Reviewerly/exHarmony huggingface dataset:
A supervised case-based reasoning approach for explainable thyroid nodule diagnosis

As an explainable experience-based artificial intelligence technique, case-based reasoning (CBR) has been widely used to help diagnose many diseases, but the application of CBR in the diagnosis of thyroid nodules (TDNs) is rarely studied. To fill this research gap, this paper proposes a supervised CBR approach to help diagnose TDNs. The proposed approach first investigates the correlation between the feature diagnoses of historical TDN cases and the corresponding overall diagnoses using the canonical correlation analysis technique. Then the learned canonical variables are used to reconstruct TDN cases. Based on the reconstructed historical case base, a classifier is constructed to provide pathological diagnosis predictions for new TDN cases. To explain these predictions with similar historical TDN cases, a convex optimization model is constructed to determine the similarity between historical TDN cases and new TDN cases. Finally, a weighted combination scheme is designed to generate an explainable pathological diagnosis for each new TDN case based on its similar historical TDN cases. The proposed approach not only avoids the burdensome parameter tuning task but also reduces the likelihood of retrieving noisy historical cases as similar cases of new cases with a supervised case retrieval process. Using a real diagnostic dataset collected from the ultrasound department of a local hospital, the effectiveness of the proposed approach in diagnosing TDNs is validated and its advantages are further highlighted by comparison with the traditional CBR approach and six mainstream machine learning models. • A supervised case-based approach (CBR) is proposed for explainable diagnosis of thyroid nodules. • Both case features and case solutions are considered to determine the similarity between different cases. • Predictions of unexplainable machine learning models are explained using similar historical cases. • The proposed approach is compared with traditional CBR approach and mainstream machine learning models.
Mutually reinforced network embedding: An integrated approach to research paper recommendation

With the rapid growth of research publications, it has become time-consuming and cumbersome for researchers to find research papers relevant to their research. Research paper recommendation, which provides a reference list for a given manuscript, providing convenience for researchers. In the past, researchers embedded papers into a low-dimensional vector space through textual information and network structure to obtain paper embeddings, and then made recommendations based on the paper embeddings. This paper argues that in addition to the network structure and textual information, the co-authorship of a paper should also be considered, as it reveals the author’s interaction preference in the papers involved and reflects the topic of the paper from the author’s perspective. In this paper, we consider network structure, textual information and co-authorship of a paper simultaneously, and develop a mutual reinforcement network embedding (MRNE) model to enhance network embedding via mutual reinforcement between co-authorship and textual information based on network structure. Based on this model, we propose a cold-start research paper recommendation approach, which is evaluated on the link prediction-based paper recommendation and cold-start-based paper recommendation, respectively. Experimental results on HepTh and AAN datasets demonstrate effectiveness of the proposed method. This paper provides a new idea for the study of the cold start problem in research paper recommendation. • A novel network embedding model is proposed for scientific paper recommendation. • Our model integrate co-authorship, text and network with mutual reinforcement. • A inductive method is developed to recommend reference papers for cold-start papers. • Our method achieves state-of-art performance in HepTh and AAN datasets.
Generic semi-supervised adversarial subject translation for sensor-based activity recognition

Performance of Human Activity Recognition (HAR) models, particularly deep neural networks, is highly contingent upon the availability of the massive amount of annotated training data. Though, data collection and manual labeling in the HAR domain are prohibitively expensive due to human resource dependence in both steps. Hence, domain adaptation techniques are proposed to adapt the knowledge from the existing source of data. More recently, adversarial transfer learning methods have shown promising results for visual classification, yet limited for HAR problems, which are still prone to the unfavorable effects of the imbalanced distribution of samples. This paper presents a novel generic semi-supervised approach that takes advantage of the adversarial framework to tackle these shortcomings by leveraging knowledge from annotated samples exclusively from the source subject and unlabeled ones of the target subject. An extensive subject translation experiments is conducted on three large, middle, and small-size datasets with different levels of imbalance to assess the robustness of the proposed model to the scale as well as the imbalance in the data. The results demonstrate the effectiveness of our proposed algorithms over state-of-the-art methods, which led to up to 13%, 4%, and 13% improvement of our high-level activities recognition metrics for Opportunity, LISSI, and PAMAP2 datasets, respectively.
The concept of usability in terms of universal design

