Reviewerly/exHarmony huggingface dataset:
Overcoming Exploration: Deep Reinforcement Learning for Continuous Control in Cluttered Environments From Temporal Logic Specifications

Model-free continuous control for robot navigation tasks using Deep Reinforcement Learning (DRL) that relies on noisy policies for exploration is sensitive to the density of rewards. In practice, robots are usually deployed in cluttered environments, containing many obstacles and narrow passageways. Designing dense effective rewards is challenging, resulting in exploration issues during training. Such a problem becomes even more serious when tasks are described using temporal logic specifications. This work presents a deep policy gradient algorithm for controlling a robot with unknown dynamics operating in a cluttered environment when the task is specified as a Linear Temporal Logic (LTL) formula. To overcome the environmental challenge of exploration during training, we propose a novel path planning-guided reward scheme by integrating sampling-based methods to effectively complete goal-reaching missions. To facilitate LTL satisfaction, our approach decomposes the LTL mission into sub-goal-reaching tasks that are solved in a distributed manner. Our framework is shown to significantly improve performance (effectiveness, efficiency) and exploration of robots tasked with complex missions in large-scale cluttered environments.
Privacy-preserving federated learning: Application to behind-the-meter solar photovoltaic generation forecasting

The growing usage of decentralized renewable energy sources has made accurate estimation of their aggregated generation crucial for maintaining grid flexibility and reliability. However, the majority of distributed photovoltaic (PV) systems are behind-the-meter (BTM) and invisible to utilities, leading to three challenges in obtaining an accurate forecast of their aggregated output. Firstly, traditional centralized prediction algorithms used in previous studies may not be appropriate due to privacy concerns. There is therefore a need for decentralized forecasting methods, such as federated learning (FL), to protect privacy. Secondly, there has been no comparison between localized, centralized, and decentralized forecasting methods for BTM PV production, and the trade-off between prediction accuracy and privacy has not been explored. Lastly, the computational time of data-driven prediction algorithms has not been examined. This article presents a FL power forecasting method for PVs, which uses federated learning as a decentralized collaborative modeling approach to train a single model on data from multiple BTM sites. The machine learning network used to design this FL-based BTM PV forecasting model is a multi-layered perceptron, which ensures privacy and security of the data. Comparing the suggested FL forecasting model to non-private centralized and entirely private localized models revealed that it has a high level of accuracy, with an RMSE that is 18.17% lower than localized models and 9.9% higher than centralized models.
Blockchain agency theory

Longstanding assumptions underlying strategic alliances, such as agency theory, are actively being revoked by dynamics in the new economy. The mechanism of inter-firm cooperation is increasingly being altered by radical developments in blockchains and artificial intelligence among other technologies. To capture and address this shift, this review takes a problematisation approach and focuses wholly on the pertinence of agency theory. First, it begins by acknowledging the established corpus in the area before, second, appraising the seven long-held assumptions in the principal-agent relationship encompassing (1) self-interest, (2) conflicting goals, (3) bounded rationality, (4) information asymmetry, (5) pre-eminence of efficiency, (6) risk aversion and (7) information as a commodity. Third, to add a fresh perspective, the review proceeds to proffer seven assumptions to advance a novel ‘Blockchain Agency Theory’ that would better describe new attributes and relaxed agency behaviour in blockchain alliances. These counter assumptions are (1) common interests, (2) congruent goals, (3) unbounded rationality, (4) information symmetry, (5) smart contracts, (6) mean risk and (7) information availability. In the fourth part, the prior audience of principals and agents is appraised and this culminates into, fifth, a consideration of a new audience of blockchain agency in algocratic environments. Altogether, the seven new assumptions extend and provoke new agency thinking among scholars and blockchain practitioners alike.
Lung cancer disease prediction with CT scan and histopathological images feature analysis using deep learning techniques

Lung cancer is characterized by the uncontrollable growth of cells in the lung tissues. Early diagnosis of malignant cells in the lungs, which provide oxygen to the human body and excrete carbon dioxide because of important processes, is critical. Because of its potential importance in patient diagnosis and treatment, the use of deep learning for the identification of lymph node involvement on histopathological slides has attracted widespread attention. The existing algorithm performs considerably less in recognition accuracy, precision, sensitivity, F-Score, Specificity, etc. The proposed methodology shows enhanced performance in the metrics with six different deep learning algorithms like Convolution Neural Network (CNN), CNN Gradient Descent (CNN GD), VGG-16, VGG-19, Inception V3 and Resnet-50. The proposed algorithm is analyzed based on CT scan images and histopathological images. The result analysis shows that the detection accuracy is better when histopathological tissues are considered for analysis.
Should large language models replace human participants?

