Reviewerly/exHarmony huggingface dataset:
Data Security in Cloud with Hybrid Homomorphic Encryption Technique Using GM–RSA Algorithm

Cloud computing is an environment which provides on-demand and convenient network access to a partitioned amount of computing resources. It is considered as amongst the most impressive development in the computing world. It is a focal database where numerous customers/organizations store their information to change and recover information, on pay what you utilize given by cloud service provider (CSP). The cloud computing is a strategy which gives a massive range of utilizations under various sort of topologies that quickly delivered with least administration effective way. Indeed, even cloud specialist co-ops like Dropbox could incidentally permit anybody to get to any client's record without client's information. This can prompted immense information penetrates and they can be outside client’s ability to control. Since customers would now have the option to will cloud organizations from wherever round the world a few affiliations may execute solid client verification as a guard against character cheats. Unfortunately, these courses of action do not actually ensure the client’s information inside the cloud. In this way, homomorphic encryption is the new strategy that permits applying express system on the encoded data to give greater security from information breaking. The proposed work is thought of half breed homomorphic encryption plot upheld the GM–RSA encryption algorithm which is single piece, i.e. additively and multiplicative homomorphic. This hybridization calculation prompted accelerate 2.9 occasions quicker, less calculation time to 66% when contrasted with existing frameworks and gives improved secrecy of the data that is put away inside the cloud.
Underwater self-supervised depth estimation

Accurate underwater depth estimation is a cornerstone of reaching autonomous underwater exploration. However, it is incredibly tricky due to the inherent attenuation character and heavy noise. Fortunately, the depth-changing trend and underwater light attenuation are closely correlated, providing powerful clues for underwater depth estimation. Rather than simulating the underwater attenuation through formulas, we propose an underwater self-supervised depth estimation neural network in our work. With the guidance of multiple constraints, which are meticulously designed based on the comprehensive analyses of underwater characters, this network can learn the depth-changing trend by itself from attenuation information in underwater monocular videos. Our detailed experiments on underwater datasets prove that the proposed framework can obtain accurate and fine-grained depth maps. We believe the work may provide an economical solution for underwater perception.
Identification of Anomalies in Mammograms through Internet of Medical Things (IoMT) Diagnosis System

Breast cancer is the primary health issue that women may face at some point in their lifetime. This may lead to death in severe cases. A mammography procedure is used for finding suspicious masses in the breast. Teleradiology is employed for online treatment and diagnostics processes due to the unavailability and shortage of trained radiologists in backward and remote areas. The availability of online radiologists is uncertain due to inadequate network coverage in rural areas. In such circumstances, the Computer-Aided Diagnosis (CAD) framework is useful for identifying breast abnormalities without expert radiologists. This research presents a decision-making system based on IoMT (Internet of Medical Things) to identify breast anomalies. The proposed technique encompasses the region growing algorithm to segment tumor that extracts suspicious part. Then, texture and shape-based features are employed to characterize breast lesions. The extracted features include first and second-order statistics, center-symmetric local binary pattern (CS-LBP), a histogram of oriented gradients (HOG), and shape-based techniques used to obtain various features from the mammograms. Finally, a fusion of machine learning algorithms including K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA are employed to classify breast cancer using composite feature vectors. The experimental results exhibit the proposed framework's efficacy that separates the cancerous lesions from the benign ones using 10-fold cross-validations. The accuracy, sensitivity, and specificity attained are 96.3%, 94.1%, and 98.2%, respectively, through shape-based features from the MIAS database. Finally, this research contributes a model with the ability for earlier and improved accuracy of breast tumor detection.
A methodical interpretation of adaptive robotics: Study and reformulation

The recent development of industrial manufacturing and social services has witnessed a significant trend of automation and intelligentization due to the wide application of robots and the technology of artificial intelligence (AI). While robots liberate humans from tedious and dangerous work in hazardous environments, AI simplifies the programming of robots by automatically inferring patterns and models from the interaction between the robots and the environment. Nevertheless, the application of robots and AI to more general manufacturing and social tasks is still limited by the lack of flexibility and adaptability to the changes in the task and the environment. Thus, a new concept, adaptive robotics, has been proposed to address the desire that an AI-powered robot should be able to properly reprogram itself to these changes without human intervention. Nevertheless, this concept is yet too abstract to provide any specific guidance to the development of robot programs. In this paper, we attempt to provide methodical redefinition and reformulation of adaptive robotics both in conceptual and mathematical manners based on the study of previous results. First of all, we introduce the essential motivation and the conceptual origination of adaptability of mechanical systems. Then, we review the previous literature and explore the related work of adaptive robotics. Based on this, we provide a uniform mathematical formulation of adaptive robotics based on adaptive and robust Markov-decision process (MDP). Through this work, we attempt to inspire the generic framework of adaptive robotics incorporating the existing immature paradigms, by which we are aiming at a clarified and well-defined context of adaptive robotics for future research on related domains.
Differential privacy medical data publishing method based on attribute correlation

