Reviewerly/exHarmony huggingface dataset:
Entangling bosons through particle indistinguishability and spatial overlap

Particle identity and entanglement are two fundamental quantum properties that work as major resources for various quantum information tasks. However, it is still a challenging problem to understand the correlation of the two properties in the same system. While recent theoretical studies have shown that the spatial overlap between identical particles is necessary for nontrivial entanglement, the exact role of particle indistinguishability in the entanglement of identical particles has never been analyzed quantitatively before. Here, we theoretically and experimentally investigate the behavior of entanglement between two bosons as spatial overlap and indistinguishability simultaneously vary. The theoretical computation of entanglement for generic two bosons with pseudospins is verified experimentally in a photonic system. Our results show that the amount of entanglement is a monotonically increasing function of both quantities. We expect that our work provides an insight into deciphering the role of the entanglement in quantum networks that consist of identical particles.
Real-Time Avoidance Strategy of Dynamic Obstacles via Half Model-Free Detection and Tracking With 2D Lidar for Mobile Robots

Avoidance is a necessary capability for a mobile robot to perform tasks, such as delivering objects in household or industrial scenarios. The existing avoidance strategy based on timed elastic band local planner and cost-map provided by robotics operating system cannot realize the excellent performance when a robot and an obstacle both move. In this article, we present a real-time, simple, and reliable approach to detecting and tracking obstacles via a two-dimensional lidar in dynamic scenarios where the mobile robot and the obstacle are moving. Obstacles are represented by a set of points against their outlines and the information of obstacles is initialized and updated via the raw laser measurement. First, the obstacle is detected by three main steps: preprocessing, segmentation, and merging, classification of consequent measurements. Second, we use a hierarchical method to realize data associations for figuring out the corresponding matches among obstacles with the consecutive time. Last, after doing the data association, we need to estimate the motion of the dynamic obstacle for being tracked by the Kalman filter. Extensive experiments performed in the simulation and practical scenarios indicate that the proposed method enables a mobile robot to perform dynamic avoidances efficiently [Real-time Avoidance Strategy of Dynamic Obstacles via Half Model-free Detection and Tracking (T-MECH)].
MMHAR-EnsemNet: A Multi-Modal Human Activity Recognition Model

In this article, we propose a new deep learning model named as MMHAR-EnsemNet (Multi-Modal Human Activity Recognition Ensemble Network) which makes use of four different modalities to perform sensor-based Human Activity Recognition (HAR).Two separate Convolutional Neural Networks (CNNs) are made for skeleton data. While one CNN and one LSTM is trained for RGB images. For Accelerometer and Gyroscope data first it is converted to signal diagram then another CNN model is trained. Finally, all the outputs of the said models have been used to form an ensemble so that performance of the HAR model gets improved. The proposed model has been evaluated on two standard benchmark datasets namely UTD-MHAD and Berkeley-MHAD which contain four different modalities of input information. Experimental results confirm that the MMHAR-EnsemNet model has outperformed some recently proposed models considered here for comparison. Source code of this work can be found at: https://github.com/abhi1998das/MMHAREnsemNet.
Combining multiple spatial statistics enhances the description of immune cell localisation within tumours

Abstract Digital pathology enables computational analysis algorithms to be applied at scale to histological images. An example is the identification of immune cells within solid tumours. Image analysis algorithms can extract precise cell locations from immunohistochemistry slides, but the resulting spatial coordinates, or point patterns, can be difficult to interpret. Since localisation of immune cells within tumours may reflect their functional status and correlates with patient prognosis, novel descriptors of their spatial distributions are of biological and clinical interest. A range of spatial statistics have been used to analyse such point patterns but, individually, these approaches only partially describe complex immune cell distributions. In this study, we apply three spatial statistics to locations of CD68+ macrophages within human head and neck tumours, and show that images grouped semi-quantitatively by a pathologist share similar statistics. We generate a synthetic dataset which emulates human samples and use it to demonstrate that combining multiple spatial statistics with a maximum likelihood approach better predicts human classifications than any single statistic. We can also estimate the error associated with our classifications. Importantly, this methodology is adaptable and can be extended to other histological investigations or applied to point patterns outside of histology.
Deep Echo State Q-Network (DEQN) and Its Application in Dynamic Spectrum Sharing for 5G and Beyond