Objectives . The paper discusses usability as the concept underlying many contemporary design trends and accompanying technological development in various industrial, graphic and digital contexts. A formulation of the concept of usability in digital design contexts is advanced based on the evolution of industrial design. Methods. Using the historical research method, usability is shown to be characterized by unification of form as applied to the relationship between a person and a design object. This conclusion has been applied to digital design using the analogy method. Basic principles of usability assessment in design are outlined, along with impacts on further development. Requirements for a designed product in terms of usability analysis should be fulfilled taking into account the principles of universality that constitute a basis for a universal design system. Results. When developing UI/UX design objects, the usability of a digital product is considered in terms of universality based on rational visual image principles of industrial design and taking contemporary requirements in graphic, digital, and website design into account. Examples of industrial and digital design developments carried out by students using the concept of universal design are presented. Conclusions. The usability of a design object can be shaped by using universal principles applying in both industrial and UI/UX design.
Traffic Sign Detection Based on SSD Combined with Receptive Field Module and Path Aggregation Network

The traditional traffic sign detection algorithm can not deal with the application scenarios such as intelligent transportation system or advanced assisted driving environment, and it is difficult to meet the application requirements in detection accuracy and efficiency. Focusing on the above problems, this paper proposes a traffic sign detection algorithm based on Single Shot Multibox Detector (SSD) combined with Receptive Field Module (RFM) and Path Aggregation Network (PAN). The proposed algorithm is abbreviated to SSD-RP. The SSD-RP uses the RFM to improve the receptive field and semantics of the predicted feature maps, thus improving the detection performance of small traffic signs. At the same time, the path aggregation network is introduced to integrate multiscale features, which makes the abstract semantic information and rich detailed information shared among multiscale feature maps, enhances the discrimination ability of feature system, and improves the location and classification accuracy of traffic signs. Following that, the spatial pyramid pooling module is used to pool the shallow features and integrate them into the bottom-up information transmission path of the path aggregation network, thus continuing to supplement the fine-grained features for the feature system and further improve the detection performance. The experimental results on GTSDB and CCTSDB data sets show that SSD-RP has higher mean average precision (mAP) than traditional SSD algorithm and can better detect small traffic signs, which means that SSD-RP has higher detection precision. In addition, the experimental results also show that, compared with the common object detection algorithms such as Faster R-CNN, RetinaNet, and YOLOv3, the SSD-RP achieves a better balance between detection time and detection precision.
Cybersecurity strategy under uncertainties for an IoE environment

Internet of Everything (IoE) technology is increasingly being used by companies to modernize their activities. The very specific characteristics of such environments, in particular, their vulnerable exchanged data and the weak nature of the connected things, expose these companies to risks and security breaches. The principal objective of our work is to provide a cybersecurity strategy capable to consider all types of attacks that can affect an IoE environment while respecting the specified budget. For this purpose, a financial approach based on portfolio management is exploited by allowing to select a portfolio of security controls that minimizes the direct costs and maximizes the security level control. To be solved, the considered problem is assimilated to a combinatorial optimization technique and more precisely to that of the knapsack. We start by modeling the cybersecurity problem under cardinality and budget constraints that should be respected. To tackle the uncertainty of the problem, a robust optimization is used by considering the min-max criterion al-lowing to consider all the possible threats that can be generated by an attacker over the IoE environment. To solve the considered problem, we use a new iterative method under constraints and we compare it to the Non-dominated Sorting Genetic Algorithm (NSGA-II) meta-heuristic to evaluate its performances. The obtained numeric results when evaluating the performances of the proposed strategy have shown its efficiency by finding efficient Pareto fronts for the two considered objective functions. Based on the iterative method, our strategy greatly outperforms the genetic algorithm by allowing good results for different problem sizes and respecting cardinality constraints in a reasonable time.
Jakarta Pandemic to Endemic Transition: Forecasting COVID-19 Using NNAR and LSTM

In December 2021, the latest COVID-19 variant, Omicron, was confirmed in Indonesia. Unlike the Delta variant, the number of deaths in the Omicron type did not increase significantly and remained constant, even though the cases increased significantly. It is hoped that Indonesia will declare COVID-19 endemic. Jakarta is the capital of Indonesia and the first city where the new COVID-19 virus emerged. Therefore, we are trying to model COVID-19 cases in Jakarta and predict future cases to see if endemic conditions are identified. We applied Neural Network Auto-Regressive (NNAR) and Long Short-Term Memory (LSTM) methods. It is found that the NNAR forecast better for positive confirmed cases with an R-squared 0.939 and the LSTM forecast better for cases of death with an R-squared 0.9337. The forecasting results for the next 7 days reveal that positive confirmed cases of COVID-19 in Jakarta will increase slightly. In addition, the death cases experienced a very small increase, only one new case. According to the results of this study, it can be concluded that COVID-19 in Jakarta will enter an endemic phase in Jakarta, with no substantial increase in cases and a low mortality rate.
For Your Eyes Only: Privacy-preserving eye-tracking datasets