The advent of the era of big data promotes the further development of medicine, and data release is an important step in it. The existing medical data release methods mostly use the k-anonymity model as the basis for data protection. With the advancement of technology, anonymous models are progressively less resistant to consistency attacks and background knowledge attacks. In order to better protect the private information of patients, this paper makes two major contributions: (1) The method of calculating the correlation between attributes is used to ensure the validity of the data after the data is released; (2) On the basis of the previous step, combined with the difference privacy-preserving model and tree model, this paper proposes an attribute association-based differential privacy classification tree data publishing method (ACDP-Tree). In this paper, simulation experiments are carried out on real medical data sets. The experimental results show that the algorithm ensures the validity and availability of the data to a certain extent while ensuring that the patient's privacy is not leaked.
AMon: A domain-specific language and framework for adaptive monitoring of Cyber–Physical Systems

Cyber–Physical Systems (CPS) are increasingly used in safety–critical scenarios where ensuring their correct behavior at runtime becomes a crucial task. Therefore, the behavior of the CPS needs to be monitored at runtime so that violations of requirements can be detected. With the inception of edge devices that facilitate runtime analysis at the edge and the increasingly diverse environments that CPS operate in, flexible monitoring approaches are needed that consider the data that needs to be monitored and the analyses performed on that data. In this paper, we propose AMon, a flexible adaptive monitoring framework that supports the specification and validation of monitoring adaptation rules, using a domain-specific language. Based on these rules, AMon automatically generates code for direct deployment onto devices. We evaluated AMon by applying it to TurtleBot Robots and a fleet of Unmanned Aerial Vehicles. Furthermore, we conducted a user study assessing the understandability and ease of use of our language. Results show that creating multiple adaptation rules with our DSL is feasible with minimal effort, and that adaptive monitoring can reduce the amount of runtime data transmitted from the edge device according to the current state of the system and its monitoring needs.
Studying the effect of symmetry in team structures on collaborative tasks in virtual reality

Studies involving collaboration in groups are frequently carried out under symmetrical conditions, meaning that all team members have the same role at the same time. In Virtual Reality, symmetrical collaboration often seems attractive as it allows all participants to be included in the VR environment, but it is not clear whether this provides any benefits over asymmetric forms of collaboration, such as when some team members are in VR while others are working on laptops. We conducted a study to compare the conditions of symmetric configurations (both team members use VR) and asymmetric configurations (one member in VR, and the other uses a laptop) when two participants performed a creativity task together. We found that there were differences in participant behaviour and the ideas generated. We conclude by proposing guidelines for future work in the area, particularly related to the use of Virtual Reality for creativity workshops.
Hard-style Selective Context Utilization for dialogue generation based on what user just said

Dialogue is a process of information exchanging, where global background is stable while local focuses are transiting. Thus, at the ongoing dialogue turn, there are both relevant and irrelevant semantics existing in dialogue contexts. How to filter out noises and selectively utilize context can pave the way to successful dialogue generation. Current work on dialogue context utilization either processes contexts as vanilla monologue text ignoring dynamic conversation flows, or depends on weighted strategies to fuse all contexts where irrelevant utterances cannot be filter out even may overwhelm relevant ones. To deal with this, this paper proposes a Hard-style Selective Context Utilization method (HardSCU). We first define and measure the information density of the last utterance (query) of a dialogue, marking it as “strong” or “weak”. For a dialogue with strong query, HardSCU directly inputs the query into a RNN-based or T5-based encoder–decoder framework to generate a response; for a dialogue with weak query, HardSCU conducts a selective context utilization for dialogue generation, where a semantic interaction module introduces relevant semantics of context to enrich the query and the co-reference relations existing in dialogue are extracted to promote the learning process of response decoder. Extensive experiments on two benchmark conversation corpora verify that our HardSCU method can outperform competitive baselines on generating appropriate responses for chit-chat-bots with yielding strong robustness to the variations of dialogue lengths.
AI-assisted programming question generation: Constructing semantic networks of programming knowledge by local knowledge graph and abstract syntax tree