Recent advances in large language models (LLMs) like OpenAI’s GPT-4 and Alphabet’s Bard have captivated people around the world, including cognitive scientists. Recently, Dillion et al. [1] asked whether LLMs can replace human participants in cognitive science research, noting some of the limitations of these models and offering a framework for integrating them into a cognitive science research pipeline. Here, we suggest that alongside asking whether LLMs can replace human participants, we ought to critically consider whether they should. What are we assuming when we explore the possibility of treating LLMs as proxies for human participants? And what are the costs of those assumptions? Examining these questions offers opportunities for us to reflect on our values as a field.
Augmented Reality and 3-Dimensional Dynamic Navigation System Integration for Osteotomy and Root-end Resection

Introduction Augmented reality (AR) superimposes high-definition computer-generated virtual content onto the existing environment, providing users with an enhanced perception of reality. This study investigates the feasibility of integrating an AR head-mounted device into a 3-dimensional dynamic navigation system (3D-DNS) for osteotomy and root-end resection (RER). It compares the accuracy and efficiency of AR + 3D-DNS to 3D-DNS for osteotomy and RER. Methods Seventy-two tooth roots of 3D-printed surgical jaw models were divided into two groups: AR + 3D-DNS (n = 36) and 3D-DNS (n = 36). Cone-beam computed tomography scans were taken pre and postoperatively. The osteotomy and RER were virtually planned on X-guide software and delivered under 3D-DNS guidance. For the AR + 3D-DNS group, an AR head-mounted device (Microsoft HoloLens 2) was integrated into the 3D-DNS. The 2D- and 3D-deviations were calculated. The osteotomy and RER time and the number of procedural mishaps were recorded. Results Osteotomy and RER were completed in all samples (72/72). AR + 3D-DNS was more accurate than 3D-DNS, showing lower 2D- and 3D-deviation values (P < .05). The AR + 3D-DNS was more efficient in time than 3D-DNS (P < .05). There was no significant difference in the number of mishaps (P > .05). Conclusions Within the limitations of this in vitro study, the integration of an AR head-mounted device to 3D-DNS is feasible for osteotomy and RER. AR improved the accuracy and time efficiency of 3D-DNS in osteotomy and RER. Head-mounted AR has the potential to be safely and reliably integrated into 3D-DNS for endodontic microsurgery.
Shortest Route to Non-Abelian Topological Order on a Quantum Processor

A highly coveted goal is to realize emergent non-Abelian gauge theories and their anyonic excitations, which encode decoherence-free quantum information. While measurements in quantum devices provide new hope for scalably preparing such long-range entangled states, existing protocols using the experimentally established ingredients of a finite-depth circuit and a single round of measurement produce only Abelian states. Surprisingly, we show there exists a broad family of non-Abelian states---namely those with a Lagrangian subgroup---which can be created using these same minimal ingredients, bypassing the need for new resources such as feed forward. To illustrate that this provides realistic protocols, we show how ${D}_{4}$ non-Abelian topological order can be realized, e.g., on Google's quantum processors using a depth-11 circuit and a single layer of measurements. Our work opens the way toward the realization and manipulation of non-Abelian topological orders, and highlights counterintuitive features of the complexity of non-Abelian phases.
Superconducting Cavity Qubit with Tens of Milliseconds Single-Photon Coherence Time

A superconducting cavity qubit with coherence time an order of magnitude larger than the state of the art is demonstrated, showing the storage of a Schr\"odinger cat state with a record size of 1024 photons.
Dual Cross-Attention for medical image segmentation

We propose Dual Cross-Attention (DCA), a simple yet effective attention module that enhances skip-connections in U-Net-based architectures for medical image segmentation. The plain and simple skip-connection scheme in U-Net-based architectures struggles with capturing the multi-scale context, resulting in a semantic gap between encoder and decoder features. Such a semantic gap causes redundancy between low and high-level features which ultimately limits the segmentation performance. In this paper, we address this issue by sequentially capturing channel and spatial dependencies across multi-scale encoder features that adaptively combine low and high-level features in various scales to effectively bridge the semantic gap. First, the Channel Cross-Attention (CCA) extracts global channel-wise dependencies by utilizing cross-attention across channel tokens of multi-scale encoder features. Then, the Spatial Cross-Attention (SCA) module performs cross-attention to capture spatial dependencies across spatial tokens. Finally, these fine-grained encoder features are up-sampled and connected to their corresponding decoder parts to form the skip-connection scheme. Our proposed DCA module can be integrated into any encoder–decoder architecture with skip-connections such as U-Net and its variants as well as advanced architectures based on vision transformers. The experimental results using six medical image segmentation datasets demonstrate that our DCA module can consistently improve the overall segmentation performance at a slight parameter increase. Our codes are available at: https://github.com/gorkemcanates/Dual-Cross-Attention.
Building motivation above trust: A blockchain-based NFT GameFi model for construction workload assessment

