Reviewerly/exHarmony huggingface dataset:
Dense Contour-Imbalance Aware framework for Colon Gland Instance Segmentation

Colon gland images contain rich information about the patient’s condition and the automated instance segmentation results have been widely used to relieve the workload of pathologists and further assist pathologists in making clinical decisions. Previous methods utilize several deep layers’ features and auxiliary contour prediction results to model this task. However, they always fail to capture the complex representation of gland images and bring the contour imbalance problem, resulting in limited performance. To overcome these problems, in this paper, we propose a dense contour-imbalance aware (DCIA) framework by leveraging the recent advances in dense convolutional neural network (DenseNet) and focal loss (FL). First, we integrate all the semantic features generated by DenseNet to explore the “optimal” representation of input image. On this basis, to mitigate the contour imbalance problem in the training stage, we replace common cross-entropy objective with FL. Also, two post-processing techniques, i.e., morphology and convolutional conditional random fields (ConvCRFs) are adopted to further refine the predicted confidence maps, aiming to achieve better performance. Experimental results demonstrate that the proposal outperforms other popular methods on visual perception. Also, compared with the recent popular method DCAN, our framework has higher scores on the testing sets B in terms of three quantitative metrics Object F1, Object Dice, and Object Hausdorff with the improvement of 5.54%, 1.37% and 12.16%, respectively.
First-order and continuous quantum phase transitions in the anisotropic quantum Rabi-Stark model

Various quantum phase transitions in the anisotropic Rabi-Stark model with both the nonlinear Stark coupling and the linear dipole coupling between a two-level system and a single-mode cavity are studied in this work. The first-order quantum phase transitions are detected by the level crossing of the ground-state and the first-excited state with the help of the pole structure of the transcendental functions derived by the Bogoliubov operators approach. As the nonlinear Stark coupling is the same as the cavity frequency, this model can be solved by mapping to an effective quantum oscillator. All energy levels close at the critical coupling in this case, indicating continuous quantum phase transitions. The critical gap exponent is independent of the anisotropy as long as the counter-rotating wave coupling is present, but essentially changed if the counter-rotating wave coupling disappears completely. It is suggested that the gapless Goldstone mode excitations could appear above a critical coupling in the present model in the rotating-wave approximation.
Modulation Classification Method Based on Clustering and Gaussian Model Analysis for VLC System

In the letter, a novel automatic modulation classification (AMC) method is proposed and experimentally demonstrated in orthogonal frequency division multiplexing (OFDM) visible light communication (VLC) system. For the proposed AMC method, the classification feature values are obtained by clustering and Gaussian model analysis. Then these classification feature values are used to classify the modulation format. In addition, the recognition accuracy at different optical signal-noise-ratio (OSNR) is investigated in OFDM-VLC system. The experimental results show that, for 4QAM, 16QAM, 32QAM, and 64QAM with the proposed method, it can be accurately recognized with 2000 symbols at OSNR of 4 dB, 11 dB, 13 dB, and 18 dB, respectively.
A machine learning forensics technique to detect post-processing in digital videos

Technology has brought great benefits to human beings and has served to improve the quality of life and carry out great discoveries. However, its use can also involve many risks. Examples include mobile devices, digital cameras and video surveillance cameras, which offer excellent performance and generate a large number of images and video. These files are generally shared on social platforms and are exposed to any manipulation, compromising their authenticity and integrity. In a legal process, a manipulated video can provide the necessary elements to accuse an innocent person of a crime or to exempt a guilty person from criminal acts. Therefore, it is essential to create robust forensic methods, which will strengthen the justice administration systems and thus make fair decisions. This paper presents a novel forensic technique to detect the post-processing of digital videos with MP4, MOV and 3GP formats. Concretely, detect the social platform and editing program used to execute possible manipulation attacks. The proposed method is focused on supervised machine learning techniques. To achieve our goal, we take advantage that the social platforms and editing programs, execute filtering and compression processes on the videos when they are shared or manipulated. The result of these transformations leaves a characteristic pattern in the videos that allow us to detect the social platform or editing program efficiently. Three phases are involved in the method: 1) Dataset preparation; 2) data features extraction; 3) Supervised model creation. To evaluate the scalability of the technique in real scenarios, we used a robust, heterogeneous and far superior dataset than that used in the literature.
Speaker Verification by Partial AUC Optimization With Mahalanobis Distance Metric Learning