Creating practice questions for programming learning is not an easy job. It requires the instructor to diligently organize heterogeneous learning resources. Although educational technologies have been adopted across levels of programming learning, programming question generation (PQG) is still predominantly performed by instructors without advanced technological support. This study proposes a knowledge-based PQG model that aims to help the instructor generate new programming questions and expand the assessment items by the Local Knowledge Graph and Abstract Syntax Tree. A group of experienced instructors was recruited to evaluate the PQG model and expressed significantly positive feedback on the generated questions.
CMAN: Leaning Global Structure Correlation for Monocular 3D Object Detection

The key to 3D object detection is proper utilization of depth data. Compared with LiDAR based approaches, 3D object detection from a single image remains a challenging task due to the lack of structure information. Recent methods leverage monocular depth estimation as a way to produce 2D depth maps, and adopt the depth maps as additional source of input to explore structure information. However, these methods either encode local structure correlations, or encode long range structure correlations by iteratively passing local messages. In this work, we propose a cross modal attention network (CMAN) for monocular 3D object detection. It is built upon the self-attention module which learns attention map from single modal data. Our CMAN is able to encode structure correlations from depth data, and embed the structure correlations with appearance information which is learned from RGB data. Thanks to the attention learning mechanism, our CMAN learns global structure correlations without iteration. In order to reduce the computational burden, our CMAN adopts a novel node sampler to eliminate redundant nodes during the attention map calculation. Experiment results on benchmark KITTI3D dataset show that our proposed CMAN outperforms the state-of-the-art methods.
Mapping the Privacy Landscape for Central Bank Digital Currencies

As central banks all over the world move to digitize cash, the issue of privacy needs to move to the forefront. The path taken may depend on the needs of each stakeholder group: privacy-conscious users, data holders, and law enforcement.
Assessing the quantum-computing landscape

A summative assessment of quantum computing's progress, based on market readiness and investment levels, and its future implications.
Generative Adversarial Networks for Cell Segmentation in Human Corneal Endothelium

We generate synthetic images with a generative adversarial network (GAN) model trained with image patches from specular microscopy corneal endothelial cells. Preliminary results show it may be a suitable approach for reliable cell segmentation.
Training and intrinsic evaluation of lightweight word embeddings for the clinical domain in Spanish

Resources for Natural Language Processing (NLP) are less numerous for languages different from English. In the clinical domain, where these resources are vital for obtaining new knowledge about human health and diseases, creating new resources for the Spanish language is imperative. One of the most common approaches in NLP is word embeddings, which are dense vector representations of a word, considering the word's context. This vector representation is usually the first step in various NLP tasks, such as text classification or information extraction. Therefore, in order to enrich Spanish language NLP tools, we built a Spanish clinical corpus from waiting list diagnostic suspicions, a biomedical corpus from medical journals, and term sequences sampled from the Unified Medical Language System (UMLS). These three corpora can be used to compute word embeddings models from scratch using Word2vec and fastText algorithms. Furthermore, to validate the quality of the calculated embeddings, we adapted several evaluation datasets in English, including some tests that have not been used in Spanish to the best of our knowledge. These translations were validated by two bilingual clinicians following an ad hoc validation standard for the translation. Even though contextualized word embeddings nowadays receive enormous attention, their calculation and deployment require specialized hardware and giant training corpora. Our static embeddings can be used in clinical applications with limited computational resources. The validation of the intrinsic test we present here can help groups working on static and contextualized word embeddings. We are releasing the training corpus and the embeddings within this publication.
SEAN: A Simple and Efficient Attention Network for Aircraft Detection in SAR Images

Due to the unique imaging mechanism of synthetic aperture radar (SAR), which leads to a discrete state of aircraft targets in images, its detection performance is vulnerable to the influence of complex ground objects. Although existing deep learning detection algorithms show good performance, they generally use a feature pyramid neck design and large backbone network, which reduces the detection efficiency to some extent. To address these problems, we propose a simple and efficient attention network (SEAN) in this paper, which takes YOLOv5s as the baseline. First, we shallow the depth of the backbone network and introduce a structural re-parameterization technique to increase the feature extraction capability of the backbone. Second, the neck architecture is designed by using a residual dilated module (RDM), a low-level semantic enhancement module (LSEM), and a localization attention module (LAM), substantially reducing the number of parameters and computation of the network. The results on the Gaofen-3 aircraft target dataset show that this method achieves 97.7% AP at a speed of 83.3 FPS on a Tesla M60, exceeding YOLOv5s by 1.3% AP and 8.7 FPS with 40.51% of the parameters and 86.25% of the FLOPs.
Initial Work on the Development of a Hardware-Based Gradient Descent Trained Expert System

