Reviewerly/exHarmony huggingface dataset:
Obstacle avoidance navigation method for robot based on deep reinforcement learning

Aiming at the navigation problem of mobile robots indoor environment, the traditional navigation algorithm based on D3QN has some problems such as sparse reward and slow training speed of the neural network. This paper proposes a deep reinforcement learning Algorithm (LN-D3QN) based on the D3QN network to realize collision-free autonomous navigation of mobile robots. To improve the efficiency of mobile robot learning and exploration, the Vision Sensor is used to acquire the input data from the environment, and the layer normalization method is used to normalize the input data. An improved reward function is designed, which improves the reward value of the algorithm, optimizes the state space, and alleviates the problem of sparse reward to some extent. The data is stored in a priority experience replay pool, and the network is trained using small batches of data. In addition, we evaluate our method by experiment related to indoor navigation. The experiments show that the robot trained by the improved D3QN algorithm can adapt to the unknown environment more quickly than the basic D3QN algorithm. The network's convergence speed is also improved, and it can complete the obstacle avoidance navigation task more efficiently.
Systematic Review on Identification and Prediction of Deep Learning-Based Cyber Security Technology and Convergence Fields

Recently, as core technologies leading the fourth industrial revolution, such as the Internet of Things (IoT), 5G, the cloud, and big data, have promoted smart convergence across national socio-economic infrastructures, cyber systems are expanding and becoming complex, and they are not effective in responding to cyber safety risks and threats using security technology solutions limited to a single system. Therefore, we developed cyber security technology that combines machine learning and AI technology to solve complex problems related to cyber safety. In this regard, this study aims to identify technology development trends to prevent the risks and threats of various cyber systems by monitoring major cyber security convergence fields and technologies through the symmetrical thesis and patent analysis. Because thesis information can explain the superiority of technology and patent information can explain the usefulness of a technology, they can be effectively used for analyzing and predicting technology development trends. Therefore, in this study, latent Dirichlet allocation is applied to extract text-document-based technical topics for the symmetrical thesis and patent information to identify security convergence fields and technologies for cyber safety. In addition, it elucidates cyber security convergence fields and technology trends by applying a dynamic topic model and long short-term memory, which are useful for analyzing technological changes and predicting trends. Based on these results, cyber security administrators, system operators, and developers can effectively identify and respond to trends in related technologies to reduce threats, and companies and experts developing cyber security solutions can present a new security approach.
Research on Biological Population Evolutionary Algorithm and Individual Adaptive Method Based on Quantum Computing

On the basis of classical computer, quantum computer has been developed. In dealing with some large-scale parallel problems, quantum computer is simpler and faster than traditional computer. Nowadays, physical qubit computers have many limitations. Classical computers have many ways to simulate quantum computing, the most effective of which are quantum superiority and quantum algorithm. Ensuring computational efficiency, accuracy, and precision is of great significance to the study of large-scale quantum computing. Compared with other algorithms, genetic algorithm has more advantages, so it can be more widely used. For example, strong adaptability and global optimization ability are the advantages of genetic algorithm. Through the research in Chapter 4, we can conclude that the variance of A2C is obviously smaller than that of PPO. Furthermore, it can be concluded that A2C has better robustness.
Geo-Environmental Approaches for the Analysis and Assessment of Groundwater Resources at the Catchment Scale

Groundwater resources constitute nearly one-third of the globe’s freshwater resources [...]
Attention-based for Multiscale Fusion Underwater Image Enhancement

Underwater images often suffer from color distortion, blurring and low contrast, which is caused by the propagation of light in the underwater environment being affected by the two processes: absorption and scattering.To cope with the poor quality of underwater images, this paper proposes a multiscale fusion underwater image enhancement method based on channel attention mechanism and local binary pattern (LBP).The network consists of three modules: feature aggregation, image reconstruction and LBP enhancement.The feature aggregation module aggregates feature information at different scales of the image, and the image reconstruction module restores the output features to high-quality underwater images.The network also introduces channel attention mechanism to make the network pay more attention to the channels containing important information.The detail information is protected by real-time superposition with feature information.Experimental results demonstrate that the method in this paper produces results with correct colors and complete details, and outperforms existing methods in quantitative metrics.
Redesigning for mobile plurilingual futures

