Reviewerly/exHarmony huggingface dataset:
Research on Current Situation of 3D face reconstruction based on 3D Morphable Models

Abstract 3D face reconstruction based on 3D morphable models (3DMM) uses single or multiple 2D face RGB images to reconstruct the 3D information of the target and restore the spatial structure of the face, which has important research significance for face recognition, film industry, medical field and so on. This paper introduces the main technical methods and research status of 3D face reconstruction based on 3dmm in recent 20 years, summarizes the advantages and disadvantages and applicability of various researches on 3D face reconstruction using 3dmm, and analyzes the current research hotspot and future development trend of 3D face reconstruction.
Reconfigurable Intelligent Surface Assisted Federated Learning with Privacy Guarantee

In this paper, we consider a wireless federated learning (FL) system concerning differential privacy (DP) guarantee, where multiple edge devices collaboratively train a shared model under the coordination of a central base station (BS) through over-the-air computation (AirComp). However, due to the heterogeneity of wireless links, it is difficult to achieve the optimal trade-off between model privacy and accuracy during the FL model aggregation. To address this issue, we propose to utilize the reconfigurable intelligent surface (RIS) technology to mitigate the communication bottleneck in FL by reconfiguring the wireless propagation environment. Specifically, we aim to minimize the model optimality gap while strictly meeting the DP and transmit power constraints. This is achieved by jointly optimizing the device transmit power, artificial noise, and phase shifts at RIS, followed by developing a two-step alternating minimization framework. Simulation results will demonstrate that the proposed RIS-assisted FL model achieves a better trade-off between accuracy and privacy than the benchmarks.
Gödel-McKinsey-Tarski and Blok-Esakia for Heyting-Lewis Implication

Heyting-Lewis Logic is the extension of intuitionistic propositional logic with a strict implication connective that satisfies the constructive counterparts of axioms for strict implication provable in classical modal logics. Variants of this logic are surprisingly widespread: they appear as Curry-Howard correspondents of (simple type theory extended with) Haskell-style arrows, in preservativity logic of Heyting arithmetic, in the proof theory of guarded (co)recursion, and in the generalization of intuitionistic epistemic logic.Heyting-Lewis Logic can be interpreted in intuitionistic Kripke frames extended with a binary relation to account for strict implication. We use this semantics to define descriptive frames (generalisations of Esakia spaces), and establish a categorical duality between the algebraic interpretation and the frame semantics. We then adapt a transformation by Wolter and Zakharyaschev to translate Heyting-Lewis Logic to classical modal logic with two unary operators. This allows us to prove a Blok-Esakia theorem that we then use to obtain both known and new canonicity and correspondence theorems, and the finite model property and decidability for a large family of Heyting-Lewis logics.
An Efficient Reversible Digital Oil Painting Technique for Smartphone and Tablet Users

With the increase in mobility in technology, artists are looking for ways to create paintings using their tablets and smartphones while on the move. Digital oil painting is a revolutionary technique where an artist uses digital paintbrush to simulate the traditional bristle paintbrush of a painting to get an oil paint image. In this article, we propose a novel realistic technique for reversible digital oil painting to accelerate the users to (re)paint an oil paint image to convey the surrounding atmosphere, express their emotions and feelings, or showcase a time of day or type of weather through that painting. Realism is all about accuracy. To repaint, the original image is retrieved from the existing oil paint image by reversing the pixels to its original value. We derive a mathematical expression to determine the probability of a pixel, with a set of neighbor pixels, being oil painted to get an oil paint image of the desired quality, which reduces the time and space complexities of the technique. We also develop prototypes on Android, Windows, and Ubuntu operating systems that successfully implement the proposed technique.
Training neural network

Training a neural network is a process of finding a suitable set of weights that results in high performance on training and validation data. This chapter provides some of the most significant techniques that used in training network models, including backpropagation, weight initialization, and batch normalization. Then, we examine the effectiveness of each weight initialization technique and verify batch normalization by applying it to train the neural network model on the CIFAR-10 dataset. Finally, we summarize, compare, and visualize the performance of all the trained neural networks in Chapter 4 and this chapter on TensorBoard to see the impact of each technique in the convergence of the model during the training process.
Torpedo: A Fuzzing Framework for Discovering Adversarial Container Workloads

