Reviewerly/exHarmony huggingface dataset:
The Uber eXtensible Micro-Hypervisor Framework (uberXMHF)

This chapter presents the uber eXtensible Micro-Hypervisor Framework (uberXMHF), a micro-hypervisor architecture and framework that focuses on three goals which are keys to achieving practical security on commodity platforms: (a) commodity compatibility (e.g., runs unmodified Linux and Windows) and unfettered access to platform hardware; (b) efficient implementation; and (c) low trusted computing base and complexity. uberXMHF strives to be a comprehensible, practical, and flexible platform for performing micro-hypervisor research and development. uberXMHF encapsulates common hypervisor core functionality in a framework that allows developers and users to build custom micro-hypervisor-based solutions (called “uberapps”) while freeing them from a considerable amount of wheel-reinventing that is often associated with such efforts. We are encouraged by the end result—a clean, bare-bones, open-source micro-hypervisor framework with desirable performance characteristics and an architecture amenable to formal analysis. Open-source development continues at: https://uberxmhf.org .
Protection to Personal Data Using Decentralizing Privacy of Blockchain.

In today's world, we deal with various online services, where each person deals with various technologies. These technologies are made for people to make our access to the new world easily. There is a tremendous use of online applications, websites which require large storage. Large data is handled by the online systems. The collection of data in the whole world is about 20% in the last few years. The data is captured from the user, controlled by the systems, and operations are performed on data. It requires more system accuracy and protection to personal data. But the person does not know about the data, where and how it is used where it is stored or whether the data is handled by some organisations for their own use or data is been hacked by another person. This chapter explores protection of data using the decentralized privacy of blockchain.
Gender Identification using Spectral Features and Glottal Closure Instants (GCIs)

Automatic identification of gender from speech may help to improve the performance of the systems such as speaker & speech recognition, forensic analysis, authentication processes. The difference in the physiological parameters of male and female vocal folds results in significant changes in their vocal fold vibration pattern. These changes can be characterized from the differences in the duration of their glottal closure. In this paper, an attempt has been made for gender recognition from speech using spectral features such as MFCCs, LPCCs, etc.; pitch (F0), excitation source features like glottal closure instants (GCIs) and its statistical variations. Western Michigan University's Gender dataset is used for experimentation. The dataset is collected from 93 speakers consisting of speech from 45 male and 48 female speakers respectively. Random forests (RFs) and Support vector machines (SVMs) are used to measure the performance of the proposed features. Random forest is observed to achieve average frame level accuracy of 96.908% using 13 MFCCs, 13 LPCCs, Pitch (F0) and GCI Stats (5). SVM is observed to achieve an average accuracy of 98.607% using 13 MFCCs, 13 LPCCs and GCI Stats (5). From the results, it is observed that the proposed features are efficient in discriminating the gender from speech.
Secure and efficient image retrieval based on global features

Abstract A few years ago, an image retrieval needs have been increased enormously. Therefore, a need for an efficiency and accurate image retrieval scheme has become desired accordingly. In fact, the images require high storage space; for this reason, cloud computing considers the best selective to outsource images. Many outsourcing images to the cloud server such as medical, police investigation, and personal images that require to encrypt before being outsourced to the cloud servers. However, in this paper a robust scheme has been presented which provides Content Based Image Retrieval (CBIR) over the encrypted images without declassing the important information to the cloud server side. Additionally, the information is exchanging in the strong secure manner among entities. The proposed scheme consists of two phases: setup phase and retrieve phase. The first phase based on build secure dictionary based on global features (intensity of histogram) and images decryption while the second phase utilizes secure query to retrieve the queried image. This scheme is a robust to measure the Euclidean Distance similarity between two queries without decryption. The security and efficiency of the proposed scheme are proofed by security analysis and experiments.
A Secure Fine-Grained Micro-Video Subscribing System in Cloud Computing

Micro-video sharing is prevalent nowadays due to the rapid development of personal smart devices, fast growing of network bandwidth and the easy-to-use cloud computing. However, the micro-video sharing system is more vulnerable compared to the conventional video service model because of the vulnerability of the cloud and the hardware computing constraints of personal devices. Ensuring the security and privacy of the micro-videos has become critical for micro-video sharing systems. Recently, a number of encryption schemes have been proposed to protect the security of the micro-videos stored in the cloud. However, these approaches cannot support micro-video sharing functionality. Moreover, the encryption-based method cannot provide the fine-grained access control on encrypted micro-videos. In this paper, we present a notion of fine-grained identity-based broadcast proxy re-encryption to address the issue of fine-grained encrypted micro-video sharing. Further, we present a concrete scheme in the proposed notion and proved its semantic security. Performance and evaluation reveal that our scheme is practical and efficient.
Instrument Designs for Validating Cross-Language Behavioral Differences

