Reviewerly/exHarmony huggingface dataset:
GsNeRF: Fast novel view synthesis of dynamic radiance fields

Synthesis of new views in dynamic 3D scenes is a challenging task in 3D vision. However, most current approaches rely on radiance fields built upon multi-view camera systems for dynamic scenes, which are expensive and time-consuming, and depend on implicit representations based on Neural Radiance Fields . In this study, we introduced a new representation method for dynamic scenes called GsNeRF, which allows for fast reconstruction of objects under motion by moving a single camera in the scene, and it can render high-quality views from arbitrary time frames and camera poses. We utilize five grids to represent the dynamic scene, employing tensor decomposition for each grid to reduce storage space usage. Since the primary task of the entire model is to optimize these planes, we enforce spatio-temporal continuity of these planes through a smoothness loss. GsNeRF is combined with a miniature MLP to regress color outputs and trained using volume rendering. Through testing on a series of synthetic and real datasets, our method reduces the training time by over 100 times compared to implicit methods and achieves better rendering quality compared to explicit methods. Our approach achieves a balance between memory usage, speed, and quality overall.
VMIFresh: Efficient and fresh caches for virtual machine introspection

Virtual machine introspection (VMI) is the process of extracting knowledge about the inner state of a virtual machine from the outside. Traditional passive introspection mechanisms have proved themselves ineffective in many application domains due to their low performance. As a remedy for this issue, caching at the level of the introspection application was introduced. However, this sacrificed the freshness of VMI and led to an inconsistent outside view. In this work, we propose a multi-purpose hybrid caching scheme with freshness and consistency guarantees that is interleaved with the guest's MMU. This scheme can easily be integrated into existing applications and frameworks such as libvmi and Volatility 3. We demonstrate its feasibility by developing a prototype for such applications. Furthermore, the experimental evaluation of our approach suggests that it even significantly exceeds the performance of previous inconsistent caches.
Graph attention-based deep embedded clustering for speaker diarization

Deep speaker embedding extraction models have recently served as the cornerstone for modular speaker diarization systems. However, in current modular systems, the extracted speaker embeddings (namely, speaker features) do not effectively leverage their intrinsic relationships, and moreover, are not tailored specifically for the clustering task. In this paper, inspired by deep embedded clustering (DEC), we propose a speaker diarization method using the graph attention-based deep embedded clustering (GADEC) to address the aforementioned issues. First, considering the temporal nature of speech signals, when segmenting the speech signal into small segments, the speech in the current segment and its neighboring segments may likely belong to the same speaker. This suggests that embeddings extracted from neighboring segments could help generate a more informative speaker representation for the current segment. To better describe the complex relationships between segments and leverage the local structural information among their embeddings, we construct a graph for the pre-extracted speaker embeddings in a continuous audio signal. On this basis, we introduce a graph attentional encoder (GAE) module to integrate information from neighboring nodes (i.e., neighboring segments) in the graph and learn latent speaker embeddings. Moreover, we further jointly optimize both the latent speaker embeddings and the clustering results within a unified framework, leading to more discriminative speaker embeddings for the clustering task. Experimental results demonstrate that our proposed GADEC-based speaker diarization system significantly outperforms the baseline systems and several other recent speaker diarization systems concerning diarization error rate (DER) on the NIST SRE 2000 CALLHOME, AMI, and VoxConverse datasets.
Enhanced neural network-based attack investigation framework for network forensics: Identification, detection, and analysis of the attack

Network forensics aids in the identification of distinct network-based attacks through packet-level analysis of collected network traffic. It also unveils the attacker's intentions and operations. After identification, it is inevitable to design an efficient network attack detection model. Therefore, this work modifies the generic network forensic framework for attack investigation with two primary objectives i.e., Analysis and detection of attacks. In the proposed framework, a three-level analysis is performed. First, packet-level analysis is performed to study the attack behavior. Second, a graphical analysis is completed to determine both the attack flow and whether a node is an attacker or a victim. Moreover, it also assigns a score to the node indicating the severity of the attack. Finally, forensics exploratory data analysis(FEDA) is performed to distinguish the distribution of different features during attack and normal scenarios. For attack detection, the framework uses a convolution neural network (CNN-1D). CSE-CIC-IDS2018 (CIC2018), UNSW-NB15 and CIC-Darknet2020 datasets are used to test the performance of the proposed framework, wherein, it classifies distinct classes of attacks with an accuracy of 99.4%, 99.0%, and 90% on each dataset respectively. The results show that the proposed framework is more effective than previous works in attack detection and network traffic classification.
ANALISIS BENTUK KESULITAN MEMBACA PERMULAAN PADA SISWA KELAS III DI MI AL-BAROKAH KEC. JATIUWUNG KOTA TANGERANG

