bluuebunny/crossref_metadata_2025 huggingface dataset:
Trust Mechanism of Feedback Trust Weight in Multimedia Network
Authors: Lv Zhihan , Song Houbing
Published: July 2020

It is necessary to solve the inaccurate data arising from data reliability ignored by most data fusion algorithms drawing upon collaborative filtering and fuzzy network theory. Therefore, a model is constructed based on the collaborative filtering algorithm and fuzzy network theory to calculate the node trust value as the weight of weighted data fusion. First, aFTWDF (Feedback Trust Weighted for Data Fusion)is proposed. Second,EEFA (Efficiency unequal Fuzzy clustering Algorithm) is introduced into FTWDF considering the defects of the clustering structure caused by ignoring the randomness of node energy consumption and cluster head selection in the practical application of the existing data fusion algorithm. Besides, the fuzzy logic is applied to cluster head selection and node clustering. Finally, an FTWDF-EEFA clustering algorithm is constructed for generating candidate cluster head nodes, which is verified by simulation experiments. The comparative analysis reveals that the accuracy of the FTWDF-EEFA clustering algorithm is 4.1% higher than that of theTMDF (Trust Multiple attributes Decision-making-based data Fusion)algorithm, and 8.3% higher than that ofLDTS(Larger Data fusion based on node Trust evaluation in wireless Sensor networks)algorithm. It performs better in accuracy and recommendation results during the processing of ML100M dataset and NF5M dataset. Besides, the new clustering algorithm increases the survival time of nodes when analyzing the number of death nodes to prolong networks’ lifespan. It improves the survival period of nodes, balances the network load, and prolongs networks’ lifespan. Furthermore, the FTWDF-EEFA clustering algorithm can balance nodes’ energy consumption and effectively save nodes’ overall energy through analysis. Therefore, the optimized algorithm can increase the lifespan of network and improve the trust mechanism effectively. The performance of the algorithm has reached the expected effect, providing a reference for the practical application of the trust mechanism in networks.
A Fast Defogging Image Recognition Algorithm Based on Bilateral Hybrid Filtering
Authors: Liang Wei , Long Jing , Li Kuan-Ching , Xu Jianbo , Ma Nanjun , Lei Xia
Published: July 2020

With the rapid advancement of video and image processing technologies in the Internet of Things, it is urgent to address the issues in real-time performance, clarity, and reliability of image recognition technology for a monitoring system in foggy weather conditions. In this work, a fast defogging image recognition algorithm is proposed based on bilateral hybrid filtering. First, the mathematical model based on bilateral hybrid filtering is established. The dark channel is used for filtering and denoising the defogging image. Next, a bilateral hybrid filtering method is proposed by using a combination of guided filtering and median filtering, as it can effectively improve the robustness and transmittance of defogging images. On this basis, the proposed algorithm dramatically decreases the computation complexity of defogging image recognition and reduces the image execution time. Experimental results show that the defogging effect and speed are promising, with the image recognition rate reaching to 98.8% after defogging.
Development Automation of Real-Time Java
Authors: Chang Wanli , Wei Ran , Zhao Shuai , Wellings Andy , Woodcock Jim , Burns Alan
Published: July 2020

Many applications in emerging scenarios, such as autonomous vehicles, intelligent robots, and industrial automation, are safety-critical with strict timing requirements. However, the development of real-time systems is error prone and highly dependent on sophisticated domain expertise, making it a costly process. This article utilises the principles of model-driven engineering (MDE) and proposes two methodologies to automate the development of real-time Java applications. The first one automatically converts standard time-sharing Java applications to real-time Java applications, using a series of transformations. It is in line with the observed industrial trend, such as for the big data technology, of redeveloping existing software without the real-time notion to realise the real-time features. The second one allows users to automatically generate real-time Java application templates with a lightweight modelling language, which can be used to define the real-time properties—essentially a synthesis process. This article opens up a new research direction on development automation of real-time programming languages and inspires many research questions that can be jointly investigated by the embedded systems, programming languages as well as MDE communities.
A Retargetable MATLAB-to-C Compiler Exploiting Custom Instructions and Data Parallelism
Authors: Latifis Ioannis , Parashar Karthick , Dimitroulakos Grigoris , Cappelle Hans , Lezos Christakis , Masselos Konstantinos , Catthoor Francky
Published: July 2020