The New London Group’s 1996 manifesto was a clarion call to educational researchers to fundamentally redesign language and literacy education for the needs of global learners communicating in evolving digital media environments. In this conceptual overview, the “how”, “what” and “why” of multiliteracies are critically re examined from the perspective of mobile digital language learning in posthumanist media ecologies, with attention drawn to paradigm shifts in language, technology, multimodality and context. We argue that Web 3.0 environments, AI and rapidly emerging algorithmic cultures have outpaced earlier critical theorizations of multiliteracies and digitally mediated learning practices as well as meaningful implementation of multiliteracies pedagogies in schools. We then reconsider the affordances and constraints of Web 3.0 tools for multilingual/plurilingual language learning, and sketch pathways for critical and productive engagements with mobile devices and multiliteracies pedagogies that reframe and advance the important critical work of the New London Group.
Bot-Based Emergency Software Applications for Natural Disaster Situations

Upon a serious emergency situation such as a natural disaster, people quickly try to call their friends and family with the software they use every day. On the other hand, people also tend to participate as a volunteer for rescue purposes. It is unlikely and impractical for these people to download and learn to use an application specially designed for aid processes. In this work, we investigate the feasibility of including bots, which provide a mechanism to get inside the software that people use daily, to develop emergency software applications designed to be used by victims and volunteers during stressful situations. In such situations, it is necessary to achieve efficiency, scalability, fault tolerance, elasticity, and mobility between data centers. We evaluate three bot-based applications. The first one, named Jayma, sends information about affected people during the natural disaster to a network of contacts. The second bot-based application, Ayni, manages and assigns tasks to volunteers. The third bot-based application named Rimay registers volunteers and manages campaigns and emergency tasks. The applications are built using common practice for distributed software architecture design. Most of the components forming the architecture are from existing public domain software, and some components are even consumed as an external service as in the case of Telegram. Moreover, the applications are executed on commodity hardware usually available from universities. We evaluate the applications to detect critical tasks, bottlenecks, and the most critical resource. Results show that Ayni and Rimay tend to saturate the CPU faster than other resources. Meanwhile, the RAM memory tends to reach the highest utilization level in the Jayma application.
ANALYSIS OF BRAIN WAVES IN THE PROCESS OF READING THE BOOK WITHIN THE CONTEXT OF DIGITALIZATION OF THE PRINTED BOOK

Digitalization, being positioned as the essential element of our lives in consequence of globalization, has brought innovation to publishing as it did with numerous fields. Nowadays, digital books appear in addition to printed books. The means of producing, recording, and storing information are available on digital devices in which caused a change in the reading habits of the readers. Thus, providing opportunities for readers to further their reading by means of a tablet, computer, or mobile phone without utilizing the concept of books. However, the effects of reading habits, the sustaining the continuity of these habits and their encodings presents itself as a matter of significance that has to be researched. The emergence of the new media technologies, in particular, with utilizing the newest research methods it provided, ensured the acquiring of data that was undiscovered until that point. One of the devices of these methods that leads to the most significant, effective, and accurate results is the monitoring of the brain waves. The detection of the brain waves by utilizing EEG (Electroencephalography) plays a crucial role in various fields of research. The research of such has helped the advancement of the interpretations about individuals’ moods, psychological and physical conditions. The features of electronic books and the frequencies of the reader’s immediate brain waves who read George Orwell’s Animal Farm in print and e-book form were studied in this research. The readers have read the first chapter in print and the second chapter through the e-book. A focus group study with three phases (pretest, monitoring the brain waves, and posttest) was conducted with 15 participants. The participants were between the ages of 18-36, consisting of 9 women and 6 men participants, and were arbitrarily chosen. The brain waves were monitored by using NeuroSky, a brain signal detection device. Analyses that were made were recorded by the Brainwave
Digital Promotion of Suicide: A Platform-Level Ethical Analysis

This article utilizes Aristotelian and Kantian philosophies to probe the social responsibilities of internet intermediaries that in one way or another assist and promote suicide. Striking a balance between freedom of expression and social responsibility, it is argued that several actors should be involved in restricting or eliminating live-streaming suicide, sites that encourage and facilitate suicide, and insult forums that drive people, especially adolescents, to take their own lives. The remediating actors are: commercial social media/website owners through their moderators; voluntary, non-profit, NGO “public defenders”; internet platform providers; regulatory agencies based on legislative authority, and advertisers. Practical remedies are suggested for each of these actors, noting as well important exceptions and caveats regarding the respective solutions.
Preface of special issue on Artificial Intelligence: The security &amp; privacy opportunities and challenges for emerging applications

With the increasing awareness of data security in emerging applications, Artificial Intelligence (AI)-enabled security mechanism has begun to receive growing attention. This special issue assembles a set of 14 papers, which provide in-depth research results to report the advance of security and privacy in AI and AI-enabled secure emerging applications. This preface provides an overview of all articles.
An Analysis of Hardware Design of MLWE-Based Public-Key Encryption and Key-Establishment Algorithms