Eye-tracking is a critical source of information for understanding human behavior and developing future mixed-reality technology. Eye-tracking enables applications that classify user activity or predict user intent. However, eye-tracking datasets collected during common virtual reality tasks have also been shown to enable unique user identification, which creates a privacy risk. In this paper, we focus on the problem of user re-identification from eye-tracking features. We adapt standardized privacy definitions of k-anonymity and plausible deniability to protect datasets of eye-tracking features, and evaluate performance against re-identification by a standard biometric identification model on seven VR datasets. Our results demonstrate that re-identification goes down to chance levels for the privatized datasets, even as utility is preserved to levels higher than 72% accuracy in document type classification.
Efficient fall detection in four directions based on smart insoles and RDAE-LSTM model

Efficient fall detection is significant for the elderly, persons with motor symptoms, and people who perform risky actions, with four types of falling postures. However, most studies focused on distinguishing fall or not, although the fall direction information is crucial to turn on the corresponding part of the airbag or quickly assess the damage level. To accurately, rapidly, and reliably recognize different directional falls (forward, backward, left, and right) during daily life, this study proposes a novel fall detection methodology based on a pair of commercial lightweight smart insoles and a long short-term memory (LSTM) framework with a trained referencing denoising autoencoder (RDAE). Compared with traditional autoencoders, the referencing sub-path, i.e., RDAE, is additionally attached to achieve the automatic feature extraction. A pair of wireless in-shoe insoles (OpenGo, Moticon GmbH), each side equipped with 13 plantar pressure sensors and a tri-axial accelerometer, was employed to capture comprehensive spatial-temporal gait parameters. Hence an effective response to a fall, together with the estimation of the corresponding direction, can be accomplished, where the accuracy and response time are two primary concerns. The proposed RDAE-LSTM network provides a reliable testing result in classification, with 98.6% accuracy and 8.7 ms response time for determining fall directions, demonstrating a more convincing performance than other algorithms. The proposed methodology is an unobtrusive choice for users whose daily life is not affected by the fall detection device. The RDAE-LSTM model was proven to accurately and quickly recognize falls in four directions for the unbalanced fall detection dataset.
Artificial life for segmentation of fusion ultrasound images of breast abnormalities

Segmentation of cancerous tumors in ultrasound (US) images of human organs is one of the critical problems in medical imaging. The US images are characterized by low contrast, irregular shapes, high levels of speckle-noise and acoustic shadows, making it difficult to segment the tumor. Yet, US imaging is considered one of the most inexpensive and safe imaging tests available to detect cancer in its early stages. However, an automatic segmentation method applicable to all types of US imagery does not exist. This paper proposes a novel segmentation method that combines image fusion, artificial life (AL) and a genetic algorithm (GA). The new algorithm has been applied to US images of breast cancer. The method is based on tracing agents (TA), which are artificial organisms with memory and the ability to communicate. They live inside a fusion image generated from the US and the elastography (EL) images. The TA can recognize the patterns of strong edges and boundary gaps allowing to outline the tumor. The new model has been tested against six types of segmentation models, i.e., machine learning, active contours, level set models, superpixel models, edge linking models and selected hybrid methods. The experiments include 16 state-of-the-art methods, which outperform 69 recent and classical segmentation routines. The tests were run on 395 breast cancer images from http://onlinemedicalimages.com and https://www.ultrasoundcases.info/. TA training employs a GA. The model has been verified on "hard" cases (complex shapes, boundary leakage, and noisy edge maps). The proposed algorithm produces more accurate results than the reference methods on high complexity images. A video demo of the algorithm is at http://shorturl.at/htBW9.
Online Troll Reviewer Detection Using Deep Learning Techniques

The concentration of this paper is on detecting trolls among reviewers and users in online discussions and link distribution on social news aggregators such as Reddit. Trolls, a subset of suspicious reviewers, have been the focus of our attention. A troll reviewer is distinguished from an ordinary reviewer by the use of sentiment analysis and deep learning techniques to identify the sentiment of their troll posts. Machine learning and lexicon-based approaches can also be used for sentiment analysis. The novelty of the proposed system is that it applies a convolutional neural network integrated with a bidirectional long short-term memory (CNN-BiLSTM) model to detect troll reviewers in online discussions using a standard troll online reviewer dataset collected from the Reddit social media platform. Two experiments were carried out in our work: the first one was based on text data (sentiment analysis), and the second one was based on numerical data (10 attributes) extracted from the dataset. The CNN-BiLSTM model achieved 97% accuracy using text data and 100% accuracy using numerical data. While analyzing the results of our model, we observed that it provided better results than the compared methods.
Application of Multi-Objective Hyper-Heuristics to Solve the Multi-Objective Software Module Clustering Problem

