Reviewerly/exHarmony huggingface dataset:
Classifying Breast Cancer Histopathological Images Using a Robust Artificial Neural Network Architecture

Pathological diagnosis is the standard for the diagnosis and identification of breast malignancies. Computer-aided diagnosis (CAD) is widely applied in pathological image analysis to help pathologists improving the accuracy, efficiency, and consistency in diagnosis. The traditional CAD methods rely on the expert domain knowledge, time-consuming feature engineering, which is insufficient to real-world systems. In recent studies, deep learning methods have been explored to improve the performance of pathological CAD. However, typical deep methods mainly suffer from the following limitations on pathological image classification. (i) The model cannot extract rich and informative features due to the shallow network structure. (ii) The commonly adopted patch-wise classification strategy makes it impossible to obtain the global features at the image level. To address the two issues, in this paper we propose to use a deep ResNet structure with Convolutional Block Attention Module (CBAM), in order to extract richer and finer features from pathological images. Moreover, we abandon the patch-wise classification strategy and perform an end-to-end training instead. The public BreakHis dataset is used to evaluate our proposed method. The results show that our model achieves a significant improvement over the baseline methods.
Out of vocabulary word detection and recovery in Arabic handwritten text recognition

Today's Arabic Handwriting recognition systems are able to recognize arbitrary words over a large but finite vocabulary. Systems operating with a fixed vocabulary are bound to encounter so-called out-of-vocabulary (OOV) words. The aim of this research is to propose a two-step approach that tackles the problem of OOV words in Arabic handwriting. In the first step, we exploit different types of sub-word units to detect the potential OOVs. In the recovery stage, a dynamic dictionary is built to extend the initial static word lexicon in order to cope with the detected OOVs. The recovery includes a selection step in which the best word candidates extracted from the external resource are kept. Experiments were conducted on the public benchmarking KHATT and AHTID/MW databases. The obtained results revealed that sub-word modeling could give cues for improving the detection and that the use of a dynamic dictionary significantly improves the recognition performance compared to one-step approaches that are based on a large static dictionary or the combination of different sub-word units. We achieve the state of the art results on the KHATT dataset.
An Improved Firefly Algorithm With Specific Probability and Its Engineering Application

Firefly algorithm (FA) belongs to the swarm intelligence algorithm, which is famous for its strong exploration, a small number of parameter settings and effortless operation. However, there are some drawbacks in the searching process for FA, as the poor accuracy of the solution, high-computational time complexity, and doughty oscillation. These phenomenons are attributed to two factors: 1) in classical FA, the firefly, which is gloomier than others can be attracted by any one of them and 2) FA cannot fully utilize the information of objective function and its fitness. In this paper, to overcome these shortcomings, based on specific probability p <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fit</sub> , a new modified firefly algorithm (pFA) is proposed. In this algorithm, for speeding up the convergence, the specific probability p <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fit</sub> determined by the value of fitness of the firefly is used to choose a neighbor among the better fireflies compared with the predefined firefly, which helps the predefined firefly to move toward a better direction. If there is no neighbor, the opposite learning strategy is employed to lead the firefly to move. The performance of pFA is tested on some well-known benchmark functions. The findings of the test show that pFA is outperformed to FA and some other state-of-the-art algorithms. Finally, we apply pFA to solve four engineering applications.
Discovering Multiple Co-Clusterings in Subspaces

Multiple clustering approaches aim at exploring alternative ways of organizing a given collection of data into various clusters from different perspectives. Although multiple one-way clusterings have been studied for more than a decade, how to explore alternative two-way clusterings (or co-clusterings) still remains an untouched topic, and an important one from an application standpoint. To solve this interesting but yet unexplored topic, we assume the existence of alternative co-clusterings embedded in different subspaces and simultaneously pursue multiple co-clusterings therein. We initially specify a subspace indicator matrix for each feature subspace, and employ matrix tri-factorization to seek row-wise and column-wise cluster indicator matrices in each subspace. To ensure diversity, we quantify the redundancy between pairwise co-clusterings using the cluster indicator and the subspace indicator matrices. We further introduce a unified objective function to simultaneously account for the two pursues, and an alternating optimization solution to iteratively optimize cluster indicator and feature indicator matrices. Our empirical study shows that the proposed solution can explore multiple meaningful co-clusterings and generally achieves better results than state-of-the-art methods.
Multi-region Coverage Path Planning for Heterogeneous Unmanned Aerial Vehicles Systems