Containers enable a computing system to host multiple isolated applications, making more cost-efficient use of the available computing resources. However, exploiting shared computing resources, adversaries can launch various real-world attacks (e.g., denial-of-service attacks) inside containers. In this paper, we present TORPEDO, a fuzzing-based approach to detecting out-of-band workloads: such workloads could largely interfere the performance of colocated container instances on the same host, gaining extra unfair advantages on the system resources without being charged appropriately. TORPEDO mutates inputs of OS syscalls and simultaneously monitors the resource consumption of multiple container instances. It uses resource-guided heuristics to find inputs that maximize the difference in resource consumption between container instances and resource limits. We evaluate TORPEDO on widely-used containerization platforms and demonstrate that it can verify adversarial workloads that are manually discovered by existing research. More importantly, TORPEDO identifies several zero-day vulnerabilities that are not known to the public.
Deciphering WEP, WPA, and WPA2 Pre-shared Keys Using Fluxion

The content in the present paper reviews the attack carried out to decipher WEP, WPA, and WPA2 passwords using a tool named Fluxion. Before understanding how the tool works, brief information about the security protocols is given so that comprehending the attack more undoubtedly. At the same time, we compare Fluxion to other pre-existing tools used to crack Wi-Fi passwords and then list out the major differences between them. This comparison is done to evaluate the efficacy and success rate of Fluxion. In conclusion, this research and attack are performed to provide a deeper insight into WEP, WPA, and WPA2 cracking.
A UNIVERSITY ELECTRONIC EDUCATIONAL ENVIRONMENT AS AN INNOVATIVE RESOURCE FOR PROFESSIONAL TRAINING OF FUTURE PSYCHOLOGISTS AND SOCIAL TEACHERS

Today an electronic educational environment is an integral innovative component of vocational education functioning. The construction of electronic educational environments in Russia is supported by government initiatives in the context of the modernization of the entire educational institution, taking into account global trends. Today, our country has accumulated a certain experience in the development and use of electronic educational resources in the educational process, which needs analysis and systematization. The research problem: what are the mechanisms of using electronic educational environment of a university in the professional training of future psychologists and social teachers? The purpose of the research is to substantiate the expediency of using electronic educational environment in the professional training of future psychologists and social teachers. The research methods used are synthesis, systematization, desk research, generalization. The research results are the following: the relevance of using the electronic educational environment of a university as an innovative resource in the professional training of future psychologists and social teachers has been confirmed; the components of the electronic educational environment of a university have been characterized; electronic resources used in educational activities for the purpose of resource and information support of psychological and pedagogical disciplines have been disclosed Key conclusions: digital service is viewed as an additional, promising innovative educational resource in the professional training of future psychologists and social educators; the components of the electronic information and educational environment of a university are the official website, an electronic library, electronic an educational environment; the effectiveness of professional training of future psychologists and social teachers is possible with the additional use of Moodle, Zoom, YouTube, Google electronic resources.
Artificial Intelligence and Tomorrow’s Education

Nowadays, there is a rapid technological progress around the world that has enabled realities long ago unimaginable. We live in a technological era that represents new possibilities and challenges for society, and for the educational models in each country [1]. Research on smart education, which has forced the educational community to rethink on new ways of learning and teaching has been developed globally. Due to the advent of artificial intelligence (AI), the educational model for both, teachers and students will change. Nevertheless, to transform educational systems, it is necessary to update and train students, educators, and administrators effectively [2]. This research aims to describe the possible applications of AI in education from: 1) the automation of administrative tasks; 2) collection and analysis of information [3] to create smart content; 3) the implementation of virtual assistants in the teaching-learning process; 4) the potential delivery of lectures by humanoid robots with AI.
Pict-Place Authentication: Recognition-based Graphical Password using Image Layout for Better Balance of Security and Operation Time

In this paper, we propose a novel recognition-based graphical authentication (RbGA) scheme, named "Pict-Place authentication" (PPA). RbGA was expected to improve the credential memorability issue of knowledge-based user authentication. However, RbGA has not been widely used owing to its low security and longer operation time. We therefore propose a new RbGA scheme to modify the credential input method in order to improve these issues. In PPA, the credential is input as a layout of multiple images. Based on this idea, we implemented a web-based prototype system and conducted evaluation experiments. The results indicate that PPA has the potential to reduce the operation time and improve theoretical security.
Deep Structured Learning for Natural Language Processing

