Reviewerly/exHarmony huggingface dataset:
Intuitionistic Level Set Segmentation for Medical Image Segmentation

Introduction: Image segmentation is one of the basic practices that involve dividing an image into mutually exclusive partitions. Learning how to partition an image into different segments is considered as one of the most critical and crucial step in the area of medical image analysis. Objective: The primary objective of the work is to design an integrated approach for automating the process of level set segmentation for medical image segmentation. This method will help to overcome the problem of manual initialization of parameters. Methods: In the proposed method, input image is simplified by the process of intuitionistic fuzzification of an image. Further segmentation is done by intuitionistic based clustering technique incorporated with local spatial information (S-IFCM). The controlling parameters of level set method are automated by S-IFCM, for defining anatomical boundaries. Results: Experimental results were carried out on MRI and CT-scan images of brain and liver. The results are compared with existing Fuzzy Level set segmentation; Spatial Fuzzy Level set segmentation using MSE, PSNR and Segmentation Accuracy. Qualitatively results achieved after proposed segmentation technique shows more clear definition of boundaries. The attain PSNR and MSE value of propose algorithm proves the robustness of algorithm. Segmentation accuracy is calculated for the segmentation results of the T-1 weighted axial slice of MRI image with 0.909 value. Conclusion: The proposed method shows good accuracy for the segmentation of medical images. This method is a good substitute for the segmentation of different clinical images with different modalities and proves to give better result than fuzzy technique.
Holistic object detection and image understanding

Abstract This paper proposes a new representation of the visual content of an image that allows learning about what elements are part of an image and the hierarchical structure that they form. Our representation is a Top-Down Visual-Tree, where every node represents a bounding box, label, and visual feature of an object existing in the image. Each image and its object annotations from a training dataset are parsed to obtain the proposed visual representation. These images and their parsed tree representations are trained using a Top-Down Tree LSTM (Long Short Term Memory) network. The encoded information, allows integrate object detection and image understanding in a single process. The presented holistic object detection is not agnostic to the overall content of the image, and it is influenced by the image composition and the parts discovered. During testing time, from an image, we are able to infer the most prominent type of objects and their locations, the parts of these objects, and having a proper understanding of the image content through the obtained Top-Down Visual-Tree representation output. The accuracy of our object detection process increases notably respect to the baseline Fast R-CNN method in the visual genome test dataset.
GuideGen: An approach for keeping requirements and acceptance tests aligned via automatically generated guidance

When software-based systems evolve, their requirements change. The changes in requirements affect the associated acceptance tests, which should be adapted accordingly. In practice, however, requirements and their acceptance tests are not always kept up-to-date nor aligned. Such inconsistencies may introduce software quality problems, unintended costs and project delays. In order to keep evolving requirements and their acceptance tests aligned, we are developing an approach called GuideGen. GuideGen automatically generates guidance in natural language about how to adapt the impacted acceptance tests when their requirements change. We have implemented GuideGen as a prototype tool and evaluated it in two studies: first, by assessing the correctness, completeness, understandability and relevance of the generated guidance using three data sets from industry and second, by assessing the applicability and usefulness of the approach and the tool with 23 practitioners from ten companies. When a requirement having more than one associated acceptance test is changed, GuideGen currently generates guidance for all of them together. As a first step towards overcoming this limitation, we assessed how well existing methods for change impact analysis can identify the tests actually impacted by the changes in a requirement. In the first study, we found that GuideGen produced correct guidance in about 67 to 89 percent of all changes. Our approach performed better for agile requirements than for traditional ones. The results of the second study show that GuideGen is perceived to be useful, but that the practitioners would prefer a GuideGen plug-in for commercial tools instead of a standalone tool. Further, in our experiment we could correctly identify the affected acceptance tests for 63% to 91% of the changes in the requirements. Our approach facilitates the alignment of acceptance tests with the actual requirements and can improve the communication between requirements engineers and testers.
Security analysis of SIMECK block cipher against related-key impossible differential

SIMECK is a family of lightweight block ciphers that relies on Feistel structure. Being proposed at CHES in 2015, the round function of SIMECK is slightly modified from SIMON. A cipher in this family with K-bit key and n-bit block is called SIMECKn/K, for n/K∈{32/64,48/96,64/128}. SIMECK has already received a number of third-party analyses. However, the security level on SIMECK against the related-key impossible differential has never been evaluated. In this paper, we consider related-key impossible differential distinguishers for the variants of SIMECK. We first propose some distinguishers on SIMECK using the miss-in-the-middle approach. More specifically, 15/16/19-round related-key impossible differential distinguishers on SIMECK32/48/64 are presented first while the best previously known results were 11/15/17-round on SIMECK32/48/64 in the single-key setting. Afterwards, thanks to MILP approach, we automatically prove that these characteristics are the best related-key impossible differentials of SIMECK when we limit the input and output differences to 1 active bit.
Travel Plans in Public Transit Networks Using Artificial Intelligence Planning Models