Recently unmanned aerial vehicles (UAVs) have been widely adopted by military and civilian applications due to their strong autonomies and adaptabilities. Although UAVs can achieve effective cost reduction and flexibility enhancement in the development of systems with search or surveillance missions, they result in a complex path planning problem. Especially in region coverage systems, coverage path planning problem, which seeks a path that covers all regions of interest, has a NP-Hard computational complexity and is difficult to solve. In this paper, we study the coverage path planning problem for heterogeneous UAVs in multiple region systems. First, with the models of UAVs and regions, an exact formulation based on mixed integer linear programming is presented to produce an optimal coverage path. Then taking into account both the scanning time on regions and the flight time between regions, an efficient heuristic is proposed to assign regions and to obtain coverage orders for UAVs. Finally, experiments are conducted to show the reliability and efficiency of the proposed heuristic from several aspects.
Empirical analysis of weapons of influence, life domains, and demographic-targeting in modern spam: an age-comparative perspective

Spam has been increasingly used for malware distribution. This paper analyzed modern spam from an age-comparative perspective to (i) discover the extent to which psychological weapons of influence and life domains were represented in today's spam emails and (ii) clarify variations in the use of these weapons and life domains by user demographics. Thirty five young and 32 older participants forwarded 18,605 emails from their spam folder to our study email account. A random set of 961 emails were submitted to qualitative content coding and quantitative statistical analysis. Reciprocation was the most prevalent weapon; financial, leisure, and independence the most prevalent life domains. Older adults received health and independence-related spam emails more frequently, while young adults received leisure and occupation-related spam emails more often. These age differences show a level of targeting by user demographics in current spam campaigns. This targeting shows the need for age-tailored demographic warnings highlighting the presence of influence and pretexting (life domains) for suspicious emails for improved response to cyber-attacks that could result from spam distribution. The insights from this study and the produced labeled dataset of spam messages can inform the development of the next generation of such solutions, especially those based on machine learning.
Modeling and Optimizing of the Multi-Layer Nearest Neighbor Network for Face Image Super-Resolution

In this paper, we propose a face super-resolution (FSR) method to handle the decreasing face recognition rate caused by low-quality images. To better model the input images, we build a nearest neighbor network (NNN) which consists of nodes and paths by introducing the second-layer nearest neighbors (SLNNs), where the paths of the network represent the distance between nodes. As the SLNN is trained in the high-resolution (HR) space and is exponentially supplementary to the traditional first-layer nearest neighbors (FLNNs), the neighbor inadequacy problem can be effectively solved by enriching the neighbor candidate set via NNN. Furthermore, we solve the NNN for the optimal weights of neighbors. Finally, we fuse the refined weights and neighbors for better reconstruction results. The effectiveness of this fusion strategy is validated by both quantitative and qualitative experimental results. The extensive experimental results on the public face datasets and real-world challenging low-resolution (LR) images demonstrate that the proposed method performs favorably against the state-of-the-art methods.
Multiple instruments motion trajectory tracking in optical surgical navigation

Optical surgical navigation system has been a hot research topic because of its high accuracy. This paper focuses on identifying and tracking multiple surgical instruments to meet the requirements of applying multiple surgical instruments in clinical medicine. The methods of instrument identification based on the marker's geometrical arrangement and instrument tracking based on markers' motion vector were applied in the proposed algorithm. The experiments of multiple instruments' identification and tracking, the instruments' stability, the space distance and rotation of a pair of instruments at the same tracking time were performed to verify the proposed algorithm. The stereoscopic camera is applied to capture images, and two 850 nm filters are added in front of the binocular camera. The tracking experiment shows that ten instruments can be fully and accurately identified, and then all of them can be quickly and accurately tracked at the same time. A pair of instruments is simultaneously measured in the stability test, such as the typical surgical instrument (TSI) and the miniature surgical instrument (MSI). The ranges of standard deviations (SD) of the stability test for the TSI in the X-, Y-, and Z- axes are from 0.016 mm to 0.127 mm, from 0.011 mm to 0.090 mm, and from 0.124 mm to 0.901 mm, respectively. And the ranges of SDs for the MSI's stability test are from 0.011 mm to 0.133 mm in the X-axis, from 0.010 mm to 0.106 mm in the Y-axis, and from 0.093 mm to 0.932 mm in the Z-axis. The sub-millimeter SDs show that the proposed algorithm has a high stability. Moreover, the space distance test and the rotation test were performed for simultaneously tracking TSI and MSI. All experimental results indicate the proposed algorithm is able to meet the clinical accuracy requirements.
Cognitive Computing Systems: Their Potential and the Future

