Reviewerly/exHarmony huggingface dataset:
Self-Adjusting Evolutionary Algorithms for Multimodal Optimization

Recent theoretical research has shown that self-adjusting and self-adaptive mechanisms can provably outperform static settings in evolutionary algorithms for binary search spaces. However, the vast majority of these studies focuses on unimodal functions which do not require the algorithm to flip several bits simultaneously to make progress. In fact, existing self-adjusting algorithms are not designed to detect local optima and do not have any obvious benefit to cross large Hamming gaps. We suggest a mechanism called stagnation detection that can be added as a module to existing evolutionary algorithms (both with and without prior self-adjusting schemes). Added to a simple (1+1) EA, we prove an expected runtime on the well-known Jump benchmark that corresponds to an asymptotically optimal parameter setting and outperforms other mechanisms for multimodal optimization like heavy-tailed mutation. We also investigate the module in the context of a self-adjusting (1+ $$\lambda $$ ) EA. To explore the limitations of the approach, we additionally present an example where both self-adjusting mechanisms, including stagnation detection, do not help to find a beneficial setting of the mutation rate. Finally, we investigate our module for stagnation detection experimentally.
A LiDAR–Camera Fusion 3D Object Detection Algorithm

3D object detection with LiDAR and camera fusion has always been a challenge for autonomous driving. This work proposes a deep neural network (namely FuDNN) for LiDAR–camera fusion 3D object detection. Firstly, a 2D backbone is designed to extract features from camera images. Secondly, an attention-based fusion sub-network is designed to fuse the features extracted by the 2D backbone and the features extracted from 3D LiDAR point clouds by PointNet++. Besides, the FuDNN, which uses the RPN and the refinement work of PointRCNN to obtain 3D box predictions, was tested on the public KITTI dataset. Experiments on the KITTI validation set show that the proposed FuDNN achieves AP values of 92.48, 82.90, and 80.51 at easy, moderate, and hard difficulty levels for car detection. The proposed FuDNN improves the performance of LiDAR–camera fusion 3D object detection in the car category of the public KITTI dataset.
A fuzzy semantic representation and reasoning model for multiple associative predicates in knowledge graph

As the latest achievement of the development in semiotics, knowledge graph has been recognized and widely used by more and more researchers for its rich semantic information and clear logical structure. How to discovery the deep relevant knowledge from the massive graph-structured data has become a hot spot of artificial intelligence. Considering that some predicates in knowledge graph express fuzzy relationships whose semantics are not certain, the basic schema of classical knowledge graph in the form of RDF triple cannot describe the fuzzy semantic information effectively. To counter above problems, in this paper, we present a new semantic representation and reasoning model for multiple associative predicates by introducing fuzzy theory. Concretely, the presented method defines a new fuzzy annotating strategy to represent the fuzzy semantics between associative predicates in different RDF triples. On this basis, some fuzzy reasoning rules are presented to realize fuzzy semantic extension for classical knowledge graph. Lastly, the experimental results show that our proposal can discover more implicit valid knowledge with fuzzy semantic and have a good consistency with the intuition of human judgments. Overall, the methods proposed in this paper constitute some effective ways of knowledge discovery of structured semantic data.
Safe from “harm”: The governance of violence by platforms

Abstract A number of issues have emerged related to how platforms moderate and mitigate “harm.” Although platforms have recently developed more explicit policies in regard to what constitutes “hate speech” and “harmful content,” it appears that platforms often use subjective judgments of harm that specifically pertains to spectacular, physical violence—but harm takes on many shapes and complex forms. The politics of defining “harm” and “violence” within these platforms are complex and dynamic, and represent entrenched histories of how control over these definitions extends to people's perceptions of them. Via a critical discourse analysis of policy documents from three major platforms (Facebook, Twitter, and YouTube), we argue that platforms' narrow definitions of harm and violence are not just insufficient but result in these platforms engaging in a form of symbolic violence. Moreover, the platforms position harm as a floating signifier, imposing conceptions of not just what violence is and how it manifests, but who it impacts. Rather than changing the mechanisms of their design that enable harm, the platforms reconfigure intentionality and causality to try to stop users from being “harmful,” which, ironically, perpetuates harm. We provide a number of suggestions, namely a restorative justice‐focused approach, in addressing platform harm.
Histopathologic and Machine Deep Learning Criteria to Predict Lymphoma Transformation in Bone Marrow Biopsies

