Reviewerly/exHarmony huggingface dataset:
Multiple-camera System for 3D Object Detection in Virtual Environment using Intelligent Approach

This work is the development of an adaptive multi-camera system for object detection which can be further adjusted for computational requirements. To get preliminary results, the whole project is simulated in a virtual environment for tracking the targeted object and to collect maximum information about the object’s features and characteristics. The virtual scene is developed using Unity software, and the images are captured by a virtual camera which is controlled by an external device (mouse). Further expected results of this project are to achieve a model which can be trained on the various classes of the 3D objects to classify objects and calculate the optimal distance between the object and the camera position, making it reliable to get the maximum information from the real-time environment which is developed by physical model by using camera and microcontroller raspberry pi4. The initial results obtained in this preliminary work clearly represent what has been achieved so far. Machine learning algorithms have been used to recognize what the object is that is in the camera’s gaze. Objects have been classified into different categories. Object detection has been implemented using Python and Opencv library.
Leveraging Geometric Structure for Label-Efficient Semi-Supervised Scene Segmentation

Label-efficient scene segmentation aims to achieve effective per-pixel classification with reduced labeling effort. Recent approaches for this task focus on leveraging unlabelled images by formulating consistency regularization or pseudo labels for individual pixels. Yet most of these methods ignore the 3D geometric structures naturally conveyed by image scenes, which is free for enhancing training segmentation models with better discrimination of image details. In this work, we present a novel Geometric Structure Refinement (GSR) framework to explicitly exploit the geometric structures of image scenes to enhance the semi-supervised training of segmentation models. In the training phase, we generate initial dense pseudo labels based on fast and coarse annotations, and then utilize the free unsupervised 3D reconstruction of the image scene to calibrate the dense pseudo labels with more reliable details. With the calibrated pseudo groundtruth, we are able to conveniently train any existing image segmentation models without increasing the costs of annotations or modifying the models' architectures. Moreover, we explore different strategies for allocating labeling effort in semi-supervised scene segmentation, and find that a combination of finely-labeled samples and coarsely-labeled samples performs better than the traditional dense-fine only annotations. Extensive experiments on datasets including Cityscapes and KITTI are conducted to evaluate our proposed methods. The results demonstrate that GSR can be easily applied to boost the performance of existing models like PSPNet, DeepLabv3+, etc with reduced annotations. With half of the annotation effort, GSR achieves 99% of the accuracy of its fully supervised state-of-the-art counterparts.
Efficient and traceable data sharing for the Internet of Things in smart cities

Data sharing in smart city applications supported by the Internet of Things (IoT) currently lacks privacy protection and accountability mechanisms for data leakage. When a data breach occurs, it is difficult for the data owner to trace the identity of the data leaker. To address this challenge, we propose an Efficient and Traceable IoT Data Sharing method (ETDS), which strives to solve the accountability problem of IoT data in multiparty sharing scenarios. The ETDS consists of a blockchain-based decentralized access control system and a commutative encryption and fingerprint system. ETDS not only implements data access authorization in a decentralized environment but also implements an efficient accountability mechanism for data leakages. Unlike other accountability solutions, the data accountability mechanism of ETDS is implemented in scenarios where the data and the applications are decoupled. This paper demonstrates the security and availability of ETDS through security analysis and an efficiency evaluation.
Optimized virtual reality-based Method of Loci memorization techniques through increased immersion and effective memory palace designs: a feasibility study

For most, an improvement in memory would always be desirable, whether from the point of view of an aging individual with declining memory, or from the perspective of someone seeking to memorize large amounts of information in the shortest period of time. One way for people to improve upon their memory performance is by using the Method of Loci (MoL), a famously complex, ancient memorization technique for non-spatial information recall. With the use of virtual reality technology, this technique can finally be easily taught to individuals for use in their daily lives. In this paper, we present an exploration into this avenue of using MoL in virtual reality and report on the design and evaluation of our new virtual memory palace that aims to prove the feasibility of improving upon designs from other studies to optimize memory recall performance. An experiment was conducted to evaluate our VR MoL environment. The results from week 1 on the pre-test (M = 62.55, SD = 24.01) and post-test (M = 82.91, SD = 15.99) memory task showed an increase in the number of words remembered was statistically significant, t(20) = -2.34, p = 0.014 where participants were able to remember approximately 20.4% more non-spatial information, when compared to traditional memorization techniques. After a second use, participants improved, remembering 22.2% more non-spatial information on the pre-test (M = 63.44, SD = 26.64) and post-test (M = 85.67, SD = 16.10) memory task, indicating that the increase in number of words remembered was statistically significant, t(16) = -2.142, p = 0.024. The results suggest that the virtual memory palace experience could be optimized to help participants learn the MoL technique with very little training time and potentially produce significant improvements in recall performance as a result.
Needs and artificial intelligence