Deep reinforcement learning (DRL) has been shown to be successful in many application domains. Combining recurrent neural networks (RNNs) and DRL further enables DRL to be applicable in non-Markovian environments by capturing temporal information. However, training of both DRL and RNNs is known to be challenging requiring a large amount of training data to achieve convergence. In many targeted applications, such as those used in the fifth-generation (5G) cellular communication, the environment is highly dynamic, while the available training data is very limited. Therefore, it is extremely important to develop DRL strategies that are capable of capturing the temporal correlation of the dynamic environment requiring limited training overhead. In this article, we introduce the deep echo state Q-network (DEQN) that can adapt to the highly dynamic environment in a short period of time with limited training data. We evaluate the performance of the introduced DEQN method under the dynamic spectrum sharing (DSS) scenario, which is a promising technology in 5G and future 6G networks to increase the spectrum utilization. Compared with conventional spectrum management policy that grants a fixed spectrum band to a single system for exclusive access, DSS allows the secondary system to share the spectrum with the primary system. Our work sheds light on the application of an efficient DRL framework in highly dynamic environments with limited available training data.
Random Forest with Sampling Techniques for Handling Imbalanced Prediction of University Student Depression

In this work, we propose a combined sampling technique to improve the performance of imbalanced classification of university student depression data. In experimental results, we found that combined random oversampling with the Tomek links under sampling methods allowed generating a relatively balanced depression dataset without losing significant information. In this case, the random oversampling technique was used for sampling the minority class to balance the number of samples between the datasets. Then, the Tomek links technique was used for undersampling the samples by removing the depression data considered less relevant and noisy. The relatively balanced dataset was classified by random forest. The results show that the overall accuracy in the prediction of adolescent depression data was 94.17%, outperforming the individual sampling technique. Moreover, our proposed method was tested with another dataset for its external validity. This dataset’s predictive accuracy was found to be 93.33%.
Explainability and Interpretability: Keys to Deep Medicine

Deep medicine, which aims to push the boundaries of artificial intelligence to reshape the health and medical intelligence and decision making, is a promising concept that is gaining attention over traditional EMR-based medical information management systems. The success of intelligent solutions in health and medicine depends on the degree to which they support interoperability, to allow consistent integration of different systems and data sources, and explainability, to make their decisions understandable, interpretable, and justifiable by humans.
Blockchain Architectures for Physical Internet: A Vision, Features, Requirements, and Applications

Physical Internet (PI) is a global and interconnected logistics system that enables freight transportation and material handling akin to transfer of data packets in computer networks and digital Internet. Blockchain is an emerging and disruptive technology that has the potential to bring ground-breaking innovation and major improvements to PI based transportation and material handling systems. In this article, we describe the advantages of integrating blockchain technology with PI and present suitable architectures. We discuss the enabling concepts required to ensure the feasibility of blockchain based PI networks leading to efficiency and trust for the next generation logistic networks and material handling systems. Furthermore, we present two permissioned block-chain architectures using Hyperledger Fabric and Besu that provide decentralization, privacy, trust, immutability, and transparency in PI networks. Finally, we highlight several use case scenarios that demonstrate the practicality of the proposed blockchain architectures for the PI networks.
Blinder -- Scalable, Robust Anonymous Committed Broadcast

Anonymous Committed Broadcast is a functionality that extends DC-nets and allows a set of clients to privately commit messages to set of servers, which can then simultaneously open all committed messages in a random ordering. Anonymity holds since no one can learn the ordering or the content of the client's committed message. We present Blinder, the first system that provides a scalable and fully robust solution for anonymous committed broadcast. Blinder maintains both properties of security (anonymity) and robustness (aka. 'guaranteed output delivery' or 'availability') in the face of a global active (malicious) adversary. Moreover, Blinder is censorship resistant, that is, an honest client cannot be blocked from participating. Blinder obtains its security and scalability by carefully combining classical and state-of-the-art techniques from the fields of anonymous communication and secure multiparty computation (MPC). Relying on MPC for such a system is beneficial since it naturally allows the parties (servers) to enforce some properties on accepted messages prior their publication. A GPU based implementation of Blinder with 5 servers, which accepts 1 million clients, incurs a latency of less than 8 minutes; faster by a factor of $>100$ than the 3-servers Riposte protocol (S&P '15), which is not robust and not censorship resistant; we get an even larger factor when comparing to AsynchroMix and PowerMix (CCS '19), which are the only ones that guarantee fairness (or robustness in the online phase).
Automated Code-Smell Detection in Microservices Through Static Analysis: A Case Study

