Reviewerly/exHarmony huggingface dataset:
Histopathology Cross-Modal Retrieval based on Dual-Transformer Network

Computer-aided cancer diagnosis (CAD) methods based on the histopathological images have achieved great development. The content-based whole slide image (WSI) retrieval is one of the important application that can search for the informative data to assist clinical diagnosis. It is notable that the current retrieval system are mainly developed based on the image content and image labels. The diagnosis report for the WSIs given by the pathologists are also valuable data, but have not yet been adequately considered in modeling. In this paper, we propose a cross-modal retrieval framework based on histopathology WSIs and diagnosis report, which can simultaneously achieve four retrieval tasks for histopathology database across WSIs and diagnosis reports. The compact binary features from both WSIs and diagnosis reports are first extracted, and then built in a common vision-language semantic feature space by the constraint of the designed cross hashing loss function. The method was verified on a gastric histopathology dataset that contains 932 gastric cases with 4 lesion categories. Experimental results have demonstrated the effectiveness of the proposed method in the cross-modal retrieval tasks for digital pathology system.
A Comparison of Deep Neural Networks for Monocular Depth Map Estimation in Natural Environments Flying at Low Altitude

Currently, the use of Unmanned Aerial Vehicles (UAVs) in natural and complex environments has been increasing, because they are appropriate and affordable solutions to support different tasks such as rescue, forestry, and agriculture by collecting and analyzing high-resolution monocular images. Autonomous navigation at low altitudes is an important area of research, as it would allow monitoring parts of the crop that are occluded by their foliage or by other plants. This task is difficult due to the large number of obstacles that might be encountered in the drone's path. The generation of high-quality depth maps is an alternative for providing real-time obstacle detection and collision avoidance for autonomous UAVs. In this paper, we present a comparative analysis of four supervised learning deep neural networks and a combination of two for monocular depth map estimation considering images captured at low altitudes in simulated natural environments. Our results show that the Boosting Monocular network is the best performing in terms of depth map accuracy because of its capability to process the same image at different scales to avoid loss of fine details.
Handheld or head-mounted? An experimental comparison of the potential of augmented reality for animal phobia treatment using smartphone and HoloLens 2

Exposure therapy is an effective treatment for specific phobia that could be further enhanced through Augmented Reality, a novel technology that can facilitate implementation of gradual exposure and promote treatment acceptability. Effective exposure interventions require stimuli evoking high levels of anxiety. Therefore, it is important to ascertain whether animals can induce anxiety in distinct Augmented Reality modalities, such as Head-Mounted Displays and smartphones, which can differ in user experience and technological embodiment. This study compared the anxiety inducing potential and experienced realism of a spider within the HoloLens 2 Augmented Reality headset and an Augmented Reality smartphone application. Sixty-five participants were exposed to a virtual spider in a 5-step Behavioral Approach Task through both the HoloLens 2 head-mounted display and the PHOBOS Augmented Reality smartphone application. Participants reported Subjective Units of Distress at each step and physiological arousal was measured using heart rate and Skin Conductance. Results show that both technological modalities induced self-reported anxiety for spiders in a Behavioral Approach Task task in a non-clinical sample. The Hololens 2 modality was also related to an skin conductance (SC) increase. Perceived realism did not differ between modalities but was associated with increased anxiety in the HoloLens 2 modality. Findings demonstrate that both implemented modalities have potential for enabling Augmented Reality Exposure Therapy, although the role of experienced realism merits additional investigation. Future research should assess the effectiveness of Augmented Reality Exposure Therapy in clinical samples and assess whether new extended reality modalities, such as passthrough virtual reality, could accommodate observed limitations and improve Augmented Reality Exposure Therapy experiences and outcomes.
Detecting Synthetic Speech Manipulation in Real Audio Recordings

Recent advances in artificial speech and audio technologies have improved the abilities of deep-fake operators to falsify media and spread malicious misinformation. Anyone with limited coding skills can use freely available speech synthesis tools to create convincing simulations of influential speakers’ voices with the malicious intent to distort the original message. With the latest technology, malicious operators do not have to generate an entire audio clip; instead, they can insert a partial manipulation or a segment of synthetic speech into a genuine audio recording to change the entire context and meaning of the original message. Detecting these insertions is especially challenging because partially manipulated audio can more easily avoid synthetic speech detectors than entirely fake messages can. This paper describes a potential synthetic speech detection system based on the x-ResNet architecture with a probabilistic linear discriminant analysis (PLDA) backend. Experimental results suggest that the PLDA backend results in a 25% average error reduction among partially synthesized datasets over a non-PLDA baseline.
Effects of Explicit Reading Strategy Instruction on Grade 9 Students’ Achievement in Reading Comprehension

