Reviewerly/exHarmony huggingface dataset:
Real-Time Polyp Detection for Colonoscopy Video on CPU

Colorectal cancer(CRC), which is derived from polyps, is a common malignant tumor with a high incidence and mortality. Currently the colonoscopy is the most effective approach for early detection of the colorectal cancer. With the development of object detection, some polyp detection algorithms have achieved satisfied results. However, most of them need the support of hardware accelerators such as GPU, which is unsuitable for actual clinical environment due to its high power consumption and loud noise. To solve the problem, this paper proposes a solution to implement real-time polyp detection for colonoscopy video on general-purpose computing platforms, i.e. CPU. Our solution incorporates several methods to improve efficiency of polyp detection. Firstly, by analyzing the similarity and difference between successive frames in colonoscopy videos, we use the abrupt shot detection method to reduce the unnecessary detection for frames that contain non-polyps; secondly, for the frames that need to be detected, we iterated a procedure of sparse training, pruning and knowledge distillation to search for compressed models. Our approach is evaluated with both public datasets and actual colonoscopy videos, and results show that our approach can process 35.28 frames per second on colonoscopy videos with satisfied precision on CPU.
Straggler Remission for Federated Learning via Decentralized Redundant Cayley Tree

Federated learning, one of the parallel machine learning models, recently has become a promising direction for large scale mobile edge computing. It leverages the computational power of edge devices (i.e., mobile devices) to train machine learning models with distributed data sets on edge devices without uploading data sets to the cloud. The straightforward benefit is avoiding data security concerns. However, federated learning also encounters challenges, like stragglers (end-devices that respond slowly) among devices due to heterogeneity of devices. Most of the existing body of research tries to address the problem through training objects replication and information coding design such as maximum distance separable (MDS) code. In this paper, we propose a decentralized redundant n-Cayley tree (DRC-tree) for federated learning. Our proposal aims to explore the hierarchical structure of the n-Cayley tree to enhance the redundancy rate in federated learning to mitigate the impact of stragglers. In the DRC- tree structure, the fusion node serves as the root node, while all the worker devices are the intermediate tree nodes and leaves that formulated through a distributed message passing interface. the redundancy of workers is constructed layer by layer with a given redundancy branch degree. The optimality of the proposed architecture is theoretically analyzed and simulated experiments are also conducted through CloudLab with synchronous stochastic gradient descent (SGD) implementation. The experimental results show that the proposed DRC-tree architecture reduces the errors caused by stragglers compared with the state of arts.
Exploiting Sequential Mobility for Recommending new Locations on Geo-tagged Social Media

The aim of the paper is to provide a novel location recommendation system exploiting user preference, social relationships and geographic constraints in social media. User preference and social ties are learned from the visited location history also accounting venues proximity to previous check-ins. A framework integrating sequential mobility and user preference is proposed. The framework formulates the recommendation task as a similarity problem among the visiting and mobility profiles of users, accounting the mobility sequentiality in the patterns. Two ranking metrics are introduced to predict places the user could like. The metrics are then combined into an overall recommendation ranking function. The candidate locations are then ranked according to the two similarity measures. The experimental results obtained by using a real-world dataset of tweets show that the proposed method is effective in recommending unseen locations, outperforming representative state-of-the-art approaches.
QMine: A Framework for Mining Quantitative Regular Expressions from System Traces

Dynamic behavior of real-time systems and the ability to distinguish between normal and abnormal behavior is critical in safety-critical systems. Temporal patterns define the order of occurrence of events. Temporal properties help draw insights over system specifications. However, given the complexity of modern-day software in cyber-physical systems, the specifications are either not specified or loosely specified. We propose a framework for automating the task of mining temporal specifications from system traces with both events and quantitative values. Our framework, QMine, is an online property mining framework that extracts properties specified in the form of Quantitative Regular Expression (QRE) templates. QMine is shown to be sound and complete. Moreover, we evaluate our framework using real-world industry-standard traces such as Arrhythmia dataset.
Nipple Localization in Automated Whole Breast Ultrasound Coronal Scans Using Ensemble Learning

Nipple is a vital landmark in the breast lesion diagnosis. Although there are advanced computer-aided detection (CADe) systems for nipple detection in breast mediolateral oblique (MLO) views of mammogram images, few academic works address the coronal views of breast ultrasound (BUS) images. This paper addresses a novel CADe system to locate the Nipple Shadow Area (NSA) in ultrasound images. Here the Hu Moments and Gray-level Co-occurrence Matrix (GLCM) were calculated through an iterative sliding window for the extraction of shape and texture features. These features are then concatenated and fed into an Artificial Neural Network (ANN) to obtain probable NSA's. Later, contour features, such as shape complexity through fractal dimension, edge distance from the periphery and contour area, were computed and passed into a Support Vector Machine (SVM) to identify the accurate NSA in each case. The coronal plane BUS dataset is built upon our own, which consists of 64 images from 13 patients. The test results show that the proposed CADe system achieves 91.99% accuracy, 97.55% specificity, 82.46% sensitivity and 88% F-score on our dataset.
Legitimizing Disruptive Technology