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A collaborative CPU‐GPU approach for deep learning on mobile devices

Summary As mobile devices become more prevalent, users tend to reassess their expectations regarding the personalization of mobile services. The data collected by a mobile device's sensors provide an opportunity to gain insight into the user's profile. Recently, deep learning has gained momentum and has become the method of choice for solving machine learning problems. Interestingly, training a deep neural network on a mobile device is often mistakenly regarded as cumbersome. For instance, several deep learning frameworks only provide a CPU‐based implementation for prediction tasks on a mobile device. In contrast to servers, a mobile computing environment imposes many domain‐specific constraints that invite us to review the general computing approach used in a deep learning framework implementation. In this paper, we propose a deep learning framework that has been specifically designed for mobile device platforms. Our approach relies on the collaboration of the multicore CPU and the integrated GPU to accelerate deep learning computation on mobile devices. Our work exploits the shared memory architecture of mobile devices to promote CPU‐GPU collaboration without any data copying. We analyze our approach with regard to three factors: performance/portability trade‐off , power efficiency , and memory management .
Anonymizing building data for data analytics in cross-organizational settings

Low-cost sensors are being installed in smart buildings to gather large amounts of sensor data on building operation and occupant comfort. These sensor data enables the development of data-driven applications and the analysis of building use. Many of such applications are cross-organizational because data are being shared between a building owner and a contractor that works with data at different spatial granularities, e.g., an open plan office or a heating ventilation and air conditioning (HVAC) zone. This is a challenge as 1) sharing the sensor data in its original form can reveal performance indexes amongst occupants and can violate occupant's privacy by revealing behavioral patterns; 2) methods proposed by previous work fails to anonymize the limited number of individual sensor streams available at smaller spatial granularities, e.g., at the zone-level. In this paper, we propose a meta-method, Time-slicer for anonymizing datasets with a limited number of individual sensor streams and for variable length to enable zone-level applications on anonymized data. The evaluation of the Time-Slicer shows that the method provides privacy protection with only a few individual data streams as it can replace the need for individual sensors with past data.
Improved empirical models for estimating surface direct and diffuse solar radiation at monthly and daily level: A case study in North China

Precise knowledge of direct and diffuse solar radiation is important for energy utilization and agricultural activities. However, field measurements in most areas of the world are only for total solar radiation. The satellite-retrieved direct and diffuse solar radiation show poor performance under overcast skies. Therefore, better empirical models are needed to estimate direct and diffuse solar radiation by considering the impact of aerosols over polluted regions. A case study is conducted in North China with the ground-measured solar radiation and satellite-retrieved aerosol optical depth to improve new empirical models at monthly (from 2000 to 2016) and daily (from 2006 to 2009) level. The improved empirical models are validated using the field measurements and compared with the existing models. Results suggest that these models perform well in estimating direct solar radiation at monthly ( R 2 = 0.86–0.91, RMSE = 0.76–0.83 MJ/m 2 ) and daily ( R 2 = 0.91–0.94, RMSE = 1.51–1.64 MJ/m 2 ) level. The accuracy of estimated monthly ( R 2 = 0.95–0.96, RMSE = 0.57–0.65 MJ/m 2 ) and daily ( R 2 = 0.91–0.93, RMSE = 1.09–1.15 MJ/m 2 ) diffuse solar radiation, particularly the maximum diffuse solar radiation value, has been improved compared to the existing models. The models presented in this study can be useful in the improvement and evaluation of solar radiation dataset over polluted regions similar to North China.
Ensembles of density estimators for positive-unlabeled learning

Positive-Unlabeled (PU) learning works by considering a set of positive samples, and a (usually larger) set of unlabeled ones. This challenging setting requires algorithms to cleverly exploit dependencies hidden in the unlabeled data in order to build models able to accurately discriminate between positive and negative samples. We propose to exploit probabilistic generative models to characterize the distribution of the positive samples, and to label as reliable negative samples those that are in the lowest density regions with respect to the positive ones. The overall framework is flexible enough to be applied to many domains by leveraging tools provided by years of research from the probabilistic generative model community. In addition, we show how to create mixtures of generative models by adopting a well-known bagging method from the discriminative framework as an effective and cheap alternative to the classical Expectation Maximization. Results on several benchmark datasets show the performance and flexibility of the proposed approach.
An Automatic Spatial Registration Method for Image-Guided Neurosurgery System

