A WebExtension framework for experimentation and evaluation of webpage segmentation methods

Current webpages contain areas with different functions and contents. Many studies and applications have used webpage segmentation methods to separate these areas or extract only specific areas for their purposes. Examining these methods requires laborious tasks, such as collecting many webpages, inspecting them with human participants, and applying various performance metrics to their results. Therefore, we developed a WebExtension (browser extension) framework to support the examination and analysis of webpage segmentation methods. This framework can build a WebExtension to collect webpages, curate data for labeling web documents, evaluate methods, and measure the results with various performance metrics in a web browser environment. Furthermore, researchers can use preloaded well-known methods and metrics in the framework and add more methods and metrics for their research purposes

Architectural Support for Secure Virtualization under a Vulnerable Hypervisor

Although cloud computing has emerged as a promising future computing model, security concerns due to malicious tenants have been deterring its fast adoption. In cloud computing, multiple tenants may share physical systems by using virtualization techniques. In such a virtualized system, a software hypervisor creates virtual machines (VMs) from the physical system, and provides each user with an isolated VM. However, the hypervisor, with a full control over hardware resources, can access the memory pages of guest VMs without any restriction. By compromising the hypervisor, a malicious user can access the memory contents of the VMs used by other users. In this paper, we propose a hardware-based mechanism to protect the memory of guest VMs from unauthorized accesses, even with an untrusted hypervisor. With this mechanism, memory isolatio

Context Dependent Memory in the Wilds

Memory retrieval is influenced by both prior and current experiences. The various factors (e.g., frequency, recency, or similarity) may interfere during retrieval due to prior experiences, while the context-dependent memory effect may enhance based on present experiences. Most memory research has been limited to controlled laboratory settings, but this study aims to examine memory retrieval in a more natural setting by using a GPS application (e.g., Traccar Client) to track participants’ daily GPS locations every 60 seconds for 5 weeks. Participants were then asked to recall their locations at a specific time, choosing from all locations visited in the previous 4 weeks. Results demonstrated the existence of the context-dependent memory effect in real-world settings, with more frequent or recent visits leading to increased correct responses. This study is the first to use the current methodology to study the context-dependent memory effect and to measure an individual’s genuine memories in a more ecologically valid way

Examining dependencies among different time scales in episodic memory - An experience sampling study

We re-examined whether memories of different time scales such as week, day of week, and hour of day are used independently during memory retrieval as has been previously argued (​i.e., independence of scales). To overcome the limitations of previous studies, we used experience sampling technology to obtain test stimuli that have higher ecological validity and used pointwise mutual information to directly measure the degree of dependency in a formal way. Participants wore a smartphone around their neck for two weeks, which was equipped with an app that automatically collected time, images, GPS, audio and accelerometry. After a one-week retention interval, participants were presented with an image that was captured during their data collection phase, and were tested on their memory of when the event happened (​i.e., ​week, day of week, and hour). We find that, in contrast to previous arguments, memories of different time scales were not retrieved independently. Moreover, through rendering recurrence plots of the images that the participants collected, we provide evidence the dependency may have originated from the repetitive events that the participants encountered in their daily life

Communication-Reduced Off-Current of NbO2 by Thermal Oxidation of Polycrystalline NbWire

The origin of high leakage current in NbO2 is investigated on the basis of grain size and grain boundary distribution. We used thermally grown and sputtered NbO2 films on polycrystalline niobium microwires. The off-current of the thermally grown film was significantly decreased. This is attributed to the large size of grains in thermally grown film over sputtered one and better quality of oxide film could be grown in the thermal process than sputtering. Our assumptions are supported by Conductive Atomic Force Microscopy studies and simulations. In addition, by introducing 15 nm HfO2 dielectric layer further reduction of the off-current was achieved.111sciescopu

Mango Leaf Disease Recognition and Classification Using Novel Segmentation and Vein Pattern Technique

Mango fruit is in high demand. So, the timely control of mango plant diseases is necessary to gain high returns. Automated recognition of mango plant leaf diseases is still a challenge as manual disease detection is not a feasible choice in this computerized era due to its high cost and the non-availability of mango experts and the variations in the symptoms. Amongst all the challenges, the segmentation of diseased parts is a big issue, being the pre-requisite for correct recognition and identification. For this purpose, a novel segmentation approach is proposed in this study to segment the diseased part by considering the vein pattern of the leaf. This leaf vein-seg approach segments the vein pattern of the leaf. Afterward, features are extracted and fused using canonical correlation analysis (CCA)-based fusion. As a final identification step, a cubic support vector machine (SVM) is implemented to validate the results. The highest accuracy achieved by this proposed model is 95.5%, which proves that the proposed model is very helpful to mango plant growers for the timely recognition and identification of diseases

Multi-layered NiOy/NbOx/NiOy fast drift-free threshold switch with high on/off ratio for selector application

NbO2 has the potential for a variety of electronic applications due to its electrically induced insulatorto-metal transition (IMT) characteristic. In this study, we find that the IMT behavior of NbO2 follows the field-induced nucleation by investigating the delay time dependency at various voltages and temperatures. Based on the investigation, we reveal that the origin of leakage current in NbOx is partly due to insufficient Schottky barrier height originating from interface defects between the electrodes and NbOx layer. The leakage current problem can be addressed by inserting thin NiOy barrier layers. The NiOy inserted NbOx device is drift-free and exhibits high I-on/I-off ratio (> 5400), fast switching speed ( 453 K) characteristics which are highly suitable to selector application for x-point memory arrays.We show that NbOx device with NiOx interlayers in series with resistive random access memory (ReRAM) device demonstrates improved readout margin (> 29 word lines) suitable for x-point memory array application.116Nsciescopu

Multiclass Skin Lesion Classification Using Hybrid Deep Features Selection and Extreme Learning Machine

The variation in skin textures and injuries, as well as the detection and classification of skin cancer, is a difficult task. Manually detecting skin lesions from dermoscopy images is a difficult and time-consuming process. Recent advancements in the domains of the internet of things (IoT) and artificial intelligence for medical applications demonstrated improvements in both accuracy and computational time. In this paper, a new method for multiclass skin lesion classification using best deep learning feature fusion and an extreme learning machine is proposed. The proposed method includes five primary steps: image acquisition and contrast enhancement; deep learning feature extraction using transfer learning; best feature selection using hybrid whale optimization and entropy-mutual information (EMI) approach; fusion of selected features using a modified canonical correlation based approach; and, finally, extreme learning machine based classification. The feature selection step improves the system’s computational efficiency and accuracy. The experiment is carried out on two publicly available datasets, HAM10000 and ISIC2018. The achieved accuracy on both datasets is 93.40 and 94.36 percent. When compared to state-of-the-art (SOTA) techniques, the proposed method’s accuracy is improved. Furthermore, the proposed method is computationally efficient