Abstract Throughout our history, we, Homo sapiens, have used technologies to better satisfy our needs . The relation between needs and technology is so fundamental that the US National Research Council defines the distinguishing characteristic of technology as its goal “to make modifications in the world [in order] to meet human needs” [1]. Artificial intelligence (AI) is one of the most promising emerging technologies of our time. Similar to other technologies, AI is expected by many “to meet [human] needs”. In this article, we reflect on the relationship between needs and AI, and call for the realization of needs-aware AI systems. We argue that re-thinking needs for , through , by , and with AI can be a very useful means towards the development of realistic approaches for sustainable H uman-aware, A ccountable, L awful, and E thical (HALE) AI systems. We discuss some of the most critical gaps, barriers, enablers, and drivers of co-creating future AI-based sociotechnical systems in which [human] needs are well considered and met. Finally, we provide an overview of potential challenges and considerations that should be carefully taken into account; and call for joint, immediate, and interdisciplinary efforts and collaborations to start on the path to needs-aware AI.
Improving Performance of Machine Learning based Detection of Network Steganography in Industrial Control Systems

In view of the strong increase of targeted attacks on industrial control systems (ICS) of manufacturies and critical infrastructures, it can be noticed that for the concealment of communication, steganographic information hiding techniques become increasingly popular for attackers. Particularly in Advanced Persistent Threats, attackers focus on hiding network information flows between infected components from any possible detection mechanism in order to remain on the invaded system for as long as possible. In order to be able to detect these kinds of threats by hidden communication in future, defense concepts such as intrusion detection systems need to be supplemented by steganalytic detectors for ICS network traffic. First state-of-the-art detection mechanisms have been proposed and deliver decent but improvable results. This paper proposes a novel, convolutional neural network (CNN) based detection approach relying on a handcrafted feature space as CNN input layer. The detection approach is evaluated extensively in experiments. The evaluation results are compared to three state-of-the-art approaches in a laboratory ICS setup. We show that our novel approach is able to outperform all state-of-the-art approaches significantly. It delivers a performance of up to 94.3% correct classified test data samples.
Health Recommendation System by Using Deep Learning and Fuzzy Technique

Data science has transformed completely the technology industry across the world. Health care ecosystems are incorporating technologies to provide assistive, necessary and personalized care to patients. There are various data science-based technologies like big data analytics, Machine Learning (ML), Neural Network (NN) which are used to help users for early detection of diseases and treatment by remote monitoring without physically visiting doctors. This study proposed a model based on deep learning. In this study, a healthcare recommendation system designed to provide a better multilevel decision-making related to the health risk and severity of different diseases.
A new multiple targets tracking algorithm based on improved MHT algorithm

In this paper, a bearing pretreatment method suitable for passive sonar tracking is proposed, when the ocean background noise fluctuation is large, the weak target can also be detected and tracked through the adaptive adjustment of the threshold. The principle and characteristics of the conventional multi-hypothesis tracking algorithm is explored in this paper, we propose a bearing information pre-processing method suitable for passive sonar tracing and an improved MHT method based on Kalman prediction framework. Finally, automatic tracking problem with multi-targets based on bearing crossing and ocean background fluctuations can be achieved. The simulation data proves that this method is reliable and practical.
JaSenpai: Towards an Adaptive and Social Interactive E-Learning Platform for Japanese Language Learning

The Japanese language is an essential skill for many foreigners who plan to study or work in Japan, but it is very hard and time-consuming to learn. Given the current COVID19 pandemic, the use of online and computer-assisted tools for Japanese language learning is indispensable. However, many of the currently available tools do not offer personalized content based on the user’s performance and lack social interaction, which can lower the engagement level of the users. In this paper, we propose JaSenpai, a Japanese language E-learning platform that features an automatic generation of vocabulary exercises, a recommendation system based on previous answers, and a multiplayer game for social interaction. We believe these elements can provide a more engaging and effective learning experience.
BI-GAN