This article presents a MATLAB-to-C compiler that exploits custom instructions present in state-of-the-art processor architectures and supports semi-automatic vectorization. A parameterized processor model is used to describe the target instruction set architecture to achieve user-friendly retargetability. Custom instructions are represented via specialized intrinsic functions in the generated code, which can then be used as input to any C/C++ compiler supporting the target processor. In addition, the compiler supports the generation of data parallel/vectorized code through the introduction of data packing/unpacking statements. The compiler has been used for code generation targeting ARM and x86 architectures for several benchmarks. The vectorized code generated by the compiler achieves an average speedup of 4.1× and 2.7× for packed fixed and floating point data, respectively, compared to scalarized code for ARM architecture and an average speedup of 3.1× and 1.5× for packed fixed and floating point data, respectively, for x86 architecture. Implementing data parallel instructions directly in the assembly code would have required a lot of design effort, and it would not been sustainable across evolving platform variants. Thus, the compiler can be employed to efficiently speed up critical sections of the target application. The compiler is therefore potentially employable to raise the design abstraction and reduce development time for both embedded and general-purpose applications.
Grading Tibetan Children’s Literature
Authors: Schmidt Dirk
Published: July 2020

Worldwide, literacy is on the rise. This historically unprecedented surge—especially over the past 200 years—has changed nearly everything about the ancient technology of reading.
Who
reads is changing: Literacy is no longer just for elite, professional readers, but for anyone and everyone.
What
and
why
we read is changing: We do not just read
difficult
texts for academic, religious, legal, or record-keeping purposes; we also read
easy
texts to be entertained, to access information, and to communicate with each other on a daily basis. And
how
we read is changing: Memorization, recitation, and oral performance has given way to a rapid, silent, individual activity.

Many of these democratizing changes have been made possible by technology. This has included advances in methods and materials that have made reading and writing easy, cheap, and widely available—like paper, the printing press, and the digital revolution. But perhaps the biggest reason literacy has become so widespread has been its ability to reach people
in their own natural languages
. More recently, this progress has been enhanced by NLP tools, like readability editors, that have helped authors, journalists, and other writing professionals make simple, clear content suitable for both beginning readers and widespread audiences.

To that end, this article introduces a new readability tool, “Dakje,” alongside a specific use case, and demonstrates how it can help benefit literacy in the Tibetan languages. This NLP software works by word-splitting Tibetan text and analyzing those words using level lists that are based on frequency analysis from corpora. Users then have instant access to statistics on the readability of their word choices so they can make edits for easy-to-read text. In our test-case, Dakje helped us reduce sentence complexity by 34%, total word count by 10%, and non-level vocabulary use from 16% to 1% when compared to an original English-to-Tibetan translation.
iSleep
Authors: Chang Xiangmao , Peng Cheng , Xing Guoliang , Hao Tian , Zhou Gang
Published: July 2020

The quality of sleep is an important factor in maintaining a healthy life style. A great deal of work has been done for designing sleep monitoring systems. However, most of existing solutions bring invasion to users more or less due to the exploration of the accelerometer sensor inside the device. This article presents iSleep—a practical system to monitor people’s sleep quality using off-the-shelf smartphone. iSleep uses the built-in microphone of the smartphone to detect the events that are closely related to sleep quality, and infers quantitative measures of sleep quality. iSleep adopts a lightweight decision-tree-based algorithm to classify various events. For two-user scenario, iSleep differentiates the events of two users either when two phones can collaborate with each other or when two phones cannot communicate with each other. The experimental results show that iSleep achieves consistently above 90% accuracy for event classification in a variety of different settings in one-user scenario and above 92% accuracy for distinguishing users in two-user scenario. By providing a fine-grained sleep profile that depicts details of sleep-related events, iSleep allows the user to track the sleep efficiency over time and relate irregular sleep patterns to possible causes.
Design and Implementation of a Compressed Certificate Status Protocol
Authors: Pachilakis Michalis , Chariton Antonios A. , Papadopoulos Panagiotis , Ilia Panagiotis , Degkleri Eirini , Markatos Evangelos P.
Published: July 2020

Trust in Secure Sockets Layer–based communications is traditionally provided by Certificate (or Certification) Authorities (CAs) in the form of signed certificates. Checking the validity of a certificate involves three steps: (i) checking its expiration date, (ii) verifying its signature, and (iii) ensuring that it is not revoked. Currently, such certificate revocation checks (i.e., step (iii) above) are done either via Certificate Revocation Lists (CRLs), or Online Certificate Status Protocol (OCSP) servers. Unfortunately, despite the existence of these revocation checks, sophisticated cyber-attackers can still trick web browsers to trust a
revoked
certificate, believing that it is still valid.

