Reviewerly/exHarmony huggingface dataset:
Explaining autonomous drones: An <scp>XAI</scp> journey

Abstract COGLE ( CO mmon G round L earning and E xplanation) is an explainable artificial intelligence (XAI) system where autonomous drones deliver supplies to field units in mountainous areas. The mission risks vary with topography, flight decisions, and mission goals. The missions engage a human plus AI team where users determine which of two AI‐controlled drones is better for each mission. This article reports on the technical approach and findings of the project and reflects on challenges that complex combinatorial problems present for users, machine learning, user studies, and the context of use for XAI systems. COGLE creates explanations in multiple modalities. Narrative “What” explanations compare what each drone does on a mission and “Why” based on drone competencies determined from experiments using counterfactuals. Visual “Where” explanations highlight risks on maps to help users to interpret flight plans. One branch of the research studied whether the explanations helped users to predict drone performance. In this branch, a model induction user study showed that post‐decision explanations had only a small effect in teaching users to determine by themselves which drone is better for a mission. Subsequent reflection suggests that supporting human plus AI decision making with pre‐decision explanations is a better context for benefiting from explanations on combinatorial tasks.
Generating Adversarial Examples With Shadow Model

The reduction in the number of queries to the object model is a hot topic in the current research of black-box adversarial attack methods. To solve this problem, in this article, we propose generating adversarial examples with shadow model (GASM) that shifts the number of queries to the object model to the shadow model. The method first determines the shadow model based on the robustness and transferability of classifiers and fine-tunes the decision boundary of the shadow model by constructing adversarial datasets. Second, accesses the shadow model and constructs adversarial examples by maximizing the output probability of the targeted class (any class other than the current one) to modify the image gradient information. Finally, the results show that GASM has the strongest transferability and outperforms white-box attacks when AlexNet (MNIST), VGG-19 (CIFAR10), and MobileNet v2 (Tiny ImageNet) are selected as shadow models.
Automobile Instrument Detection Using Prior Information and Fuzzy Sets

Since there are many kinds of automobile instruments and the industrial environment is usually complex, automatic instrument detection during automobile production test becomes a challenging task. This article presents an automobile pointer instrument detection method based on prior information and fuzzy sets. The proposed method consists of two frameworks built around a pointer meter prior information model (PMPIM). The first one targets PMPIM construction to obtain the required prior information. With this purpose, a pointer-free template is obtained from a template generation algorithm and pointer positions are mapped into an energy function for optimization, using an energy function-based pointer positioning algorithm. The energy function is defined based on the distance between the crisp and fuzzy sets. The second framework targets PMPIM utilization to detect pointer meters during production test. A fuzzy-based image enhancement method is proposed to enhance test images and the template simultaneously. A prior information and energy function-based pointer positioning algorithm is also proposed to locate pointers in test images. Finally, the indicator value (the value the pointer points to) is calculated according to the positions of the pointer and scale marks. Experimental results show that the proposed method achieves better generalization and robustness than existing state-of-the-art methods.
Identification of Prediabetes Discussions in Unstructured Clinical Documentation: Validation of a Natural Language Processing Algorithm

Prediabetes affects 1 in 3 US adults. Most are not receiving evidence-based interventions, so understanding how providers discuss prediabetes with patients will inform how to improve their care.This study aimed to develop a natural language processing (NLP) algorithm using machine learning techniques to identify discussions of prediabetes in narrative documentation.We developed and applied a keyword search strategy to identify discussions of prediabetes in clinical documentation for patients with prediabetes. We manually reviewed matching notes to determine which represented actual prediabetes discussions. We applied 7 machine learning models against our manual annotation.Machine learning classifiers were able to achieve classification results that were close to human performance with up to 98% precision and recall to identify prediabetes discussions in clinical documentation.We demonstrated that prediabetes discussions can be accurately identified using an NLP algorithm. This approach can be used to understand and identify prediabetes management practices in primary care, thereby informing interventions to improve guideline-concordant care.
Not Only Why but Also How to Trust Science: Reshaping Science Education Based on Science Studies for a Better Post-pandemic World