Programmers are expected to use multiple programming languages frequently. Studies have found that programmers try to reuse existing knowledge from their previous languages. However, this strategy can result in misconceptions from previous languages. Current learning resources that support the strategy are limited because there is no systematic way to produce or validate the material. We designed three instruments that can help identify and validate meaningful behavior differences between two languages to pinpoint potential misconceptions. To validate the instruments, we examined how Python programmers predict behavior in a less familiar language like R, and whether they expect various R semantics. We found that the instruments are effective in validating differences between Python and R which were linked to misconceptions. We discuss design trade-offs between the three instruments and provide guidelines for researchers and educators in systematically validating programming misconceptions when switching to a new language.
Robust human tracking using harmonious polling tracker

Human tracking is one of the challenging and essential components of an intelligent surveillance system. Variety of correlation filter-based tracking algorithms has presented their brilliance in human tracking. However, few of them have demonstrated disappointments in the presence of occlusion, background clutters, illumination variation, scale variation, fast motion, in-plane rotation, and out of the plane rotation. The paper presents, an improved correlation filter-based tracking algorithm, harmonious polling of patched correlation technique for tracking a human in a video sequence thinking about all the challenging attributes. A human to be tracked is represented by using multiple image patches, as patch-based tracking framework resolves the issues based on occlusion and global scene changes to a great extent. An innovative methodology utilized in the proposed framework is, every individual patch is treated independently thought the process and applied to the polling mechanism, which improves the performance of the system to an extraordinary degree. Kernelized correlation filter is applied to each patch individually generating the correlation score. A polling mechanism is a novel technique used in the proposed framework, which generates the confidence map from the correlation score. The maximum score achieved from the confidence map gives an exact position of the target. The tracker is applied to the number of challenging sequences and contrasted with various outperforming algorithms. The precision and success rate of the proposed tracker is improved by 15% and 19%, respectively. From the qualitative and quantitative analysis, it can be demonstrated that the proposed algorithm beats the cutting-edge execution.
Optical Stereo-System for Full-Field High-Frequency 3D Vibration Measurements Based on Low-Frame-Rate Cameras

Dynamic characterization of vibrating targets represents a critical issue for many industrial fields. In this paper, a stereo-camera system integrated with a Digital Image Correlation (DIC) algorithm is proposed with the aim at performing 3D full-field vibration measurements in the range of kHz. The system exploits two industrial low-speed cameras, and the Nyquist-Shannon frequency limitation is overcome by a down-sampling approach under the hypothesis that the vibration signal is characterized by a single known frequency component. Experimental results obtained from the measurement of vibrational responses of a cantilever plate excited at three high-frequency resonance values (1121 Hz, 2956 Hz and 4010 Hz) are provided. A comparison with numerical analyses evidences the effectiveness of the proposed approach.
A Structure-Behavior Coalescence Method for Integrating SysML Internal Block Diagram with Activity Diagram

SysML uses the internal block diagram (IBD) and the activity diagram (AD) to describe the structure and behavior of a system respectively. Since IBD and AD are heterogeneous and separate models, they are prone to inconsistencies between them, often referred to as model multiplicity problems. This paper proposes a model singularity structure-behavior coalescence (SBC) IBD-AD process algebra to solve the inconsistency between IBD and AD. In this model singularity SBC IBD-AD process algebra, both IBD and AD are its projected views, so the inconsistency between IBD and AD naturally disappears.
Software Engineering Evolution

Software Engineering knowledge has continuously grown at a very high speed. In this work, we aim to provide a perspective on how the body of knowledge of Software Engineering has evolved. For this purpose, we analyzed data of 3,300 works published from 1988 to 2018 in one of the most important conferences on Software Engineering, the International Conference on Software Engineering (ICSE). We identified the main topics investigated in Software Engineering and how the investigation of those topics has evolved over the time. The results bring a compilation of Software Engineering evolution that may be of value to the software community.
Co-Detection in Images Using Saliency and Siamese Networks