The main objective of the study is to investigate the effects of explicit reading strategy instruction on students’ achievement in reading comprehension. In two intact classes, the treatment group received reading lessons accompanied by the three basic reading instructions, i.e., the pre-, the while, and the post-reading instructions through explicit reading strategy instruction, but the nontreatment group received reading lessons in the usual way of learning/teaching reading. The study employed an embedded design since data were collected using quantitative data-gathering instruments through reading comprehension tests before and after the intervention, and an interview was also employed to gather data about the treatment from the randomly selected participants from the treatment group. Statistical results were analyzed with an independent sample t-test and paired sample t-test from nontreatment (33) and treatment (35). The findings emphasized the importance of employing explicit reading strategy instruction in enabling readers to comprehend reading comprehension questions, especially it lets students properly handle reading comprehension questions that seek higher level thinking. On the top priority, to be good readers at comprehending reading comprehension questions that seek higher level thinking, classroom reading teachers are required to create opportunities for the readers while delivering reading lessons by presenting activities that go with the pre-, while, and post-reading instructions. Being one of the most important instructions in reading lesson presentations, secondary school language teachers are expected to present reading lessons through explicit reading strategy instruction.
Improving Defect Prediction Using Combination of Software Metrics

Software defect is one of the interesting challenges in the software development process. This is because a defect in the software can cause a failure in a software or its components so that it does not meet the design in the software requirements document. Software defect can be detected by testing all system components, but this process can be a allocated resource issue, especially limited time and manpower. Moreover, the cost of overcoming defects in software requires high costs. Software metrics are metrics obtained from software development data in a repository. From this repository data can be extracted into software product metrics, change metrics and profile metrics. The main problem faced is the prediction results are less accurate. To solve this problem, this study proposes using a combination of software metrics. To improve the prediction results, the missing value is handled before the modeling is carried out. The experimental results show that the classification model with a combination of metrics produces better predictive results with the highest recall in a combination of three metrics (software product metrics, change metrics, profile metrics v3 (with the handling of missing values replaced by the median value)) in the Random Forest model and model with the best AUC value obtained in a combination of change metrics and profile metrics v3 with the Logistic Regression model.
Anomaly localization for copy detection patterns through print estimations

Copy detection patterns (CDP) are recent technologies for protecting products from counterfeiting. However, in contrast to traditional copy fakes, deep learning-based fakes have shown to be hardly distinguishable from originals by traditional authentication systems. Systems based on classical supervised learning and digital templates assume knowledge of fake CDP at training time and cannot generalize to unseen types of fakes. Authentication based on printed copies of originals is an alternative that yields better results even for unseen fakes and simple authentication metrics but comes at the impractical cost of acquisition and storage of printed copies. In this work, to overcome these shortcomings, we design a machine learning (ML) based authentication system that only requires digital templates and printed original CDP for training, whereas authentication is based solely on digital templates, which are used to estimate original printed codes. The obtained results show that the proposed system can efficiently authenticate original and detect fake CDP by accurately locating the anomalies in the fake CDP. The empirical evaluation of the authentication system under investigation is performed on the original and ML-based fakes CDP printed on two industrial printers.
Machine Learning for Uterine Cervix Screening

Cervical cancer develops in the lower part of the uterus, the organ of the female apparatus where the embryo is received and develops during pregnancy. In this paper we investigate the possibility to automatically detect the presence of cancerous cells and to predict of the stage of the cancerous lesion of the uterine cervix by exploiting images of cervical cells captured by the microscope. We extract a set of numerical features from each images and we build supervised machine learning models to diagnose the cervix cancer. The experimental analysis show that the proposed method is promising in distinguish between healthy and cancerous cells and to detect also high and low-grade squamous intraepithelial lesions.
Experiential Learning in Digital Contexts―A Case Study