This study aims to describe the forms of initial reading difficulties experienced by third grade students and the factors that cause initial reading difficulties for third grade students in elementary schools. Reading difficulty is one of the obstacles to the learning process, because reading difficulty is the inability of students to follow learning properly. This study used a qualitative approach with descriptive analysis methods while collecting data using reading tests, observations, interviews, and documentation. Through the data from the research results, it can be concluded that the initial reading difficulties of class III students are: students still have difficulty reading, many errors in reading, repeating when spelling, difficulty recognizing almost the same letters, errors in word pronunciation, omitting letters, not paying attention to punctuation marks . Factors causing initial reading difficulties are: lack of interest in reading, lack of motivation, environmental factors and school library facilities.
Protocols for classically training quantum generative models on probability distributions

Quantum generative modeling (QGM) relies on preparing quantum states and generating samples from these states as hidden---or known---probability distributions. As distributions from some classes of quantum states (circuits) are inherently hard to sample classically, QGM represents an excellent test bed for quantum supremacy experiments. Furthermore, generative tasks are increasingly relevant for industrial machine learning applications, and thus QGM is a strong candidate for demonstrating a practical quantum advantage. However, this requires that quantum circuits are trained to represent industrially relevant distributions, and the corresponding training stage has an extensive training cost for current quantum hardware in practice. In this work, we propose protocols for classical training of QGMs based on circuits of the specific type that admit an efficient gradient computation, while remaining hard to sample. In particular, we consider instantaneous quantum polynomial (IQP) circuits and their extensions. Showing their classical simulability in terms of the time complexity, sparsity, and anticoncentration properties, we develop a classically tractable way of simulating their output probability distributions, allowing classical training to a target probability distribution. The corresponding quantum sampling from IQPs can be performed efficiently, unlike when using classical sampling. We numerically demonstrate the end-to-end training of IQP circuits using probability distributions for up to 30 qubits on a regular desktop computer. When applied to industrially relevant distributions this combination of classical training with quantum sampling represents an avenue for reaching advantage in the noisy intermediate-scale quantum (NISQ) era.
Rockfall alarm system for railway monitoring: Integrating seismic detection, localization, and characterization

Rockfalls pose a threat to human infrastructure below cliffs. Sensitive and reactive alarm systems are needed for rail traffic safety because small rockfalls ([Formula: see text]) impacting the railroad may cause train derailment. We develop a seismic processing workflow for rockfall early warning, powered by dense arrays deployed along the track. The method is evaluated by dropping rocks from a controlled height and triggering rockfalls on a cliff. We indicate that seismic arrays are highly sensitive to small impacts and are able to detect them, locate them, and estimate their magnitude. The detection can be performed in near real-time with a simple algorithm because small-scale rockfalls produce impulsive waveforms near the impact. Precise localization with matched field processing is able to track the trajectory of a rockfall. Impacts against the steel rails may be recognized by their source signature. The seismic amplitudes are related to the rockfall volume by the Hertz law, which may be used to estimate their volume. These results indicate the potential of seismic-driven near real-time rockfall alarm systems.
FEATURES OF THE MANIFESTATION OF CREATIVE POTENTIAL STUDENTS IN EDUCATIONAL ACTIVITIES

Abstract. In the world of the problem of the formation and development of students' personality, the study of their creative potential becomes important, since the modern speed of life constantly requires from the individual skills that allow to be creative and react productively to any changes. The relevance of the problem of studying and developing creative potential is especially increasing in modern conditions of existence, with a high rate of change in the world, with the ever-growing need to adapt to new conditions, to show flexibility, originality and speed both in any field of professional activity and in one's own life.&#x0D; During the Russian-Ukrainian war, the conditions of students' education changed significantly due to the introduction of distance education, the introduction of irregular vacations, the interruption of classes due to air raids or the disappearance of the Internet, etc. In this regard, the organization of the educational process has become much more difficult for teachers and students, as everyone is forced to constantly adapt to changing conditions. At the same time, the use of the information and communication capabilities of the Internet makes educational activity flexible, variable, multi-vector, enables interaction between the teacher and the student and, on this basis, promotes the development of personal needs, interests and creative abilities of the students of education.&#x0D; Despite the fact that Ukrainian students flexibly respond to the challenges of the realities of life, it is still a very difficult task for teachers to notice and develop creative potential in such conditions. In this regard, the study of the creative potential of students and the peculiarities of its manifestation in educational activities, in our opinion, is relevant and timely for institutions of higher education.&#x0D; In the process of research, the psychological characteristics of students with a high level of creative potential, but with different levels of academic achievements, were studied. Disagreements were established according to the following indicators: general anxiety, level of self-esteem, level of professional motivation and attitude to the future profession.&#x0D; Based on the theoretical analysis of scientific literature and the results of empirical research, it was found that there are psychological factors that determine the manifestation of students' creative potential in educational activities. Students who do not fully demonstrate their creative potential in educational activities need additional psychological influence on the development and correction of professional motivation, self-esteem and emotional state.
The figure of the narrator as a way to create a “documentary” plot in the historical plays-parables by E. Radzinsky