Although frequently
updated
,
nonced
, and
timestamped
certificates can reduce the frequency and impact of such cyber-attacks, they add a huge burden to the CAs and OCSP servers. Indeed, CAs and/or OCSP servers need to
timestamp
and
sign
on a regular basis all the responses, for every certificate they have issued, resulting in a very high overhead. To mitigate this and provide a solution to the described cyber-attacks, we present CCSP : a new approach to provide timely information regarding the status of certificates, which capitalizes on a newly introduced notion called
Signed Collections
. In this article, we present in detail the notion of
Signed Collections
and the complete design, implementation, and evaluation of our approach. Performance evaluation shows that CCSP (i) reduces space requirements by more than an order of magnitude, (ii) lowers the number of signatures required by six orders of magnitude compared to OCSP-based methods, and (iii) adds only a few milliseconds of overhead in the overall user latency.
Hardware and Software Co-optimization for the Initialization Failure of the ReRAM-based Cross-bar Array
Authors: Kim Youngseok , Kim Seyoung , Yeh Chun-Chen , Narayanan Vijay , Choi Jungwook
Published: July 2020

Recent advances in deep neural network demand more than millions of parameters to handle and mandate the high-performance computing resources with improved efficiency. The cross-bar array architecture has been considered as one of the promising deep learning architectures that shows a significant computing gain over the conventional processors. To investigate the feasibility of the architecture, we examine non-idealities and their impact on the performance. Specifically, we study the impact of failed cells due to the initialization process of the resistive memory-based cross-bar array. Unlike the conventional memory array, individual memory elements cannot be rerouted and, thus, may have a critical impact on model accuracy. We categorize the possible failures and propose hardware implementation that minimizes catastrophic failures. Such hardware optimization bounds the possible logical value of the failed cells and allows us to compensate for the loss of accuracy via off-line training. By introducing the random weight defects during the training, we show that the model becomes more resilient on the device initialization failures, therefore, less prone to degrade the inference performance due to the failed devices. Our study sheds light on the hardware and software co-optimization procedure to cope with potentially catastrophic failures in the cross-bar array.
Joint Modelling of Cyber Activities and Physical Context to Improve Prediction of Visitor Behaviors
Authors: Kaur Manpreet , Salim Flora D. , Ren Yongli , Chan Jeffrey , Tomko Martin , Sanderson Mark
Published: July 2020

This article investigates the cyber-physical behavior of users in a large indoor shopping mall by leveraging anonymized (opt in) Wi-Fi association and browsing logs recorded by the mall operators. Our analysis shows that many users exhibit a high correlation between their cyber activities and their physical context. To find this correlation,propose a mechanism to semantically label a physical space with rich categorical information from DBPedia concepts and compute a contextual similarity that represents a user’s activities with the mall context. We demonstrate the application of cyber-physical contextual similarity in two situations: user visit intent classification and future location prediction. The experimental results demonstrate that exploitation of contextual similarity significantly improves the accuracy of such applications.
Scalable Spatial Scan Statistics for Trajectories
Authors: Matheny Michael , Xie Dong , Phillips Jeff M.
Published: July 2020

We define several new models for how to define anomalous regions among enormous sets of trajectories. These are based on spatial scan statistics, and identify a geometric region which captures a subset of trajectories which are significantly different in a measured characteristic from the background population. The model definition depends on how much a geometric region is contributed to by some overlapping trajectory. This contribution can be the full trajectory, proportional to the length within the spatial region, or dependent on the flux across the boundary of that spatial region. Our methods are based on and significantly extend a recent two-level sampling approach which provides high accuracy at enormous scales of data. We support these new models and algorithms with extensive experiments on millions of trajectories and also theoretical guarantees.
Requet
Authors: Gutterman Craig , Guo Katherine , Arora Sarthak , Gilliland Trey , Wang Xiaoyang , Wu Les , Katz-Bassett Ethan , Zussman Gil
Published: July 2020

As video traffic dominates the Internet, it is important for operators to detect video quality of experience (QoE) to ensure adequate support for video traffic. With wide deployment of end-to-end encryption, traditional deep packet inspection--based traffic monitoring approaches are becoming ineffective. This poses a challenge for network operators to monitor user QoE and improve upon their experience. To resolve this issue, we develop and present a system for
RE
al-time
QU
ality of experience metric detection for
E
ncrypted
T
raffic—
Requet
—which is suitable for network middlebox deployment. Requet uses a detection algorithm that we develop to identify video and audio chunks from the IP headers of encrypted traffic. Features extracted from the chunk statistics are used as input to a machine learning algorithm to predict QoE metrics, specifically buffer warning (low buffer, high buffer), video state (buffer increase, buffer decay, steady, stall), and video resolution. We collect a large YouTube dataset consisting of diverse video assets delivered over various WiFi and LTE network conditions to evaluate the performance. We compare Requet with a baseline system based on previous work and show that Requet outperforms the baseline system in accuracy of predicting buffer low warning, video state, and video resolution by 1.12×, 1.53×, and 3.14×, respectively.
Homomorphic Encryption for Machine Learning in Medicine and Bioinformatics
Authors: Wood Alexander , Najarian Kayvan , Kahrobaei Delaram
Published: July 2020

Machine learning and statistical techniques are powerful tools for analyzing large amounts of medical and genomic data. On the other hand, ethical concerns and privacy regulations prevent free sharing of this data. Encryption techniques such as fully homomorphic encryption (FHE) enable evaluation over encrypted data. Using FHE, machine learning models such as deep learning, decision trees, and Naive Bayes have been implemented for privacy-preserving applications using medical data. These applications include classifying encrypted data and training models on encrypted data. FHE has also been shown to enable secure genomic algorithms, such as paternity and ancestry testing and privacy-preserving applications of genome-wide association studies.