Previous research demonstrates that actors seek acceptance of innovations during the diffusion and adoption process, but exactly what kind of acceptance they seek and how they go about obtaining this remains unclear. This study interrogated academic databases and Twitter to identify and analyze research and grey literature concerning blockchain adoption in human resources (HR). The data was analyzed through the theoretical lens of pragmatic, moral, and cognitive legitimacy to explore how potential adopters of this innovation conceive its value and alignment with their goals. The results show how pragmatic considerations are emphasized around streamlining current HR processes. Arguments for moral legitimacy are considered far less, while narratives on cognitive legitimacy are absent. The findings also show high levels of conformity around legitimation strategies within the community, with only some evidence of selection and manipulation strategies. The authors discuss the implications of these findings for the adoption and diffusion of blockchain in the HR sector.
Hierarchical Planner with Composable Action Models for Asynchronous Parallelization of Tasks and Motions

Task and motion planning is a relevant yet hard to solve problem in robotic manipulation. Large number of degrees of freedom with multiple manipulators and several objects require specialized algorithms, which can deal with the hybrid planning and optimization problem. An additional challenge is the asynchronous parallelization of single robot actions on interacting manipulators. In this paper we propose a system with a hierarchical planner, which solves the task and motion problem and optimizes for a subsequent parallelization. We use action models based on a constraint formulation; thus, the execution engine can parallelize the sequential plan without synchronization between different tasks. In the experiment, we solve a task and motion problem with difficult geometric constraints and combinatorial complexity. The asynchronously parallel execution of that plan is demonstrated on a real world dual-arm robot.
Research on electrochemical discharge machining based on image features and SVM algorithm

The electrochemical discharge machining process is affected by many factors, so the machining process is difficult to be qualitatively analyzed. In order to further understand the characteristics of the electrochemical discharge machining process and better master the machining skills, based on the image features, this article uses the SVM algorithm to build an electrochemical discharge machining system, and uses image feature recognition technology to effectively control the electrochemical discharge machining process. Moreover, this article analyzes the laser back-wet etching process mechanism and discusses the material removal mechanism of electrochemical discharge machining according to the three processes of bubble generation, gas layer formation, and spark discharge removal material. In addition, this article analyzes the system performance according to the actual situation and displays the results through statistical methods. The research results show that the electrochemical discharge machining system based on image features and SVM algorithm constructed in this paper has a good effect, and it can be applied to the management and control of the electrochemical discharge machining process.
Flexible and Efficient Partial Migration of Split-memory VMs

Recently, virtual machines (VMs) with a large amount of memory are being widely used. For flexible migration of such large-memory VMs without large hosts, split migration has been proposed. It transfers VM fragments to multiple smaller hosts and runs a split-memory VM across those hosts with remote paging. However, the traditional method cannot migrate a split-memory VM efficiently because it always migrates the entire VM. In addition, it has to gather all the VM fragments to one host and transfer them from that host. To address these issues, this paper proposes flexible and efficient partial migration of split-memory VMs. In particular, subst migration migrates only part of a split-memory VM to enable the maintenance of some of the hosts running the VM. Merge migration efficiently consolidates VM fragments distributed across multiple hosts into one host by directly transferring a VM fragment from each host. Even if a split-memory VM itself causes remote paging during such partial migration, the consistency of the VM is maintained by retransferring and invalidating target memory. We have implemented partial migration in KVM and showed its efficiency.
Task Scheduling in Cloud Computing Using Spotted Hyena Optimizer

Cloud computing provides internet users with quick and efficient tools to access and share the data. One of the most important research problems that need to be addressed is the effective performance of cloud-based task scheduling. Different cloud-based task scheduling algorithms based on metaheuristic optimization techniques like genetic algorithm (GA) and particle swarm optimization (PSO) scheduling algorithms are demonstrated and analyzed. In this chapter, cloud computing based on the spotted hyena optimizer (SHO) is proposed with a novel task scheduling technique. SHO algorithm is population-based and inspired by nature's spotted hyenas to achieve global optimization over a given search space. The findings show that the suggested solution performs better than other competitor algorithms.
On-line Dense 3D Reconstruction Method based on ANN us-ing a Single Structured Light Pattern