Receiver operating characteristic (ROC) and detection error tradeoff (DET) curves are two widely used evaluation metrics for speaker verification. They are equivalent since the latter can be obtained by transforming the former's true positive y-axis to false negative y-axis and then re-scaling both axes by a probit operator. Real-world speaker verification systems, however, usually work on part of the ROC curve instead of the entire ROC curve given an application. Therefore, we propose in this article to use the area under part of the ROC curve (pAUC) as a more efficient evaluation metric for speaker verification. A Mahalanobis distance metric learning based back-end is applied to optimize pAUC, where the Mahalanobis distance metric learning guarantees that the optimization objective of the back-end is a convex one so that the global optimum solution is achievable. To improve the performance of the state-of-the-art speaker verification systems by the proposed back-end, we further propose two feature preprocessing techniques based on length-normalization and probabilistic linear discriminant analysis respectively. We evaluate the proposed systems on the major languages of NIST SRE16 and the core tasks of SITW. Experimental results show that the proposed back-end outperforms the state-of-the-art speaker verification back-ends in terms of seven evaluation metrics.
Rethinking domain adaptation for machine learning over clinical language

Building clinical natural language processing (NLP) systems that work on widely varying data is an absolute necessity because of the expense of obtaining new training data. While domain adaptation research can have a positive impact on this problem, the most widely studied paradigms do not take into account the realities of clinical data sharing. To address this issue, we lay out a taxonomy of domain adaptation, parameterizing by what data is shareable. We show that the most realistic settings for clinical use cases are seriously under-studied. To support research in these important directions, we make a series of recommendations, not just for domain adaptation but for clinical NLP in general, that ensure that data, shared tasks, and released models are broadly useful, and that initiate research directions where the clinical NLP community can lead the broader NLP and machine learning fields.
Quantum algorithms for typical hard problems: a perspective of cryptanalysis

Abstract In typical well-known cryptosystem, the hardness of classical problems plays a fundamental role in ensuring its security. While, with the booming of quantum computation, some classical hard problems tend to be vulnerable when confronted with the already-known quantum attacks, as a result, it is necessary to develop the post-quantum cryptosystem to resist the quantum attacks. With the purpose to bridge the two disciplines, it is significant to summarize known quantum algorithms and their threats toward these cryptographic intractable problems from a perspective of cryptanalysis. In this paper, we discussed the designing methodology, algorithm framework and latest progress of the mathematic hard problems on which the typical cryptosystems depend, including integer factorization problem, discrete logarithmic problem and its variants, lattice problem, dihedral hidden subgroup problems and extrapolated dihedral coset problem. It illustrated the reason why some cryptosystems such as RSA and ECC are not resistant to quantum attacks, yet some of them like lattice cryptosystems remain intact facing quantum attacks.
Interacting Multiple Model UAV Navigation Algorithm Based on a Robust Cubature Kalman Filter

To improve the precision and robustness of Unmanned Aerial Vehicle (UAV) integrated navigation systems, this paper presents an Interacting Multiple Model (IMM) navigation algorithm based on a Robust Cubature Kalman Filter (RCKF) with modified Zero Velocity Update (ZUPT) method assistance.This algorithm has a two-level fusion structure.At the bottom level, the Global Positioning System/Inertial Navigation System (GPS/INS) integrated navigation model and the Dynamic Zero Velocity Update/Inertial Navigation System (DZUPT/INS) integrated navigation model are established by modifying the Zero Velocity Update (ZUPT) method.Subsequently, the RCKF algorithm adopts a robust factor to weaken the influence of measurement outliers on the filter solution.At the top level, the estimation results of the GPS/INS integrated navigation model and the DZUPT/INS integrated navigation model are fused by the IMM algorithm.In addition to enhancing the robustness of filter estimation in the presence of measurement outliers, the proposed navigation algorithm also corrects navigation errors with ZUPT method assistance.Simulation and experimental analyses demonstrate the performance of the proposed navigation algorithm for UAVs.
Deep-Learning and Depth-Map Based Approach for Detection and 3-D Localization of Small Traffic Signs

The three-dimensional (3-D) geographic locations of street furniture, such as traffic signs, comprise the basic content of 3-D city construction, and such information is indispensable for periodic statistics for road management and maintenance. This article presents a novel solution for acquiring 3-D information on small traffic signs based on mobile mapping system (MMS) data. First, a lightweight backbone network called VGG-L under an optimized faster region-based convolutional neural network detection framework is proposed for the detection of small traffic signs. An urban traffic sign detection (UTSD) dataset is created based on panoramic images obtained from test areas. Detection results from the UTSD dataset show that VGG-L outperforms other popular networks and achieves a mean average precision of 75.4%, which is 4.2%–14.8% higher than those of VGG16, MobileNet, ResNet, and YOLOv3. Second, a novel depth-map-based 3-D spatial geolocation method is proposed for obtaining the 3-D geographic locations of the objects. Then, a center-based method is proposed to automatically extract the final 3-D vector of the target. Experimental results illustrate that the proposed method performs 3-D positioning and vectorization of the milestones and circular and triangular traffic signs, accurately and effectively, achieving greater than 86% recall and precision for the three types of targets in the test areas. The experiments demonstrate that the overall 3-D information acquisition scheme is feasible and has great application potential.
A Survey on Security and Privacy Challenges in Device Discovery for Next-Generation Systems

