Reviewerly/exHarmony huggingface dataset:
Visual Analysis of Hyperproperties for Understanding Model Checking Results

Model checkers provide algorithms for proving that a mathematical model of a system satisfies a given specification. In case of a violation, a counterexample that shows the erroneous behavior is returned. Understanding these counterexamples is challenging, especially for hyperproperty specifications, i.e., specifications that relate multiple executions of a system to each other. We aim to facilitate the visual analysis of such counterexamples through our HyperVis tool, which provides interactive visualizations of the given model, specification, and counterexample. Within an iterative and interdisciplinary design process, we developed visualization solutions that can effectively communicate the core aspects of the model checking result. Specifically, we introduce graphical representations of binary values for improving pattern recognition, color encoding for better indicating related aspects, visually enhanced textual descriptions, as well as extensive cross-view highlighting mechanisms. Further, through an underlying causal analysis of the counterexample, we are also able to identify values that contributed to the violation and use this knowledge for both improved encoding and highlighting. Finally, the analyst can modify both the specification of the hyperproperty and the system directly within HyperVis and initiate the model checking of the new version. In combination, these features notably support the analyst in understanding the error leading to the counterexample as well as iterating the provided system and specification. We ran multiple case studies with HyperVis and tested it with domain experts in qualitative feedback sessions. The participants' positive feedback confirms the considerable improvement over the manual, text-based status quo and the value of the tool for explaining hyperproperties.
Dynamic Nearest Neighbor: An Improved Machine Learning Classifier and Its Application in Finances

The presence of machine learning, data mining and related disciplines is increasingly evident in everyday environments. The support for the applications of learning techniques in topics related to economic risk assessment, among other financial topics of interest, is relevant for us as human beings. The content of this paper consists of a proposal of a new supervised learning algorithm and its application in real world datasets related to finance, called D1-NN (Dynamic 1-Nearest Neighbor). The D1-NN performance is competitive against the main state of the art algorithms in solving finance-related problems. The effectiveness of the new D1-NN classifier was compared against five supervised classifiers of the most important approaches (Bayes, nearest neighbors, support vector machines, classifier ensembles, and neural networks), with superior results overall.
Engineering and graphic education during and after the COVID-19 pandemic: Challenges and opportunities

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation R. R. Anamova; Engineering and graphic education during and after the COVID-19 pandemic: Challenges and opportunities. AIP Conf. Proc. 23 September 2021; 2389 (1): 100007. https://doi.org/10.1063/5.0063501 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Nighttime Thermal Infrared Image Colorization with Dynamic Label Mining

Translating a nighttime thermal infrared (NTIR) image into a daytime color image, denoted as NTIR2DC, may be a promising way to facilitate nighttime scene perception. Despite recent impressive advances in the field of image-to-image translation, content distortion in the NTIR2DC task remains under-addressed. Previous approaches resort to annotated pixel-level labels to enforce semantic invariance during translation, which greatly limits their practicality. To ameliorate this intractability, we propose a generative adversarial network with Dynamic LAbel Mining called DlamGAN, which encourages a semantically consistent translation without requiring pixel-wise manual annotation. Specifically, a dynamic label mining strategy is proposed to extract the semantic cues of NTIR images online. The obtained cues are served as pseudo labels to constrain the semantic consistency in the translation process. In addition, an adaptive surrounding response integration module is introduced for adaptively integrating contextual information to reduce coding ambiguity of NTIR images. Experimental results on FLIR and KAIST datasets demonstrate the superiority of the proposed model in semantic preservation.
A Brief Review of Convolutional Neural Network Techniques for Masked Face Recognition

Masked face recognition is now an essential part of health safety, security and surveillance systems which offers incredible advantages in our daily lives, especially in the era of the pandemic ushered in by the outbreak of coronavirus disease in the year 2019 (COVID-19). Applications such as; face mask compliance checks, facial security checks, facial attendance records and facial authentication for access control now requires an effective masked face recognition system. The existing systems of masked face recognition were developed to automatically detect and understand faces occluded with masks using computer vision and deep learning techniques, the systems are yet to work effectively in real-time. This study gives an analysis of some techniques used for the implementation of masked face recognition system, with emphasis on Convolutional Neural Network (CNN). The strengths and enhancement areas of the highlighted techniques towards real-time implementation were discussed.
Crop Recommendation System with Cloud Computing