Large cell transformation (LCT) of indolent B-cell lymphomas, such as follicular lymphoma (FL) and chronic lymphocytic leukemia (CLL), signals a worse prognosis, at which point aggressive chemotherapy is initiated. Although LCT is relatively straightforward to diagnose in lymph nodes, a marrow biopsy is often obtained first given its ease of procedure, low cost, and low morbidity. However, consensus criteria for LCT in bone marrow have not been established.To study the accuracy and reproducibility of a trained convolutional neural network in identifying LCT, in light of promising machine learning tools that may introduce greater objectivity to morphologic analysis.We retrospectively identified patients who had a diagnosis of FL or CLL who had undergone bone marrow biopsy for the clinical question of LCT. We scored morphologic criteria and correlated results with clinical disease progression. In addition, whole slide scans were annotated into patches to train convolutional neural networks to discriminate between small and large tumor cells and to predict the patient's probability of transformation.Using morphologic examination, the proportion of large lymphoma cells (≥10% in FL and ≥30% in CLL), chromatin pattern, distinct nucleoli, and proliferation index were significantly correlated with LCT in FL and CLL. Compared to pathologist-derived estimates, machine-generated quantification demonstrated better reproducibility and stronger correlation with final outcome data.These histologic findings may serve as indications of LCT in bone marrow biopsies. The pathologist-augmented with machine system appeared to be the most predictive, arguing for greater efforts to validate and implement these tools to further enhance physician practice.
Development and validation of a deep learning model for detection of breast cancers in mammography from multi-institutional datasets

The objective of this study was to develop and validate a state-of-the-art, deep learning (DL)-based model for detecting breast cancers on mammography.Mammograms in a hospital development dataset, a hospital test dataset, and a clinic test dataset were retrospectively collected from January 2006 through December 2017 in Osaka City University Hospital and Medcity21 Clinic. The hospital development dataset and a publicly available digital database for screening mammography (DDSM) dataset were used to train and to validate the RetinaNet, one type of DL-based model, with five-fold cross-validation. The model's sensitivity and mean false positive indications per image (mFPI) and partial area under the curve (AUC) with 1.0 mFPI for both test datasets were externally assessed with the test datasets.The hospital development dataset, hospital test dataset, clinic test dataset, and DDSM development dataset included a total of 3179 images (1448 malignant images), 491 images (225 malignant images), 2821 images (37 malignant images), and 1457 malignant images, respectively. The proposed model detected all cancers with a 0.45-0.47 mFPI and had partial AUCs of 0.93 in both test datasets.The DL-based model developed for this study was able to detect all breast cancers with a very low mFPI. Our DL-based model achieved the highest performance to date, which might lead to improved diagnosis for breast cancer.
The Impact of Using TED Talks as a Learning Instrument on Enhancing Indonesian EFL Learners’ Listening Skill

Regarding the popularity of TED Talks which are freely accessible online, we were encouraged to examine the impacts of this online resource on Indonesian EFL learners’ listening skills. The Oxford Quick Placement Test (OQPT) was used to recruit 70 Indonesian intermediate respondents for the research. They were then split into two groups: one experimental group (EG) and one control group (CG). After that, a listening test was administered to both groups. After that, the EG participants watched TED Talks via mobile devices, and the CG participants practiced listening skills via DVDs and CDs of the course textbook for 13 sessions. When the treatment ended, a posttest of listening skills was administered to the participants of both groups, and the collected data were analyzed via running paired samples and independent samples <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>t</mi> </math> -tests. The results indicated that both groups had better listening performances in their listening posttests, but the EG outperformed the CG after the treatment. Overall, the results showed that using TED Talks was more advantageous for listening development than using DVDs and CDs of the course textbook. The implications of this study can encourage EFL teachers to integrate TED Talks into their English classes.
The Impact of Artificial Intelligence on Sustainable Development in Electronic Markets

With the emergence of artificial intelligence (AI), the technological revolution has transformed human lives and processes, empowering the products and services in today’s marketplaces. AI introduces new ways of doing jobs and business, and of exploring new global market opportunities. However, on the other hand, it provides many challenges to comprehend. Therefore, our study’s main objective was to examine the behavioral, cultural, ethical, social, and economic challenges of AI-enabled products and services in consumer markets and discuss how businesses might shape their approaches to address AI-related ethical issues. AI offers numerous social, ethical, and behavioral difficulties for people, organizations, and societies that endanger the sustainable development of economies. These fundamental challenges due to AI technologies have raised serious questions for the sustainable development of electronic markets. Based on this, the current study presents a framework highlighting these issues. Systematic reviewing was our research method; we looked for explicit information and methods that indicate the credibility of research or reduce biases. This paper is of great importance, as it highlights several behavioral, societal, ethical, and cultural aspects in electronic markets which were not presented in previous studies. Some key issues are the security and privacy of consumers, AI biases, individual autonomy, wellbeing, and issues of unemployment. Therefore, companies that use AI systems need to be socially responsible and make AI systems as secure as possible to promote the sustainable development of countries. The results suggest that AI has undoubtedly transformed life and has both positive and negative effects. However, the main aim regarding AI should be to use it for the overall goals of humanity. Moreover, authorities operating in e-business environments need to create proper rules and regulations and make the systems as secure as possible for people.
Cultivating FAIR principles for agri-food data

