Reviewerly/exHarmony huggingface dataset:
Assessment of Gradient Descent Trained Rule-Fact Network Expert System Multi-Path Training Technique Performance

The use of gradient descent training to optimize the performance of a rule-fact network expert system via updating the network’s rule weightings was previously demonstrated. Along with this, four training techniques were proposed: two used a single path for optimization and two use multiple paths. The performance of the single path techniques was previously evaluated under a variety of experimental conditions. The multiple path techniques, when compared, outperformed the single path ones; however, these techniques were not evaluated with different network types, training velocities or training levels. This paper considers the multi-path techniques under a similar variety of experimental conditions to the prior assessment of the single-path techniques and demonstrates their effectiveness under multiple operating conditions.
Robustness Testing of AI Systems: A Case Study for Traffic Sign Recognition

In the last years, AI systems, in particular neural networks, have seen a tremendous increase in performance, and they are now used in a broad range of applications. Unlike classical symbolic AI systems, neural networks are trained using large data sets and their inner structure containing possibly billions of parameters does not lend itself to human interpretation. As a consequence, it is so far not feasible to provide broad guarantees for the correct behaviour of neural networks during operation if they process input data that significantly differ from those seen during training. However, many applications of AI systems are security- or safety-critical, and hence require obtaining statements on the robustness of the systems when facing unexpected events, whether they occur naturally or are induced by an attacker in a targeted way. As a step towards developing robust AI systems for such applications, this paper presents how the robustness of AI systems can be practically examined and which methods and metrics can be used to do so. The robustness testing methodology is described and analysed for the example use case of traffic sign recognition in autonomous driving.
Quantum Wheatstone Bridge

We propose a quantum Wheatstone bridge as a fully quantum analog to the classical version. The bridge is a few-body boundary-driven spin chain exploiting quantum effects to gain an enhanced sensitivity to an unknown coupling. The sensitivity is explained by a drop in population of an entangled Bell state due to destructive interference as the controllable coupling approaches the unknown coupling. A simple criterion for the destructive interference is found, and an approximate expression for the width of the drop is derived. The sensitivity to the unknown coupling is quantified using the quantum Fisher information, and we show that the state of the bridge can be measured indirectly through the spin current. Our results are robust toward calibration errors and generic in the sense that several of the current state-of-the-art quantum platforms could be used as a means of realization. The quantum Wheatstone bridge may thus find use in fields such as sensing and metrology using near-term quantum devices.
Investigating coupling preprocessing with shallow and deep convolutional neural networks in document image classification

Convolutional neural networks (CNNs) are effective for image classification, and deeper CNNs are being used to improve classification performance. Indeed, as needs increase for searchability of vast printed document image collections, powerful CNNs have been used in place of conventional image processing. However, better performances of deep CNNs come at the expense of computational complexity. Are the additional training efforts required by deeper CNNs worth the improvement in performance? Or could a shallow CNN coupled with conventional image processing (e.g., binarization and consolidation) outperform deeper CNN-based solutions? We investigate performance gaps among shallow (LeNet-5, -7, and -9), deep (ResNet-18), and very deep (ResNet-152, MobileNetV2, and EfficientNet) CNNs for noisy printed document images, e.g., historical newspapers and document images in the RVL-CDIP repository. Our investigation considers two different classification tasks: (1) identifying poems in historical newspapers and (2) classifying 16 document types in document images. Empirical results show that a shallow CNN coupled with computationally inexpensive preprocessing can have a robust response with significantly reduced training samples; deep CNNs coupled with preprocessing can outperform very deep CNNs effectively and efficiently; and aggressive preprocessing is not helpful as it could remove potentially useful information in document images.
Personality Trait Detection Based on ASM Localization and Deep Learning

Global competition is the competition of human resources, the social demand for high-quality talents is increasing, and the demand for all kinds of talents is increasing. Therefore, how to scientifically and efficiently complete the preliminary screening of college students’ mental health, so as to provide services for them, has become an important task. In order to solve the above problems, by combining the relevant professional knowledge of psychology, statistics, image processing, and artificial intelligence technology, a personality trait detection method based on active shape model (ASM) localization and deep learning is proposed. Firstly, the traditional ASM algorithm is improved and applied to facial feature point location, which provides training basis for further deep learning. It mainly includes three aspects of improvement: (1) 2D texture model based on Gabor wavelet and gradient features; (2) new multiresolution pyramid decomposition method; and (3) improved multiresolution pyramid search strategy. Secondly, the deep belief network model is used to train and classify the students’ four personality traits and facial features, so as to dig out the relationship between the four personality traits and facial features. The experimental results show that the localization effect of the improved ASM algorithm is obviously better than that of the traditional algorithm, and the classifier after learning and training has a good effect in analyzing the four personality traits.
The The Impact of Behavioral Aspects on Investment Decision Making

