Reviewerly/exHarmony huggingface dataset:
On Future Development of Autonomous Systems: A Report of the Plenary Panel at IEEE ICAS’21

Autonomous Systems (AS) are perceived as the most advanced intelligent systems evolved from those of reflexive, imperative, and adaptive intelligence. A plenary panel on “Future Development of Autonomous Systems” is organized at the inaugural IEEE ICAS’21. This paper reports the panel discussions about the-state-of-the-art and paradigms of AS, the basic research on theoretical foundations and mathematical means of AS, and challenges to the future development of AS. As an emerging and increasingly demanded field, AS provide an unprecedented approach to contemporary intelligent industries including deep machine learning, highly intelligent robotics, cognitive computers, general AI technologies, and industrial applications enabled by transdisciplinary advances in intelligence science, system science, brain science, cognitive science, robotics, computational intelligence, and intelligent mathematics.
Systematizing core properties of pairing-based attribute-based encryption to uncover remaining challenges in enforcing access control in practice

Abstract Attribute-based encryption (ABE) cryptographically implements fine-grained access control on data. As such, data can be stored by an entity that is not necessarily trusted to enforce access control, or an entity that is not even trusted to have access to the plaintext data at all. Instead, access control can be externally enforced by a trusted entity. Additionally, some multi-authority variants of ABE—which do not have a central authority—can effectively and securely implement access control in multiple-domain settings. Furthermore, ABE is the only cryptographic approach to fine-grained access control that does not require an online trusted third party during access requests, and thus provides better availability properties. The actual realization of these theoretical advantages in practice depends on whether current state-of-the-art ABE schemes support the necessary core properties. Much progress has been made in the last two decades in pairing-based ABE schemes, owing to their versatility and efficiency. In fact, it is possible to support most core properties under strong security guarantees, while incurring acceptable storage and computational costs. It is therefore a good time to ask ourselves whether pairing-based ABE has reached its full practical potential. To answer this question, we provide a comprehensive systematized overview of various existing pairing-based ABE schemes and their core properties. We also investigate the relationship between these core properties and real-world access control requirements. We show that a few challenges remain, that must be overcome for ABE to reach its full potential as a mechanism to implement efficient and secure access control in practice.
RCD: Radial Cell Decomposition Algorithm for Mobile Robot Path Planning

Finding the optimum path for mobile robots is now an essential task as lots of autonomous mobile robots are widely used in factories, hospitals, farms, etc. Many path planning algorithms have been developed to finding the optimum path with the minimum processing time. The vertical cell decomposition algorithm (VCD) is one of the popular path planning algorithms. It is able to find a path in a very short time. In this paper, we present a new algorithm, called the Radial cell decomposition (RCD) algorithm, which can generate shorter paths and a slightly faster than VCD algorithm. Furthermore, the VCD algorithm cannot be applied directly to obstacles in special cases, like two vertices have the same x-coordinate; on the other hand, the RCD algorithm can be applied to these special cases directly. In addition to that, the RCD algorithm is very suitable for corridor environments, unlike the VCD algorithm. In this paper, the RCD algorithm is described and tested for both cluttered and corridor environments. Furthermore, Two different algorithms A*, and Vertical cell decomposition are compared to the RCD algorithm. Simulation results confirm the effectiveness of the RCD algorithm in terms of path length and processing time.
Strategies to increase downloads of COVID–19 exposure notification apps: A discrete choice experiment

Exposure notification apps have been developed to assist in notifying individuals of recent exposures to SARS-CoV-2. However, in several countries, such apps have had limited uptake. We assessed whether strategies to increase downloads of exposure notification apps should emphasize improving the accuracy of the apps in recording contacts and exposures, strengthening privacy protections and/or offering financial incentives to potential users. In a discrete choice experiment with potential app users in the US, financial incentives were more than twice as important in decision-making about app downloads, than privacy protections, and app accuracy. The probability that a potential user would download an exposure notification app increased by 40% when offered a $100 reward to download (relative to a reference scenario in which the app is free). Financial incentives might help exposure notification apps reach uptake levels that improve the effectiveness of contact tracing programs and ultimately enhance efforts to control SARS-CoV-2. Rapid, pragmatic trials of financial incentives for app downloads in real-life settings are warranted.
Counterfactually Fair Automatic Speech Recognition