The articles in this special section focus on cognitive computing systems. Humans are arguably the most intelligent entities in the known universe; the objective of cognitive computing is to understand and replicate the essence of human intelligence. Autonomous systems are self-contained and self-regulated entities that continuously evolve in real time in response to changes to their environment. Fundamental to this evolution is learning and development. Cognition is the basis for autonomous systems. Human cognition refers to processes and systems that enable humans to perform both mundane and specialized tasks. Machine cognition refers to similar processes and systems that enable computers to perform tasks at a level that rivals human performance. While human cognition employs biological and natural means—the brain and mind—for its realization, machine cognition is a type of computation. Cognitive computing systems are autonomous systems that are based on machine cognition. A cognitive system views the mind as a highly parallel information processor, uses various models for representing information, and employs algorithms for transforming and reasoning with the information.
Preliminary application of mxed reality in neurosurgery: Development and evaluation of a new intraoperative procedure

During neurological surgery, neurosurgeons have to transform the two-dimensional (2D) sectional images into three-dimensional (3D) structures at the cognitive level. The complexity of the intracranial structures increases the difficulty and risk of neurosurgery. Mixed reality (MR) applications reduce the obstacles in the transformation from 2D images to 3D visualization of anatomical structures of central nervous system. In this study, the holographic image was established by MR using computed tomography (CT), computed tomography angiography (CTA) and magnetic resonance imaging (MRI) data of patients. The surgeon's field of vision was superimposed with the 3D model of the patient's intracranial structure displayed on the mixed reality head-mounted display (MR-HMD). The neurosurgeons practiced and evaluated the feasibility of this technique in neurosurgical cases. We developed the segmentation image masks and texture mapping including brain tissue, intracranial vessels, nerves, tumors, and their relative positions by MR technologies. The results showed that the three-dimensional imaging is in a stable state in the operating room with no significant flutter and blur. And the neurosurgeon’s feedback on the comfort of the equipment and the practicality of the technology was satisfactory. In conclusion, MR technology can holographically construct a 3D digital model of patient's lesions and improve the anatomical perception of neurosurgeons during craniotomy. The feasibility of the MR-HMD application in neurosurgery is confirmed.
A two-streamed network for estimating fine-scaled depth maps from single RGB images

Estimating depth from a single RGB image is an ill-posed and inherently ambiguous problem. State-of-the-art deep learning methods can now estimate accurate 2D depth maps, but when the maps are projected into 3D, they lack local detail and are often highly distorted. We propose a fast-to-train two-streamed CNN that predicts depth and depth gradients, which are then fused together into an accurate and detailed depth map. To overcome the challenge of learning from limited sized datasets, we define a novel set loss over multiple images. By regularizing the estimation between a common set of images, the network is less prone to over-fitting and achieves better accuracy than competing methods. Our method is applicable to both entire scenes and individual objects and we demonstrate this by evaluating on the NYU Depth v2 and ScanNet datasets for indoor scenes and on the ShapeNet dataset for single man-made objects. Experiments shows that our depth predictions are competitive with state-of-the-art and lead to faithful 3D projections rich in detailing and structure.
Approaching the Communication Constraints of Ethereum-Based Decentralized Applications

Those working on Blockchain technologies have described several new innovative directions and novel services in the Internet of things (IoT), including decentralized trust, trusted and verifiable execution of smart contracts, and machine-to-machine communications and automation that reach beyond the mere exchange of data. However, applying blockchain principles in the IoT is a challenge due to the constraints of the end devices. Because of fierce cost pressure, the hardware resources in these devices are usually reduced to the minimum necessary for operation. To achieve the high coverage needed, low bitrate mobile or wireless technologies are frequently applied, so the communication is often constrained, too. These constraints make the implementation of blockchain nodes for IoT as standalone end-devices impractical or even impossible. We therefore investigated possible design approaches to decentralized applications based on the Ethereum blockchain for the IoT. We proposed and evaluated three application architectures differing in communication, computation, storage, and security requirements. In a pilot setup we measured and analyzed the data traffic needed to run the blockchain clients and their applications. We found out that with the appropriate designs and the remote server architecture we can strongly reduce the storage and communication requirements imposed on devices, with predictable security implications. Periodic device traffic is reduced to 2400 B/s (HTTP) and 170 B/s (Websocket) from about 18 kB/s in the standalone-device full client architecture. A notification about a captured blockchain event and the corresponding verification resulted in about 2000 B of data. A transaction sent from the application to the client resulted in an about 500 B (HTTP) and 300 B message (Websocket). The key store location, which affects the serialization of a transaction, only had a small influence on the transaction-related data. Raw transaction messages were 45 B larger than when passing the JSON transaction objects. These findings provide directions for fog/cloud IoT application designers to avoid unrealistic expectations imposed upon their IoT devices and blockchain technologies, and enable them to select the appropriate system design according to the intended use case and system constraints. However, for very low bit-rate communication networks, new communication protocols for device to blockchain-client need to be considered.
Know What You Don’t Know: Modeling a Pragmatic Speaker that Refers to Objects of Unknown Categories