Microservice Architecture (MSA) is becoming the predominant direction of new cloud-based applications. There are many advantages to using microservices, but also downsides to using a more complex architecture than a typical monolithic enterprise application. Beyond the normal poor coding practices and code smells of a typical application, microservice-specific code smells are difficult to discover within a distributed application setup. There are many static code analysis tools for monolithic applications, but tools to offer code-smell detection for microservice-based applications are lacking. This paper proposes a new approach to detect code smells in distributed applications based on microservices. We develop an MSANose tool to detect up to eleven different microservice specific code smells and share it as open-source. We demonstrate our tool through a case study on two robust benchmark microservice applications and verify its accuracy. Our results show that it is possible to detect code smells within microservice applications using bytecode and/or source code analysis throughout the development process or even before its deployment to production.
Deeply supervised <scp>U‐Net</scp> for mass segmentation in digital mammograms

Abstract Mass detection is a critical process in the examination of mammograms. The shape and texture of the mass are key parameters used in the diagnosis of breast cancer. To recover the shape of the mass, semantic segmentation is found to be more useful rather than mere object detection (or) localization. The main challenges involved in the mass segmentation include: (a) low signal to noise ratio (b) indiscernible mass boundaries, and (c) more false positives. These problems arise due to the significant overlap in the intensities of both the normal parenchymal region and the mass region. To address these challenges, deeply supervised U‐Net model (DS U‐Net) coupled with dense conditional random fields (CRFs) is proposed. Here, the input images are preprocessed using CLAHE and a modified encoder‐decoder‐based deep learning model is used for segmentation. In general, the encoder captures the textual information of various regions in an input image, whereas the decoder recovers the spatial location of the desired region of interest. The encoder‐decoder‐based models lack the ability to recover the non‐conspicuous and spiculated mass boundaries. In the proposed work, deep supervision is integrated with a popular encoder‐decoder model (U‐Net) to improve the attention of the network toward the boundary of the suspicious regions. The final segmentation map is also created as a linear combination of the intermediate feature maps and the output feature map. The dense CRF is then used to fine‐tune the segmentation map for the recovery of definite edges. The DS U‐Net with dense CRF is evaluated on two publicly available benchmark datasets CBIS‐DDSM and INBREAST. It provides a dice score of 82.9% for CBIS‐DDSM and 79% for INBREAST.
Long Short-Term Memory With Gate and State Level Fusion for Light Field-Based Face Recognition

Long Short-Term Memory (LSTM) is a prominent recurrent neural network for extracting dependencies from sequential data such as time-series and multi-view data, having achieved impressive results for different visual recognition tasks. A conventional LSTM network, hereafter referred only as LSTM network, can learn a model to posteriorly extract information from one input sequence. However, if two or more dependent sequences of data are simultaneously acquired, the LSTM networks may only process those sequences consecutively, not taking benefit of the information carried out by their mutual dependencies. In this context, this paper proposes two novel LSTM cell architectures that are able to jointly learn from multiple sequences simultaneously acquired, targeting to create richer and more effective models for recognition tasks. The efficacy of the novel LSTM cell architectures is assessed by integrating them into deep learning-based methods for face recognition with multi-view, light field images. The new cell architectures jointly learn the scene horizontal and vertical parallaxes available in a light field image, to capture richer spatio-angular information from both directions. A comprehensive evaluation, with the IST-EURECOM LFFD dataset using three challenging evaluation protocols, shows the advantage of using the novel LSTM cell architectures for face recognition over the state-of-the-art light field-based methods. These results highlight the added value of the novel cell architectures when learning from correlated input sequences.
Bayesian information fusion and multitarget tracking for maritime situational awareness

The goal of maritime situational awareness (MSA) is to provide a seamless wide-area operational picture of ship traffic in coastal areas and the oceans in real time. Radar is a central sensing modality for MSA. In particular, oceanographic high-frequency surface-wave (HFSW) radars are attractive for surveying large sea areas at over-the-horizon distances, due to their low environmental footprint and low power requirements. However, their design is not optimal for the challenging conditions prevalent in MSA applications, thus calling for the development of dedicated information fusion and multisensor-multitarget tracking algorithms. In this study, the authors show how the multisensor-multitarget tracking problem can be formulated in a Bayesian framework and efficiently solved by running the loopy sum-product algorithm on a suitably devised factor graph. Compared to previously proposed methods, this approach is advantageous in terms of estimation accuracy, computational complexity, implementation flexibility, and scalability. Moreover, its performance can be further enhanced by estimating unknown model parameters in an online fashion and by fusing automatic identification system (AIS) data and context-based information. The effectiveness of the proposed Bayesian multisensor-multitarget tracking and information fusion algorithms is demonstrated through experimental results based on simulated data as well as real HFSW radar data and real AIS data.
The Experience of Social Touch in Multi-User Virtual Reality