Agriculture is the backbone of the developing countries and plays a primary role in the economy in these countries. For bringing in the most productivity, the decision of planting a suitable crop in a particular location is necessary. But, there is a general problem among farmers and other agricultural activists that they don't opt for better scientifically proven methods for crop recommendation. Thus, our proposed work would help farmers in selecting the right crop based on factors like cost of cultivation, cost of production, yield to increase productivity and get more profit out of this proposed technique. This paper discusses about the different machine learning algorithms to know about them, their metrics evaluation for a certain dataset, and finally, a proposed methodology that performs better than other learners. The paper proposes a methodology in which decision tree, k <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> nearest neighbor, logistic regression, random forest and gradient boosting classifier are used to process the data set and then, these learners are passed through an ensemble model called voting classifier to get a more improved outcome. The comparison between these algorithms is also shown in terms of metrics – accuracy, f1 score and execution time on the certain dataset used. This paper also discusses cloud computing and the cloud server processing machine learning algorithms to give required output enquired by the end user.
SERVAS! Secure Enclaves via RISC-V Authenticryption Shield

Isolation is a long-standing security challenge. Privilege rings and virtual memory are increasingly augmented with capabilities, protection keys, and powerful enclaves. Moreover, we are facing an increased need for physical protection, e.g., via transparent memory encryption, resulting in a complex interplay of various security mechanisms. In this work, we tackle the isolation challenge with a new extensible isolation primitive called authenticryption shield that unifies various isolation policies. By using authenticated memory encryption, we streamline the security reasoning towards cryptographic guarantees. We showcase the versatility of our approach by designing and prototyping SERVAS – a novel enclave architecture for RISC-V. SERVAS facilitates a new efficient and secure enclave memory sharing mechanism. While the memory encryption constitutes the main overhead, invoking SERVAS enclave requires only 3.5x of a simple syscall instead of 71x for Intel SGX.
Masked Face Analysis via Multi-Task Deep Learning

Face recognition with wearable items has been a challenging task in computer vision and involves the problem of identifying humans wearing a face mask. Masked face analysis via multi-task learning could effectively improve performance in many fields of face analysis. In this paper, we propose a unified framework for predicting the age, gender, and emotions of people wearing face masks. We first construct FGNET-MASK, a masked face dataset for the problem. Then, we propose a multi-task deep learning model to tackle the problem. In particular, the multi-task deep learning model takes the data as inputs and shares their weight to yield predictions of age, expression, and gender for the masked face. Through extensive experiments, the proposed framework has been found to provide a better performance than other existing methods.
Practical Location Privacy Attacks and Defense on Point-of-interest Aggregates

Location-based services have significantly affected mobile users' everyday life, and location privacy is also an essential issue in these services. In some applications (e.g., location-based recommendation, mobility analytic), the raw data is not required, and the service providers adopt aggregation to protect users' location traces. However, some works show that even these aggregation data may disclose users' location privacy when other prior knowledge is available to an adversary. We consider the location privacy problem in the presence of Location Uniqueness, which is a property that some geographical locations can be re-identified based on the aggregated point-of-interest (POI) information. We first study whether previous protection mechanisms are effective for defending against this novel type of attack. Then we present two practical attacks for inferring users' actual locations based on the POI aggregates. Furthermore, we propose a secure POI aggregate release mechanism that can defend against this type of re-identification attack and achieve differential privacy at the same time. We conduct extensive experiments on real-world datasets. The results show that the existing protection mechanisms cannot provide sufficient protection. The proposed enhanced attacks can significantly improve the inference performance, and the proposed protection mechanism achieves satisfactory performance.
Attitude, knowledge and constraints associated with the use of mobile phone applications by farmers in North West Nigeria

The evolution of mobile phone applications has opened up a platform for easy and real time dissemination and exchange of agricultural information among agricultural extension officers, farmers, agricultural institutions and non-governmental institutions. This study examined attitude, knowledge and constraint associated with the use of mobile phone apps by farmers in North West region of Nigeria. A descriptive survey design was adopted; data collection tool was pre-tested and administered as interview schedule to randomly sampled farmers. Data were analyzed using descriptive statistics and correlation. The findings showed that farmers have positive attitude towards mobile phone applications, and knowledgeable of simple phone operation techniques. The constraints to the use of mobile phone applications are high cost of phones, poor network, poor power supply, high cost of airtime and complexity in operating phones. This study recommends training and re-retraining of farmers and extension agents on the use of mobile phone apps for effective information sharing among farmers in the region.
AudienceMR: Extending the Local Space for Large-Scale Audience with Mixed Reality for Enhanced Remote Lecturer Experience