Co-Detection is an important problemZinzuvadiya, Milan in computer visionDhameliya, Vatsalkumar, which involves detecting common objects from multiple imagesVaghela, Sanjay. In this paper, we address the co-detectionPatki, Sahil problem and propose an integrated deep learning modelNanavati, Nirali involving two networks for co-detection. Our proposed model detects the objects of individual images using a convolutional neural networkBhavsar, Arnav by generating the saliency maps, which are passed as input in a Siamese neural network to ascertain whether the salient objects in both the images are similar or different. We have tested our model on the iCoseg dataset achieving high-quality results.
On minimal assumptions to obtain a universally composable quantum bit commitment

It is well known, due to the no-go theorems, that perfectly secure bit commitment is impossible. For that reason, there exist many proposals for bit commitment protocols that are secure under certain assumptions (such as the existence of a trusted party, computational hardness, or noisy memories). In this paper, we investigate the minimal assumptions required to obtain a secure bit commitment protocol. We show that it is possible to construct a bit commitment scheme with two parties Alice and Bob as long as there is a third party that is willing to broadcast either entangled qubits for the parties or to produce classical strings that are xor-correlated. We also show that the bit commitment scheme proposed is universally composable.
Analysis of Graph Cut Technique for Medical Image Segmentation

Segmentation plays an important role in image analysis as it is used to identify and differentiate foreground and background regions. Image segmentation in brain MRI analysis performs several roles like extraction of abnormal region for better diagnosis of the disease aiding in the therapy planning. Various brain tumors comprise diverse properties like their shapes, intensity distribution and location, hence reducing the possibility of developing a single general algorithm. In this paper authors have illustrated two methods for performing extraction which includes histogram thresholding and centroid based graph cut segmentation. On the basis of their potential, advantages and limitation comparison is made, that emphasize better performance of centroid based graph cut segmentation method. To measure the performance some quality parameters are evaluated. This paper also solves the problem of initial seed selection by using graph cut segmentation technique.
Development of the Maintainability Index for SPLs Feature Models Using Fuzzy Logic

The variability of the common features in an Software Product Line (SPL) can be managed by an feature model, an artifact that consist of a tree-shaped diagram, that describe the features identified in the products and the possible relationships between them. Guarantee the quality of the feature model may be essential to ensure that errors do not propagate across all products. The process of evaluating the quality of a product or artifact can be done using measures, which may reflect the characteristics, sub-characteristics or attributes of quality. However, the isolated values of each measure do not allow access to a whole quality of the feature model, since most of the measures cover several specific aspects that are not correlated. In this context, this paper proposes the aggregation of measures in order to evaluate the maintainability of the feature model in SPL. We aim to investigate how to aggregate these measures and access the respective sub-characteristics by means of a single aggregate value that has the same available information as a set of measures. For this, we have used the theory of Fuzzy Logic as a technique for aggregation of these measures. The new aggregate measure represents the maintainability index of a feature models (MIFM) was obtained. Moreover, to evaluate the MIFM, we applied it to a set of models. It was verified that the aggregate measure obtained allows to measure if a feature models has a high or low maintainability index, supporting the domain engineer in the evaluation of the maintenance of the feature model in a faster and more precise way.
An Anomaly Pattern Detection Method for Sensor Data

With the development of the Internet of Things (IOT) technology, a large number of sensor data have been produced. Due to the complex acquisition environment and transmission condition, anomalies are prevalent. Sensor data is a kind of typical time series data, its anomaly refers to not only outliers, but also the anomaly of continuous data fragments, namely anomaly patterns. To achieve anomaly pattern detection on sensor data, the characteristics of sensor data are analyzed including temporal correlation, spatial correlation and high dimension. Then based on these characteristics and the real-time processing requirements of sensor data, a sensor data oriented anomaly pattern detection approach is proposed in this paper. In the approach, the frequency domain features of sensor data are obtained by Fast Fourier Transform, the dimension of the feature space is reduced by describing frequency domain features with statistical values, and the high-dimensional sensor data is processed in time on the basis of Isolation Forest algorithm. In order to verify the feasibility and effectiveness of the proposed approach, experiments are carried out on the open dataset IBRL. The experimental results show that the approach can effectively identify the pattern anomalies of sensor data, and has low time cost while ensuring the high accuracy.
Weighted Compositional Vectors for Translating Collocations Using Monolingual Corpora