Zero-shot learning in Language & Vision is the task of correctly labelling (or naming) objects of novel categories.Another strand of work in L&V aims at pragmatically informative rather than "correct" object descriptions, e.g. in reference games.We combine these lines of research and model zero-shot reference games, where a speaker needs to successfully refer to a novel object in an image.Inspired by models of "rational speech acts", we extend a neural generator to become a pragmatic speaker reasoning about uncertain object categories.As a result of this reasoning, the generator produces fewer nouns and names of distractor categories as compared to a literal speaker.We show that this conversational strategy for dealing with novel objects often improves communicative success, in terms of resolution accuracy of an automatic listener.
Robust adaptive visual tracking control for uncertain robotic systems with unknown dead-zone inputs

This paper is concerned with the image-based visual servoing (IBVS) control for uncalibrated camera-robot system with unknown dead-zone constraint, where the uncertain kinematics and dynamics are also considered. The control implementation is achieved by constructing a smooth inverse model for dead-zone-input to eliminate the nonlinear effect resulting from the actuator constraint. A novel adaptive algorithm, which does not require a priori knowledge of the parameter intervals of dead-zone model, is proposed to update the parameter values online, and the dead-zone slopes are not required the same. Furthermore, to accommodate the uncertainties of uncalibrated camera-robot system, adaptation laws are developed to estimate the uncertain parameters, simultaneously avoiding singularity of the image Jacobian matrix. With the full consideration of unknown dead-zone constraint and system uncertainties, an adaptive robust visual tracking control scheme together with dead-zone compensation is subsequently established such that the image tracking error converges to the origin. Based on a 3-DOF manipulator, simulations are conducted to verify the tracking performance of the proposed controller.
Triplet Fusion Network Hashing for Unpaired Cross-Modal Retrieval

With the dramatic increase of multi-media data on the Internet, cross-modal retrieval has become an important and valuable task in searching systems. The key challenge of this task is how to build the correlation between multi-modal data. Most existing approaches only focus on dealing with paired data. They use pairwise relationship of multi-modal data for exploring the correlation between them. However, in practice, unpaired data are more common on the Internet but few methods pay attention to them. To utilize both paired and unpaired data, we propose a one-stream framework triplet fusion network hashing (TFNH), which mainly consists of two parts. The first part is a triplet network which is used to handle both kinds of data, with the help of zero padding operation. The second part consists of two data classifiers, which are used to bridge the gap between paired and unpaired data. In addition, we embed manifold learning into the framework for preserving both inter and intra modal similarity, exploring the relationship between unpaired and paired data and bridging the gap between them in learning process. Extensive experiments show that the proposed approach outperforms several state-of-the-art methods on two datasets in paired scenario. We further evaluate its ability of handling unpaired scenario and robustness in regard to pairwise constraint. The results show that even we discard 50% data under the setting in [19], the performance of TFNH is still better than that of other unpaired approaches and that only 70% pairwise relationships are preserved, TFNH can still outperform almost all paired approaches.
Give Me More Feedback II: Annotating Thesis Strength and Related Attributes in Student Essays

While the vast majority of existing work on automated essay scoring has focused on holistic scoring, researchers have recently begun work on scoring specific dimensions of essay quality.Nevertheless, progress in dimensionspecific essay scoring research is hindered in part by the lack of annotated corpora.To facilitate advances in this area of research, we design a rubric for scoring an important, yet unexplored dimension of persuasive essay quality, thesis strength, and annotate a corpus of essays with thesis strength scores.We additionally identify the attributes that could impact thesis strength and annotate the essays with the values of these attributes, which, when predicted by computational models, could provide feedback to students on why her essay receives a particular thesis strength score.