Federated Learning is a growing advanced collaborative machine learning framework that aims to preserve user-privacy data. However, multiple researchers have investigated attack methods from the server side via gradient inversion techniques or Generative Adversarial Networks (GAN) to reconstruct the raw data distributions from users. In this paper, we propose Batch Inversion GAN (BI-GAN), a novel membership inference attack that can recover user-level batch images from local updates, utilizing both gradient inversion techniques and GAN. Our attack is more stealthy since it only requires access to gradients and does not interfere with the global model performance and is more robust in terms of image batch recovery and victim classification. The experiments show that our attack recovers higher quality images of the victim with higher accuracy compared to other attacks.
Interpreting sarcasm on social media using attention-based neural networks

Posting sarcastic comments is a common trend on social media in which the intended meaning of the expression by the user is opposite to the literal meaning. Most existing approaches have mainly focused on sarcasm detection and left sarcasm interpretation rather underexplored, whereas what can really improve the efficiency of social text analysis techniques are generating the correct interpretation of sarcastic posts. In this paper, we present a deep learning neural network architecture for sarcasm interpretation in which the effect of adapting the idea of different attention mechanisms with the proposed architecture is explored to generate a non-sarcastic post conveying the same meaning as the original sarcastic one. This is based on the idea that, in interpreting a sarcastic post, the incongruity between a positive word and a negative situation plays a key role and consequently making it necessary to pay more attention to some words in the sentence to interpret it accurately. In addition, through extensive experiments, we demonstrate the effectiveness of our methods in interpreting sarcastic posts compared to state-of-the-art baselines. Moreover, we further show that the impact of our methods is especially meaningful when considered for long posts.
Mass surveillance of VoIP calls in the data plane

Over the last decade, programmable data planes have enabled highly customizable and efficient packet processing in commercial off-the-shelf hardware. Although researchers have demonstrated various use cases of this technology, its potential misuse has gained much less traction. This work investigates a typical surveillance scenario, VoIP call identification and monitoring, through a tailored data-plane attack.
A two-level architecture for deep learning applications in mobile edge computing

As the deep learning models keep growing larger, it becomes more difficult to carry out highly accurate inference on mobile and Internet of things (IoT) devices with acceptable latency. By using edge computing, a popular approach to reducing inference latency is to partition the deep neural network (DNN) model and off-load part of the model inference to the edge server for processing. However, the partition approach still has some limitations, such as data transmission latency and inflexible model deployment. In this research, we propose a novel two-level inference architecture for deep learning applications in mobile edge computing. Instead of partitioning a large DNN model, we use small models to do two levels of inferences on edge devices and edge servers respectively. With our architecture, the on-device first-level inference is conducted in parallel with the feature uploading to the edge server and different types of second-level models can be flexibly deployed in edge servers according to their environments. Experiments demonstrate that our architecture can decrease computing costs and inference latency in complicated edge environments.
Medication based machine learning to identify subpopulations of pediatric hemodialysis patients in an electronic health record database

Electronic health records (EHRs) have given rise to large and complex databases of medical information that have the potential to become powerful tools for clinical research. However, differences in coding systems and the detail and accuracy of the information within EHRs can vary across institutions. This makes it challenging to identify subpopulations of patients and limits the widespread use of multi-institutional databases. In this study, we leveraged machine learning to identify patterns in medication usage among hospitalized pediatric patients receiving renal replacement therapy and created a predictive model that successfully differentiated between intermittent (iHD) and continuous renal replacement therapy (CRRT) hemodialysis patients. We trained six machine learning algorithms (logistical regression, Naïve Bayes, k-nearest neighbor, support vector machine, random forest, and gradient boosted trees) using patient records from a multi-center database (n = 533) and prescribed medication ingredients (n = 228) as features to discriminate between the two hemodialysis types. Predictive skill was assessed using a 5-fold cross-validation, and the algorithms showed a range of performance from 0.7 balanced accuracy (logistical regression) to 0.86 (random forest). The two best performing models were further tested using an independent single-center dataset and achieved 84-87% balanced accuracy. This model overcomes issues inherent within large databases and will allow us to utilize and combine historical records, significantly increasing population size and diversity within both iHD and CRRT populations for future clinical studies. Our work demonstrates the utility of using medications alone to accurately differentiate subpopulations of patients in large datasets, allowing codes to be transferred between different coding systems. This framework has the potential to be used to distinguish other subpopulations of patients where discriminatory ICD codes are not available, permitting more detailed insights and new lines of research.
El futurible de la educación a distancia la diferenciación