This study aims to investigate the impact of behavioral aspects (sentiment investor, overconfidence, salience, overreaction, and herd behavior) on investment decision making. The sample contained 413 individual investors—used partial least square structural equation modeling (PLS-SEM) as a data analysis technique. The results showed that sentiment investors, overconfidence, salience, overreaction, and herd behavior positively affect investment decision making. The finding of this study has important implications for the investor to understand themselves to anticipate bias in investment decision making.
The Teachers’ Online Readiness: an evaluation of Online Learning during Covid-19 Pandemic in Indonesia

Online learning is the right choice to do during the Covid-19 pandemic. So that the challenges that teachers in implementing learning must face are in themselves. Therefore, this study assesses the influence of attitudes, interests, and motivation on teachers' online readiness in learning from home due to the Covid-19 pandemic. This research is quantitative by using a questionnaire for data collection. The sample was determined using an accidental sampling method, namely elementary school teachers and equivalent to senior high school teachers and equivalent from all regions of Indonesia, totaling 281 respondents. While the analysis technique used is multiple linear regression. The results of this study indicate that attitude has a significant influence on online readiness, with the most dominant indicator being behavior, interest also has a significant influence on online readiness, with the most dominant indicator being personal, and motivation has a minor influence on online readiness with the most indicator dominant being external. While simultaneously, attitudes, interests, and motivation positively affect online readiness with a significance level of less than 0.005. Due to the limited variables chosen, it is recommended to enrich it using other variables in further research.
Decoding Methods in Neural Language Generation: A Survey

Neural encoder-decoder models for language generation can be trained to predict words directly from linguistic or non-linguistic inputs. When generating with these so-called end-to-end models, however, the NLG system needs an additional decoding procedure that determines the output sequence, given the infinite search space over potential sequences that could be generated with the given vocabulary. This survey paper provides an overview of the different ways of implementing decoding on top of neural network-based generation models. Research into decoding has become a real trend in the area of neural language generation, and numerous recent papers have shown that the choice of decoding method has a considerable impact on the quality and various linguistic properties of the generation output of a neural NLG system. This survey aims to contribute to a more systematic understanding of decoding methods across different areas of neural NLG. We group the reviewed methods with respect to the broad type of objective that they optimize in the generation of the sequence—likelihood, diversity, and task-specific linguistic constraints or goals—and discuss their respective strengths and weaknesses.
Cross-Modal Knowledge Distillation Method for Automatic Cued Speech Recognition

Cued Speech (CS) is a visual communication system for the deaf or hearing impaired people. It combines lip movements with hand cues to obtain a complete phonetic repertoire. Current deep learning based methods on automatic CS recognition suffer from a common problem, which is the data scarcity. Until now, there are only two public single speaker datasets for French (238 sentences) and British English (97 sentences). In this work, we propose a cross-modal knowledge distillation method with teacher-student structure, which transfers audio speech information to CS to overcome the limited data problem. Firstly, we pretrain a teacher model for CS recognition with a large amount of open source audio speech data, and simultaneously pretrain the feature extractors for lips and hands using CS data. Then, we distill the knowledge from teacher model to the student model with frame-level and sequence-level distillation strategies. Importantly, for frame-level, we exploit multi-task learning to weigh losses automatically, to obtain the balance coefficient. Besides, we establish a five-speaker British English CS dataset for the first time. The proposed method is evaluated on French and British English CS datasets, showing superior CS recognition performance to the state-of-the-art (SOTA) by a large margin.
Robust Retrieval Augmented Generation for Zero-shot Slot Filling

Automatically inducing high quality knowledge graphs from a given collection of documents still remains a challenging problem in AI.One way to make headway for this problem is through advancements in a related task known as slot filling.In this task, given an entity query in form of [ENTITY, SLOT, ?], a system is asked to 'fill' the slot by generating or extracting the missing value exploiting evidence extracted from relevant passage(s) in the given document collection.The recent works in the field try to solve this task in an end-to-end fashion using retrieval-based language models.In this paper, we present a novel approach to zero-shot slot filling that extends dense passage retrieval with hard negatives and robust training procedures for retrieval augmented generation models.Our model reports large improvements on both T-REx and zsRE slot filling datasets, improving both passage retrieval and slot value generation, and ranking at the top-1 position in the KILT leaderboard.Moreover, we demonstrate the robustness of our system showing its domain adaptation capability on a new variant of the TACRED dataset for slot filling, through a combination of zero/few-shot learning.We release the source code and pre-trained models 1 .
Quantum circuit engineering for correcting coherent noise

