Reviewerly/exHarmony huggingface dataset:
Underwater image enhancement method based on semi-supervised domain adaptation

Methods based on supervised learning perform well in underwater image enhancement. However, it is difficult to obtain clear labels of underwater images, and there are domain differences among various databases due to different acquisition equipment and waters, resulting in poor generalization ability of these methods. To solve the above problems, this paper proposes an underwater image enhancement algorithm based on semi-supervised domain adaptation, which is composed of domain adaptive module and image enhancement module. A domain adaptive module based on cycle-consistent generative adversarial networks (CycleGAN) is designed to eliminate the domain differences between different datasets. An image enhancement module based on semi-supervised learning strategy is proposed to solve the training problem of unlabeled images by adding physical priors of underwater images. Consistency constraint is used to ensure the stability of training and further improve network performance. The experimental results on the public datasets show that the proposed method is superior to the existing methods. In addition, the algorithm also perform well on the self-collected offshore underwater dataset.
Cryptanalysis and Improvements on Quantum Key Agreement Protocol Based on Quantum Search Algorithm

Recently, Huang et al. (2021) presented a quantum key agreement schemeto securely negotiate on a secret key employing the properties of a quantumsearch algorithm. First, the authors proposed the two-party quantum key agreement, and then they extended their work to the three-party case. Huang et al.‘s protocol employs the unitary operation and single-particle measurements to negotiate on a secret key without using complex quantum technologies such as quantum memory or entangled quantum particles. The authors claimed that their protocol is secure and efficient. However, this work shows that Huang et al.‘s protocol has a significant pitfall, where the private key of one user could be easily leaked to the attackers. Hence, the properties of security and fairness are not achieved. Accordingly, thetwo-party and three-party of Huang et al.’s protocol have been reviewed, and an improvementto address the shortcoming is suggested.
Implications For Research In Artificial Intelligence

Recently, these contemporary technologies have been thriving. Depending on the Augmented/Virtual Reality File 2017, the VR/AR market must reach out to $108 billion profits by 2021, while a summary using Goldman Sachs (2016) proposes a company of $80 billion in 2025. Development along with next-generation modern technologies have entirely tweaked the method our teamwork, stay and also probably likewise the strategy our staff thinks.
Optimization Of Classification Results By Minimizing Class Imbalance On Decision Tree Algorithm

Imbalance in the number of training datasets for each class is a problem occurring in classification boundaries accuracy. This class imbalance causes unmaximal classification accuracy. It is necessary to increase the accuracy in class classification for minority data, so as not to cause a decrease in the accuracy of class classification with majority data. Thus an appropriate method is required to solve such minority training dataset. The method dealing with imbalance classes in this study is applied to a classification with Decision Tree algorithm. The dataset used is Breast Cancer (286 instances) with two classes, i.e. no-recurrence-events class (201 instance) and recurrence-events class (85 instances). Ensemble classifier method applied to overcome imbalance class in this study is conducted using a combination of AdaBoost, Bagging and SMOTE algorithms. The comparison of algorithms combination use tested in this study resulted in a combination of AdaBoost, Bagging and SMOTE algorithm methods which creates the highest accuracy value, i.e. 84.3%.
Generating and Validating Contextually Relevant Justifications for Conversational Recommendation

Providing a justification or explanation for a recommendation has been shown to improve the users’ experience with recommender systems, in particular by increasing confidence in the recommendations. However, in order to be effective in a conversational setting, the justifications have to be appropriate for the conversation so far. Previous approaches rely on a user history of reviews and ratings of related items to personalize the recommendation, but this information is not generally available when conversing with a new user, and as such a cold-start problem imposes a challenge in generating suitable justifications. To address this problem, we propose and validate a new method, CONJURE (CONversational JUstificatons for REcommendations) to generate contextually relevant justifications for conversational recommendations. Specifically, we investigate whether the conversation itself can be used effectively to model the user, identify relevant review content from other users, and generate a justification that boosts the user’s confidence in and understanding of the recommendation. To implement CONJURE, we test several novel extensions to prior algorithms, by exploiting an auxiliary corpus of movie reviews to construct the justifications from extracted pieces of those reviews. In particular, we explore different conversation representations and ranking approaches. To evaluate CONJURE, we developed a pairwise crowd task to compare justifications. Our results show large, significant improvements in Efficiency and Transparency metrics over the previous non-contextualized template-based methods. We plan to release our code and an augmented conversation corpus on Github.
A direct product theorem for quantum communication complexity with applications to device-independent QKD

