that enables outsourcing the model aggregation to MPC parties on untrusted cloud environments and offers correctness verification to the model owners. Unlike differential privacy-based solutions, the proposed framework offers the same level of accuracy as models that are trained on the clear and minimize the possibility of collusion among clients and MPC parties. We implemented and evaluated the proposed framework under various conditions. The results showed that our framework can match the accuracy of centralized FL training while maintaining the required level of privacy and security in malicious cross-silo settings.
A Steganographic Method for Color Images using the Salp Swarm Algorithm and Chaotic Maps

The availability of high resolution images allows for using steganography to hide the presence of secret information. An issue of importance is to guarantee that the hidden data neither affects the quality of the images nor is it visually perceived. Another factor to consider is the capacity of embedding in the cover images without detection. In this paper we employ the Discrete Cosine Transform compression with quantization for lossy compression. We use the Salp Swarm Algorithm in conjunction with chaotic maps to find optimal locations in the cover images for data embedding in the LSBs. When chaotic maps are inserted into the SSA algorithm environment, the proposed method has achieved satisfactory results with minimal impact on the PSNR.