This survey provides an overview of fully homomorphic encryption and its applications in medicine and bioinformatics. The high-level concepts behind FHE and its history are introduced, and details on current open-source implementations are provided. The state of fully homomorphic encryption for privacy-preserving techniques in machine learning and bioinformatics is reviewed, along with descriptions of how these methods can be implemented in the encrypted domain.
Touch-dynamics based Behavioural Biometrics on Mobile Devices – A Review from a Usability and Performance Perspective
Authors: Ellavarason Elakkiya , Guest Richard , Deravi Farzin , Sanchez-Riello Raul , Corsetti Barbara
Published: July 2020

Over the past few years, there has been an exponential increase in the percentage of people owning and using a smart phone. These devices have sensor-rich touchscreens that can capture sensitive biometric features such as keystroke typing and finger-swiping patterns. Touch-dynamics based behavioural biometrics is a time-based assessment of how a user performs a particular touch task on a mobile device. Several performance-focused surveys already exist. In this article, building upon the existing reviews, we have examined studies on touch-dynamics based behavioural biometrics based on usability and its impact on authentication performance. We also emphasize the need for shifting the focus on usability during performance evaluations by presenting a consolidated list of usability and ergonomic-based factors that influence user interaction and cause performance variations. In this article, we report and review the usability evaluations: user acceptance studies and performance-based studies influencing the user interaction process on three specific touch-dynamics based modalities—signature, keystroke, and swipe. With regards to performance, we present a comparative analysis of error rates and accuracy of various research works undertaken. Additionally, we present a consolidated list of public datasets and discuss evolving vulnerabilities of touch-dynamics based behavioural biometrics, their adopted attack models, and their feasibility. Finally, we present our assessment of this domain's existing unresolved problems that could pave the way for future research.
Output-Bounded and RBFNN-Based Position Tracking and Adaptive Force Control for Security Tele-Surgery
Authors: Wang Ting , Ji Xiangjun , Song Aiguo , Madani Kurosh , Chohra Amine , Lu Huimin , Monero Ramon
Published: July 2020

In security e-health brain neurosurgery, one of the important processes is to move the electrocoagulation to the appropriate position in order to excavate the diseased tissue.
1
However, it has been problematic for surgeons to freely operate the electrocoagulation, as the workspace is very narrow in the brain. Due to the precision, vulnerability, and important function of brain tissues, it is essential to ensure the precision and safety of brain tissues surrounding the diseased part. The present study proposes the use of a robot-assisted tele-surgery system to accomplish the process. With the aim to achieve accuracy, an output-bounded and RBF neural network–based bilateral position control method was designed to guarantee the stability and accuracy of the operation process. For the purpose of accomplishing a minimal amount of bleeding and damage, an adaptive force control of the slave manipulator was proposed, allowing it to be appropriate to contact the susceptible vessels, nerves, and brain tissues. The stability was analyzed, and the numerical simulation results revealed the high performance of the proposed controls.
SDN-Assisted DDoS Defense Framework for the Internet of Multimedia Things
Authors: Sahoo Kshira Sagar , Puthal Deepak
Published: July 2020

The Internet of Things is visualized as a fundamental networking model that bridges the gap between the cyber and real-world entity. Uniting the real-world object with virtualization technology is opening further opportunities for innovation in nearly every individual’s life. Moreover, the usage of smart heterogeneous multimedia devices is growing extensively. These multimedia devices that communicate among each other through the Internet form a unique paradigm called the
Internet of Multimedia Things
(IoMT). As the volume of the collected data in multimedia application increases, the security, reliability of communications, and overall quality of service need to be maintained. Primarily, distributed denial of service attacks unveil the pervasiveness of vulnerabilities in IoMT systems. However, the Software Defined Network (SDN) is a new network architecture that has the central visibility of the entire network, which helps to detect any attack effectively. In this regard, the combination of SDN and IoMT, termed
SD-IoMT
, has the immense ability to improve the network management and security capabilities of the IoT system. This article proposes an SDN-assisted two-phase detection framework, namely SD-IoMT-Protector, in which the first phase utilizes the entropy technique as the detection metric to verify and alert about the malicious traffic. The second phase has trained with an optimized machine learning technique for classifying different attacks. The outcomes of the experimental results signify the usefulness and effectiveness of the proposed framework for addressing distributed denial of service issues of the SD-IoMT system.
A Survey of Fake News
Authors: Zhou Xinyi , Zafarani Reza
Published: July 2020