We give a direct product theorem for the entanglement-assisted interactive quantum communication complexity in terms of the quantum partition bound for product distributions. The quantum partition or efficiency bound is a lower bound on communication complexity, a non-distributional version of which was introduced by Laplante, Lerays and Roland (2012). For a two-input boolean function, the best result for interactive quantum communication complexity known previously was due to Sherstov (2018), who showed a direct product theorem in terms of the generalized discrepancy. While there is no direct relationship between the maximum distributional quantum partition bound for product distributions, and the generalized discrepancy method, unlike Sherstov's result, our result works for two-input functions or relations whose outputs are non-boolean as well. As an application of our result, we show that it is possible to do device-independent quantum key distribution (DIQKD) without the assumption that devices do not leak any information after inputs are provided to them. We analyze the DIQKD protocol given by Jain, Miller and Shi (2020), and show that when the protocol is carried out with devices that are compatible with several copies of the Magic Square game, it is possible to extract a linear (in the number of copies of the game) amount of key from it, even in the presence of a linear amount of leakage. Our security proof is parallel, i.e., the honest parties can enter all their inputs into their devices at once, and works for a leakage model that is arbitrarily interactive, i.e., the devices of the honest parties Alice and Bob can exchange information with each other and with the eavesdropper Eve in any number of rounds, as long as the total number of bits or qubits communicated is bounded.
A Proposed Load Balancer Using Naïve Bayes to Enhance Response Time on Cloud Computing

Cloud computing is a modern technology that brings challenges to all organizations worldwide. Response time is one of the most important challenges that cloud computing are facing. Improving response times for user requests is a big issue that can help reduce bottlenecks in cloud. With rapid growth as the number of applications grows, leading to the number of accessing the cloud is also rapidly increasing, easily cause the overload. This paper proposes an improvement using application of Naïve Bayes algorithm (based on Bayes theorem on probability theory to make judgments as well as classify data based on observed and statistical data) to improve the response time of VMs in the cloud, enhance the performance of cloud and cloud infrastructure. This new proposal is named as RCBA (Response Time Classification with Naive Bayes Algorithm). We simulated this proposed RCBA algorithm with the CloudSim engine and the result has improved from 4 popular load balancing algorithms: Round-Robin, FCFS, MaxMin and MinMin.
Applications of Artificial Intelligence in Biomedical Image Processing

The biomedical applications of nanoparticles are growing in recent years. The uses range from diagnostic to therapeutic purposes, and researchers are constantly introducing new ways. Biosensors, miniaturization techniques, surface enhancement in implantable biomaterials, and better nanofibers in medical textiles are some of the contributions of biomedical nanotechnology. Therapeutic (medical) imaging successfully maps the anatomy of the human body. Image enhancement, analysis, categorization, and image based representation are all possible areas of computer processing and analysis of medical images. Therapeutic image understanding uses computer vision, applied mathematics, signal analysis, and artificial intelligence to recognise objects in medical images. Image processing is the use of computer algorithms to manipulate digital images. Hand-held transducers emit and receive pulsed acoustic waves for medical ultrasound imaging. In this study, a computer-aided diagnosis method is developed to help cytotechnicians locate and classify aberrant cells in cervical cytology images. With the outcomes of morphological and statistical edge based segmentation are compared to traditional segmentation techniques. The proposed segmentation methodology outperforms the other methods. This proposed technique’s segmentation accuracy is 98%.
SCU-Net: shape constraint U-Net for prostate segmentation in MR images

Accurate segmentation of prostate magnetic resonance (MR) images provides sufficient information for the diagnosis and treatment of prostate cancer. However, the automatic segmentation of prostate based on 3D MR images faces some challenges. First, the prostate differs from other anatomical structures in that its gland lacks clear boundaries. Moreover, there are large differences in the background texture, shape, and size of different samples of prostate MR images, which makes segmentation accuracy difficult to improve. To address this issue, we propose an automatic segmentation model called the Shape Constraint U-Net (SCU-Net) for prostate MR images. The network focuses on the segmentation of the prostate boundary region by introducing a shape constraint stream based on a parallel encoder–decoder structure that can handle different shape and texture information. Specifically, the shape constraint stream is composed of a multi-level boundary attention module that can process boundary-related information using advanced activation features in the regular segmentation stream. Finally, the multi-scale context information extracted from the regular segmentation stream is fused with the multi-level boundary information obtained from the shape constraint stream to generate a segmentation prediction map. Owing to the additional shape constraints, the network substantially improves prostate boundary region segmentation. We evaluated our method on two different real clinical MR prostate datasets. The experimental results demonstrate that SCU-Net achieves state-of-the-art prostate segmentation accuracy, especially in boundary-region voxel prediction. Further analysis indicates that the proposed shape constraint stream can be used to improve the boundary voxel prediction performance of other segmentation networks.
Sixth HUMANIZE Workshop on Transparency and Explainability in Adaptive Systems Through User Modeling Grounded in Psychological Theory: Summary