Many authors blame postmodernism and studies on Sociology and Anthropology of Science (Science Studies) for the rise of relativism and anti-science movements. Despite such criticism, Science Studies have always been concerned with the construction of the common world (a shared reality), while the anti-science movement goes in the opposite direction, denying science to defend economic and political interests of specific groups. In this sense, the post-truth movement is part of a political agenda and therefore science education will not be able to face the dilemmas of such scenario unless it takes a clear political stance. Thus, our objective is to present a discussion on why we should trust science as well as what it means to trust science precisely from the so-called ontological turn of science studies. We argue that, based on this sociological framework, it is possible to recognize the value of science as a community capable of producing networks and actors that mobilize the world and that respond to day-to-day problems. Next, we discuss the fact that trusting in science does not mean blind trusting specialists. It is necessary to increase the participation of different actors in the construction of the common world, especially by calling into debate those who were made invisible in the process of colonialism. Finally, we argue that recovering confidence in science is a political process, in a way that public opinion can only changed by politically organizing the field of science and science education.
<i>Retracted:</i> Quality management in higher education: Developing the methodology on the basis of total quality management

Abstract Understanding the needs of end users (customers, stakeholders) and the simultaneous application of new methodologies for the implementation of strategic plans in order to meet the needs of external (employers) and internal consumers (students and teachers) are essential elements of quality management in higher education. This article aimed to investigate the general state of modern quality management in higher education. A survey of consumers of educational services in Ukraine was conducted in order to study the current state of quality management in higher education. Interested parties’ theory and total quality management methodology in quality management of higher education served as the basis for the research methodology. The requirements of the administration and departments of higher educational institutions, students and employers in Ukraine to the quality of education were studied. Requirements for meeting the needs of consumers of educational services have been developed. During the study, the principle of ‘student‐centred learning’ and the protection of key interests of students were implemented. It was found that the quality of higher education in Ukraine is characterised by a significant level of dissatisfaction. An empirical assessment of the gap between expectations and actual experience of obtaining higher education was carried out and the discrepancy between education in Ukraine and the requirements of stakeholders was proved.
Learning Highly Recursive Input Grammars

This paper presents Arvada, an algorithm for learning context-free grammars from a set of positive examples and a Boolean-valued oracle. Arvada learns a context-free grammar by building parse trees from the positive examples. Starting from initially flat trees, Arvada builds structure to these trees with a key operation: it bubbles sequences of sibling nodes in the trees into a new node, adding a layer of indirection to the tree. Bubbling operations enable recursive generalization in the learned grammar. We evaluate Arvada against GLADE and find it achieves on average increases of 4.98× in recall and 3.13× in F1 score, while incurring only a 1.27× slowdown and requiring only 0.87× as many calls to the oracle. Arvada has a particularly marked improvement over GLADE on grammars with highly recursive structure, like those of programming languages.
NemesisGuard: Mitigating interrupt latency side channel attacks with static binary rewriting

Internet of Things (IoT) is becoming integrated into nearly every aspect of our modern life. Indeed, exploitation of such devices can directly lead to physical consequences in the real world. Previous work has shown that IoT devices can be compromised by exploits in lower software layers such as the Operating System (OS). Embedded Trusted Execution Environments (TEEs) provide a small Trusted Computing Base (TCB) to protect sensitive codes and data in such devices. TEEs assume a strong threat model where even a privileged attacker (e.g. OS) cannot compromise the confidentiality and integrity of the execution. Nevertheless, it has been shown that side channel attacks make it challenging to keep secrets during application execution. Interrupt latency side channel attacks (a.k.a. Nemesis) are a novel type of timing attacks that target embedded TEEs and extract application secrets from them. Nemesis attacks exploit the CPU's interrupt mechanism to reveal microarchitectural instruction timings from embedded TEEs. Specifically, the attacker measures the latency of a precisely timed interrupt to differentiate between secret-dependent branches. In this paper, we present NemesisGuard, the first mitigation mechanism against such side channel attacks that does not require a modified compiler or hardware and can protect COTS binaries without access to source code. NemesisGuard applies a novel static binary instrumentation technique to balance secret-dependent branches in IoT application binaries. Evaluation of NemesisGuard shows that it mitigates Nemesis side channel attacks effectively and efficiently.
Social Media Popularity Prediction Based on Multi-Modal Self-Attention Mechanisms

Popularity prediction using social media is an important task because of its wide range of real-world applications such as advertisements, recommendation systems, and trend analysis. However, this task is challenging because social media is affected by multiple factors that cannot be easily modeled (e.g. quality of content, relevance to viewers, real-life events). Usually, other methods adopt the greedy approach to include as many modalities and factors as possible into their model but treat these features equally. To solve this phenomenon, our proposed method leverages the self-attention mechanism to effectively and automatically fuse different features to achieve better performance for the popularity prediction of a post, where the features used in our model can be mainly categorized into two modalities, semantic (text) and numeric features. With extensive experiments and ablation studies on the training and testing data of the challenging ACM Multimedia SMPD 2020 Challenge dataset, the evaluation results demonstrate the effectiveness of the proposed approach as compared with other methods.
Computer Vision Approach for the detection of Thrombocytopenia from Microscopic Blood Smear Images