Abstract Dense 3D reconstruction techniques to measuring dynamic scenes and deformable objects with little texture have been widely studied for various applications like surface deformation measurement. We assume that the actual situation of scanning is acquiring sequential shape and measuring surface deformation online at the moment when an object deforms under the action of aerodynamic forces. This paper presents such a technique for on-line acquisition of 3D surface data based on a one-shot scanning method that reconstructs 3D shape from a single image where a simple color-coded pattern using de Bruijn sequence is projected onto an object. The proposed approach has advantages that it requires no assumption of global smoothness and continuous surface. To realize 3D reconstruction from a single image, there are sever-al issues to be solved, for example, difficulty on decoding structured light pattern because of influence of chromatic aberration in both projection systems and imaging systems, and difficul-ty on establishing accurate correspondences between projector and camera pixels. This paper describes the solutions of the issues by combining two methods, that is (1) an efficient and robust pattern decoding method based on artificial neural networks (ANN), and (2) a sub-pixel matching method using phase map and multiple view geometry. Furthermore, we adopt some effective strategies to improve algorithm robustness. Practical experiments were carried out to test the accuracy and efficiency of the scanning system in typical configuration. The results show that the scanning system can reconstruct 3D shape on-line in high-resolution with the accuracy of 0.09 mm and efficiency of 10 fps.
Data Mining Model Based on Improved Ant Colony Algorithm

Abstract Data mining technology is an important means and way of data analysis in the Internet era, and it is a process of extracting potentially useful information from large databases. From the perspective of theory and technology, data mining is a process of discovering the relationship between data and models or between data from a huge database. From the application level, data mining can provide the government and enterprises with valuable and different levels of knowledge, and provide strong technical support for social development. The existing data methods mainly include classification method, association analysis method, cluster analysis method and anomaly detection method, but these methods have some shortcomings. Aiming at the problems of big randomness, sensitivity to parameters and slow convergence speed of existing ant colony clustering algorithm, this paper proposes a data mining model based on improved ant colony algorithm, which will be applied to cluster analysis of experimental data sets. The experimental results show that the improved algorithm has higher accuracy and faster convergence speed, and can effectively realize data information mining.
Differentially Private Machine Learning Model against Model Extraction Attack

Machine learning model is vulnerable to model extraction attacks since the attackers can send plenty of queries to infer the hyperparameters of the machine learning model thus stealing confidential information of the learning models. Therefore, there is a urgent need to defend against such an attack. Differential privacy is a promising technique to protect the valuable information. We propose a differential privacy-based method applied in the linear neural network to obfuscate the output of the machine learning model. The security and utility issue of injecting a noise layer to the linear neural network is mathematically analyzed. The experiment results show that our proposed method can lower the attacker's extraction rate while keeping high utility.
Named Entity Recognition in English Text

Abstract Natural Language processing has been one of the challenging field of computational linguistics. Language processing occurs in several steps in which Named Entity Recognition is one of the prominent phase. NER frameworks have been contemplated and created generally for a considerable length of time, however precise frameworks utilizing profound neural systems (NN) have just been presented over the most recent couple of years. We present a far reaching review of profound neural system models for NER, and balance them with past ways to deal with NER dependent on highlight designing and other regulated or semi-administered learning calculations.
A genetic algorithm-based deep RBF neural network for medical classification

In this paper, we introduce a deep RBF neural network for medical classification. The proposed classifier consists of two parts: an auto-encoder and an RBF neural network. The auto-encoder is used to decrease the number of the characteristics of the presented samples. Then, the obtained new features are presented to the RBF neural network. The design of the RBF neural networks is performed in two stages. First, the subtractive clustering method is used to define the centers of RBFs. Second, the genetic algorithm is used to optimize the widths of RBFs. To assess the proposed classifier, we perform tests over three medical datasets from the UCI machine-learning repository and we compare its performances with other methods.
Large-Scale Unbiased Neuroimage Indexing via 3D GPU-SIFT Filtering and Keypoint Masking

We propose a feature extraction method via a novel description and a scalable GPU implementation (the first to our knowledge) of the 3D scale-invariant feature transform (SIFT). The feature extraction is first represented as a shallow convolutional neural network with pre-computed filters, followed by a masked keypoint analysis. We use the implementation in order to investigate feature extraction for specific instance identification on natural non-skull-stripped magnetic resonance image (MRI) neuroimaging data. The proposed implementation is invariant to 3D similarity transforms and aims to improve robustness by reducing noise and bias for image processing convolution operations. We show interpretable feature visualizations, which help explain the obtained results. We demonstrate state-of-the-art results in large-scale neuroimage family indexing experiments on 3D data from the Human Connectome Project repository, and show significant speed gains compared to a CPU implementation. The results imply that using feature extraction using SIFT for neuroimaging analysis can lead to less noisy results without the need for hard masking during preprocessing. The resulting interpretable features can help understand brain similarities between family members, and can also be used on arbitrary image modalities and anatomical structures.
Geochemical behaviour of heavy metals in sludge effluents and solid deposits on the Zambian Copperbelt: Implication for effluent treatment and sludge reuse