The explosive growth in fake news and its erosion to democracy, justice, and public trust has increased the demand for fake news detection and intervention. This survey reviews and evaluates methods that can detect fake news from four perspectives: the false
knowledge
it carries, its writing
style
, its
propagation
patterns, and the credibility of its
source
. The survey also highlights some potential research tasks based on the review. In particular, we identify and detail related fundamental theories across various disciplines to encourage interdisciplinary research on fake news. It is our hope that this survey can facilitate collaborative efforts among experts in computer and information sciences, social sciences, political science, and journalism to research fake news, where such efforts can lead to fake news detection that is not only efficient but, more importantly, explainable.
coSense
Authors: Schmeißer Stephan , Schiele Gregor
Published: July 2020

We present
coSense
—the collaborative, fault-tolerant, and adaptive sensing middleware for the Internet-of-Things (IoT). By actively harnessing the greatest asset of the IoT, the sheer number of devices, coSense is able to provide easy data acquisition with quality-of-service-based data cleaning by combining unsupervised learning and information fusion. It can also greatly improve sensor accuracy and fault tolerance to produce measurements specifically tailored for modern data-driven IoT empowered applications. In this article, we focus on the general concepts behind coSense and evaluate the accuracy gain based on a real-world dataset.
Optimizing Tensor Contractions for Embedded Devices with Racetrack and DRAM Memories
Authors: Khan Asif Ali , Rink Norman A. , Hameed Fazal , Castrillon Jeronimo
Published: July 2020

Tensor contraction
is a fundamental operation in many algorithms with a plethora of applications ranging from quantum chemistry over fluid dynamics and image processing to machine learning. The performance of tensor computations critically depends on the efficient utilization of on-chip/off-chip memories. In the context of low-power embedded devices, efficient management of the memory space becomes even more crucial, in order to meet energy constraints. This work aims at investigating strategies for performance- and energy-efficient tensor contractions on embedded systems, using
racetrack memory
(RTM)-based
scratch-pad memory
(SPM) and DRAM-based off-chip memory. Compiler optimizations such as the loop access order and data layout transformations paired with architectural optimizations such as prefetching and preshifting are employed to reduce the shifting overhead in RTMs. Optimizations for off-chip memory such as memory access order, data mapping and the choice of a suitable memory access granularity are employed to reduce the contention in the off-chip memory. Experimental results demonstrate that the proposed optimizations improve the SPM performance and energy consumption by 32% and 73%, respectively, compared to an iso-capacity SRAM. The overall DRAM dynamic energy consumption improvements due to memory optimizations amount to 80%.
A Secure Multimedia Processing through Blockchain in Smart Healthcare Systems
Authors: Alghazo Jaafar , Rathee Geetanjali , Gupta Sharmidev , Tabrez Quasim Mohammad , Murugan Sivaram , Latif Ghazanfar , Dhasarathan Vigneswaran
Published: July 2020

The rapidly growing, complex and key area of multimedia processing is medical area where the huge amount of patient?s data is disregarded. Further, medial fields used to interrelate with multimedia formats because of their daily real communication. Healthcare associations are renovating themselves into added efficient, coordinated and user-centered methods through several upcoming techniques. Though, the management of large information such as patient information, medical reports escorts to augments the human labors and security hazards. Therefore, to conquer these problems, IoT-healthcare came into existence that enhances the patients care by reducing the cost of resources in an effective way. Along with lot of IoT benefits, associations are afraid to use them because of its compromise initiated by several intruders. For benefiting their own purposes, intruders hack the patient?s online record for selling to third party for doing the researchers analysis. Further, doctors also make the money for their concession by forcing the patients to buy the medicines from their recommended firms. So as to avert these problems, Blockchain technology has been came across in healthcare systems that may keep track of every activity of the entities. The patients and other entities are recognized through bio metric methods so that even if hackers try to compromise the devices or to access the stored information; the patients may easily identify the intruders or alteration in stored record. In this paper, we have projected a secure IoT healthcare mechanism using Blockchain technique that initially identify the patients and other entities using bio metric passwords and further can analyze the modifications in their records. The proposed scheme results have been validated against traditional method by offering 88% success rate over certain parametric scenarios such as grey hole attack, falsification attack and probabilistic authentication scenarios.
Unsupervised Derivation of Keyword Summary for Short Texts
Authors: Cao Bin , Wu Jiawei , Wang Sichao , Gao Honghao , Fan Jing , Deng Shuiguang , Yin Jianwei , Liu Xuan
Published: July 2020