Data generated by the global food system is crucial in the transformation towards sustainable, resilient, and high-quality food production. Although the amount of potentially useful data is growing rapidly, its (re)use is still limited. The FAIR-principles have been developed for making data findable, accessible, interoperable, and reusable both by humans and machines. This paper explores the further operationalization of the FAIR principles in agriculture and food. Experience shows that several conditions must be fulfilled before data can be effectively shared and reused. First, automated tools must be available for data providers and users. Secondly, we need a community-based approach in developing tools and vocabularies. Thirdly, data cannot be shared by an open-by-default policy only. Finally, scientific insight is needed in how data is actually (re)used in scientific communities. We conclude that bringing the FAIR-principles to full maturity requires a fair balance of efforts within the agri-food communities, supported by a proper infrastructure.
Evolutionary background-oriented schlieren tomography with self-adaptive parameter heuristics

For volumetric reconstruction of the refractive index field in a flow, background-oriented schlieren (BOS) imaging which measures the deflection of light rays due to refractive index variations is combined with an evolutionary tomographic algorithm for the first time, called evolutionary BOS tomography (EBOST). In this work application to reactive flows is presented. Direct non-linear ray-tracing of the reconstruction domain is used to evaluate the fitness of solution candidates during the evolutionary strategy that was implemented to run on a multi-GPU system. The use of a diversity measure and its consideration in a migration policy was tested against a simple scheme that distributes the best chromosome (solution candidate) in an island-based genetic algorithm. The extensive set of control parameters of the presented algorithm was harnessed by a self-adaptive strategy taking into account the fitness function and operator rates. Quantitative characterisation of the EBOST via numerical phantom studies, using flame simulations as ground truth data is presented. A direct comparison to a state-of-the-art BOST algorithm demonstrates similar accuracy for a turbulent swirl flame phantom reconstruction. A series of experimental applications of the EBOST on several unsteady and turbulent flames is also presented. In all cases, the instantaneous and time-averaged flame structure is revealed, proving the benefit of EBOST for volumetric flow diagnostics.
Detecting fake reviews with supervised machine learning algorithms

This study provides an applicable methodological procedure applying Artificial Intelligence (AI)-based supervised Machine Learning (ML) algorithms in detecting fake reviews of online review platforms and identifies the best ML algorithm as well as the most critical fake review determinants for a given restaurant review dataset. Our empirical findings from analyzing 16 determinants (review-related, reviewer-related, and linguistic attributes) measured from over 43,000 online restaurant reviews reveal that among the seven ML algorithms, the random forest algorithm outperforms the other algorithms and, among the 16 review attributes, time distance is found to be the most important, followed by two linguistic (affective and cognitive cues) and two review-related attributes (review depth and structure). The present study contributes to the literature on fake online review detection, especially in the hospitality field and the body of knowledge on supervised ML algorithms.
Evolving CNN with Paddy Field Algorithm for Geographical Landmark Recognition

Convolutional Neural Networks (CNNs) operate within a wide variety of hyperparameters, the optimization of which can greatly improve the performance of CNNs when performing the task at hand. However, these hyperparameters can be very difficult to optimize, either manually or by brute force. Neural architecture search or NAS methods have been developed to address this problem and are used to find the best architectures for the deep learning paradigm. In this article, a CNN has been evolved with a well-known nature-inspired metaheuristic paddy field algorithm (PFA). It can be seen that PFA can evolve the neural architecture using the Google Landmarks Dataset V2, which is one of the toughest datasets available in the literature. The CNN’s performance, when evaluated based on the accuracy benchmark, increases from an accuracy of 0.53 to 0.76, which is an improvement of more than 40%. The evolved architecture also shows some major improvements in hyperparameters that are normally considered to be the best suited for the task.
Blockchain technology in the pharmaceutical industry: a systematic review