The counting and analysis of blood cells is the first step in detecting diseases such as COVID-19, dengue, and leukemia. In primary health care centers, platelet counting is usually performed manually from peripheral blood smears, which is labor-intensive and requires an experienced laboratory technician, which invites error. The complete blood count test is carried out using the automatic hematology analyzer with the development of technology. However, this is an expensive method to count the blood cells. This paper introduces an efficient and cost-effective computer vision technique known as blob detection for automatic platelet counting. The technology uses digitized microscopic blood smear images to identify platelets as blobs used to quantify clinically relevant features, i.e., detection of thrombocytopenia, leading to further medical analysis. Detection of thrombocytopenia is imperative as the reduced platelet count may cause small gum bleeds to severe conditions like hemorrhage or even death. The algorithm is developed using a dataset of 50 digitized microscopic blood smear images. The clinically estimated manual platelet count is considered as a reference. The developed algorithm gives an accuracy of about 96.4% for the detection of platelets. The automated count will ensure greater accuracy and allow hematologists to perform faster analysis. This system can be deployed in remote areas as a supporting aid for telemedicine technology.
A GPU Algorithm for Detecting Contextual Outliers in Multiple Concurrent Data Streams

A data stream is an infinite sequence of data points generated from a source continuously at a fast rate, which is characterized by the transiency of the data points, the temporal relationship among the data points, concept drift, and multi-dimensionality of data points. Outlier detection in data streams thus needs to deal with the characteristics of Big Data applications such as volume, velocity, and variety. The problem of detecting outliers in multiple concurrent data streams introduces additional challenges to the problem. In this paper, we propose a parallel outlier detection technique CODS to detect Contextual Outliers in multiple concurrent independent multi-dimensional Data Streams using a Graphics Processing Unit (GPU). The proposed algorithm addresses all the aforesaid characteristics of data streams. A set of experiments demonstrates reasonable outlier detection accuracy and scalability of CODS with the number of data streams.
Authenticating Spatial Queries on Blockchain Systems

In many blockchain networks, light nodes (e.g.mobile clients) with few computational resources must rely on more powerful full nodes to retrieve transactions from the chain.However, in this untrusted environment a malicious full node could deliver altered or incomplete information, requiring query authentication techniques to ensure the integrity of the results.To this aim, we study an authentication mechanism for spatial information (i.e.data representing the location, size, and shape of objects in a geographical coordinate system).We assume that light nodes issue range queries to obtain data from a single block.To enable authentication, we propose to construct a Merkle R-tree for each block and embed its root into the corresponding header, so that full nodes can exploit it to fetch information and construct a proof of integrity for lightweight clients.We also develop a new algorithm based on sorting and partitioning for constructing Merkle R-trees from a set of spatial transactions and employ space-filling curves to preserve the locality of elements.We examine its theoretical complexity, evaluate it experimentally on a real data set and compare it against other popular construction strategies.Results show that, as queries become more selective, trees generated with our solution improve query performance and reduce verification times with respect to other approaches.Moreover, we observe that the overhead induced by the tree construction is negligible if compared to the average inter-block time of popular blockchain protocols such as Bitcoin and Ethereum.
A Comprehensive Review on Power Efficient Fault Tolerance Models in High Performance Computation Systems

For the purpose of high performance computation, several machines are developed at an exascale level. These machines can perform at least one exaflop calculations per second, which corresponds to a billion billon or 108. The universe and nature can be understood in a better manner while addressing certain challenging computational issues by using these machines. However, certain obstacles are faced by these machines. As huge quantity of components is encompassed in the exascale machines, frequent failure may be experienced and also the resilience may be challenging. High progress rate must be maintained for the applications by incorporating certain form of fault tolerance in the system. Power management has to be performed by incorporating the system in a parallel manner. All layers inclusive of fault tolerance layer must adhere to the power limitation in the system. Huge energy bills may be expected on installation of exascale machines due to the high power consumption. For various fault tolerance models, the energy profile must be analyzed. Parallel recovery, message-logging, and restart or checkpoint fault tolerance models for rollback recovery are evaluated in this paper. For execution with failure, the most energy efficient solution is provided by parallel recovery when programs with various programming models are used. The execution is performed faster with parallel recovery when compared to the other techniques. An analytical model is used for exploring these models and their behavior at extreme scales.
Deep learning for historical books: classification of printing technology for digitized images