This paper presents a review of module ring learning with errors-based (MLWE-based) public-key encryption and key-establishment algorithms. In particular, we introduce the preliminaries of public key cryptography, MLWE-based algorithms, and arithmetic operations in post-quantum cryptography. We then focus on analyzing the state-of-the-art hardware architecture designs of CRYSTALS-Kyber at different security levels, including hardware architectures for Kyber-512, Kyber-768, and Kyber-1024. This analysis is dedicated to providing complete guidelines for selecting the most suitable CRYSTALS-Kyber hardware architecture to apply in post-quantum cryptography-based security systems in reality, with different requirements of security levels and hardware efficiency.
Accelerating Concurrency Control with Active Thread Adjustment

We attempted to improve the performance of Silo, a concurrency control protocol for inmemory DataBase Management System that performs well under high-contention work-loads. Adaptive backoff is known as an effective optimization method under high-contention workloads. As a result of analyzing, we found that its efficacy lies in the non-existence of conflict events rather than in the reduction of the conflict rate, which has been considered in the past. On the basis of this analysis, we propose a method of adjusting the number of active threads. We conducted experiments comparing Cicada, another concurrency control protocol, and our method applied to Silo. The results indicate that the proposed method enabled Silo to significantly outperform. We found that cache misses are related to the performance.
Pilot Study on the Intercalibration of a Categorisation System for FAIRer Digital Objects Related to Sensitive Data in the Life Sciences

Abstract Sharing sensitive data is a specific challenge for research infrastructures in the field of life sciences. For that reason a toolbox has been developed, providing resources for researchers who wish to share and use sensitive data, to support the workflows for handling these kinds of digital objects. Common and community approved annotations are required to be compliant with FAIR principles (Findability, Accessibility, Interoperability, Reusability). The toolbox makes use of a tagging (categorisation) system, allowing consistent labelling and categorisation of digital objects, in terms relevant to data sharing tasks and activities. A pilot study was performed within the Horizon 2020 project EOSC-Life, in which 2 experts from 6 life sciences research infrastructures were recruited to independently assign tags to the same set of 10 to 25 resources related to sensitive data management and data sharing (in total 110). Summary statistics of agreement and observer variation per research infrastructure are provided. The pilot study has shown that experts were able to attribute tags but in most cases with a considerable observer variation between experts. In the context of CWFR (Canonical Workflow Frameworks for Research), this indicates the necessity for careful definition, evaluation and validation of parameters and processes related to workflow descriptions. The results from this pilot study were used to tackle this issue by revising the categorisation system and providing an updated version.
Controlling the Focus of Pretrained Language Generation Models

The finetuning of pretrained transformer-based language generation models are typically conducted in an end-to-end manner, where the model learns to attend to relevant parts of the input by itself.However, there does not exist a mechanism to directly control the model's focus.This work aims to develop a control mechanism by which a user can select spans of context as "highlights" for the model to focus on, and generate relevant output.To achieve this goal, we augment a pretrained model with trainable "focus vectors" that are directly applied to the model's embeddings, while the model itself is kept fixed.These vectors, trained on automatic annotations derived from attribution methods, act as indicators for context importance.We test our approach on two core generation tasks: dialogue response generation and abstractive summarization.We also collect evaluation data where the highlight-generation pairs are annotated by humans.Our experiments show that the trained focus vectors are effective in steering the model to generate outputs that are relevant to user-selected highlights.
Learn2Sing 2.0: Diffusion and Mutual Information-Based Target Speaker SVS by Learning from Singing Teacher

Building a high-quality singing corpus for a person who is not good at singing is non-trivial, thus making it challenging to create a singing voice synthesizer for this person. Learn2Sing is dedicated to synthesizing the singing voice of a speaker without his or her singing data by learning from data recorded by others, i.e., the singing teacher. Inspired by the fact that pitch is the key style factor to distinguish singing from speaking voice, the proposed Learn2Sing 2.0 first generates the preliminary acoustic feature with averaged pitch value in the phone level, which allows the training of this process for different styles, i.e., speaking or singing, share same conditions except for the speaker information. Then, conditioned on the specific style, a diffusion decoder, which is accelerated by a fast sampling algorithm during the inference stage, is adopted to gradually restore the final acoustic feature. During the training, to avoid the information confusion of the speaker embedding and the style embedding, mutual information is employed to restrain the learning of speaker embedding and style embedding. Experiments show that the proposed approach is capable of synthesizing high-quality singing voice for the target speaker without singing data with 10 decoding steps.
An Evaluation of 2D Human Pose Estimation based on ResNet Backbone