Sludge effluents and solid deposits generated from the conventional lime treatment processes on the Zambian Copperbelt have led to reports of copper (Cu) and cobalt (Co) contamination into the nearby water bodies. To better understand the behaviour of the metals; partitioning, adsorption and their specific binding forms were studied through sequential extraction, batch adsorption experiments and surface complexation modeling (SCM). Results of mineral composition analyses indicated that micas, kaolinite, quartz and feldspar are abundant with hydrous ferric oxide (HFO) precipitates that formed as a result of the weathering of biotite grains existing as grain surface coating. Sequential extractionrevealed that Cu and Co metals are partitioned in the order of: exchangeable (F1: 600–1500 mg/kg Cu; 100–200 mg/kg Co), acid-soluble (F2: 2200–5500 mg/kg Cu; 190–220 mg/kg Co) and reducible fraction (F3: 2200–5500 mg/kg Cu; 260–300 mg/kg Co). Metals in F1 are hosted by kaolinite, F2 by both kaolinite and HFO whereas in F3 by dominantly HFO. Equal Cu concentration between F2 and F3 is due to both the limited amount of HFO (i.e. 5–10 g/kg) and desorption of loosely adsorbed Cu and Co metals to HFO surfaces. Batch adsorption experiments revealed adsorption as the dominant metal retention mechanism. According to modeling predictions, HFO sites are the dominant metal adsorption sites. At HFO site; >(s)FeOCo+, Co showed adsorption decrease from 40% in single system to 25% in binary system between pH 7 - 7.5 due to metal competition for adsorption sites. The high Cu concentration (i.e. 0.5–1.1% Cu) displaced low Co (i.e. 0.03–0.07% Co) concentration from the adsorption sites present in sludge, thus rendering Co mobile into the environment. To keep the adsorbed metals stable from release, optimal pH of 7.5 is suggested during treatment with lime. At this optimal pH, metals are decreased to below the regulation standard values and with less generation of voluminous sludge. Adsorbed Cu and Co can be recoverable from sludge through acid treatment at pH <3 based on sequential extraction results. The resultant metal-free sludge material has potential of been used as aggregate in construction.
Low Cost System for Fall Detection in the Elderly

Over the recent years, Internet of Things have been used to monitor biosignals in a large scale. Large scale demands the use of many biosensors and a complex control system, which may results in high cost technology. This article proposes a simple fall detection system targeting the elderly population at low cost. A prototype based on the Arduino Uno, accelerator and gyroscope was implemented and fixed in a individual's torso. Five types of movement were analyzed (walking, sitting, running, and falling). Data were transferred to a mobile phone using the HC-06 Bluetooth transmission module. The final proposed prototype was about $18.00 and have the advantage of detecting falls automatically. Measurements showed that a fall can be detected by either using the gyroscope or the accelerometer. The findings from this article mighty be useful when working on a multi-parameter device which uses intelligent algorithms to control health and fall among the elderly. With these, algorithms, it is expected to improve the fall detection system so that it also provides the individual's location and history of movements that precede a possible fall.
The N-Tuple Subspace Classifier: Extensions and Survey

This article is written in recognition of W. Bledsoe, who with Browning, introduced the N-tuple subspace classifier in 1959. This 1959 article was the first article to introduce subspace classifiers and the sum rule to combine the outputs of the classifiers. A mathematical notation is given to easily express in a precise and unambiguous way everything going on in the N-tuple subspace classifier. Extensions of the N-tuple method are discussed using a generalized product expression and we relate the generalization to graphical models. We discuss the sum rule, the product rule, and the plurality voting rule for combining the scores of the subspace classifiers. We selected a representative sample of papers that the 1959 N-tuple subspace classifier inspired. Some of the papers introduced specialized improvements. Many of the papers showed the value of the N-tuple subspace classifier in all kinds of applications and compared the results of one or more varieties of the N-tuple subspace classifier with other state-of-the-art classifiers. Their experiments showed that the N-tuple subspace classifier was competitive with the state-of-the-art classifiers and often had a higher accuracy. Finally, we highlight some papers that describe experiments of the N-tuple subspace classifier executing in a quantum computer.