We present user study results on virtual body contact experience in a two-user VR scenario, in which participants performed different touches with a research assistant. The interaction evoked different emotional reactions in perceived relaxation, happiness, desire, anxiety, disgust, and fear. Congruent to physical social touch, the evaluation of virtual body contact was modulated by intimacy, touch direction, and sex. Further, individual comfort with interpersonal touch was positively associated with perceived relaxation and happiness. We discuss the results regarding implications for follow-up studies and infer implications for the use of social touch in social VR applications.
Activities of Daily Living Recognition With Binary Environment Sensors Using Deep Learning: A Comparative Study

The power of end-to-end deep learning techniques to automatically learn latent high-level features from raw signals has been demonstrated in numerous application fields, however, few studies systematically investigate how to properly encode the time-series firings of binary environment sensors that typically work in an event-triggering scheme and have irregular sampling rates for in-home human activity recognition. To this end, we here propose two different methods to process the streaming sensor readings and accordingly evaluate their combinations with deep learning models. Specifically, we divide the multichannel sensor events into segments and encode each segment into either a vector or two-dimensional matrix. Particularly, three different feature representations are presented for the vector form. Afterwards, we combine the encoded features with four typical deep learning models and optimize corresponding activity recognizers to study their sensitivity to different feature encodings. Furthermore, we include seven commonly used shallow classification models for comparison purposes. Finally, we conduct extensive experiments on three publicly available smart home datasets. Results indicate that the performance of both deep learning and shallow models is closely associated with the raw signal encodings and demonstrate the superiority of one-dimensional convolutional neural networks over its competitors in terms of generalization across scenarios. Besides, we preliminarily explore the influence of NULL class on an activity recognizer and experimentally show its negative impact on overall accuracy, enlightening relevant studies to consider it in developing a practical activity recognition system.
Classifying and resolving software product line redundancies using an ontological first-order logic rule based method

Software product line engineering improves software quality and diminishes development cost and time by efficiently developing software products. Its success lies in identifying the commonalities and variabilities of a set of software products which are generally modeled using feature models. The success of software product lines heavily relies upon the quality of feature models to derive high quality products. However, there are various defects that reduce profits of software product line. One of such defect is redundancy. While the majority of research work focuses on the identification of redundancies, their causes and corrections have been poorly explored. Causes and corrections must be as accurate and comprehensible as possible in order to support the developer in resolving the cause of a redundancy. This research work classified redundancies in the form of a typology. An ontological first-order logic rule based method is proposed to deal with redundancies. A two-step process is presented for mapping model to ontology based on predicate logic. First-order logic based rules are developed and applied to the generated ontology for identifying redundancies, their causes and corrections to resolve redundancies. The proposed method is illustrated using a case study from software product lines online tools repository. The results of experiments performed on 35 models with varied sizes of real world models as well as automatically-generated models from the Software Product Line Online Tools repository and models created via FeatureIDE tool conclude that the method is accurate, efficient and scalable with FM up to 30,000 features. Thus, enables deriving redundancy free end products from the product line and ultimately, improves its quality.
KAICD: A knowledge attention-based deep learning framework for automatic ICD coding

Automatic International Classification of Diseases (ICD) coding is an important task in the future of artificial intelligence healthcare. In recent years, a lot of traditional machine learning-based methods have been proposed, and they achieved good results on this task. However, these traditional machine learning-based methods for automatic ICD coding only focus on the semantic features of clinical notes and ignore the feature extraction of ICD titles that are the descriptions of ICD codes. In this paper, we propose a knowledge attention-based deep learning framework called KAICD for automatic ICD coding. KAICD makes full use of the clinic notes and the ICD titles. The semantic features of clinic notes are extracted by a multi-scale convolutional neural network. For ICD titles, we use attention-based Bidirectional Gated Recurrent Unit (Bi-GRU) to build a knowledge database, which can offer additional information. Depending on input clinic notes, we can use the attention mechanism to obtain different knowledge vectors from the knowledge database where some ICD titles are more relevant to the input clinic notes. Last, we concatenate the knowledge vectors and the semantic features of clinic notes, and use them for the final prediction. KAICD is tested on a public dataset Medical Information Mart for Intensive Care III (MIMIC III); it achieves micro-precision of 0.502, micro-recall of 0.428, and micro-f1 of 0.462, which outperforms other competing methods. Furthermore, the results of the ablation study show that the knowledge database of ICD titles learned by the attention-based Bi-GRU enhances the feature expression and improves the prediction performance.