Focusing on the intimate relation of experience and education, this paper discusses evaluation findings from a digital project-based course in the higher education sector. The course engages students in the implementation and reflection of projects in the field of education. The empirical findings, therefore, focus on students’ experiences in digital project-based settings. This paper analyses the planning, execution, and critical reflection of projects in the context of experiential learning, considering the various digital settings of the course and the projects. It thereby discusses the structure, the digital methodology, and the outcomes of the course with a focus on the experience of the participating students. The study uses digital qualitative interviews for the evaluation research. By doing so, the study investigates and reflects the quality of students’ experiences and their possible influences upon learning. It also discusses the question of how these experiences can be constructively implemented to improve future digital or blended-learning scenarios in the higher education sector.
Quantum Speed-Up Induced by the Quantum Phase Transition in a Nonlinear Dicke Model with Two Impurity Qubits

In this paper, we investigate the effect of the Dicke quantum phase transition on the speed of evolution of the system dynamics. At the phase transition point, the symmetry associated with the system parity operator begins to break down. By comparing the magnitudes of the two types of quantum speed limit times, we find that the quantum speed limit time of the system is described by one of the quantum speed limit times, whether in the normal or superradiant phase. We find that, in the normal phase, the strength of the coupling between the optical field and the atoms has little effect on the dynamical evolution speed of the system. However, in the superradiant phase, a stronger atom–photon coupling strength can accelerate the system dynamics’ evolution. Finally, we investigate the effect of the entanglement of the initial state of the system on the speed of evolution of the system dynamics. We find that in the normal phase, the entanglement of the initial state of the system has almost no effect on the system dynamics’ evolution speed. However, in the superradiant phase, larger entanglement of the system can accelerate the evolution of the system dynamics. Furthermore, we verify the above conclusions by the actual evolution of the system.
Explainable Regression Via Prototypes

Model interpretability/explainability is increasingly a concern when applying machine learning to real-world problems. In this article, we are interested in explaining regression models by exploiting prototypes, which are exemplar cases in the problem domain. Previous works focused on finding prototypes that are representative of all training data but ignore the model predictions, i.e., they explain the data distribution but not necessarily the predictions. We propose a two-level model-agnostic method that considers prototypes to provide global and local explanations for regression problems and that account for both the input features and the model output. M-PEER (Multiobjective Prototype-basEd Explanation for Regression) is based on a multi-objective evolutionary method that optimizes both the error of the explainable model and two other “semantics”-based measures of interpretability adapted from the context of classification, namely, model fidelity and stability. We compare the proposed method with the state-of-the-art method based on prototypes for explanation—ProtoDash—and with other methods widely used in correlated areas of machine learning, such as instance selection and clustering. We conduct experiments on 25 datasets, and results demonstrate significant gains of M-PEER over other strategies, with an average of 12% improvement in the proposed metrics (i.e., model fidelity and stability) and 17% in root mean squared error (RMSE) when compared to ProtoDash.
Detecting quantum phase transition via magic resource in the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>X</mml:mi><mml:mi>Y</mml:mi></mml:mrow></mml:math> spin model

Quantum phase transition in the $XY$ spin model with three-spin interaction is investigated using magic resource (non-stabilizerness), which is crucial in universal fault-tolerant quantum computation. The magic quantifier we employ here is defined straightforwardly via characteristic functions of quantum states, which are well defined for all dimensional quantum systems (in sharp contrast to those defined by discrete Wigner functions) and can be easily calculated. We show that the magic quantifier of both the reduced single-site spins and two-site spins of the system ground state increase to their maximum around the critical points for quantum phase transition. This indicates that the magic resource can be used to detect the critical phenomena in the $XY$ spin model and reveals a connection between quantum phase transition in many-body systems and quantum resource in stabilizer quantum computation.
Updating the study of ethical problems of artificial intelligence by modern schoolchildren

The main purpose of writing this article is to update the problem of developing the content of teaching artificial intelligence at school and to draw the attention of a modern teacher to the leading trends in artificial intelligence, indicated in the roadmap of the Program for the Development of Artificial Intelligence for the period up to 2024. And, as a consequence of this, the identification of new directions for the development of the content of teaching informatics at school, both during school hours and after school hours. In addition, it seems to us necessary to convey to a wide range of the pedagogical community the results of a survey of schoolchildren — finalists of the first All-Russian Olympiad in artificial intelligence (2021) on issues that actualize the need to study the humanistic component of artificial intelligence at school, its ethical and philosophical aspects.
Certificateless Searchable Encryption Scheme in Multi-User Environment