In the VUCA (Volatile, Uncertain, Complex, Ambiguous) era, construction management faces unique challenges, particularly in the realm of workload assessment. This paper proposes an innovative solution to address these challenges: a blockchain-based GameFi model for construction workload assessment. This model leverages blockchain Non-Fungible Token (NFT) technology to issue various badges, reliably representing employees' workload. The technology ensures the authenticity and tamper-proof nature of the badges, thereby fostering employee motivation through a distributed trust system. This study provides fresh insights into the construction industry's digital transformation and is anticipated to lay the groundwork for similar applications in other sectors.
Prospective implementation of AI-assisted screen reading to improve early detection of breast cancer

Abstract Artificial intelligence (AI) has the potential to improve breast cancer screening; however, prospective evidence of the safe implementation of AI into real clinical practice is limited. A commercially available AI system was implemented as an additional reader to standard double reading to flag cases for further arbitration review among screened women. Performance was assessed prospectively in three phases: a single-center pilot rollout, a wider multicenter pilot rollout and a full live rollout. The results showed that, compared to double reading, implementing the AI-assisted additional-reader process could achieve 0.7–1.6 additional cancer detection per 1,000 cases, with 0.16–0.30% additional recalls, 0–0.23% unnecessary recalls and a 0.1–1.9% increase in positive predictive value (PPV) after 7–11% additional human reads of AI-flagged cases (equating to 4–6% additional overall reading workload). The majority of cancerous cases detected by the AI-assisted additional-reader process were invasive (83.3%) and small-sized (≤10 mm, 47.0%). This evaluation suggests that using AI as an additional reader can improve the early detection of breast cancer with relevant prognostic features, with minimal to no unnecessary recalls. Although the AI-assisted additional-reader workflow requires additional reads, the higher PPV suggests that it can increase screening effectiveness.
Redefining Data Transparency: A Multidimensional Approach

The use of big data combined with powerful machine-learning algorithms raises major concerns over potential adverse effects. Consequently, data transparency is critical for many data-intensive applications. We provide a comprehensive definition, elaborate on various concerns, and articulate an initial road map for critical research challenges.
Evaluating Image Tracking Approaches for Surface Velocimetry With Thermal Tracers

Abstract In this paper an automatic approach is proposed to measure flow velocity with an uncooled thermal camera. Hot water is used as thermal tracer. The introduced tracking algorithm utilizes the pyramidal Lucas‐Kanade method and is especially suitable for thermal image data. The performance of the new tool is compared to traditional image‐based tracking tools, that is, PIVlab and PTVlab. Experiments are performed in the laboratory for three different flow velocities and tests are conducted in a small stream to illustrate the suitability of the tool for field measurements. Results of the laboratory experiments as well as of the field experiments show that our tracking algorithm, applied to imagery from a thermal camera, outperforms commonly used tracking methods. Our tool provides velocity fields with very high resolution and is in close agreement with reference measurements, whereas PTVlab and PIVlab tend to overestimate and underestimate flow velocities, respectively.
Boosting Arabic Named-Entity Recognition With Multi-Attention Layer

Sequence labeling models with recurrent neural network variants, such as long short-term memory (LSTM) and gated recurrent unit (GRU), show promising performance on several natural language processing (NLP) problems, including named-entity recognition (NER).Most existing models utilize word embeddings for capturing similarities between words.However, they lag when handling previously unobserved or infrequent words.Moreover, the attention mechanism has been used to improve sequence labeling tasks.In this paper, we propose an efficient multi-attention layer system for the Arabic named-entity recognition (ANER) task.In addition to word-level embeddings, we adopt character-level embeddings and combine them via an embedding-level attention mechanism.The output is fed into an encoder unit with bidirectional-LSTM, followed by another self-attention layer that is used to boost the system performance.Our model achieves approximately matched F1 score of 91% on the ''ANERCorpus.''The overall experimental results demonstrate that our method is superior to other systems.Our approach using multi-layer attention mechanism yields a new state-of-the-art result for the ANER.
Absolute Cluster Validity