This study aimed to investigate the feasibility of an automatic marker-free patient-to-image spatial registration method based on the 4-points congruent sets (4PCS) and iterative closest point (ICP) algorithm for the image-guided neurosurgery system (IGNS).A portable scanner was used to obtain the point cloud of the patient's entire head. The 4PCS algorithm, which is resilient to noise and outliers, automatically registered the point cloud in the patient space to the surface reconstructed from the patient's preoperative images in the image space without any assumptions about initial alignment. A variant of the ICP algorithm was then used to finish the fine registration. Two phantoms and 3 patients' experiments were performed to demonstrate the effectiveness of the proposed method.In the phantom experiments, the mean target registration error of 15 targets on the surface of the rigid and the elastic phantoms were 1.02 ± 0.18 mm and 1.27 ± 0.36 mm, respectively. In the clinical experiments, the mean target registration error of 7 targets on the first, second and third patient's head were 1.88 ± 0.19 mm, 1.84 ± 0.19 mm, and 1.89 ± 0.18 mm, respectively, which was sufficient to meet clinical requirements. The registration accuracy and registration time using the proposed method are better than that using the method based on manually coarse registration and automatic fine registration.It is feasible to use the automatic spatial registration method based on the 4PCS and ICP algorithm for the IGNS. Moreover, it can replace the spatial registration method based on manually selected anatomical landmarks combined with the automatic fine registration in the currently used IGNS.
Human Activity Recognition Using Multi-modal Data Fusion

The automated recognition of human activity is an important computer vision task, and it has been the subject of an increasing number of interesting home, sports, security, and industrial applications. Approaches using a single sensor have generally shown unsatisfactory performance. Therefore, an approach that efficiently combines data from a heterogeneous set of sensors is required. In this paper, we propose a new method for human activity recognition fusing data obtained from inertial sensors (IMUs), surface electromyographic recording electrodes (EMGs), and visual depth sensors, such as the Microsoft Kinect®. A network of IMUs and EMGs is scattered on a human body and a depth sensor keeps the human in its field of view. From each sensor, we keep track of a succession of primitive movements over a time window, and combine them to uniquely describe the overall activity performed by the human. We show that the multi-modal fusion of the three sensors offers higher performance in activity recognition than the combination of two or a single sensor. Also, we show that our approach is highly robust against temporary occlusions, data losses due to communication failures, and other events that naturally occur in non-structured environments.
Cross-View Self-Similarity Using Shared Dictionary Learning for Cervical Cancer Staging

Dictionary Learning (DL) has gained large popularity in solving different computer vision and medical image problems. However, to the best of our knowledge, it has not been used for cervical tumor staging. More importantly, there have been very limited works on how to aggregate different interactions across data views using DL. As a contribution, we propose a novel cross-view self-similarity low rank shared dictionary learning-based (CVSS-LRSDL) framework, which introduces three major contributions in medical image-based cervical cancer staging: (1) leveraging the complementary of axial and sagittal T2w-magnetic resonance (MR) views for cervical cancer diagnosis, (2) introducing self-similarity (SS) patches for DL training, which explore the unidirectional interaction from a source view to a target one, and (3) extracting features that are shared across tumor grades and grade-specific features using the CVSS-LRSDL learning approach. For the first and second contributions, given an input patch in the source view (axial T2w-MR images), we generate its SS patches within a fixed neighborhood in the target view (sagittal T2w-MR images). Specifically, we produce a unidirectional patch-wise SS from a source to a target view, based on mutual and additional information between both views. As for the third contribution, we represent each individual subject using the weighted distance matrix between views, which is used to train our DL-based classifier to output the label for a new testing subject. Overall, our framework outperformed several DL based multi-label classification methods trained using: (i) patch intensities, (ii) SS single-view patches, and (iii) weighted-SS single-view patches. We evaluated our CVSS-LRSDL framework using 15 T2w-MRI sequences with axial and sagittal views. Our CVSS-LRSDL significantly (p<;0.05) outperformed several comparison methods and obtained an average accuracy of 81.73% for cervical cancer staging.
Novel and Practical SDN-based Traceback Technique for Malicious Traffic over Anonymous Networks

Diverse anonymous communication systems are widely deployed as they can provide the online privacy protection and Internet anti-censorship service. However, these systems are severely abused and a large amount of anonymous traffic is malicious. To mitigate this issue, we propose a novel and practical traceback technique to confirm the communication relationship between the suspicious server and the user. We leverage the software-defined network (SDN) switch at a destination server side to intercept target traffic towards the server and alter the advertised TCP window sizes so as to stealthily vary the traffic rate at the server. By carefully varying the traffic rate, we can successfully modulate a secret signal into the traffic. The traffic carrying the signal passes through the anonymous communication system and reaches the SDN switch at the user side. Then we can detect the modulated signal from the traffic so as to confirm the communication relationship between the server and the user. To validate the feasibility and effectiveness of our technique, extensive real-world experiments are performed using three popular anonymous communication systems, i.e., SSH tunnel, OpenVPN tunnel, and Tor. The results demonstrate that the detection rates approach 100% for SSH and Open VPN and 95% for Tor while the false positive rates are significantly low, approaching 0% for these three systems.
Using Deep Learning to Classify Class Imbalanced Gene-Expression Microarrays Datasets