In this paper, the authors will talk about the development of technological platforms and tools that will support the creation and implementation of techno-pedagogical paradigms to tech the XXI Century generations. By proving how the innovative model of the open and distance learning universities (UNAD, for its Spanish acronym) is not run out and can be renewed, taking as an example the COVID-19 pandemic and analyzing the carriers that provide change to a new education, where the need of innovation in higher education in open and distance modalities is an urgent matter. Undoubtedly, learning is one of the human capacities that has most influenced their development and evolution. Thus, the e-education concept is presented as an effort to develop talents and human capacities using the Internet and new information and communication technologies (ICT). This will allow introducing a generalized remote mode, which will create a different culture adjusting the school curriculum to an online strategy, and not a rigid one, that involves technological improvements, so general and specialized knowledge are combined to the needs of scientific, technological, and social development. Teachers work with students differently and provide help through more knowledge and experience. Recent studies are showing that there are more open and online options for teacher professional development and teachers access the Internet to expand their development opportunities through social media platforms. In this sense, the non-attendance process of education has improved its relevance globally. Although, the COVID-19 pandemic caused an accelerator effect on the digital transformation of the educational institutions.
Semantic Modelling of Document Focus-Time for Temporal Information Retrieval

An accurate understanding of the temporal dynamics of Web content and user behaviors plays a crucial role during the interactive process between search engine and users. In this work, we focus on how to improve the retrieval performance via a better understanding of the time factor. On the one hand, we proposed a novel method to estimate the focus-time of documents leveraging their semantic information. On the other hand, we introduced query trend time for understanding the temporal intent underlying a search query based on Google Trend. Furthermore, we applied the proposed methods to two search scenarios: temporal information retrieval and temporal diversity retrieval. Our experimental results based on NTCIR Temporalia test collections show that: (1) Semantic information can be used to predict the temporal tendency of documents. (2) The semantic-based model works effectively even when few temporal expressions and entity names are available in documents. (3) The effectiveness of the estimated focus-time was comparable to that of the article’s publication time in relevance modelling, and thus, our method can be used as an alternative or supplementary tool when reliable publication dates are not available. (4) The trend time can improve the representation of temporal intents behind queries over query issue time.
The relative class number one problem for function fields, I

Abstract We reduce the classification of finite extensions of function fields (of curves over finite fields) with the same class number to a finite computation; complete this computation in all cases except when both curves have base field $$\mathbb {F}_2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>F</mml:mi> <mml:mn>2</mml:mn> </mml:msub> </mml:math> and genus $$&gt;1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>&gt;</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:math> ; and give a conjectural answer in the remaining cases. The conjecture will be resolved in subsequent papers.
An Efficient Parallel Architecture for Convolutional Neural Networks Accelerator on FPGAs

Convolutional Neural Networks (CNNs) have been widely used in the field of computer vision. Due to the computational complexity of CNNs, their computational efficiency has become a major concern. Field Programmable Gate Array (FPGA) is an ideal embedded device for accelerating CNNs due to its parallelism and programmability. However, the key challenge is how to efficiently deploy CNNs on embedded platform FPGA. Based on the inherent parallelism of CNNs, this paper proposes an efficient parallel accelerator architecture with two processing element (PE) arrays to accelerate CNNs through layer-wise calculation. Using three tile strategies, the accelerator can be reconfigured to accelerate different CNNs, including VGG, and tiny-YOLO v2. Consuming 448 dsps, the experimental results show that the accelerator can reach a peak performance of 164.25 Giga Operations Per Second (GOPS) on VGG-16 and 138.77 GOPS on tiny-YOLO v2. Compared with previous research, this accelerator achieves enhanced power efficiency and performance density.
Research on Infrared Image Super-Resolution Based on Enhanced Generative Adversarial Network

Infrared image super-resolution reconstruction is an effective way to improve the quality of infrared images. Aiming at the problem that the reconstructed image of the enhanced generative adversarial network Esrgan is prone to artifacts, This paper proposes a multi-branch enhanced generative adversarial network model, which adds an attention mechanism module and a residual module to the backbone of the original enhanced generative adversarial network model generator. The attention mechanism module strengthens the learning of useful channel information and suppresses useless channel information through the learning of global channel information. The residual module is a stack of the original Esrgan model block, which is the normalization of the channel dimension and retains the high-frequency features of the image. Experiments show that, compared with the original Esrgan model, the image texture details generated by the improved model are clearer and the resolution is higher, and the PSNR and SSIM of the image are significantly improved.