Blockchain technology is accelerating digital transformation across multiple industries, including the pharmaceutical industry. The pharmaceutical industry suffers from a lack of transparency, difficulty tracking products, lack of trust, and the shipment of expired products. Blockchain technology has been applied to solve several of these problems. In this paper, we present a systematic review of the literature focusing on the adoption of blockchain technology in the pharmaceutical industry. We collected, analyzed, qualified, and discussed studies retrieved from seven databases. The initial search yielded 2,185 papers, which were screened, discussed, voted on, critically appraised, and collected by a snowball workflow that finally yielded 38 papers. The blockchain application areas covered in the papers were classified as counterfeit drug prevention, drug distribution, tracking and tracing, and safety and security. The most frequent category was counterfeit drug prevention, which is consistent with the primary objective of the pharmaceutical industry. The newer topics discussed in this study were data governance, data quality, pharmaceutical turnover, and prescription drug monitoring. We discuss issues surrounding each of these topics and research studies, along with their limitations and solutions. We also examine the challenges and future research directions of applying blockchain technology in the pharmaceutical industry.
A survey on biometric recognition using wearable devices

Thanks to their ability to monitor physical activity and health-related parameters, wearable devices are becoming more and more popular. In addition to what they already offer, an interesting capability achievable through such devices is biometric recognition. The physiological traits recorded by wearable devices may in fact possess distinctive properties which could allow to recognize their legitimate users, and detect unauthorized usage. In this paper, the most recent advances accomplished in this field are reviewed, and a critical analysis on the current state of the art, as well as on the issues still open, is provided.
Hand Gesture Recognition for an Off-the-Shelf Radar by Electromagnetic Modeling and Inversion

Microwave radar sensors in human-computer interactions have several advantages compared to wearable and image-based sensors, such as privacy preservation, high reliability regardless of the ambient and lighting conditions, and larger field of view. However, the raw signals produced by such radars are high-dimension and relatively complex to interpret. Advanced data processing, including machine learning techniques, is therefore necessary for gesture recognition. While these approaches can reach high gesture recognition accuracy, using artificial neural networks requires a significant amount of gesture templates for training and calibration is radar-specific. To address these challenges, we present a novel data processing pipeline for hand gesture recognition that combines advanced full-wave electromagnetic modelling and inversion with machine learning. In particular, the physical model accounts for the radar source, radar antennas, radar-target interactions and target itself, i.e.,, the hand in our case. To make this processing feasible, the hand is emulated by an equivalent infinite planar reflector, for which analytical Green’s functions exist. The apparent dielectric permittivity, which depends on the hand size, electric properties, and orientation, determines the wave reflection amplitude based on the distance from the hand to the radar. Through full-wave inversion of the radar data, the physical distance as well as this apparent permittivity are retrieved, thereby reducing by several orders of magnitude the dimension of the radar dataset, while keeping the essential information. Finally, the estimated distance and apparent permittivity as a function of gesture time are used to train the machine learning algorithm for gesture recognition. This physically-based dimension reduction enables the use of simple gesture recognition algorithms, such as template-matching recognizers, that can be trained in real time and provide competitive accuracy with only a few samples. We evaluate significant stages of our pipeline on a dataset of 16 gesture classes, with 5 templates per class, recorded with the Walabot, a lightweight, off-the-shelf array radar. We also compare these results with an ultra wideband radar made of a single horn antenna and lightweight vector network analyzer, and a Leap Motion Controller.
Polar transform network for prostate ultrasound segmentation with uncertainty estimation

Automatic and accurate prostate ultrasound segmentation is a long-standing and challenging problem due to the severe noise and ambiguous/missing prostate boundaries. In this work, we propose a novel polar transform network (PTN) to handle this problem from a fundamentally new perspective, where the prostate is represented and segmented in the polar coordinate space rather than the original image grid space. This new representation gives a prostate volume, especially the most challenging apex and base sub-areas, much denser samples than the background and thus facilitate the learning of discriminative features for accurate prostate segmentation. Moreover, in the polar representation, the prostate surface can be efficiently parameterized using a 2D surface radius map with respect to a centroid coordinate, which allows the proposed PTN to obtain superior accuracy compared with its counterparts using convolutional neural networks while having significantly fewer (18%∼41%) trainable parameters. We also equip our PTN with a novel strategy of centroid perturbed test-time augmentation (CPTTA), which is designed to further improve the segmentation accuracy and quantitatively assess the model uncertainty at the same time. The uncertainty estimation function provides valuable feedback to clinicians when manual modifications or approvals are required for the segmentation, substantially improving the clinical significance of our work. We conduct a three-fold cross validation on a clinical dataset consisting of 315 transrectal ultrasound (TRUS) images to comprehensively evaluate the performance of the proposed method. The experimental results show that our proposed PTN with CPTTA outperforms the state-of-the-art methods with statistical significance on most of the metrics while exhibiting a much smaller model size. Source code of the proposed PTN is released at https://github.com/DIAL-RPI/PTN.