Widely used automatic speech recognition (ASR) systems have been empirically demonstrated in various studies to be unfair, having higher error rates for some groups of users than others. One way to define fairness in ASR is to require that changing the demographic group affiliation of any individual (e.g., changing their gender, age, education or race) should not change the probability distribution across possible speech-to-text transcriptions. In the paradigm of counterfactual fairness, all variables independent of group affiliation (e.g., the text being read by the speaker) remain unchanged, while variables dependent on group affiliation (e.g., the speaker's voice) are counterfactually modified. Hence, we approach the fairness of ASR by training the ASR to minimize change in its outcome probabilities despite a counterfactual change in the individual's demographic attributes. Starting from the individualized counterfactual equal odds criterion, we provide relaxations to it and compare their performances for connectionist temporal classification (CTC) based end-to-end ASR systems. We perform our experiments on the Corpus of Regional African American Languages (CORAAL) and the LibriSpeech dataset to accommodate for differences due to gender, age, education, and race. We show that with counterfactual training, we can reduce average character error rates while achieving lower performance gap between demographic groups, and lower error standard deviation among individuals.
Sentiment Analysis in Twitter Based on Knowledge Graph and Deep Learning Classification

The traditional way to address the problem of sentiment classification is based on machine learning techniques; however, these models are not able to grasp all the richness of the text that comes from different social media, personal web pages, blogs, etc., ignoring the semantic of the text. Knowledge graphs give a way to extract structured knowledge from images and texts in order to facilitate their semantic analysis. This work proposes a new hybrid approach for Sentiment Analysis based on Knowledge Graphs and Deep Learning techniques to identify the sentiment polarity (positive or negative) in short documents, such as posts on Twitter. In this proposal, tweets are represented as graphs; then, graph similarity metrics and a Deep Learning classification algorithm are applied to produce sentiment predictions. This approach facilitates the traceability and interpretability of the classification results, thanks to the integration of the Local Interpretable Model-agnostic Explanations (LIME) model at the end of the pipeline. LIME allows raising trust in predictive models, since the model is not a black box anymore. Uncovering the black box allows understanding and interpreting how the network could distinguish between sentiment polarities. Each phase of the proposed approach conformed by pre-processing, graph construction, dimensionality reduction, graph similarity, sentiment prediction, and interpretability steps is described. The proposal is compared with character n-gram embeddings-based Deep Learning models to perform Sentiment Analysis. Results show that the proposal is able to outperforms classical n-gram models, with a recall up to 89% and F1-score of 88%.
Fast Drivable Areas Estimation with Multi-Task Learning for Real-Time Autonomous Driving Assistant

Autonomous driving is a safety-critical application that requires a high-level understanding of computer vision with real-time inference. In this study, we focus on the computational efficiency of an important factor by improving the running time and performing multiple tasks simultaneously for practical applications. We propose a fast and accurate multi-task learning-based architecture for joint segmentation of drivable area, lane line, and classification of the scene. An encoder–decoder architecture efficiently handles input frames through shared representation. A comprehensive understanding of the driving environment is improved by generalization and regularization from different tasks. The proposed method learns end-to-end through multi-task learning on a very challenging Berkeley Deep Drive dataset and shows its robustness for three tasks in autonomous driving. Experimental results show that the proposed method outperforms other multi-task learning approaches in both speed and accuracy. The computational efficiency of the method was over 93.81 fps at inference, enabling execution in real-time.
Knowledge-infused Learning for Entity Prediction in Driving Scenes

Scene understanding is a key technical challenge within the autonomous driving domain. It requires a deep semantic understanding of the entities and relations found within complex physical and social environments that is both accurate and complete. In practice, this can be accomplished by representing entities in a scene and their relations as a knowledge graph (KG). This scene knowledge graph may then be utilized for the task of entity prediction, leading to improved scene understanding. In this paper, we will define and formalize this problem as Knowledge-based Entity Prediction (KEP). KEP aims to improve scene understanding by predicting potentially unrecognized entities by leveraging heterogeneous, high-level semantic knowledge of driving scenes. An innovative neuro-symbolic solution for KEP is presented, based on knowledge-infused learning, which 1) introduces a dataset agnostic ontology to describe driving scenes, 2) uses an expressive, holistic representation of scenes with knowledge graphs, and 3) proposes an effective, non-standard mapping of the KEP problem to the problem of link prediction (LP) using knowledge-graph embeddings (KGE). Using real, complex and high-quality data from urban driving scenes, we demonstrate its effectiveness by showing that the missing entities may be predicted with high precision (0.87 Hits@1) while significantly outperforming the non-semantic/rule-based baselines.
How humans obtain information from AI: Categorizing user messages in human-AI collaborative conversations

