Reviewerly/exHarmony huggingface dataset:
Anomaly Detection and Identification Using Visual Techniques in Streaming Video

There are many intelligent systems and tools which uses highly efficient processing models to identify different anomalies with high accuracy. The anomaly detection is of high importance and mostly will come as an absolute requirement at high risk environments and situations. The amount of processing involved in quick decision taking systems bare high deployment costs which restricts the anomaly detection only to a selected few who are capable of building such resource centered systems. Modern world uses drones and other video feeds in order to find and keep track of any anomalous events around a specific area. But most such detection requires absolute manual attention as well as processing power to keep up with real time detection and recognition. The proposed research solution aims to automate this process and includes a two-step anomaly detection system which gives a quicker anomaly detection in an average processing unit time with an advanced recognition model with up to 90% accuracy. The deep learning model (VGG 16) together with alert system and comparison techniques on videos leads into unsupervised anomaly detection of a landscape. The system generates alerts and recognizes anomalies on the alerted video frames. The proposed solution can also be used by any source and does not require high capacity of capability system to get the optimal output. Moreover, the solution brings a simple yet sophisticated technique to address modern anomaly detection and quick alerting system.
Software Product Quality Evaluation Guide for Electronic Health Record Systems

Software product quality evaluation is an important process that benefits the system developer and the end user. Software products in domain specific, such as those in the health field, need specific treatments for their evaluation. This article presents a proposal for adaptations of a generic software product quality evaluation guide to evaluate a software domain specific. For the adaptations, the MEDE-PROS software product quality evaluation guide, the ISO/IEC 25010:2011, and the SBIS/CFM certification for electronic health record (EHR) systems were used. The purpose of these adaptations was to obtain an update of the MEDE-PROS guide, allowing to inspect quality attributes in a EHR system. To demonstrate the applicability of the guide after the adaptations, two case studies were performed in three health information systems. With the preliminary results of the case study, it was checked that the adaptations made in the guide were consistent, updating the guide with the ISO/IEC 25010:2011 standard and making it specialist in EHR systems, contributing with system evaluation more effectively in both guide versions.
Disruptive Engineering and Education in Emerging Economies: Challenges and Prospects

A plethora of literature have existed on disruptive technologies revealing its relevance in academia and application in the field of engineering education. The directions of possible problems of the application in the field of engineering education however are still obscured despite the abundant available literature with different opinions and inconsistent results which necessitates a review to harmonize the challenges and prospects to ensure continuity of study during this COVID-19 health crisis in emerging economies. In this work, disruptive engineering education in emerging economies is thus investigated and reviewed, and the challenges and prospects are examined and presented. The work also presents the emerging trends in disruptive engineering, proposes some solutions to overcome the key challenges by giving some novel recommendations on technical requirements and potential enablers.
Confidence-based Local Feature Selection for Material Classification

With the rise of Convolutional Neural Network (CNN) in the recent years, image classification has shown outstanding performances in the computer vision field. Many wellknown state of the art's CNN architectures such as the ResNet family are applying a Global Average Pooling (GAP) to reduce the number of parameters of the fully connected layers. Most of the time, this pooling operation helps to prevent overfitting but we claim that it has a serious weakness for specific images where small details are crucial to predict their category, such as material images. In this case, the details are lost in the global average, providing non accurate global features. In this paper, we propose to select the most important local features before applying the GAP. In this aim, we add a branch in the classification network that predicts the confidence the network should have in each local feature vector. The less confident features are filtered out before applying the GAP. Experimental results on three datasets show that our approach outperforms recent alternatives in terms of classification accuracy and output probability calibration.
Realtime Global optimization of a Fail-Safe Emergency Stop Maneuver for Arbitrary Electrical/ Electronical Failures in Automated Driving

In the event of a critical system failures in auto-mated vehicles, fail-operational or fail-safe measures provide minimum guarantees for the vehicle's performance, depending on which of its subsystems remain operational. Various such methods have been proposed which, upon failure, use different remaining sets of operational subsystems to execute maneuvers that bring the vehicle into a safe state under different environmental conditions. One particular such method proposes a fail-safe emergency stop system that requires no particular electric or electronic subsystem to be available after failure, and still provides a basic situation-dependent emergency stop maneuver. This is achieved by preemptively setting parameters to a hydraulic / mechanical system prior to failure, which after failure executes the preset maneuver “blindly”. The focus of this paper is the particular challenge of implementing a lightweight planning algorithm that can cope with the complex uncertainties of the given task while still providing a globallyoptimal solution at regular intervals, based on the perceived and predicted environment of the automated vehicle.
A Digital Data Hiding Technique with Missing Puzzle and Seek Algorithm