Automatically summarizing a group of short texts that mainly share one topic is a fundamental task in many applications, e.g., summarizing the main symptoms for a disease based on a group of medical texts that are usually short, i.e., tens of words. Conventional unsupervised short text summarization techniques tend to find the most representative short text document. However, they may cause privacy issues, e.g., personal information in the medical texts may be exposed. Moreover, compared with the complete short text where some unimportant words may exist, a summary consisting of only a few keywords is more preferable by the user due to its clear and concise form. Due to the above reasons, in this article, we aim to solve the problem of unsupervised derivation of keyword summary for short texts. Existing keyword extraction methods such as Latent Dirichlet Allocation cannot be applied to solve this problem, since (1) the ordering relations among the extracted keywords are ignored, which causes troubles for people to capture the main idea of the event, and (2) short texts contain limited context, which makes it hard to find the optimal words for semantic coverage. Hence, we propose a simple but yet effective method named
Frequent Closed Wordsets Ranking (FCWRank)
to derive the keyword summary from a short text cluster. FCWRank is an unsupervised method that builds on the idea of frequent closed itemset mining in transaction database. FCWRank first mines all frequent closed wordsets from a cluster of short texts and then selects the most important wordset based on an importance model where the similarity between closed wordsets and the relation between the closed wordset and the short text document are considered simultaneously. To make the keywords within the wordset more understandable, FCWRank further unfolds the semantics behind them by sorting them. Experiments on real-world short text collections show that FCWRank outperforms the state-of-the-art baselines in terms of Recall-Oriented Understudy for Gisting Evaluation-Longest common subsequence F1, precision and recall scores.
Woman-Centered Design through Humanity, Activism, and Inclusion
Authors: Almeida Teresa , Balaam Madeline , Comber Rob
Published: July 2020

Women account for over half of the global population, however, continue to be subject to systematic and systemic disadvantage, particularly in terms of access to health and education. At every intersection, where systemic inequality accounts for greater loss of life or limitations on full and healthy living, women are more greatly impacted by those inequalities. The design of technologies is no different, the very definition of technology is historically cast in terms of male activities, and advancements in the field are critical to improve women's quality of life. This article views HCI, a relatively new field, as well positioned to act critically in the ways that technology serve, refigure, and redefine women's bodies. Indeed, the female body remains a contested topic, a restriction to the development of women's health. On one hand, the field of women's health has attended to the medicalization of the body and therefore is to be understood through medical language and knowledge. On the other hand, the framing of issues associated with women's health and people's experiences of and within such system(s) remain problematic for many. This is visible today in, e.g., socio-cultural practices in disparate geographies or medical devices within a clinic or the home. Moreover, the biological body is part of a great unmentionable, i.e., the perils of essentialism. We contend that it is necessary, pragmatically and ethically, for HCI to turn its attention toward a
woman-centered design
approach. While previous research has argued for the dangers of gender-demarcated design work, we advance that designing for and with women should not be regarded as ghettoizing, but instead as critical to improving women's experiences in bodily transactions, choices, rights, and access to and in health and care. In this article, we consider how and why designing with and for woman matters. We use our design-led research as a way to speak to and illustrate alternatives to designing for and with women within HCI.
On Proximity and Structural Role-based Embeddings in Networks
Authors: Rossi Ryan A. , Jin Di , Kim Sungchul , Ahmed Nesreen K. , Koutra Danai , Lee John Boaz
Published: July 2020

Structural roles define sets of structurally similar nodes that are more similar to nodes inside the set than outside, whereas communities define sets of nodes with more connections inside the set than outside. Roles based on structural similarity and communities based on proximity are fundamentally different but important complementary notions. Recently, the notion of structural roles has become increasingly important and has gained a lot of attention due to the proliferation of work on learning representations (node/edge embeddings) from graphs that preserve the notion of roles. Unfortunately, recent work has sometimes confused the notion of structural roles and communities (based on proximity) leading to misleading or incorrect claims about the capabilities of network embedding methods. As such, this article seeks to clarify the misconceptions and key differences between structural roles and communities, and formalize the general mechanisms (e.g., random walks and feature diffusion) that give rise to community- or role-based structural embeddings. We theoretically prove that embedding methods based on these mechanisms result in either community- or role-based structural embeddings. These mechanisms are typically easy to identify and can help researchers quickly determine whether a method preserves community- or role-based embeddings. Furthermore, they also serve as a basis for developing new and improved methods for community- or role-based structural embeddings. Finally, we analyze and discuss applications and data characteristics where community- or role-based embeddings are most appropriate.
Binary Tree Classification of Rigid Error Detection and Correction Techniques
Authors: Kritikakou Angeliki , Psiakis Rafail , Catthoor Francky , Sentieys Olivier
Published: July 2020