The real-time and dissemination characteristics of network information make net-mediated public opinion become more and more important food safety early warning resources, but the data of petabyte (PB) scale growth also bring great difficulties to the research and judgment of network public opinion, especially how to extract the event role of network public opinion from these data and analyze the sentiment tendency of public opinion comment. First, this article takes the public opinion of food safety network as the research point, and a BLSTM-CRF model for automatically marking the role of event is proposed by combining BLSTM and conditional random field organically. Second, the Attention mechanism based on vocabulary in the field of food safety is introduced, the distance-related sequence semantic features are extracted by BLSTM, and the emotional classification of sequence semantic features is realized by using CNN. A kind of Att-BLSTM-CNN model for the analysis of public opinion and emotional tendency in the field of food safety is proposed. Finally, based on the time series, this article combines the role extraction of food safety events and the analysis of emotional tendency and constructs a net-mediated public opinion early warning model in the field of food safety according to the heat of the event and the emotional intensity of the public to food safety public opinion events.
Application of Queuing Model in Library Service

Determining the number of service facilities to be purchased or allocated, in order to relieve the contradiction between reader waiting time and effective use of facilities, has always been a hot topic of research. As a service organization, there are various types of service facilities in library, such as access control system, self-service loan and return system, information desk, and even books can be used as service facilities in a broad sense. In this paper, we attempt to apply a queuing model to determine the library resource allocation based on the quality of reader service. In the model, the quantitative measurement of library resource allocation can be determined through the birth and death process of the number of readers. The initial model was improved by using to determine the number of access control channels in our library, thus promoting the effective allocation of library facilities.
Dynamic landscape analysis for open-ended stacking

Fitness landscape analysis (FLA) is a useful tool in the domain of (meta-)heuristic optimization but depends on explicitly knowing what fitness value is assigned to each solution. Dynamic optimization problems often do not provide their fitness landscape in such an explicit form, but by employing problem-specific knowledge, information about the problem itself and its current state can still be obtained. In this paper, a type of gray-box analysis of states of the open-ended stacking problem in two variations is presented. The current states obtained by monitoring the problem and algorithm during optimization are described via statistical measures similar to FLA measures. From this the distribution of possible states (the state landscape) and the transitions between problem states are analyzed. Visualization of the empirically obtained results reveals insights into algorithm-problem dynamics.
ViReader: A Wikipedia-based Vietnamese reading comprehension system using transfer learning

Machine Reading Comprehension has attracted significant interest in research on natural language understanding, and large-scale datasets and neural network-based methods have been developed for this task. However, most developments of resources and methods in machine reading comprehension have been investigated using two resource-rich languages, English and Chinese. This article proposes a system called ViReader for open-domain machine reading comprehension in Vietnamese by using Wikipedia as the textual knowledge source, where the answer to any particular question is a textual span derived directly from texts on Vietnamese Wikipedia. Our system combines a sentence retriever component, based on techniques of information retrieval to extract the relevant sentences, with a transfer learning-based answer extractor trained to predict answers based on Wikipedia texts. Experiments on multiple datasets for machine reading comprehension in Vietnamese and other languages demonstrate that (1) our ViReader system is highly competitive with prevalent machine learning-based systems, and (2) multi-task learning by using a combination consisting of the sentence retriever and answer extractor is an end-to-end reading comprehension system. The sentence retriever component of our proposed system retrieves the sentences that are most likely to provide the answer response to the given question. The transfer learning-based answer extractor then reads the document from which the sentences have been retrieved, predicts the answer, and returns it to the user. The ViReader system achieves new state-of-the-art performances, with values of 70.83% EM (exact match) and 89.54% F1, outperforming the BERT-based system by 11.55% and 9.54% , respectively. It also obtains state-of-the-art performance on UIT-ViNewsQA (another Vietnamese dataset consisting of online health-domain news) and BiPaR (a bilingual dataset on English and Chinese novel texts). Compared with the BERT-based system, our system achieves significant improvements (in terms of F1) with 7.65% for English and 6.13% for Chinese on the BiPaR dataset. Furthermore, we build a ViReader application programming interface that programmers can employ in Artificial Intelligence applications.
Parallel Random Embedding with Negatively Correlated Search

Evolutionary algorithm (EA) is proved to be a promising way for parameter optimization in deep reinforcement learning (RL) in recent years. However, it still suffers from the curse of dimensionality when dealing with high-dimensional inputs. Based on experiments, we observe that only a few variables contribute significantly to the performance of large-scale RL policy. Intuitively, we propose a parallel random embedding framework to optimize strategies on multiple parameter sub-spaces to incorporate classical Evolution algorithms and techniques for the million-scale RL policy optimization. Experiments show that our approach has outperforming performance with Negatively Correlation Search (NCS) in the framework.
Source Code Interoperability based on Ontology