The application of clustering involves the interpretation of objects placed in multi-dimensional spaces. The task of clustering itself is inherently submitted to subjectivity, the optimal solution can be extremely costly to discover and sometimes even unreachable or nonexistent. This fact introduces a trade-off between accuracy and computational effort, moreover given that engineering applications usually work well with suboptimal solutions. In such applied scenarios, cluster validation is mandatory to refine algorithms and ensure that solutions are meaningful. Validity indices are commonly intended to benchmark diverse clustering setups, therefore they are coefficients with a relative nature, i.e., useful when compared to one another. In this paper, we propose a validation methodology that enables absolute evaluations of clustering results. Our method performs geometric measurements of the solution space and provides a coherent interpretation of the data structure by using indices based on inter- and intra-cluster distances, density, and multimodality within clusters. Conducted tests and comparisons with well-known indices show that our validation methodology improves the robustness of the clustering application for knowledge discovery. While clustering is often performed as a black box technique, our index is construable and therefore allows for the implementation of systems enriched with self-checking capabilities.
Classification of multi-carrier digital modulation signals using NCM clustering based feature-weighting method

This work presents a novel digital modulation signal classification model by combining Neutrosophic c-means (NCM) based feature weighting (NCMBFW) and classifier algorithms. As the digital modulation signal, the multi-carrier amplitude shift keying (MC-ASK), frequency shift keying (MC-FSK), and phase shift keying (MC-PSK) modulation types are employed. In the first step, the feature extraction process has been conducted from the raw digital modulation signals and thereby extracted time, frequency, and time-frequency domain features from the multi-carrier ASK, FSK, and PSK signals. After that, these features have been weighted by using NCMBFW. Finally, classifier algorithms including Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), k-nearest neighbor (k-NN), AdaBoostM1, and Random Forest, have been used to determine the types of digital modulation signals automatically. Many metrics are used to evaluate the performance in the experiments. The proposed method in the classification of MC digital modulation signals is the first work with respect to the classification of MC modulation signals in the literature. For worst case (in 5 dB), while the obtained f-measure values are 0.842, 0.848, 0.863, 0.842, and 0.894 using LDA, SVM, k-NN, AdaBoostM1, and Random Forest classifiers without NCMBFW, respectively, while the f-measure values by combining NCMBFW with classifier algorithms are 0.983, 0.976, 0.992, 0.988, and 0.991, respectively. The experimental results show that the proposed NCMBFW can be considered as a promising tool to improve the classification performance of digital multi-carrier modulation signals.
Conversational UX design process

With recent advances in natural language understanding techniques and farfield microphone arrays, natural language interfaces, such as voice assistants and chatbots, are emerging as a popular new way to interact with computers. They have made their way out of the industry research labs and into the pockets, desktops, cars and living rooms of the general public. But although such interfaces recognize bits of natural language, and even voice input, they generally lack conversational competence, or the ability to engage in natural conversation. Today's platforms provide sophisticated tools for analyzing language and retrieving knowledge, but they fail to provide adequate support for modeling interaction. The user experience (UX) designer or software developer must figure out how a human conversation is organized, usually relying on commonsense rather than on formal knowledge. Fortunately, practitioners can rely on conversation science. This book adapts formal knowledge from the field of Conversation Analysis (CA) to the design of natural language interfaces. It outlines the Natural Conversation Framework (NCF), developed at IBM Research, a systematic framework for designing interfaces that work like natural conversation. The NCF consists of four main components: 1) an interaction model of "expandable sequences," 2) a corresponding content format, 3) a pattern language with 100 generic UX patterns and 4) a navigation method of six basic user actions. The authors introduce UX designers to a new way of thinking about user experience design in the context of conversational interfaces, including a new vocabulary, new principles and new interaction patterns. User experience designers and graduate students in the HCI field as well as developers and conversation analysis students should find this book of interest.
Efficient learning from big data for cancer risk modeling: A case study with melanoma

Building cancer risk models from real-world data requires overcoming challenges in data preprocessing, efficient representation, and computational performance. We present a case study of a cloud-based approach to learning from de-identified electronic health record data and demonstrate its effectiveness for melanoma risk prediction. We used a hybrid distributed and non-distributed approach to computing in the cloud: distributed processing with Apache Spark for data preprocessing and labeling, and non-distributed processing for machine learning model training with scikit-learn. Moreover, we explored the effects of sampling the training dataset to improve computational performance. Risk factors were evaluated using regression weights as well as tree SHAP values. Among 4,061,172 patients who did not have melanoma through the 2016 calendar year, 10,129 were diagnosed with melanoma within one year. A gradient-boosted classifier achieved the best predictive performance with cross-validation (AUC = 0.799, Sensitivity = 0.753, Specificity = 0.688). Compared to a model built on the original data, a dataset two orders of magnitude smaller could achieve statistically similar or better performance with less than 1% of the training time and cost. We produced a model that can effectively predict melanoma risk for a diverse dermatology population in the U.S. by using hybrid computing infrastructure and data sampling. For this de-identified clinical dataset, sampling approaches significantly shortened the time for model building while retaining predictive accuracy, allowing for more rapid machine learning model experimentation on familiar computing machinery. A large number of risk factors (>300) were required to produce the best model.