Although there is an increasingly number of research about the design and use of conversational agents, it is still difficult for conversational agents to completely replace human service. Therefore, more and more companies have adopted human-AI collaborative systems to deliver customer service. It is important to understand how people obtain information from human-AI collaborative conversations. While the existing work relies on self-reported methods to elicit qualitative feedback from users, we have concluded a categorization system for user messages in human-AI collaborative conversations after a thorough examination of a real-world customer service log, which could objectively reflect the user's information needs. We categorize user messages into five categories and 15 specific types related to three high-level intentions. Two annotators independently classified the same set of 1,478 user messages from 300 conversations and reached a moderate consistency. We summarize and report the characteristics of different message types and compare their usage in sessions with only human, AI, or both representatives. Our results show that different message types vary significantly in usage frequency, length, and text similarities with other messages in a session. Also, the frequency of using different message types in our dataset seems consistent over sessions with different types of representatives. But we also observed some significant differences in a few specific message types across the sessions with different representatives. Our results are used to suggest some areas for improvement and future work in human-AI collaborative conversational systems.
Flexible and high-resolution surface metrology based on stitching interference microscopy

Small optics of 10 mm-scale are widely used in miniature cameras with increasingly sophisticated surface shape. The surface features ranging from low to high frequency need to be measured efficiently for in-process quality control. Wavefront interferometry is applicable with specifically designed null optics for each surface, but not preferred for small optics because the surface radius is approaching the shortest measurable radius limited by the interferometer optics. We present a strategy for flexible surface metrology based on stitching interference microscopy. The full aperture surface is divided into a series of small subapertures with reduced size and slope which are then measurable with a standard interference microscope. The measurement principle is introduced and a method for subaperture design is proposed by checking the subaperture size and slope after coordinate transformation. A simultaneous stitching algorithm is proposed to minimize the overlapping deviations with overlapping points and subaperture configurations determined automatically in a way of alternating optimization. Instrumentation is easy by integrating a multi-axis platform with a commercial interference microscope. Both the lateral and the vertical scales need to be calibrated to give accurate subaperture measurement of three coordinates. The capability of the instrument is finally verified through a series of experiments compared with cross tests such as the null test and the stylus profilometry. It shows high flexibility to measure a variety of complex surfaces including the high-order even asphere, the “gull-wing” surface and microstructured curved surfaces without using any auxiliary null optics. The instrument enables “one-stop” measurement of surface form, waviness and roughness simultaneously.
Opportunities and Barriers of Using Blockchain in Public Administration: The Case of Real Estate Registration in Kazakhstan

Abstract Blockchain technology has a great potential for improving public administration – its transparency and efficiency. It is also discussed as an instrument for reducing corruption and transaction costs. This paper discusses the potential use of block-chain technology in public administration. It is based on a case-study approach focusing on real estate registration in Kazakhstan. Particular attention is paid to identifying factors hindering the development of the blockchain technology. The paper indicates that the main barriers to further use of blockchain technology in Kazakhstan are insufficient legislation and also the complexity of the technical implementation of blockchain projects and integration with existing systems.
An irrelevant attributes resistant approach to anomaly detection in high-dimensional space using a deep hypersphere structure

It is a grand challenge to detect anomalies existing in subspaces from a high-dimensional space. Most existing state-of-the-art methods implicitly or explicitly rely on distances. Since the contrast, e.g., distances, between data objects in a high-dimensional space becomes more and more similar. Moreover, high-dimensional spaces may include many irrelevant attributes masking anomalies (if the prior probability for a class remains unchanged regardless of the value observed for attribute att, att is said to be irrelevant to a class, i.e., att is an irrelevant attribute). Obviously, anomalies can exist in any of subspaces, so it is difficult to select subspaces that highlight the relevant attributes in an exponential searching space. To address this issue, we proposed a hybrid method consisting of a deep network and a hypersphere to detect anomalies. The deep network in the proposed method is used as a feature extractor to capture the low-dimensional features from the background space. Then, anomalies are separated by using the hypersphere in the feature space reconstructed by probability distribution. To prevent irrelevant attributes from being mistaken for anomalies during mining anomalies, the upper of the number of anomalies is estimated by the Chebyshev theorem. Finally, the proposed method was verified on synthetic datasets and real-world datasets. Experimental results show that the proposed method outperforms the existing state-of-the-art detection methods in regard to the accuracy of mining anomalies and the ability of noise resistance. We find that feature extractors can improve the ability of noise resistance for anomalous detection methods. In the feature space reconstructed by probability distribution, anomalous features are easily identified from irrelevant features and normal features. We also indicate that irrelevant attributes increase the complexity of the feature space, through calculating the probability distribution of data in the background space, the layered features can be extracted to distinguish anomaly classes, normal classes, and irrelevant attribute classes.
High resolution measurement network of global horizontal and tilted solar irradiance in southern Germany with a new quality control scheme