Performance of deep learning neural networks to classify class imbalanced gene-expression microarrays datasets is studied in this work. The low number of samples and high dimensionality of this type of datasets represent a challenging situation. Three sampling methods which have shown favorable results to deal with the class imbalance problem were used, namely: Random Over-Sampling (ROS), Random Under-Sampling (RUS) and Synthetic Minority Oversampling Technique (SMOTE). Moreover, artificial noise and greater class imbalance were included in the datasets in order to analyze these situations in the context of classification of gene-expression microarrays datasets. Results show that the noise or separability of the dataset is more determinant than its dimensionality in the classifier performance.
Analysis of green manure decomposition parameters in northeast Brazil using association rule networks

The food sector is one of the most critical areas of the economy, and consumers are seeking safer, more readily available, more affordable, and better quality food. Therefore, organic agriculture has become a possible approach for optimizing the characteristics of processed foods. Vegetables have essential uses as green manure, but the greatest difficulty encountered when using these species is related to the time required for their residues to decompose. Computational intelligence and data mining techniques are used widely in agricultural studies and for process improvement purposes. Association rules are used as data mining techniques to identify patterns in large databases, where the patterns identified are analyzed and transformed into a graph to facilitate further analyses. In this study, we employed the patterns identified by association rule networks (ARNs) to determine directly the key parameters related to the half-life decomposition times of legumes used as green manure in northeast Brazil. We validated this method by comparing the results obtained by the ARNs and a decision tree. The proposed approach obtained promising results, thereby demonstrating its capacity to describe a set of objective items and facilitate the development of more integrated hypotheses. Thus, it was possible to optimize the discovery of the key parameters related to green fertilizers in order to identify the best species according to its culture requirements and to enhance productivity.
Model of Integration of Distance Education in a Traditional University: Migration of Cross-Cutting Courses to Distance Learning

Traditional higher education institutions face new challenges, including a new generation of highly connected students who focus on digital tools to learn, learn and stay in touch with the outside world. Indeed, the advent of the internet and the increasing use of digital tools (computers, smartphones, tablets, etc.) require adapting learning models and teaching methods in higher education institutions in Africa and elsewhere in the world. Senegal, which in recent years has begun implementing distance education in public universities, finds in these measures something to take a new boost to improve educational content while optimizing limited resources. However, the distance learning methods offered in these universities are generally geared towards certifying and degree-based distance learning. This does not solve the problems encountered in classical pedagogical training. Based on these limitations, we propose a new model of distance education at the service of a classical university for the optimization of human, financial, material and educational resources. The model is implemented at Alioune Diop University in Bambey, a classical public university in Senegal; and the results obtained have a positive impact on the pedagogy and resources of the university.
Toward Cryptocurrency Lending

Lending has been posited as an application of blockchain technology but it has seen little real deployment. In this paper, we discuss the roadblocks preventing the effortless lending of cryptocurrencies, and we survey a number of possible paths forward. We then provide a novel system, Ụgẉo, consisting of experimental smart contracts written in Solidity and deployed on Ethereum to demonstrate how a decentralized lending infrastructure might be constructed.
Mineralogical and petrographical studies of agricultural soil, Assiut Governorate, Egypt

The lack of information about the Egyptian soil needs more attention, especially mineralogical, petrographical studies, and quality data. Thus, the aim of this study was to evaluate the physicochemical parameters of north Assiut agricultural soil; pH, moisture%, CaCO3%, organic matter (OM%), and mineralogical contents of the studied soil. The studied soil is characterized by alkaline nature, low organic matter, and high CaCO3%. Mineralogically, the studied agricultural soil samples consist mainly of quartz, plagioclase, and minor calcite (as non-clay minerals), as well as montmorillonite, vermiculite, and illite in decreasing abundance order as clay minerals. The plagioclase consists of two minerals; calcined albite and anorthite. The clay minerals in the studied soils may be derived from the old alluvial plain (Plio-Pleistocene sediments) by successive Nile floods and reworking. The recorded clay minerals in the studied soils are montmorillonite, vermiculite, and illite in decreasing order of abundance. They have the ability to retain the irrigation water and adsorb more exchangeable cations in aqueous media which can feed the plants.