Prior work has introduced a form of explainable artificial intelligence that is able to precisely explain, in a human-understandable form, why it makes decisions. It is also able to learn to make better decisions without potentially learning illegal or invalid considerations. This defensible system is based on fractional value rule-fact expert systems and the use of gradient descent training to optimize rule weightings. This software system has demonstrated efficacy for many applications; however, it utilizes iterative processing and thus does not have a deterministic completion time. It also requires comparatively expensive general-purpose computing hardware to run on. This paper builds on prior work in the development of hardware-based expert systems and presents and assesses the efficacy of a hardware implementation of this system. It characterizes its performance and discusses its utility and trade-offs for several application domains.
Perception Enhancement and Improving Driving Context Recognition of an Autonomous Vehicle Using UAVs

The safety of various road users and vehicle passengers is very important in our increasingly populated roads and highways. To this end, the correct perception of driving conditions is imperative for a driver to react accordingly to a given driving situation. Various sensors are currently being used in recognizing driving context. To further enhance such driving environment perception, this paper proposes the use of UAVs (unmanned aerial vehicles, also known as drones). In this work, drones are equipped with sensors (radar, lidar, camera, etc.), capable of detecting obstacles, accidents, and the like. Due to their small size and capability to move places, drones can be used collect perception data and transmit them to the vehicle using a secure method, such as an RF, VLC, or hybrid communication protocol. These data obtained from different sources are then combined and processed using a knowledge base and some set of logical rules. The knowledge base is represented by ontology; it contains various logical rules related to the weather, the appropriateness of sensors with respect to the weather, and the activation mechanism of UAVs containing these sensors. Logical rules about which communication protocols to use also exist. Finally, various driving context cognition rules are provided. The result is a more reliable environment perception for the vehicle. When necessary, users are provided with driving assistance information, leading to safe driving and fewer road accidents. As a proof of concept, various use cases were tested in a driving simulator in the laboratory. Experimental results show that the system is an effective tool in improving driving context recognition and in preventing road accidents.
Design elements in immersive virtual reality: the impact of object presence on health-related outcomes

Purpose Immersive virtual reality (IVR) has been frequently proposed as a promising tool for learning. However, researchers have commonly implemented a plethora of design elements in these IVR systems, which makes the specific aspects of the system that are necessary to achieve beneficial outcomes unclear. Against this background, this study aims to combine the literature on presence with learning theories to propose that the ability of IVR to present 3D objects to users improves the presence of these objects in the virtual environment compared with 2D objects, leading to increased learning performance. Design/methodology/approach To test this study’s hypotheses, the authors conducted a 2 (training condition: approach vs avoid) x 2 (object presence: high vs low) between-subjects laboratory experiment that used IVR with 83 female participants. Findings The results support this study’s hypotheses and show that training with high object presence leads to greater reactions to cues (chocolate cravings) and improved health behaviour (chocolate consumption). Originality/value This study shows that increased object presence leads to unique experiences for users, which help reinforce training effects. Moreover, this work sheds further light on how immersive computer technologies can affect user attitudes and behaviour. Specifically, this work contributes to IVR research by showing that learning effects can be enhanced through an increased degree of object presence.
World on Data Perspective

It is not simple to consider the world from only one side, but analyzing all sides can cloud comprehension without reaching deep insight found at the core. In a word as a whole, there is potential for telling the whole world in one word, i.e., data, leading to interpretations as phenomena and paradigms at the core of this review. The tug of war between the two sides explains that data represent the world, or vice versa, and present a fundamental view that systems or subsystems frame the world, even though they are encoded and composed of culture, rules, or approaches such as the threshold of democracy. When the COVID-19 pandemic posed a threat, human efforts contributed to finding potentially answers to questions presented by the world: what, who, where, when, why, and how (5 wh); a calling in the form of a challenge, where facts show something. All these questions resulted in research, education, and service activities, with their respective data frameworks producing results. This paper aims to reveal the meaning of the outcomes through an observation from an outside perspective. Therefore, like COVID-19 and its vaccines, the assertion of convexity and concave contradictions in the treatment of data leads to a mutually conjugate treatment of data. In this regard, statistics and artificial intelligence play separate and complementary roles.