2D Human Pose Estimation (2D-HPE) has been widely applied in many practical applications in life such as sports analysis, medical fall detection, human-robot interaction, using Convolutional Neural Networks (CNNs), which has achieved many good results.In particular, the 2D-HPE results are intermediate in the 3D Human Pose Estimation (3D-HPE) process.In this paper, we perform a study to compare the results of 2D-HPE using versions of Residual Network (ResNet/RN) (RN-10, RN-18, RN-50, RN-101, RN-152) on HUman 3.6M Dataset (HU-3.6M-D).We transformed the original 3D annotation data of the Human 3.6M dataset to a 2D human pose.The estimated models are fine-tuning based on two protocols of the HU-3.6M-D with the same input parameters in the RN versions.The best estimate has an error of 34.96 pixels with Protocol #1 and 28.48 pixels with Protocol #3 when training with 10 epochs, increasing the number of training epochs reduces the estimation error (15.8 pixels of Protocol #1, 12.4 pixels of Protocol #3).The results of quantitative evaluation, comparison, analysis, and illustration in the paper.
Co-factor Clearing and Subgroup Membership Testing on Pairing-Friendly Curves

An important cryptographic operation on elliptic curves is hashing to a point on the curve. When the curve is not of prime order, the point is multiplied by the cofactor so that the result has a prime order. This is important to avoid small subgroup attacks for example. A second important operation, in the composite-order case, is testing whether a point belongs to the subgroup of prime order. A pairing is a bilinear map $$e :\mathbb G_1 \times \mathbb G_2 \rightarrow \mathbb G_T$$ where $$\mathbb G_1$$ and $$\mathbb G_2$$ are distinct subgroups of prime order r of an elliptic curve, and $$\mathbb G_T$$ is a multiplicative subgroup of the same prime order r of a finite field extension. Pairing-friendly curves are rarely of prime order. We investigate cofactor clearing and subgroup membership testing on these composite-order curves. First, we generalize a result on faster cofactor clearing for BLS curves to other pairing-friendly families of a polynomial form from the taxonomy of Freeman, Scott and Teske. Second, we investigate subgroup membership testing for $$\mathbb G_1$$ and $$\mathbb G_2$$ . We fix a proof argument for the $$\mathbb G_2$$ case that appeared in a preprint by Scott in late 2021 and has recently been implemented in different cryptographic libraries. We then generalize the result to both $$\mathbb G_1$$ and $$\mathbb G_2$$ and apply it to different pairing-friendly families of curves. This gives a simple and shared framework to prove membership tests for both cryptographic subgroups.
Denoising Neural Network for News Recommendation with Positive and Negative Implicit Feedback

News recommendation is different from movie or e-commercial recommendation as people usually do not grade the news.Therefore, user feedback for news is always implicit (click behavior, reading time, etc).Inevitably, there are noises in implicit feedback.On one hand, the user may exit immediately after clicking the news as he dislikes the news content, leaving the noise in his positive implicit feedback; on the other hand, the user may be recommended multiple interesting news at the same time and only click one of them, producing the noise in his negative implicit feedback.Opposite implicit feedback could construct more integrated user preferences and help each other to minimize the noise influence.Previous works on news recommendation only used positive implicit feedback and suffered from the noise impact.In this paper, we propose a Denoising neural network for news Recommendation with Positive and Negative implicit feedback, named DRPN.DRPN utilizes both feedback for recommendation with a module to denoise both positive and negative implicit feedback to further enhance the performance.Experiments on the real-world large-scale dataset demonstrate the state-of-the-art performance of DRPN.
Embedding arabic questions by feature-level fusion of word representations for questions classification: It is worth doing?

Questions classification remains one of the most critical phases of Question Answering Systems. It aims to reduce the answers search space by assigning a predefined class label to each question. Recently, contextualized word representations methods based on deep learning approach have achieved state of the art performance in various fields of Natural Language Processing. However, few works have applied these representations to classify Arabic questions. In this research, we propose an Arabic question classification method based on a sentence transformers-based representation. Besides, we investigate the fusion of various word representations including Bidirectional Encoder Representations from Transformers (BERT), Embeddings from Language Models (ELMo), and word embeddings enriched by subwords information (W2V). Our contribution is threefold. First, our method handles out of vocabulary words. Second, we apply the BERT representation to extract the most valuable features from words and then construct better question representation. Third, we study the impact of fusing various word embeddings on Arabic question classification. To evaluate the proposed models, we perform stratified 5-folds cross-validation on a dataset containing 3173 questions labeled with Arabic and Li & Roth taxonomies. The experimental results show that all our models surpassed previous works related to Arabic question classification task. In the Arabic taxonomy case, we scored the maximum accuracy of 94.20% with our AraBERT-based model. As for Li & Roth taxonomy, the model based on the concatenation of AraBERT and W2V scored the highest overall accuracy of 93.51%.