The different ways of representing a source code in different programming languages create a heterogeneous context. In addition, the use of multiple programming languages in a single source code (polyglot programming) brings a wide choice of terms from different languages, libraries and structures. These facts prevent the direct exchange of information between source codes of different programming languages, requiring specialized knowledge of the programming languages involved. In this article, we present an ontology-based method for source code interoperability that provides an alternative to mitigate heterogeneity problems, aiming to semantically represent the source code written in different programming languages and apply it from different perspectives in a unified way. In this sense, the method is applied in a lab experiment with the objective of validating its methodological aspects, instantiating their respective phases in different subdomains (object orientation and object/relational mapping) and programming languages (Java and Python) in the code smells detection perspective. In addition, the code smell detector produced is evaluated with a set of real-world software projects written in Java and Python.
Privacy-preserving computation in the post-quantum era

This perspectives article surveys the most promising privacy-preserving cryptographic technologies including secure multiparty computation, zero-knowledge proofs and fully homomorphic encryption, and their various real-world applications.
Sensor-Based Toilet Instructions for People with Dementia

Care for people suffering from cognitive impairment is demanding and requires a high level of personnel attendance by nurses and driving costs as well as the need for additional human resources. Specifically, the toileting process is a neglected area and taboo topic that has not been tackled so far. People with cognitive impairments like dementia often cannot go to the toilet on their own. Instead, caregivers need to accompany them to provide assistance. We present a computer vision solution which detects the current pose of a person on the toilet based on privacy-preserving 3D depth data. Instructions of the next step the user needs to perform is provided on a screen. The proposed toileting guide is regarded to be useful in terms of increased autonomy, especially for persons with mild dementia. Additionally, time is expected to be relieved care givers which leads to improvement of work load and efficiency.
Multicraft: A Multimodal Interface for Supporting and Studying Learning in Minecraft

In this paper, we present work on bringing multimodal interaction to Minecraft. The platform, Multicraft, incorporates speech-based input, eye tracking, and natural language understanding to facilitate more equitable gameplay in Minecraft. We tested the platform with elementary, middle school students and college students through a collection of studies. Students found each of the provided modalities to be a compelling way to play Minecraft. Additionally, we discuss the ways that these different types of multimodal data can be used to identify the meaningful spatial reasoning practices that students demonstrate while playing Minecraft. Collectively, this paper emphasizes the opportunity to bridge a multimodal interface with a means for collecting rich data that can better support diverse learners in non-traditional learning environments.
Comparison of search strategies for feature location in software models

Search-based model-driven engineering is the application of search-based techniques to specific problems that are related to software engineering that is driven using software models. In this work, we make use of measures from the literature to report feature location problems in models (size and volume of the model and density, multiplicity, and dispersion of the feature being located) and a set of search strategies (random search, iterated local search, hill climbing, an evolutionary algorithm, and a hybrid between an evolutionary algorithm and hill climbing). The goal is to analyze of the impact of different values that are used to describe the feature location problems and the performance obtained by the different search strategies. We apply the search strategies to 1895 feature location problems that are obtained from 40 industrial software product lines. This work shows that: 1) the best results overall are obtained by a hybrid between evolutionary algorithm and hill climbing; 2) the size of the search space has the greatest impact on the results obtained by the search strategies; and 3) the impact of each of the measures is not the same in the five search strategies. This work highlights the use of the search strategy that produces the best results. In addition, we provide recommendations on when to use each search strategy.
Storystamp: Evaluating the impact of a place-oriented approach for placemaking

Storystamp is an interactive installation designed using the place-oriented design approach that we have developed previously as part of a research project titled: A place-oriented approach: from field knowledge to design framework. The approach was developed based on the literature of place and a field study of existing media architecture artefacts. It aims to provide place-oriented design framework that enables designers to engage with and incorporate the uniqueness of places when designing media architecture artefacts. In this study, we provide an evaluation on the impact of the framework on designing media architecture artefact for placemaking. Using the case of Storystamp, we conducted a field study that consists of observations on how people interact it. We also interviewed people who interacted with the Storystamp. Based on the findings from this field study and interviews, we report on people's interactions and experiences with the Storystamp to identify the potential for place-oriented design artefacts to facilitate placemaking.