Due to technology scaling and harsh environments, a wide range of fault-tolerant techniques exists to deal with the error occurrences. Selecting a fault-tolerant technique is not trivial, whereas more than the necessary overhead is usually inserted during the system design. To avoid over-designing, it is necessary to have an in-depth understanding of the available design options. However, an exhaustive listing is neither possible to create nor efficient to use due to its prohibitive size. In this work, we present a top-down binary tree classification for error detection and correction techniques. At each split, the design space is clearly divided into two complementary parts using one single attribute, compared with existing classifications that use splits with multiple attributes. A leaf inherits all the attributes of its ancestors from the root to the leaf. A technique is decomposed into primitive components, each one belonging to a different leaf. The single attribute splits can be used to efficiently compare the techniques and to prune the incompatible parts of the design space during the design of a technique. This essential single attribute division of the design space is required for the improvement of the techniques and for novel contributions to the fault-tolerance domain.
EPRT: An Efficient Privacy-Preserving Medical Service Recommendation and Trust Discovery Scheme for eHealth System
Authors: Peng Cong , He Debiao , Chen Jianhua , Kumar Neeraj , Khan Muhammad Khurram
Published: July 2020

As one of the essential applications of health information technology, the eHealth system plays a significant role in enabling various internet medicine service scenes, most of which primarily rely on service recommendation or an evaluation mechanism. To avoid privacy leakage, some privacy-preserving mechanisms must be adopted to protect raters’ privacy and make evaluation trust reliable. To tackle this challenge, this article proposes an efficient service recommendation and evaluation scheme, called
EPRT
, which is based on a similarity calculation and trust discovery method. This scheme uses homomorphic encryption technology to encrypt the sensitive data and combines the threshold mechanism and double-trap mechanism to realize the secure computing on the encrypted data, so as to ensure that the plaintexts of the final calculation results (e.g., recommendation value and evaluation truth) are only obtained by the authorized subject. In addition, a detailed security analysis shows that the proposed EPRT scheme can achieve the expected security. In addition, performance comparison results are carried out, demonstrating its effectiveness and accuracy.
A Novel Multiobjective GDWCN-PSO Algorithm and Its Application to Medical Data Security
Authors: Bharti Vandana , Biswas Bhaskar , Shukla Kaushal Kumar
Published: July 2020

Nature-inspired optimization is one of the most prevalent research domains with a confounding history that fascinates the research communities.
Particle Swarm Optimization
is one of the well-known optimizers that belongs to the family of nature-inspired algorithms. It often suffers from premature convergence leading to a local optimum. To address this, several methods were presented using different network topologies of the particles, but either lacked accuracy or were slow. To solve these problems, an improved version of the
Directed Weighted Complex Network Particle Swarm Optimization using the Genetic Algorithm (GDWCN-PSO)
is presented. This method uses the concept of the Genetic Algorithm after each update to enhance convergence and diversity. Since most of the real-world applications and complex optimization problems involve more than one objective function so to suit this problem, a multiobjective version of GDWCN-PSO is also proposed and validated on standard benchmarks. To demonstrate its applicability in real-world applications, GDWCN-PSO is applied to solve the optimal key-based medical image encryption. It is one of the most challenging problems in health IoTs for protecting sensitive and confidential patient data as well as addressing the major concern of integrity and security of data in today’s advanced digital world.
A Query Processing Framework for Efficient Network Resource Utilization in Shared Sensor Networks
Authors: Verma Rahul Kumar , Pattanaik K. K. , Bharti Sourabh , Saxena Divya , Cao Jiannong
Published: July 2020

Shared Sensor Network (SSN) refers to a scenario where the same sensing and communication resources are shared and queried by multiple Internet applications. Due to the burgeoning growth in Internet applications, multiple application queries can exhibit overlapping in their functional requirements, such as the region of interest, sensing attributes, and sensing time duration. This overlapping results in redundant sensing tasks generation leading to the increased overall network traffic and energy consumption. Existing approaches operate on data sharing among various tasks to minimize the upstream traffic. However, no existing work attempts to prevent the redundant task generation to reduce the downstream traffic. Moreover, the allocation of suitable sensor nodes to meet the Quality of Service (QoS) requirements of the queries is still an open issue. This article proposes an end-to-end query processing framework (named, QueryPM) that first, calculates the functional requirements similarity among queries to prevent the redundant task generation. Then, it takes the QoS and functional requirements into account while allocating the tasks on the sensor nodes. Extensive simulations on the proposed approach show that downstream traffic, upstream traffic, and energy consumption reduced to 60%, 20--40%, and 40%, respectively, as compared to state-of-the-art mechanisms.
Cyberbullying Detection, Based on the FastText and Word Similarity Schemes
Authors: Wang Kun , Cui Yanpeng , Hu Jianwei , Zhang Yu , Zhao Wei , Feng Luming
Published: July 2020