Upscaling techniques are applied to estimate the total power feed-in by PV systems in control areas by grid operators. In order to improve the real-time upscaling of PV power production in the control area of the German transmission system operator TransnetBW we set up a network of 40 solar irradiance measurement stations in southern Germany. The measurement stations consist of a pyranometer, measuring global horizontal irradiance, and three silicon cells oriented east, south, and west with tilt angles of 25°, measuring global tilted irradiance. Irradiance values in minute resolution are transferred in near real-time. In order to ensure a good quality of our data we have developed a new quality control scheme exploiting the sensors with different orientations. Starting with data from September 2020, we will make the quality controlled minute-resolution measurements publicly available on a monthly basis.
Automated Detection Model in Classification of B-Lymphoblast Cells from Normal B-Lymphoid Precursors in Blood Smear Microscopic Images Based on the Majority Voting Technique

Introduction. Acute lymphoblastic leukemia (ALL) is the most common type of leukemia, a deadly white blood cell disease that impacts the human bone marrow. ALL detection in its early stages has always been riddled with complexity and difficulty. Peripheral blood smear (PBS) examination, a common method applied at the outset of ALL diagnosis, is a time-consuming and tedious process that largely depends on the specialist’s experience. Materials and Methods. Herein, a fast, efficient, and comprehensive model based on deep learning (DL) was proposed by implementing eight well-known convolutional neural network (CNN) models for feature extraction on all images and classification of B-ALL lymphoblast and normal cells. After evaluating their performance, four best-performing CNN models were selected to compose an ensemble classifier by combining each classifier’s pretrained model capabilities. Results. Due to the close similarity of the nuclei of cancerous and normal cells, CNN models alone had low sensitivity and poor performance in diagnosing these two classes. The proposed model based on the majority voting technique was adopted to combine the CNN models. The resulting model achieved a sensitivity of 99.4, specificity of 96.7, AUC of 98.3, and accuracy of 98.5. Conclusion. In classifying cancerous blood cells from normal cells, the proposed method can achieve high accuracy without the operator’s intervention in cell feature determination. It can thus be recommended as an extraordinary tool for the analysis of blood samples in digital laboratory equipment to assist laboratory specialists.
IncBL: Incremental Bug Localization

Numerous efforts have been invested in improving the effectiveness of bug localization techniques, whereas little attention is paid to making these tools run more efficiently in continuously evolving software repositories. This paper first analyzes the information retrieval model behind a classic bug localization tool, BugLocator, and builds a mathematical foundation illustrating that the model can be updated incrementally when codebase or bug reports evolve. Then, we present IncBL, a tool for Incremental Bug Localization in evolving software repositories. IncBL is evaluated on the Bugzbook dataset, and the results show that IncBL can significantly reduce the running time by 77.79% on average compared with the re-computing the model, while maintaining the same level of accuracy. We also implement IncBL as a Github App that can be easily integrated into open-source projects on GitHub. Users can deploy and use IncBL locally as well. The demo video for IncBL can be viewed at https://youtu.be/G4gMuvlJSb0, and the source code can be found at https://github.com/soarsmu/IncBL.
Quality and Cost Aware Service Selection in IoT-Context Management Platforms

The IoT-context management platforms (CMPs) have evolved from IoT silos to advanced systems that seamlessly access context providers (third-party IoT-data providers). Such evolution enabled them to respond adequately to dynamically varying needs of context consumers (context-aware IoT appli-cations). Nevertheless, the densely deployed context providers lead to multiple options that potentially complete context re-quests, resulting in the problem of 'which context provider to select ?‘. Moreover, these providers often vary in their service characteristics-Cost of Context (CoC) and Quality of Context (QoC). Therefore, the CMPs should be enabled with QoC and CoC-aware selection mechanisms to select effective context providers. Unfortunately, the Service Level Agreements (SLAs) between the context providers and CMPs that helps design these mechanisms have substantial shortcomings. This paper proposes an SLA template along with a CoC-aware selection mechanism called the Assurance. Using this SLA template, the context providers negotiate their QoC and CoC levels with the CMP. Hence, by having SLAs as a base, the CMP developers can design effective selection mechanisms. When implemented with QoC-aware selection mechanisms, Assurance improves the CMP's cost-effectiveness by selecting the low-cost context providers that deliver the required QoC. This paper also proposes metrics to measure the QoC and CoC outcomes from the invoked provider at the CMP level. Furthermore, it presents a framework that implements these contributions and performs QoC and CoC-aware selection. The evaluation demonstrates that our selection framework improves QoC outcomes and cost-effectiveness com-pared to using a naive approach and QoC-aware selection models without Assurance.