presently a day’s data dissemination over the world become progressively simpler because of quick web and advancement of various kind of technology, for this explanation individuals become increasingly stressed about their information security. For this nowadays people use steganography to make the information secure by hiding and blending the data that make them hard to perceive by hackers. For concealing mystery data in content and pictures, there exists a huge assortment of Steganography methods some are more mind-boggling than others and every one of them has particular solid and feeble focuses. We are looking for the calculation to discover the missing puzzle word which otherwise called mystery calculation by using seek algorithm. For improving the security of mystery message, the message is mixed utilizing onetime cushion plot before being covered and Figure content is at that point hidden in the spread. This is the most efficient data hiding security system and probably its increases the data security all over the world and maintain our privacy.
Supervised learning for parameterized Koopmans–Beckmann’s graph matching

In this paper, we discuss a novel graph matching problem, namely the parameterized Koopmans–Beckmann's graph matching (KBGMw). KBGMw is defined by a weighted linear combination of a series of Koopmans–Beckmann's graph matching. First, we show that KBGMw can be taken as a special case of the parameterized Lawler's graph matching, subject to certain conditions. Second, based on structured SVM, we propose a supervised learning method for automatically estimating the parameters of KBGMw. Experimental results on both synthetic and real image matching data sets show that the proposed method achieves relatively better performances, even superior to some deep learning methods.
An approach for outlier and novelty detection for text data based on classifier confidence

In this paper we present an approach for novelty detection in text data. The approach can also be considered as semi-supervised anomaly detection because it operates with the training dataset containing labelled instances for the known classes only. During the training phase the classification model is learned. It is assumed that at least two known classes exist in the available training dataset. In the testing phase instances are classified as normal or anomalous based on the classifier confidence. In other words, if the classifier cannot assign any of the known class labels to the given instance with sufficiently high confidence (probability), the instance will be declared as novelty (anomaly). We propose two procedures to objectively measure the classifier confidence. Experimental results show that the proposed approach is comparable to methods known in the literature.
Leveraging Temporal Joint Depths for Improving 3D Human Pose Estimation in Video

The effectiveness of the approaches to predict 3D poses from 2D poses estimated in each frame of a video has been demonstrated for 3D human pose estimation. However, 2D poses without appearance information of persons have much ambiguity with respect to the joint depths. In this paper, we propose to estimate a 3D pose in each frame of a video and refine it considering temporal information. The proposed approach reduces the ambiguity of the joint depths and improves the 3D pose estimation accuracy.
Reliability Service Assurance in Public Clouds based on Blockchain

The public cloud is a type of cloud computing offered by third-party providers over the public Internet, making them available to Internet users. The public cloud is featured in large-scale, high complexity, dynamic resource change. However, how to provide secure and reliable cloud services to the widest range of Internet users is a big challenge for the development of cloud computing. Blockchain is a new decentralized distributed computing paradigm. The data stored in the blockchain has the characteristics of unforgeability, whole process trace, traceability, openness and transparency, and collective maintenance. Based on these characteristics, blockchain has laid a solid foundation of trust and created a reliable cooperation mechanism. Applying blockchain technology to the cloud computing platform and improving the service quality of the cloud computing platform by using the blockchain mechanism is a research topic with great application prospects.
A Metadata Handling API for Framework Development

Frameworks play an essential role in software development, providing not only code reuse, but also design reuse. Several Java frameworks and APIs such as Spring, JPA, and CDI rely on the use of metadata, mainly defined by code annotations. These frameworks usually use the Java Reflection API to consume code annotations, which only returns the annotations in a given code element. This strategy, however, is far from the needs of a real framework. The goal of this paper is to propose a novel API, named Esfinge Metadata, to assist in the development of frameworks based on metadata and applications based on custom annotations. Being based on annotations itself, this new API uses them to map metadata to class members. We carried out an experiment to evaluate our API and its impact on aspects such as code structure, complexity, and coupling, while also performing a comparison with the direct use of the Java Reflection API. The participants implemented a metadata-based framework based on realistic requirements in a sequence of 10 tasks that took an average of nine hours. As a result, participants that used our API maintained a more stable code evolution, regarding complexity and coupling as opposed to participants using the Java Reflection API, where the code metrics evolution and structure vary greatly.
A Tor Anonymity Attack Experiment Platform Driven by Raspberry Pi