Searchable encryption technology enables users to access data that has been made publicly encrypted without divulging the original content. The majority of the currently available multi-user certificateless searchable encryption technologies are based on identity-based public key encryption as well as conventional public key cryptosystems. Thus, they are challenged to adapt to the security needs of today’s large-scale network computing environment. As a result, issues such as excessive overhead, poor security, and the inability to handle large-scale applications are unavoidable. In order to address the aforementioned issues, this paper uses the method of combining public key authentication encryption and searchable encryption to propose a certificateless searchable encryption scheme in multi-user circumstances. The stochastic prediction model demonstrates that the scheme can effectively fend off keyword guessing attacks. The proposed algorithm not only performs well in terms of computation but also significantly reduces the amount of computation in simulations.
Impact of Damping on Oscillation Patterns on the Plain Piano Soundboard

The influence of internal damping on the vibration of a piano soundboard is investigated using a Finite-Difference Time Domain (FDTD) physical model and experimental measurements. The damping constant of the model is varied according to a range similar to those found with measurements on a real grand piano at different production stages. With strong damping, a clear driving-point dependency of the forced string oscillation on the oscillation pattern of the soundboard is found. When decreasing the damping, this driving-point dependency is decreasing, nevertheless, it is still present. High damping, therefore, decreases soundboard vibration when strings drive the soundboard at the soundboard’s eigenfrequencies. However, such large damping increases soundboard vibrations when strings drive the soundboard at frequencies which are not eigenfrequencies. Therefore, strong damping smooths out the frequency response spectrum of an instrument. Extreme damping without any presence of distinct eigenmodes leads to a radiation of the strings sound spectrum without soundboard filtering. Low damping leads to a strong influence of the soundboard on the string’s radiated sound. Therefore, the amount of soundboard characteristics can be designed to alter internal damping process by choice of materials, including wood or varnish, and geometry. Additionally, damping reduces the presence of ’dead spots’, notes which are considerably lower in volume compared to other notes.
Quick and easy game bot detection based on action time interval estimation

Abstract Game bots are illegal programs that facilitate account growth and goods acquisition through continuous and automatic play. Early detection is required to minimize the damage caused by evolving game bots. In this study, we propose a game bot detection method based on action time intervals (ATIs). We observe the actions of the bots in a game and identify the most frequently occurring actions. We extract the frequency, ATI average, and ATI standard deviation for each identified action, which is to used as machine learning features. Furthermore, we measure the performance using actual logs of the Aion game to verify the validity of the proposed method. The accuracy and precision of the proposed method are 97% and 100%, respectively. Results show that the game bots can be detected early because the proposed method performs well using only data from a single day, which shows similar performance with those proposed in a previous study using the same dataset. The detection performance of the model is maintained even after 2 months of training without any revision process.
Exploring Web-based Translation Resources Applied to Hindi-English Cross-Lingual Information Retrieval

Internet users perceive a multilingual web but are unfamiliar with it due to communication in their regional language called Cross-Lingual Information Retrieval (CLIR). In CLIR, a translation technique is used to translate the user queries into the target documents language. Conventional translation techniques are based on either a manual dictionary or a parallel corpus. While the trending Statistical Machine Translation (SMT) and Neural Machine Translation (NMT) techniques are trained on a parallel corpus. NMT is not so mature for Hindi-English translation, according to the literature, SMT performs better than the NMT. SMT provides a static translation due to the limited vocabularies in the available parallel corpus. It may not provide the translations for missing or unseen words while the web provides a dynamic interface where multiple users are updating information at the same time. The web may provide the translations for missing or unseen words, therefore, the web is effectively used for technically developed languages like English, German, Spanish, Russian, and Chinese. In this paper, different web resources such as Wikipedia, Hindi WordNet &amp; Indo WordNet, ConceptNet, and online dictionary-based translation techniques are proposed and applied to Hindi-English CLIR. Wikipedia-based translation approach incorporates three modules, i.e., exactly matched, partially matched, and disambiguation to address the issues of wrong inter-wiki links, partially matched terms, and ambiguous articles. Hindi WordNet &amp; Indo WorNet attribute ”English synset” and ConceptNet attributes ”Related term” &amp; ”Synonymy” are used for obtaining translations. Further, WordNet path similarity is used to disambiguate translations. Various online dictionaries are available that return multiple relevant and irrelevant translations. The proposed approaches are compared to the SMT where the Wikipedia-based approach achieves approximately similar mean average precision to SMT.