The sixth HUMANIZE workshop1 on Transparency and Explainability in Adaptive Systems through User Modeling Grounded in Psychological Theory took place in conjunction with the 27th annual meeting of the Intelligent User Interfaces (IUI)2 community that was hosted virtually by the University of Helsinki (Finland) on March 22, 2022. The 2022 edition of the workshop was held together with TExSS (Transparency and Explanations in Smart Systems) 3. The workshop provided a venue for researchers from different fields to interact by accepting contributions on the intersection of practical data mining methods and theoretical knowledge for personalization. A total of two papers was accepted for this edition of the workshop.
Transient chaos in bidirectional encoder representations from transformers

Language is an outcome of our complex and dynamic human-interactions and the technique of natural language processing (NLP) is hence built on human linguistic activities. Bidirectional Encoder Representations from Transformers (BERT) has recently gained its popularity by establishing the state-of-the-art scores in several NLP benchmarks. A Lite BERT (ALBERT) is literally characterized as a lightweight version of BERT, in which the number of BERT parameters is reduced by repeatedly applying the same neural network called Transformer's encoder layer. By pre-training the parameters with a massive amount of natural language data, ALBERT can convert input sentences into versatile high-dimensional vectors potentially capable of solving multiple NLP tasks. In that sense, ALBERT can be regarded as a well-designed high-dimensional dynamical system whose operator is the Transformer's encoder, and essential structures of human language are thus expected to be encapsulated in its dynamics. In this study, we investigated the embedded properties of ALBERT to reveal how NLP tasks are effectively solved by exploiting its dynamics. We thereby aimed to explore the nature of human language from the dynamical expressions of the NLP model. Our short-term analysis clarified that the pre-trained model stably yields trajectories with higher dimensionality, which would enhance the expressive capacity required for NLP tasks. Also, our long-term analysis revealed that ALBERT intrinsically shows transient chaos, a typical nonlinear phenomenon showing chaotic dynamics only in its transient, and the pre-trained ALBERT model tends to produce the chaotic trajectory for a significantly longer time period compared to a randomly-initialized one. Our results imply that local chaoticity would contribute to improving NLP performance, uncovering a novel aspect in the role of chaotic dynamics in human language behaviors.
Robust observable control of open and closed quantum systems

Abstract In recent work, we introduced the asymptotic theory of quantum robust control, which enables control of moments of quantum observables and gates in the presence of Hamiltonian uncertainty or field noise. In this paper, we extend this theory of quantum robust control to encompass two of the most important generalizations: robust control of arbitrary quantum observables and robust control of quantum systems sustaining environmental decoherence. In addition, we present deterministic Pareto optimization algorithms that can be applied in conjunction with either asymptotic or leading order measures of robustness. This enables robust control of any observable in quantum systems with any initial density matrix, and for which the entropy can change arbitrarily during the time evolution. Methods for robust optimal control of open quantum systems are presented that maximize the expected value of a quantum control objective while minimizing the expected environmentally induced decoherence.
Graphical Models for Recovering Probabilistic and Causal Queries from Missing Data

chapter Share on Graphical Models for Recovering Probabilistic and Causal Queries from Missing Data Authors: Karthika Mohan Search about this author , Judea Pearl Search about this author Authors Info & Claims Probabilistic and Causal Inference: The Works of Judea PearlFebruary 2022 Pages 413–432https://doi.org/10.1145/3501714.3501739Online:04 March 2022Publication History 0citation2DownloadsMetricsTotal Citations0Total Downloads2Last 12 Months2Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Self-Supervised Stereo Matching Method Based on SRWP and PCAM for Urban Satellite Images

In this paper, we propose a self-supervised stereo matching method based on superpixel random walk pre-matching (SRWP) and parallax-channel attention mechanism (PCAM). Our method is divided into two stages, training and testing. First, in the training stage, we obtain pre-matching results of stereo images based on superpixel random walk, and some matching points with high confidence are selected as labeled samples. Then, a stereo matching network is constructed to describe the matching correlation by calculating the attention scores of any two points between different images through the parallax-channel attention mechanism, superimposing the scores of each layer to calculate the disparity. The network is trained using the labeled samples and some unsupervised constraint criteria. Finally, in the testing stage, the trained network is used to obtain stereo matching relations of stereo images. The proposed method does not need manually labeled training samples and is more suitable for 3D reconstruction under mass satellite remote sensing data. Comparative experiments on multiple datasets show that our method has a stereo matching EPE of 2.44 and a 3D reconstruction RMSE of 2.36 m. Especially in the weak texture and parallax abrupt change regions, we can achieve more advanced performance than other methods.
Deep Anomaly Detection Based on Variational Deviation Network