Private Tor network is essential for analyzing security issues both for Tor project as well as for numerous researchers. However, current research work mainly focus on improving scalability and efficiency by simulating traffic discretely with single node and restoration level by deploying a complete private network with high cost. In this paper, we propose a novel methodology constructing real private tor network by editing and controlling Raspberry Pis, called Tor anonymity attack experiment platform. Compared with other platforms of the same type, the platform has the advantages of low cost, easy to scale, high quality of real-time data transmission, high degree of network protocol restoration as well as convenient configuration. In order to demonstrate the platform can be used as a private and complete Tor anonymous network experiment environment, we conduct a source tracing attack as a study case. Experimental results and theoretical analysis both show that the platform can support researchers to carry out numerous Tor network security research with high degree of restoration efficiently and cheaply.
Duplicacy: A New Generation of Cloud Backup Tool Based on Lock-Free Deduplication

The pervasive deployment of cloud services poses an ever-increasing demand for cross-client deduplication solutions to save network bandwidth, lower storage costs, and improve backup speeds. However, existing solutions typically depend on lock-based approaches relying on a centralized chunk database, which tends to hinder performance scalability. In this article, we present a new cross-client cloud backup solution, named Duplicacy, based on a Lock-Free Deduplication approach. Lock-Free Deduplication stores chunks to network or cloud storage using content hashes as file names. It then adopts a two-step fossil deletion algorithm to solve the hard problem of deleting unreferenced chunks in the presence of concurrent backups, without the need for any locks. Experiments demonstrate that Duplicacy enables significant performance improvement for backups over previous well-known backup tools. In addition, Duplicacy can work with many general-purpose network or cloud storage services which only support a basic set of file operations, and turn them into sophisticated deduplication-aware storage servers without server-side changes.
Resolving Structural Ambiguity in Language Processing: A Systematic Review

This paper addresses these research questions: (1) What are the main ideas presented in the published articles (2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019)(2020) on structural ambiguity resolution in language processing?(2) What are the main venues for unveiling research on structural ambiguity resolution in language processing?For that, a systematic review is performed, which reports on the eight most relevant studies.It is found the investigations into the topic of interest are conducted across multidisciplinary areas and primarily in the European institutions and the US.This research is circulated in journals, which are peer-reviewed and indexed by Scopus, Web of Science, and other databases.The other major finding is that psychophysical tests are more popular in the field, and reasons for that are explained.The polarity of results on syntactic disambiguation leaves room for much to be discovered.
Identification of the forged images using image forensic tools

The contents of the digital images can be easily manipulated with image editing software like Adobe Photoshop, Pixelmator, Inkscape, Fireworks, etc. In real life applications, it is indispensable to check the authenticity of the digital images because forged images could deliver misleading information and messages to our community. Different tools have been developed to detect the forged images. In literature, there is no study which presents an insight into image forensic tools and their evaluation on the basis of different criteria. Therefore, to address this issue, we present an insight into digital image forensic tools; and evaluate it on the basis of 15 different parameters like “error level analysis”, “metadata analysis”, “JPEG luminance and chrominance data”, etc. For our experimental work, we choose “FotoForensics” tool to show the forged region in digital images; and JPEGsnoop tool has been used to extract the metadata of the images.
A Trust-based Message Passing Algorithm against Persistent SSDF

As a key technology in cognitive radio, cooperative spectrum sensing has been paid more and more attention. In cooperative spectrum sensing, multi-user cooperative spectrum sensing can effectively alleviate the performance degradation caused by multipath effect and shadow fading, and improve the spectrum utilization. However, as there may be malicious users in the cooperative sensing users, sending forged false messages to the fusion center or neighbor nodes to mislead them to make wrong judgments, which will greatly reduce the spectrum utilization. To solve this problem, this paper proposes an intelligent anti spectrum sensing data falsification (SSDF) attack algorithm using trust-based non consensus message passing algorithm. In this scheme, only one perception is needed, and the historical propagation path of each message is taken as the basis to calculate the reputation of each cognitive user. Every time a node receives different messages from the same cognitive user, there must be malicious users in its propagation path. We reward the nodes that appear more times in different paths with reputation value, and punish the nodes that appear less. Finally, the real value of the tampered message is restored according to the calculated reputation value. The MATLAB results show that the proposed scheme has a high recovery rate for messages and can identify malicious users in the network at the same time.
Zero-Keep Filter Pruning for Energy Efficient Deep Neural Network