This paper presents a method to automatically identify bilingual equivalents of collocations using only monolingual corpora in two languages. The method takes advantage of cross-lingual distributional semantics models mapped into a shared vector space, and of compositional methods to find appropriate translations of non-congruent collocations (e.g., pay attention–prestar atenção in English–Portuguese). This strategy is evaluated in the translation of English–Portuguese and English–Spanish collocations belonging to two syntactic patterns: adjective-noun and verb-object, and compared to other methods proposed in the literature. The results of the experiments performed show that the compositional approach, based on a weighted additive model, behaves better than the other strategies that have been evaluated, and that both the asymmetry and the compositional properties of collocations are captured by the combined vector representations. This paper also contributes with two freely available gold-standard data sets which are useful to evaluate the performance of automatic extraction of multilingual equivalents of collocations.
Enhancing Software Requirements Cluster Labeling Using Wikipedia

Clustering plays an important role in reusable requirements retrieval from the ever-growing software project repositories. The literature on requirements cluster labeling is still emerging. Researchers have investigated clustering to support various software engineering activities such as requirements prioritization, feature identification, automated tracing, and code navigation. The primary task in analyzing the clustering results is to "label" the clusters by means of some representative words to summarize and comprehend the requirements data. Despite the development of automatic cluster labeling techniques for software requirements, very little is understood about enhancing the cluster labels using external knowledge sources such as Wikipedia. In this paper, we review the literature on enhancing cluster labeling, present a framework for requirements cluster labeling and conduct an experiment to evaluate how the Wikipedia-based enhancement performs in labeling requirements clusters. The results show that Wikipedia-based labeling outperforms traditional Information Retrieval (IR) techniques. Our work sheds light on improving automated ways to supp.ort information reuse and management in the context of requirements engineering (RE).
Deception Jamming Against Doppler Beam Sharpening Radars

Missile seekers are becoming increasingly more capable of using Doppler Beam Sharpening (DBS) modes as part of the homing cycle. This paper develops jamming theory and uses it for practical implementation of inserting false targets into a DBS image. The theory is also developed for how incorrect estimations of the seeker trajectory can affect the quality and location of the false target. The simulated and experimental results show how received missile seeker waveforms can be modified to create false targets at desired locations.
Conditional anomaly detection based on a latent class model

In recent years, the progress of the Internet of Things has promoted data utilisation in manufacturing industries and has created new possibilities for monitoring the condition of production equipment. By applying anomaly detection procedures to the data acquired from sensors, it is possible to capture early signs of occurring anomalies, which leads to improvement of operating rates and prevention of accidents. However, in conventional anomaly detection procedures, it is not always possible to properly detect anomalies when usage situations change. This is because the definition of anomalies changes depending on the usage situation. In other words, when ‘environment variables’ indicating usage conditions and ‘monitoring variables’ indicating monitoring targets exist, it is necessary to regard them as a conditional anomaly detection problem, which is a problem of detecting anomalies occurring in a monitoring variable on the condition that an environmental variable has occurred. In this paper, we propose a novel analysis procedure to solve such conditional anomaly detection problems. In particular, we propose a conditional anomaly detection procedure when categorical environmental variables and continuous monitoring variables are observed. Through Monte Carlo simulation, we show that the proposed procedure can accurately detect ‘conditional anomalies’ that cannot be detected by conventional procedures.
Performance Analysis of Collaborative Data Mining vs Context Aware Data Mining in a Practical Scenario for Predicting Air Humidity

Predictions in data mining are a difficult process but useful in various areas. The purpose of this article is to make a parallel between the classical data mining process and two new approaches in the process of data mining: collaborative data mining and context-aware data mining. Data gathered from seven meteorological stations in Transylvania served as baseline for the research. Processes for predicting the air humidity were designed and analyzed using the same machine learning algorithms and data. The results obtained prove that collaborative and context-aware data mining approaches bring better results than the standalone approach and highlight some of the algorithms that are more suitable for each approach. The combination of the two notions could be another example of a successful approach for future research.
Multi-Scale Receptive Field Detection Network

Deep convolutional neural networks have contributed much to various computer vision problems including object detection. However, there are still many problems to be solved. Scale variation across object instances is one of the major challenges for object detection. In this paper, we propose a multi-scale receptive field detection network (MS-RFDN), a one-stage approach to detect objects of different scales in the image. The proposed network combines predictions of different scales from feature maps of different scales and receptive fields. To generate s scale-specific feature maps in specific layer, we design a scale-specific concatenation module (SSC module). This scale-specific feature maps are merged from the dense block and dilated block, which has the same size of the receptive field. Through our multi-scale layer network structure and scale-specific feature maps, our model has a significant improvement in small object detection. On the VOC 2007 test dataset, our method almost achieves the effect of the state-of-the-art one-stage methods, which confirmed the effectiveness of our model.