Crosstalk and several forms of coherent noise are invisible when a qubit or a gate is calibrated or benchmarked in isolation. These are unlocked during the execution of a full quantum circuit applying entangling gates to several qubits simultaneously. Unitary crosstalk noise, such as an unwanted Z-Z coupling, limits the state fidelity during the execution of cross-resonance controlled-not (cnot) gates in superconductor quantum computers. This work presents (1) a method of tracing coherent errors by exploiting their sensitivity to the arrangement of cnot gates in the circuit and (2) a correction scheme that modifies the original circuit by inserting carefully the chosen compensating gates (single- or two-qubit) to possibly undo coherent errors. On two vastly different types of IBMQ processors offering quantum volume 8 to 32, our experimental results show up to 25% reduction in the infidelity of $[[7,1,3]]$ code $|+\ensuremath{\rangle}$ state (Clifford circuits) and five- to 15-qubit $W$ states (non-Clifford circuits). Our experimental circuits aggressively deploy forced commutation of cnot gates to obtain low-noise state-preparation circuits. An encoded state initialized with fewer errors marks an important step towards successful demonstration of fault-tolerant quantum computers.
Misplaced confidence: limits to statistical inference in cyclostratigraphy.

Spectral (frequency-domain) analysis is used for quantitative confirmation of cyclicity in climate-proxy data.Cyclostratigraphic power spectra are typically accompanied by 'confidence limits', whether or not a statistical test has been explicitly invoked.Peaks in spectral power suggest candidate cyclic frequencies; confidence limits (CLs) appear to provide a visual guide to their relative importance, and are conventionally used in a correspondingly informal way.Confidence limits are, however, inseparable from formal tests of statistical significance; they derive from a statistical null hypothesis, and provide a threshold for its acceptance or rejection.In the procedure conventionally used in cyclostratigraphy (and implemented in several specialised software packages), noise models and confidence limits are generated automatically.Although the user may be unaware of it, the null hypothesis on which these CLs are based is calibrated for a (confirmatory) test of significance at exactly one frequency.Extending their application to an exploratory search of spectral peaks at all frequencies is statistically inadmissible.Debate over the role and correct calculation of CLs in cyclostratigraphy remains unresolved: this contribution seeks to clarify the disagreement over their use by explaining the role of CLs in statistical significance tests generally, and comparing it with their conventional use in cyclostratigraphy.Through examples of the correct and incorrect use of the conventional method, I show that the customary informal use of statistical test criteria cannot be sustained.Significance thresholds cannot be calculated in most cases; wrongly estimated confidence limits lead to false positive cycle identifications, with adverse consequences for calibration of the geological time scale.
Soft-self and Hard-cross Graph Attention Network for Knowledge Graph Entity Alignment

Knowledge Graph (KG) entity alignment aims to identify entities across different KGs that refer to the same real world object, and it is the key step towards KG integration and KG complement. Recently, Graph Attention Network (GAT) based models become a popular paradigm in entity alignment community owing to its ability in modeling structural data. But current GAT based models either ignore relation semantics and edge directions when learning entity neighbor representations or make no distinction between incoming neighbors and outgoing neighbors when calculating their attention scores. Furthermore, softmax functions utilized in soft attention mechanisms of current models always assign small but nonzero probabilities to trivial elements, which is unsuitable for learning alignment oriented entity embeddings. Taking these issues into account, this paper proposes a novel GAT based entity alignment model SHEA (Soft-self and Hard-cross Graph Attention Networks for Knowledge Graph Entity Alignment), which takes both relation semantics and edge directions into consideration when modeling single KG, and distinguishes prior aligned neighbors from the general ones to take full advantage of prior aligned information. Specifically, a type of four-channels graph attention layer is conceived to aggregate information from entity neighbors in different cases. The first two channels teach entities to aggregate information from their neighbors with soft-self attention, where both neighboring entities and the linked relations are used to obtain attention values. The other two channels teach entities to aggregate information from their neighbors with hard-cross graph attention, where tf_idf1 is utilized to measure the importance of entity neighbors. Extensive experiments on five publicly available datasets demonstrate our superior performances.
A Survey on Affective and Cognitive VR