Software maintenance is an important step in the software lifecycle. Software module clustering is a HHMO_CF_GDA optimization problem involving several targets that require minimization of module coupling and maximization of software cohesion. Moreover, multi-objective software module clustering involves assembling a specific group of modules according to specific cluster criteria. Software module clustering classifies software modules into different clusters to enhance the software maintenance process. A structure with low coupling and high cohesion is considered an excellent software module structure. In this study, we apply a multi-objective hyper-heuristic method to solve the multi-objective module clustering problem with three objectives: (i) minimize coupling, (ii) maximize cohesion, and (iii) ensure high modularization quality. We conducted several experiments to obtain optimal and near-optimal solutions for the multi-objective module clustering optimization problem. The experimental results demonstrated that the HHMO_CF_GDA method outperformed the individual multi-objective evolutionary algorithms in solving the multi-objective software module clustering optimization problem. The resulting software, in which HHMO_CF_GDA was applied, was more optimized and achieved lower coupling with higher cohesion and better modularization quality. Moreover, the structure of the software was more robust and easier to maintain because of its software modularity.
Identifying and Mapping Alteration Minerals Using HySpex Airborne Hyperspectral Data and Random Forest Algorithm

Airborne hyperspectral remote sensing data provide rapid, non-destructive, and near laboratory quality reflectance spectra for mineral mapping and lithological discrimination, thereby ushering an innovative era of remote sensing. In this study, NEO HySpex cameras, which comprise 504 spectral channels in the spectral ranges of 0.4–1.0 μm and 1.0–2.5 μm, were mounted on a delta wing XT-912 aircraft. The designed flexibility and modular nature of the HySpex aircraft hyperspectral imaging system made it relatively easy to test, transport, install, and remove the system multiple times before the acquisition flights. According to the design fight plan, including the route distance, length, height, and flight speed, we acquired high spectral and spatial resolutions airborne hyperspectral images of Yudai porphyry Cu (Au, Mo) mineralization in Kalatag District, Eastern Tianshan terrane, Northwest China. By comparing the features of the HySpex hyperspectral data and standard spectra data from the United States Geological Survey database, endmember pixels of spectral signatures for most alteration mineral assemblages (goethite, hematite, jarosite, kaolinite, calcite, epidote, and chlorite) were extracted. After a HySpex data processing workflow, the distribution of alteration mineral assemblages (iron oxide/hydroxide, clay, and propylitic alterations) was mapped using the random forest (RF) algorithm. The experiments demonstrated that the workflow for processing data and RF algorithm is feasible and active, and show a good performance in classification accuracy. The overall classification accuracy and Kappa classification of alteration mineral identification were 73.08 and 65.73%, respectively. The main alteration mineral assemblages were primarily distributed around pits and grooves, consistent with field-measured data. Our results confirm that HySpex airborne hyperspectral data have potential application in basic geology survey and mineral exploration, which provide a viable alternative for mineral mapping and identifying lithological units at a high spatial resolution for large areas and inaccessible terrains.
Multi-Entanglement Routing Design over Quantum Networks

Quantum networks are considered as a promising future platform for quantum information exchange and quantum applications, which have capabilities far beyond the traditional communication networks. Remote quantum entanglement is an essential component of a quantum network. How to efficiently design a multi-routing entanglement protocol is a fundamental yet challenging problem. In this paper, we study a quantum entanglement routing problem to simultaneously maximize the number of quantum-user pairs and their expected throughput. Our approach is to formulate the problem as two sequential integer programming steps. We propose efficient entanglement routing algorithms for the two integer programming steps and analyze their time complexity and performance bounds. Results of evaluation highlight that our approach outperforms existing solutions in both served quantum-user pairs numbers and the network expected throughput.
Extended Reality in Marketing—A Multiple Case Study on Internet of Things Platforms

This research highlights how cloud platform as a service technologies host extended reality technologies and convergent technologies in integrated solutions. It was only around 2019 that scholarly literature conceptualized the role of extended reality, that is, augmented reality, virtual reality, and mixed reality, in the marketing function. This article is a multiple case study on the leading eleven platform as a service vendors. They provide the programming technology required to host software as a service in the cloud, making the software available from everywhere. Of the eleven cases, 10% integrate technologies in solutions. Research results show that extended reality technologies reinvent digital marketing; as part of this, they shape the customer delivery model in terms of customer value proposition; favor the choice of customer channel (the omnichannel); possibly lead to new customer relationships, such as cocreation; and reach global mass customers. Extended reality in the delivery model is complemented by other technologies in the operating model. These combinations provide the foundations of the business models, which are either network or platform business models. This study identifies a number of solutions enabled by extended reality, which have an integrated goal in the form of customer value contribution and are to be studied in further articles.