The article studies the peculiarities of dramatic narration in E. Radzinsky’s plays-parables. Modern theater and dramaturgy strive to reflect the qualitative changes in representative forms and their potential stage possibilities. One of these forms is the reception of dramatic narration, when the event that is being shown is replaced by the event that is being told about. This technique is actively used by the playwright E. Radzinsky in his historical plays-parables “Conversations with Socrates”, “Lunin, or the Death of Jacques”, “Theater of the Times of Nero and Seneca”, “The Executioner, or Conversations on the Way to the Guillotine”. Despite the fact that the plot is based on a documentary (or pseudo-documentary) text: the story of the life and execution of Socrates, described by Plato; “Moral Letters” to Lucilius (procurator of Sicily) by Seneca; “Letters from Siberia” by Mikhail Lunin; “Notes of an Executioner” by Charles Henri Sanson, the famous executioner during the era of the Great French Revolution, the play has a setting for oral speech. The playwright uses various techniques in constructing a plot to introduce this kind of a narrator into the text. The protagonist acts as a narrator, his position, the system of evidence, the testimony that the narrator uses – all these create an attitude of authenticity, the semblance of documentarity.
Optimal and Differentially Private Data Acquisition: Central and Local Mechanisms

The data for many machine learning tasks are owned by individuals who are typically concerned about privacy. Here, the authors study the optimal design of a data acquisition mechanism aimed at learning the mean of a population. This data acquisition scheme includes the design of a payment rule to compensate users for their privacy loss. It also involves selecting an estimator that minimizes estimation error while simultaneously providing privacy guarantees to users in line with their privacy preferences. The authors formulate this problem as a Bayesian mechanism design problem and propose approximately optimal data acquisition mechanisms.
Comparative Analysis of Transaction Speed and Throughput in Blockchain and Hashgraph: A Performance Study for Distributed Ledger Technologies

Blockchain technology garners significant attention and recognition due to several key advantages it offers. Trust, reliability, speed, and transparency are among the prominent benefits that contribute to its growing prominence. The decentralized nature of blockchain allows for a high level of trust as transactions are recorded and verified by multiple participants across the network. This, in turn, enhances reliability as there is no single point of failure. Speed is also a notable advantage, particularly when compared to traditional systems that involve intermediaries and complex processes for verification. Blockchain enables faster and more efficient transaction processing, reducing delays and costs. This research paper aims to provide a comprehensive comparative analysis of two prominent distributed ledger technologies, namely blockchain and hashgraph. Both blockchain and hashgraph offer decentralized and secure systems for recording and validating transactions or information. It explores the underlying mechanisms, consensus algorithms, advantages, and limitations of these technologies. It also examines their potential applications and discusses the implications of their respective design choices. By understanding the nuances of blockchain and hashgraph, seeks to contribute to the ongoing discourse on distributed ledger technologies and aids in decision-making for their appropriate adoption in various domains and applications.
Hierarchical Learning and Dummy Triplet Loss for Efficient Deepfake Detection