Recent deep learning models succeed to achieve high accuracy and fast inference time, but they require high-performance computing resources because of a large number of parameters. However, not all systems have high-performance hardware. Sometimes, deep learning model needs to be run on edge devices such as IoT devices or smartphones. The edge devices have limited performance and energy consumption. On these devices, the amount of computation must be reduced. Pruning is one of the well-known approaches to solve this problem. In this work, we propose "zero-keep filter pruning" for an energy-efficient deep neural network. The proposed method maximizes the number of zero elements in filters by replacing small values with zero and pruning the filter that has the lowest number of zeros. In the conventional approach, the filters that have the highest number of zeros are generally pruned. As a result, through this zero-keep filter pruning, we can have the filters that have many zeros in a model. We compared the results of the proposed method with the random filter pruning and proved that our method shows better performance with much fewer non-zero elements with marginal accuracy drop. We also compare the number of remained filters with random and proposed pruning methods after pruning. Finally, we discuss a possible multiplier architecture, zero-skip multiplier circuit, which skips the multiplications with zero to accelerate and reduce energy consumption.
A Software Tool for Accuracy Evaluation of Calculated Similarity Factors between Papers and Reviewers

The assignment of reviewers to papers is one of the most important and challenging tasks in organizing scientific conferences. Its accuracy has a direct impact on the quality and the fairness of the review process. The accuracy of the automatic assignment depends on both the assignment algorithm and the way similarity factors between papers and reviewers are calculated. This paper proposes an architecture and a working model of a software tool for accuracy evaluation of the calculated similarity factors between papers and reviewers. It has been used over a hundred times and proved to be very useful for evaluation and comparative analysis of existing methods of describing papers and reviewers' competences.
Fast temperature extraction via Echo State Network for BOTDA sensors

Brillouin optical time-domain analyzer (BOTDA) assisted by echo state network (ESN) for fast and accurate temperature extraction is proposed. Compared to curve fitting methods, the processing speed of proposed ESN algorithm is accelerated up to 20 times. Experimental work is carried out and verified with good agreement of the simulation results.
Covid-19 Series: A rule-based decision support system for analysis behavior of people while working from home

Abstract COVID-19 makes the community must carry out activities such as school, work, and worship at home. However, the long-running activities from home make people experience boredom which can lead to stress. On the other side, the entertainment obtained by the public through smartphones by reading articles by their interests can reduce boredom. This paper proposed a rules-based decision support system to help the people to make choices of their activities from home while COVID-19 e rules-based approach for make an application decision support system. Ruled base used in application to selected process through characteristics following the interests of the community. This decision support system is implemented in mobile web applications. The system can display articles based on interests by questions or statements through the front end system. The results showed that most users of the application in a happy condition while working from home.
Phase Aware Performance Modeling for Cloud Applications

In this paper we propose a new methodology for performance modeling of applications deployed in the cloud based on automatically discovered phases along with their inputs. Our method is based on lightweight sampling that can predict the performance of applications with up to 95% accuracy for previously unseen input configurations at less than 5% overhead. We show the effectiveness of the performance modeling methodology in case of anomaly detection for a variety of real world workloads. As compared to the state-of-the-art, our method gives significant improvements in reducing both false positives and false negatives for anomalous test cases.
Towards Multiple Pattern Type Privacy Protection in Complex Event Processing Through Event Obfuscation Strategies

For a Complex Event Processing (CEP) system to be widely accepted, mitigating leaks of private information is paramount. In CEP systems, often private information are revealed through patterns instead of single events. There are very few mechanisms that protect privacy at the level of patterns. However these mechanisms consider only sequential patterns, one of the common pattern types in CEP. But this is highly confined since there are other common pattern types like conjunction, negation etc. So as a first step towards multiple pattern type privacy protection, in this paper we present a hybrid pattern level privacy protection mechanism that considers three common pattern types: sequence, conjunction and negation. Our approach is based on three event obfuscation strategies: event reordering, event suppression and introduction of fake events to conceal private patterns on the one hand, while minimizing impact on useful non-sensitive information required by IoT services to provide a certain Quality of Service (QoS) on the other hand. Our evaluations over real-world datasets show that our algorithms are effective in maximizing QoS while preserving privacy.