AudienceMR is designed as a multi-user mixed reality space that seamlessly extends the local user space to become a large, shared classroom where some of the audience members are seen seated in a real space, and more members are seen through an extended portal. AudienceMR can provide a sense of the presence of a large-scale crowd/audience with the associated spatial context. In contrast to virtual reality (VR), however, with mixed reality (MR), a lecturer can deliver content or conduct a performance from a real, actual, comfortable, and familiar local space, while interacting directly with real nearby objects, such as a desk, podium, educational props, instruments, and office materials. Such a design will elicit a realistic user experience closer to an actual classroom, which is currently prohibitive owing to the COVID-19 pandemic. This paper validated our hypothesis by conducting a comparative experiment assessing the lecturer’s experience with two independent variables: (1) an online classroom platform type, i.e., a 2D desktop video teleconference, a 2D video screen grid in VR, 3D VR, and AudienceMR, and (2) a student depiction, i.e., a 2D upper-body video screen and a 3D full-body avatar. Our experiment validated that AudienceMR exhibits a level of anxiety and fear of public speaking closer to that of a real classroom situation, and a higher social and spatial presence than 2D video grid-based solutions and even 3D VR. Compared to 3D VR, AudienceMR offers a more natural and easily usable real object-based interaction. Most subjects preferred AudienceMR over the alternatives despite the nuisance of having to wear a video see-through headset. Such qualities will result in information conveyance and an educational efficacy comparable to those of a real classroom, and better than those achieved through popular 2D desktop teleconferencing or immersive 3D VR solutions.
MMF: A Loss Extension for Feature Learning in Open Set Recognition

The objective of open set recognition (OSR) is to classify the known classes as well as the unknown classes when the collected samples cannot exhaust all the classes. This paper proposes a loss extension that emphasizes features with larger and smaller magnitudes to find representations that can more effectively separate the known from the unknown classes. Our contributions include: First, we introduce an extension that can be incorporated into different loss functions to find more discriminative representations. Second, we show that the proposed extension can significantly improve the performances of two different types of loss functions on datasets from two different domains. Third, we show that with the proposed extension, one loss function outperforms the others in training time and model accuracy.
Cooperative coevolutionary instance selection for multilabel problems

Multilabel classification as a data mining task has recently attracted greater research interest. Many current data mining applications address problems having instances that belong to more than one class, which requires the development of new efficient methods. Advantageously using the correlation among different labels can provide better performance than methods that deal with each label separately. Instance-based classification models, such as k-nearest neighbors for multilabel datasets, ML-kNN, are among the best performing methods on any classification task and have also shown very goog performance in multilabel problems. Despite their simplicity, they achieve comparable performance to considerably more complex methods. One of the challenges associated with instance-based classification models is their requirement for storing all the training instances in memory. To ameliorate this problem, instance selection methods have been proposed. However, their application to multilabel problems is problematic because the adaptation of most of their concepts to multilabel problems is difficult. In this paper, we propose a cooperative coevolutionary algorithm for instance selection for multilabel problems. Two different populations evolve together cooperatively. One of the populations is devoted to obtaining solutions for each label, whereas the other population combines these results into solutions for the instance selection for multilabel dataset tasks. On a large set of 70 real-world problems, our approach improves the results of both the ML-kNN method with the whole dataset and an instance selection method using a standard evolutionary algorithm.
A novel quantum‐inspired binary bat algorithm for leukocytes classification in blood smear

Abstract In the field of medical sciences, day‐to‐day procedure is followed for identification of bone marrow and immune system related diseases, which is most of the time carried out manually. The notion is to perform differential and qualitative analysis of leukocytes for the timely diagnosis of these diseases. In this article, a systematized solution is offered for the classification of leukocytes in blood smear. The proposed model incorporates the optimistic aspects of nature‐inspired and quantum‐inspired algorithms; this model tends to be perfect blend of both the techniques. For reducing the dimensionality, that is, irrelevant features; the quantum‐inspired binary bat algorithm (QBBA) has been used in the proposed model. The optimality of features selected has been computed with the help of accuracy measure using various machine learning classifiers like Logistic Regression, KNN, Random Forest, Decision Tree. The performance of QBBA and its customary algorithms has been compared and the results depict that QBBA outperforms binary bat algorithm for the same set of population. QBBA comes out as an influential algorithm with an average accuracy of 98.31% and also possess enhanced noise invulnerability. The proposed QBBA can also find its usage in thorough haematological analysis.
SPMS-ALS: A Single-Point Memetic structure with accelerated local search for instance reduction