With recent developments in online social networks (OSNs), these services are widely applied in daily lives. On the other hand, cyberbullying, which is a relatively new type of harassment through the internet-based electronic devices, is rising in online social networks. Accordingly, scholars are attracted to investigating cyberbullying behaviors. Studies show that cyberbullying has a devastating effect on mental health, especially for teenagers. In order to reduce or even stop cyberbullying, different machine learning techniques are applied and numerous studies have been conducted so far. However, conventional detection schemes still have challenges, such as low accuracy. Therefore, it is of significant importance to find an efficient detection solution in the natural language processing and machine learning communities. In the present study, characteristics of cyberbullying are initially analyzed from vocabulary and syntax points of view. Then a new detection algorithm is proposed based on FastText and word similarity schemes. Finally, experiments are carried out to evaluate the effectiveness and performance of the proposed method. Obtained results show that the proposed algorithm can effectively improve the detection accuracy and recall rate of cyberbullying detection.
SQL Education
Authors: Taipalus Toni , Seppänen Ville
Published: July 2020

Structured Query Language (SQL) skills are crucial in software engineering and computer science. However, teaching SQL effectively requires both pedagogical skill and considerable knowledge of the language. Educators and scholars have proposed numerous considerations for the betterment of SQL education, yet these considerations may be too numerous and scattered among different fora for educators to find and internalize, as no systematic mappings or literature reviews regarding SQL education have been conducted. The two main goals of this mapping study are to provide an overview of educational SQL research topics, research types, and publication fora, and to collect and propagate SQL teaching practices for educators to utilize. Additionally, we present a short future research agenda based on insights from the mapping process. We conducted a systematic mapping study complemented by snowballing techniques to identify applicable primary studies. We classified the primary studies according to research type and utilized directed content analysis to classify the primary studies by their topic. Out of our selected 89 primary studies, we identified six recurring topics:
(i)
student errors in query formulation;
(ii)
characteristics and presentation of the exercise database;
(iii)
specific and
(iv)
non-specific teaching approach suggestions;
(v)
patterns and visualization; and
(vi)
easing teacher workload. We list 66 teaching approaches the primary studies argued for (and in some cases against). For researchers, we provide a systematic map of educational SQL research and future research agenda. For educators, we present an aggregated body of knowledge on teaching practices in SQL education over a time frame of 30 years. In conclusion, we suggest that replication studies, studies on advanced SQL concepts, and studies on aspects other than data retrieval are needed to further educational SQL research.
Large-Scale Least Squares Twin SVMs
Authors: Tanveer M. , Sharma S. , Muhammad K.
Published: July 2020

In the last decade, twin support vector machine (TWSVM) classifiers have achieved considerable emphasis on pattern classification tasks. However, the TWSVM formulation still suffers from the following two shortcomings: (1) TWSVM deals with the inverse matrix calculation in the Wolfe-dual problems, which is intractable for large-scale datasets with numerous features and samples, and (2) TWSVM minimizes the empirical risk instead of the structural risk in its formulation. With the advent of huge amounts of data today, these disadvantages render TWSVM an ineffective choice for pattern classification tasks. In this article, we propose an efficient large-scale least squares twin support vector machine (LS-LSTSVM) for pattern classification that rectifies all the aforementioned shortcomings. The proposed LS-LSTSVM introduces different Lagrangian functions to eliminate the need for calculating inverse matrices. The proposed LS-LSTSVM also does not employ kernel-generated surfaces for the non-linear case, and thus uses the kernel trick directly. This ensures that the proposed LS-LSTSVM model is superior to the original TWSVM and LSTSVM. Lastly, the structural risk is minimized in LS-LSTSVM. This exhibits the essence of statistical learning theory, and consequently, classification accuracy on datasets can be improved due to this change. The proposed LS-LSTSVM is solved using the sequential minimal optimization (SMO) technique, making it more suitable for large-scale problems. We further proved the convergence of the proposed LS-LSTSVM. Exhaustive experiments on several real-world benchmarks and NDC-based large-scale datasets demonstrate that the proposed LS-LSTSVM is feasible for large datasets and, in most cases, performed better than existing algorithms.
Deterministic Sparse Suffix Sorting in the Restore Model
Authors: Fischer Johannes , I Tomohiro , Köppl Dominik
Published: July 2020

Given a text
T
of length
n
, we propose a deterministic online algorithm computing the sparse suffix array and the sparse longest common prefix array of
T
in O(
c
√ lg
n
+
m
lg
m
lg
n
lg
*
n
) time with O(
m
) words of space under the premise that the space of 
T
is rewritable, where
m
≤
n
is the number of suffixes to be sorted (provided online and arbitrarily), and
c
is the number of characters with
m
≤
c
≤
n
that must be compared for distinguishing the designated suffixes.