In Virtual Reality (VR), users can be immersed in emotionally intense and cognitively engaging experiences. Yet, despite strong interest from scholars and a large amount of work associating VR and Affective and Cognitive States (ACS), there is a clear lack of structured and systematic form in which this research can be classified. We define "Affective and Cognitive VR" to relate to works which (1) induce ACS, (2) recognize ACS, or (3) exploit ACS by adapting virtual environments based on ACS measures. This survey clarifies the different models of ACS, presents the methods for measuring them with their respective advantages and drawbacks in VR, and showcases Affective and Cognitive VR studies done in an Immersive Virtual Environment (IVE) in a non-clinical context. Our article covers the main research lines in Affective and Cognitive VR. We provide a comprehensive list of references with the analysis of 63 research articles and summarize future works directions.
System and activity approach to learning artificial intelligence in basic school

Problem and goal. The problem of insufficient substantive and methodological support for the training of basic school pupils in the field of artificial intelligence (AI) is considered. The aim is to identify the features of the application of the system-activity approach to teaching basic school pupils in the field of artificial intelligence, to describe the principles of teaching school pupils the basics of artificial intelligence and the conditions for their implementation, to highlight the types of school pupils activities in the context of the system-activity approach. Methodology. A set of methods was used: analysis of regulatory documents that determine the priority tasks of our country; analysis of research and scientific and methodological publications in the field of teaching AI for basic school pupils in the domestic and foreign education system; reflection of the content of the knowledge gained; identification of methodological approaches; search for principles, conditions and opportunities for teaching elements of AI to basic school pupils; local pedagogical experiment. Results. A comparative analysis of the methodological experience of predecessor teachers made it possible to identify the possibility and effectiveness of the application of the system-activity approach to teaching basic school pupils in the field of AI. The revealed approach made it possible to determine the fundamental principles of teaching the elements of artificial intelligence, the conditions for their implementation, as well as the most appropriate types of activities for basic school pupils. Conclusion. Today AI technologies are actively developing information technologies and possession of them has a positive effect on the level of information culture of a school pupils, which, in accordance with the requirements of the information society, must be formed, at least, in basic school. The results of the study made it possible to substantiate the possibility and expediency of applying the system-activity approach to teaching the elements of artificial intelligence to basic school pupils, starting from grade 5.
Vision Matters When It Should: Sanity Checking Multimodal Machine Translation Models

Multimodal machine translation (MMT) systems have been shown to outperform their textonly neural machine translation (NMT) counterparts when visual context is available.However, recent studies have also shown that the performance of MMT models is only marginally impacted when the associated image is replaced with an unrelated image or noise, which suggests that the visual context might not be exploited by the model at all.We hypothesize that this might be caused by the nature of the commonly used evaluation benchmark, also known as Multi30K, where the translations of image captions were prepared without actually showing the images to human translators.In this paper, we present a qualitative study that examines the role of datasets in stimulating the leverage of visual modality and we propose methods to highlight the importance of visual signals in the datasets which demonstrate improvements in reliance of models on the source images.Our findings suggest the research on effective MMT architectures is currently impaired by the lack of suitable datasets and careful consideration must be taken in creation of future MMT datasets, for which we also provide useful insights.
Detection of multipartite entanglement via quantum Fisher information

In this paper, we focus on two different kinds of multipartite correlation, $k$-nonseparability and $k$-partite entanglement, both of which can describe the essential characteristics of multipartite entanglement. We propose effective methods to detect $k$-nonseparability and $k$-partite entanglement in terms of quantum Fisher information. We illustrate the significance of our results and show that they identify some $k$-nonseparability and $k$-partite entanglement that cannot be identified by known criteria by several concrete examples.
Three-dimensional videography using omnidirectional cameras: An approach inspired by the direct linear transformation method

Direct linear transformation (DLT) is one of the most frequently used methods in motion analysis. This method was developed in 1971 and has subsequently been used in numerous studies. However, owing to the structure of the camera, unfavorable challenges are associated with this method. Because the DLT method is based on the structure of ordinary optical cameras in which the perspective projection is used, the camera's angle of view is limited. In this study, we propose a three-dimensional (3-D) calibration and reconstruction method using omnidirectional cameras, which have significantly wider angles of view than conventional cameras. For the interest points, the longitude and latitude of the celestial sphere are obtained from the image captured with an omnidirectional camera. The direction of the interest points relative to the camera's reference frame is determined via the longitude and latitude. In the calibration phase, the position and orientation of the omnidirectional camera relative to the global reference frame are determined from the image of the control points with known 3-D coordinates. In the reconstruction phase, the intersection point of the rays from the cameras is determined. Using an evaluation experiment, we verified the accuracy of the proposed method. The wide angle of view provided by omnidirectional cameras can be fully exploited using this method.