Device to Device (D2D) communications is a candidate technology for the fifth-generation (5G) and beyond mobile networks and certainly that results in high throughput, less energy consumption, reduce delay, and data traffic offload.Proximity services are the key enablers of D2D communications.A D2D technology boosts the performance and capacity of a conventional cellular system through the proximity services.To initiate the proximity services, Device Discovery (DD) is one of the the primary tasks.A DD makes the decision for effective D2D communications in terms of accuracy, speed, and minimum energy consumption.To discover the neighbor devices, the discovery signal is transmitted directly or through some access points.The discovery signal is affected by invaders during transmission which causes inaccuracy, energy consumption, and latency.Therefore, security and privacy issues must be addressed, especially in discovery signal transmission.In this paper, security and privacy issues in DD are highlighted.It is comprehensive and proves that in-band is much better than out-band with practical and technological reasons.To enhance the scope of the research, network level, and system level Security and Privacy (S&P) issues in the distributed and centralized systems environment with or without central management are surveyed.Along with an extensive survey is provided for the most recent work on DD concerning security and privacy issues, and comparison among in-band and out-band DD is performed.In the end, open issues are identified as future work on DD security and privacy in D2D communications.It is a novel survey in terms of security and privacy aspects of DD with possible suggested solutions for readers' motivation.
Target tracking in the framework of possibility theory: The possibilistic Bernoulli filter

The Bernoulli filter is a Bayes filter for joint detection and tracking of a target in the presence of false and miss detections. This paper presents a mathematical formulation of the Bernoulli filter in the framework of possibility theory, where uncertainty is represented using possibility functions, rather than probability distributions. Possibility functions model the uncertainty in a non-additive manner, and have the capacity to deal with partial (incomplete) problem specification. Thus, the main advantage of the possibilistic Bernoulli filter, derived in this paper, is that it can operate even in the absence of precise measurement and/or dynamic model parameters. This feature of the proposed filter is demonstrated in the context of target tracking using multi-static Doppler shifts as measurements.
Design of a 5 degree of freedom–voice coil motor actuator for smartphone camera modules

Consumers pay most attention to the camera function of smartphones. Thus, manufacturers focus on improving the capabilities of camera modules to deliver clearer and sharper images to consumers. Today, camera modules with auto focusing (AF) and optical image stabilization (OIS) functions are standard in the industry. Specifically, OIS is of the linear movement and rotational movement types. In contrast to the rotational movement type, the commonly used linear movement type cannot compensate for rotational error. Therefore, this paper proposes a novel voice coil motor (VCM) actuator that can supply a 5 degree of freedom (DOF)–compensation that consists of three linear movements and two rotational movements for smartphone camera modules. This novel VCM actuator with 5-DOF compensation is realized using an innovative electromagnetic structure. This paper further verifies the properties of the proposed VCM actuator with a laboratory-built prototype. The experimental results verify the robust properties and feasibility of the proposed VCM actuator.
A Linear-Time Algorithm for Seeds Computation

A seed in a word is a relaxed version of a period in which the occurrences of the repeating subword may overlap. Our first contribution is a linear-time algorithm computing a linear-size representation of all seeds of a word (the number of seeds might be quadratic). In particular, one can easily derive the shortest seed and the number of seeds from our representation. Thus, we solve an open problem stated in a survey by Smyth from 2000 and improve upon a previous O ( n log n )-time algorithm by Iliopoulos et al. from 1996. Our approach is based on combinatorial relations between seeds and subword complexity (used here for the first time in the context of seeds). In previous papers, compact representations of seeds consisted of two independent parts operating on the suffix tree of the input word and the suffix tree of its reverse, respectively. Our second contribution is a novel and significantly simpler representation of all seeds that avoids dealing with the suffix tree of the reversed word. This result is also of independent interest from a combinatorial point of view. A preliminary version of this work, with a much more complex algorithm constructing a representation of seeds on two suffix trees, was presented at the 23rd Annual ACM-SIAM Symposium on Discrete Algorithms (SODA’12).
Integrating Prior Knowledge in Mixed-Initiative Social Network Clustering