Abstract Printing technology has evolved through the past centuries due to technological progress. Within Digital Humanities, images are playing a more prominent role in research. For mass analysis of digitized historical images, bias can be introduced in various ways. One of them is the printing technology originally used. The classification of images to their printing technology e.g. woodcut, copper engraving, or lithography requires highly skilled experts. We have developed a deep learning classification system that achieves very good results. This paper explains the challenges of digitized collections for this task. To overcome them and to achieve good performance, shallow networks and appropriate sampling strategies needed to be combined. We also show how class activation maps (CAM) can be used to analyze the results.
Decentralized Identity and Access Management of Cloud for Security as a Service

Many cyber-related untoward incidents and multiple instances of a data breach of system are being reported. User identity and its usage for valid entry to system depend upon successful authentication. Researchers have explored many threats and vulnerabilities in a centralized system. It has initiated concept of a decentralized way to overcome them. In this work, we have explored application of Self-Sovereign Identity and Verifiable Credentials using decentralized identifiers over cloud.
Physically-secured high-fidelity free-space optical data transmission through scattering media using dynamic scaling factors

In this paper, we propose a method of physically-secured high-fidelity free-space optical data transmission through scattering media using physically- and dynamically-generated scaling factors. Optical channel characteristics are explored, and scaling factors are physically and dynamically generated to serve as security keys in the developed free-space optical data transmission system. The generated dynamic scaling factors provide a security layer for free-space optical data transmission. To the best of our knowledge, it is the first time to physically and dynamically generate scaling factors in free-space optical data transmission system to realize data encryption. The scaling factors existing in free-space optical data transmission channel are physically and dynamically controlled by using two optical devices, i.e., variable beam attenuator (VBA) and amplitude-only spatial light modulator (SLM). Nonlinear and dynamic variation of scaling factors is realized in different free-space wave propagation environments. It is experimentally demonstrated that high security can be guaranteed in the developed physically-secured high-fidelity free-space optical data transmission system, since one random scaling factor is physically and dynamically generated for the transmission of each signal pixel value. In addition, the proposed physically-secured free-space optical data transmission scheme is robust to noise and scattering, and high-fidelity signals are retrieved at the receiving end. The proposed method could open up a new research perspective for the secured free-space optical data transmission.
Parallel ‘same’ and ‘valid’ convolutional block and input-collaboration strategy for histopathological image classification

Histopathological image classification is of great importance in pathological diagnosis, such as tumor grading and cancer type identification. However, the traditional pathological examination is time-consuming and requires the subjective judgments and rich experience of pathologists. In order to alleviate these problems and provide quantitative analysis results, this paper proposes a parallel ‘same’ and ‘valid’ convolutional block (PSV-CB) and an input-collaboration strategy for performing histopathological image classification. The core of PSV-CB is to employ different convolutional operations for learning hidden representations of each input respectively and then correspondingly integrate them together to highlight interesting contents, where an operational flow is constructed via multiple ‘same’ convolutions and followed by a max-pooling, which can be considered as a ‘hard’ feature coding. Another one is established using step-by-step ‘valid’ convolutions that consider feature extraction and down-sampling simultaneously, which can be regarded as a ‘soft’ feature coding. Therefore, the parallel ‘same’ and ‘valid’ convolutional neural network (PSV-ConvNet) is constructed using stacked PSV-CB according to the specific task. To compensate the loss of spatial information, we introduce an input-collaborative PSV-ConvNet (IC-PSV-ConvNet) that adds grayscale version of original inputs into the outputs of each PSV-CB for better fusing global information and learned features. The proposed IC-PSV-ConvNet is evaluated on three histopathological image datasets (lymphoma, breast cancer, and liver cancer) with satisfactory results. Our comparative experiments demonstrate that the proposed IC-PSV-ConvNet can achieve more accurate classification results compared to other existing ConvNets.
Smart Pill Dispenser for Aged Patients

It’s quite common for elder people to easily become the victims of age-related diseases making them to rely on someone for their survival. With people running behind their duties and tasks all round, it has really become a cause of concern to take utmost care of their elders. The most common issue they face is providing them the right medicines in right amount at right time. There are situations where the aged people cannot arrange for the medicines on their own. It is this problem that made us to propose an idea of an autonomous bot, that would administer the medication process. Based on the user’s programmed instructions, the bot will travel to the place of the user and dispenses the required medicines. All these information’s are stored in the cloud for future references and decision making. The proposed bot is developed and tested for different test case and monitored using a separate mobile application.