There is relatively little research on deep learning for anomaly detection within the field of deep learning. Existing deep anomaly detection methods focus on the learning of feature reconstruction, but such methods mainly learn new feature representations, and the new features do not fully reflect the original features, leading to inaccurate anomaly scores; in addition, there is an end-to-end deep anomaly detection algorithm, but the method cannot accurately obtain a reference score that matches the data themselves. In addition, in most practical scenarios, the data are unlabeled, and there exist some datasets with labels, but the confidence and accuracy of the labels are very low, resulting in inaccurate results when put into the model, which makes them often designed for unsupervised learning, and thus in such algorithms, the prior knowledge of known anomalous data is often not used to optimize the anomaly scores. To address the two problems raised above, this paper proposes a new anomaly detection model that learns anomaly scores mainly through a variational deviation network (i.e., not by learning the reconstruction error of new features, distance metrics, or random generation, but by learning the normal distribution of normal data). In this model, we force the anomaly scores to deviate significantly from the normal data by a small amount of anomalous data and a reference score generated by variational self-encoding. The experimental results in multiple classes of data show that the new variational deviation network proposed in this paper has higher accuracy among the mainstream anomaly detection algorithms.
Searching for Fast Demosaicking Algorithms

We present a method to automatically synthesize efficient, high-quality demosaicking algorithms, across a range of computational budgets, given a loss function and training data. It performs a multi-objective, discrete-continuous optimization which simultaneously solves for the program structure and parameters that best tradeoff computational cost and image quality. We design the method to exploit domain-specific structure for search efficiency. We apply it to several tasks, including demosaicking both Bayer and Fuji X-Trans color filter patterns, as well as joint demosaicking and super-resolution. In a few days on 8 GPUs, it produces a family of algorithms that significantly improves image quality relative to the prior state-of-the-art across a range of computational budgets from 10 s to 1000 s of operations per pixel (1 dB–3 dB higher quality at the same cost, or 8.5–200× higher throughput at same or better quality). The resulting programs combine features of both classical and deep learning-based demosaicking algorithms into more efficient hybrid combinations, which are bandwidth-efficient and vectorizable by construction. Finally, our method automatically schedules and compiles all generated programs into optimized SIMD code for modern processors.
Postdigital Applied Systems Science Education: Toward an Integral Framework, Curriculum, and Pedagogy

Abstract The design of systems to support sustainable wellbeing is contingent upon lifespan education of Homo sapiens and ongoing efforts to cultivate individual and collective intelligence. The Postdigital Applied Systems Science Education (PASSE) framework presented in this paper highlights the need for greater investment in educational infrastructures that support the development of collective intelligence, teamwork, and system design skills. We propose that the implementation of PASSE involves group- and project-based work focused on developing (1) an understanding of systems, (2) an understanding of group dynamics relevant to the management and design of systems, and (3) skill in the application of applied systems science methods that can be used by groups in the management and redesign of systems. To showcase curricular and pedagogical challenges and opportunities, we describe key features of our current delivery of PASSE along with future plans and prospects. Aligned with postdigital perspectives and innovations at the nexus of biology, information, and society, we highlight the potential for ongoing redesign of educational infrastructures and technologies that enhance societal teamwork and system design capabilities that allow us to address increasingly complex societal challenges.
A Framework for Robotic Path Planning Based on Enhanced Fluid Potential Dynamical Models

Path planning is one of the main sectors of robotics that deals with path calculations from a starting point to a goal in a defined field. Physical algorithms that rely on artificial potential fields and fluid fields have been developed to solve the path planning task. The main problem for the former is the creation of local minima in the field which requires extra algorithms to solve with increased path cost and added inefficiencies, while for the latter, the problem is the computational cost. This paper proposes an enhanced computational-physical path planning algorithm based on fluid stream equations and mechanics – named the Stream Field Navigation (SFN) – with modifications on how the residuals are calculated to improve the computational efficiency by introducing the Directional Residuals. SFN also introduces the Stream Reversal approach to represent a navigation field with no local minima. A comparison between the SFN algorithm and the Artificial Potential Field method is carried out to show the fundamental differences between the two methods and the results include empirical comparisons between the execution times of multiple algorithms which show that the SFN algorithm is at least 75% faster than PRM while A* is the fastest approach.