We propose a new approach -- called PK-clustering -- to help social scientists create meaningful clusters in social networks. Many clustering algorithms exist but most social scientists find them difficult to understand, and tools do not provide any guidance to choose algorithms, or to evaluate results taking into account the prior knowledge of the scientists. Our work introduces a new clustering approach and a visual analytics user interface that address this issue. It is based on a process that 1) captures the prior knowledge of the scientists as a set of incomplete clusters, 2) runs multiple clustering algorithms (similarly to clustering ensemble methods), 3) visualizes the results of all the algorithms ranked and summarized by how well each algorithm matches the prior knowledge, 4) evaluates the consensus between user-selected algorithms, and 5) allows users to review details and iteratively update the acquired knowledge. We describe our approach using an initial functional prototype, then provide two examples of use and early feedback from social scientists. We believe our clustering approach offers a novel constructive method to iteratively build knowledge while avoiding being overly influenced by the results of often randomly selected black-box clustering algorithms.
Detection and Classification of Public Security Threats in the Philippines Using Neural Networks

Life being put into jeopardy when in public has always been Filipinos' concern. While there are reinforcements of laws, and common practices taught, these are no more than just band-aid solutions to the problem. With the immediate detection and classification of common public security threats through the videos fed from CCTVs, it will be an immense help to protect Filipinos. In this study, the use of pre-trained R-CNN model inception v2 alongside tools for other phases such as annotation, training, and testing will be discussed. The process through which the study attained the goal of the system will be highlighted.
On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games: 25 years later

@article{dung:aij95, author =P. Dung, title= and its fundamental role in non-monotonic reasoning, logic programming and n
A Particle Swarm Optimization Algorithm Based on Time-Space Weight for Helicopter Maritime Search and Rescue Decision-Making

One of the important problems to be solved in maritime search and rescue (MSAR) is decision-making, and the premise of it is determining the mission area for search and rescue unit. To solve the problem that classical cellular iterative search (CIS) algorithm is easy to fall into local optimal solution when determining the mission area, the particle swarm optimization algorithm based on time-space weight (TS-PSO) is proposed in this paper. This algorithm summarizes the optimization objectives and constraint conditions of the MSAR mission area planning according to search theory, carries out the parametric modeling of mission area legitimately and obtains the global optimal solution by continuous exploration in the parameter definition domain. On this basis, by analyzing the time-space weight of drift prediction data, the optimization results are further improved. Finally, through the case simulation analysis, it can be seen that the TS-PSO algorithm can effectively make up for the deficiency of the CIS algorithm and further improve the success probability of optimal MSAR mission area.
Cross-regression for multi-view feature extraction

The traditional multi-view feature extraction (MvFE) method usually seeks a latent common subspace where the samples from different views are maximally correlated. Recently, the regression-based method has become one of the most effective feature extraction methods. However, the existing regression-based methods are only suitable for single-view cases. In this paper, we firstly propose a new MvFE method named as cross-regression for MvFE (CRMvFE). CRMvFE designs a novel cross-regression regularization term to discover the relationship between multiple views in the original space, and simultaneously obtains the low-dimensional projection matrix for each view. Furthermore, inspired by the robustness of L2,1-norm, we also propose a robust CRMvFE (RCRMvFE) and an iterative algorithm to find the optimal solution. Theoretical analysis of the convergence and the relationship with CRMvFE demonstrate the effectiveness of the proposed RCRMvFE. Experiments on datasets show that the proposed CRMvFE and RCRMvFE have better performance than other related methods.
Breast Cancer Detection and Classification

Breast Cancer is more common hence, identification of BC and detection of region of breast affected is more important. Mammography screening images two views CC and MLO are widely use in diagnosis process. This paper presents the method to detect cancer region and classify normal and cancerous patient. Pre-processing operation perform on the input Mammogram image and undesirable part removed from the image, tumor region segmented from the image using morphological operation and highlighted the region on original mammogram image or if mammogram image is normal case then it shows that patient is normal. Random Forest (RF) classifiers is used for classification of BC patient and normal patient. Classification accuracy of RF is 95% for image of different patient. Processing time of RF classifier is 6.25s.
YOLO-v3 Based Currency Detection and Recognition System for Visually Impaired Persons

It is easy for a normal human being to perceive and recognize any banknote effortlessly but it is really much difficult for any visually impaired or blind person to perform the same task. As money has an important role in daily lives for any business transaction, real-time detection and recognition of banknotes become a necessity for a person especially who is blind or visually impaired. For that purpose, the YOLO-v3 CNN model based banknote detection and recognition system is proposed which is fast and accurate. Images of different denominations and in different conditions were are collected initially and then, these images are augmented with different geometric and image transformations on images, to make the system robust. These augmented images are then annotated manually, from which training sets and validation image sets are prepared. Later, the performance of the trained model has evaluated on a real-time scene as well as a test dataset. The test result shows that the proposed YOLO-v3 model based method has detection and recognition accuracy of 95.71% and 100%, respectively. The whole system is standalone and works in real-time.