Real-world optimisation problems pose domain specific challenges that often require an ad-hoc algorithmic design to be efficiently addressed. The present paper investigates the optimisation of a key stage in data mining, known as instance reduction, which aims to shrink the input data prior to applying a learning algorithm. Performing a smart selection or creation of a reduced number of samples that represent the original data may become a complex large-scale optimisation problem, characterised by a computationally expensive objective function, which has been often tackled by sophisticated population-based metaheuristics that suffer from a high runtime. Instead, by following the Ockham's Razor in Memetic Computing, we propose a Memetic Computing approach that we refer to as fast Single-Point Memetic Structure with Accelerated Local Search (SPMS-ALS). Using the k-nearest neighbours algorithm as base classifier, we first employ a simple local search for large-scale problems that exploits the search logic of Pattern Search, perturbing an n-dimensional vector along the directions identified by its design variables one by one. This point-by-point perturbation mechanism allows us to design a strategy to re-use most of the calculations previously made to compute the objective function of a candidate solution. The proposed Accelerated Local Search is integrated within a single-point memetic framework and coupled with a resampling mechanism and a crossover. A thorough experimental analysis shows that SPMS-ALS, despite its simplicity, displays an excellent performance which is as good as that of the state-of-the-art while reducing up to approximately 85% of the runtime with respect to any other algorithm that performs the same number of function calls.
SPOT: A Framework for Selection of Prototypes Using Optimal Transport

In this work, we develop an optimal transport (OT) based framework to select informative prototypical examples that best represent a given target dataset. Summarizing a given target dataset via representative examples is an important problem in several machine learning applications where human understanding of the learning models and underlying data distribution is essential for decision making. We model the prototype selection problem as learning a sparse (empirical) probability distribution having the minimum OT distance from the target distribution. The learned probability measure supported on the chosen prototypes directly corresponds to their importance in representing the target data. We show that our objective function enjoys a key property of submodularity and propose an efficient greedy method that is both computationally fast and possess deterministic approximation guarantees. Empirical results on several real world benchmarks illustrate the efficacy of our approach.
Semi-Supervised Unpaired Multi-Modal Learning for Label-Efficient Medical Image Segmentation

Multi-modal learning using unpaired labeled data from multiple modalities to boost the performance of deep learning models on each individual modality has attracted a lot of interest in medical image segmentation recently. However, existing unpaired multi-modal learning methods require a considerable amount of labeled data from both modalities to obtain satisfying segmentation results which are not easy to obtain in reality. In this paper, we investigate the use of unlabeled data for label-efficient unpaired multi-modal learning, with a focus on the scenario when labeled data is scarce and unlabeled data is abundant. We term this new problem as Semi-Supervised Unpaired Multi-Modal Learning and thereupon, propose a novel deep co-training framework. Specifically, our framework consists of two segmentation networks, where we train one of them for each modality. Unlabeled data is effectively applied to learn two image translation networks for translating images across modalities. Thus, labeled data from one modality is employed for the training of the segmentation network in the other modality after image translation. To prevent overfitting under the label scarce scenario, we introduce a new semantic consistency loss to regularize the predictions of an image and its translation from the two segmentation networks to be semantically consistent. We further design a novel class-balanced deep co-training scheme to effectively leverage the valuable complementary information from both modalities to boost the segmentation performance. We verify the effectiveness of our framework with two medical image segmentation tasks and our framework outperforms existing methods significantly.
Non-adaptive Heisenberg-limited metrology with multi-channel homodyne measurements

We show a protocol achieving the ultimate Heisenberg-scaling sensitivity in the estimation of a parameter encoded in a generic linear network, without employing any auxiliary networks, and without the need of any prior information on the parameter nor on the network structure. As a result, this protocol does not require a prior coarse estimation of the parameter, nor an adaptation of the network. The scheme we analyse consists of a single-mode squeezed state and homodyne detectors in each of the $M$ output channels of the network encoding the parameter, making it feasible for experimental applications.