The advancement of generative models has made it easier to create highly realistic Deepfake videos. This accessibility has led to a surge in research on Deepfake detection to mitigate potential misuse. Typically, Deepfake detection models utilize binary backbones, even though the training dataset contains additional exploitable information, such as the Deepfake generation method employed for each video. However, recent findings suggest that inferring a binary class from a multi-class backbone yields superior performance compared to directly employing a binary backbone. Building upon this research, our article introduces two novel methods to infer a binary class from a multi-class backbone. The first method, named root dummies , leverages the dummy triplet loss, which employs fixed vectors (i.e., dummies) instead of mined positives and negatives in the triplet loss. By training the multi-class backbone with these dummies, we can easily infer a binary class during testing by adjusting the number of dummies (from six during training to two during inference). Through this approach, we achieve an accuracy improvement of 0.23% compared to the existing inference method, without requiring additional training. The second proposed method is transfer learning. It involves training a classifier, such as a support vector machine, to predict binary classes based on the image embeddings generated by the multi-class backbone. Although this method necessitates additional training, it further enhances the model’s performance, resulting in an accuracy increase of 1.79%. In summary, our proposed methods improve the accuracy of Deepfake detection by simply modifying the number of classes during training, making them suitable for integration into a variety of existing Deepfake training pipelines. Additionally, to foster reproducible research, we have made the source code of our solution publicly available at https://github.com/beuve/DmyT .
Leningrad Gosdrama in search of modern repertoire: N. Ya. Beresnev’s production “Hero” (1925) based on J. M. Synge’s play

The aim of the research is to identify the key moments in the history of the staging of N. Ya. Beresnev’s production “Hero” based on the play by J. M. Synge (State Academic Drama Theatre (Gosdrama, 1925)) examined in the context of the repertoire-related and production-related searches of the Russian academic stage in the first half of the 1920s. The paper investigates the reasons for the appearance of Synge’s play “Hero” (“The Playboy of the Western World”) on the stages of the First Studio of the Moscow Art Theatre (1923) and the Gosdrama (1925); analyses the dramatic poetics of the play “Hero”, in which A. D. Dikiy discovered the potential for studying the national mentality by production means, namely, by means of play theatre and mask theatre as the basis of the structure of the stage image embodied by an actor. The author of the paper considers the grounds that led to the difficulties in the staging of Synge’s play faced by the First Studio of the Moscow Art Theatre and the Gosdrama; determines the significance of I. V. Ilyinsky’s participation in the 1923 and the 1925 productions. The scientific novelty of the research lies in the fact that the use of formal methods of literary and theatre studies, as well as the structural method allowed the researcher to identify the reasons that had not given the academic stage the opportunity to master the poetics of the play “Hero” either in the production of the First Studio of the Moscow Art Theatre, or in the production of the Gosdrama. As a result, it was proved that Synge’s play is a tragic farce with elements of absurdist drama, i.e., drama of paradox. Attempts to use the techniques of play theatre and mask as the basis of the stage image when creating a production of Synge’s play showed that the academic stage was not prepared for such experiments and turned the adaptations of Synge’s play in the First Studio of the Moscow Art Theatre and in the Gosdrama into tour performances by I. V. Ilyinsky.
Optimization of Navigation Route Planning Based on Image Capture Technology

In recent years, navigation systems have become an integral part of our daily lives, helping us find the most efficient route to our destination. However, traditional navigation systems rely heavily on existing road maps and satellite data, which may not always provide accurate real-time information. To address this limitation, image capture technology has emerged as a promising solution to collect real-time visual data for optimized navigation route planning. This article aims to explore various aspects of image capture technology and its potential to enhance navigation systems. We discuss the challenges of navigation route planning using image capture techniques and propose several optimization techniques to overcome these challenges. The research presented in this paper highlights the benefits of incorporating image capture technology into navigation systems, including improved accuracy, real-time updates, and enhanced user experience. Through an extensive review of existing literature, case studies, and practical implementations, we provide valuable insights into optimizing navigation route planning using image capture techniques.
PROCESSES OF TRANSFORMATION OF SCIENTIFIC KNOWLEDGE IN CONDITIONS OF DIGITALIZATION

Issues related to transformations and development of scientifi c knowledge have been and remain one of Issues related to transformations and development of scientifi c knowledge have been and remain one of the priorities of ongoing philosophical research. The most obvious circumstances, my image of modern science, are the processes of digitalization, covering all aspects of serious life activity. It is too early to talk about the consequences of digitalization for the existence of science as a whole, but it is necessary to monitor the dynamics of its development.
Development and Application of 3D Modeling in Game

The development and progress of computer graphics and 3D modeling technology play an important role in the development of 3D game animation. Three-dimensional modeling technology combines artistic design and computer technology perfectly with a real sense of depth, so it has been widely used in the game industry. This paper introduces the development process of 3D modeling technology and the application of 3D modeling technology in games. At the same time, it gives suggestions on the problems existing in the application of 3D modeling technology in games, as well as researches and looks forward to the development trend of 3D modeling technology.