bluuebunny/crossref_metadata_2025 huggingface dataset:
Detection of intracranial hypertension in children using optical coherence tomography: a systematic review protocol
Authors: Rufai Sohaib R , Jeelani Noor ul Owase , McLean Rebecca J
Published: July 2020

Introduction

Intracranial hypertension (ICH) in children can have deleterious effects on the brain and vision. It is notoriously difficult to estimate intracranial pressure (ICP) in children and existing methods deliver suboptimal diagnostic accuracy to be used as screening tools. Optical coherence tomography (OCT) may represent a valuable, non-invasive surrogate measure of ICP, as has been demonstrated in a number of associated conditions affecting adults. More recently, OCT has been employed within the paediatric age group. However, the role of OCT in detecting ICH in children has not been rigorously assessed in a systematic review for all relevant conditions. Here, we propose a systematic review protocol to examine the role of OCT in the detection of ICH in children.

Methods and analysis

Electronic searches in the Cochrane Central Register of Controlled Trials, Medline, Embase, Web of Science and PubMed will identify studies featuring OCT in detecting ICH in children. Two independent screeners will identify studies for inclusion using a screening questionnaire. The systematic search and screening will take place between 2 April 2020 and 1 June 2020, while we aim to complete data analysis by 1 September 2020. Quality assessment will be performed using the National Institutes of Health Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies. The primary outcome measure is the sensitivity and specificity of OCT in detecting ICH in children. Secondary outcomes measures include conditions associated with ICH per study, direct ICP monitoring, sensitivity and specificity of other measures for ICP and OCT parameters used.

Ethics and dissemination

Ethical approval is not required for the proposed systematic review as no primary data will be collected. The findings will be disseminated through presentations at scientific meetings and peer-reviewed journal publication.

PROSPERO registration number

CRD42019154254.
Immediate parent-infant skin-to-skin study (IPISTOSS): study protocol of a randomised controlled trial on very preterm infants cared for in skin-to-skin contact immediately after birth and potential physiological, epigenetic, psychological and neurodevelopmental consequences
Authors: Linnér Agnes , Westrup Björn , Lode-Kolz Karoline , Klemming Stina , Lillieskold Siri , Markhus Pike Hanne , Morgan Barak , Bergman Nils Johannes , Rettedal Siren , Jonas Wibke
Published: July 2020

Introduction

In Scandinavia, 6% of infants are born preterm, before 37 gestational weeks. Instead of continuing in the in-utero environment, maturation needs to occur in a neonatal unit with support of vital functions, separated from the mother’s warmth, nutrition and other benefits. Preterm infants face health and neurodevelopment challenges that may also affect the family and society at large. There is evidence of benefit from immediate and continued skin-to-skin contact (SSC) for term and moderately preterm infants and their parents but there is a knowledge gap on its effect on unstable very preterm infants when initiated immediately after birth.

Methods and analysis

In this ongoing randomised controlled trial from Stavanger, Norway and Stockholm, Sweden, we are studying 150 infants born at 28+0 to 32+6 gestational weeks, randomised to receive care immediately after birth in SSC with a parent or conventionally in an incubator. The primary outcome is cardiorespiratory stability according to the stability of the cardiorespiratory system in the preterm score. Secondary outcomes are autonomic stability, thermal control, infection control, SSC time, breastfeeding and growth, epigenetic profile, microbiome profile, infant behaviour, stress resilience, sleep integrity, cortical maturation, neurodevelopment, mother-infant attachment and attunement, and parent experience and mental health.

Ethics and dissemination

The study has ethical approval from the Swedish Ethical Review Authority (2017/1135-31/3, 2019–03361) and the Norwegian Regional Ethical Committee (2015/889). The study is conducted according to good clinical practice and the Helsinki declaration. The results of the study will increase the knowledge about the mechanisms behind the effects of SSC for very preterm infants by dissemination to the scientific community through articles and at conferences, and to the society through parenting classes and magazines.

Study status

Recruiting since April 2018. Expected trial termination June 2021.

Trial registration number

NCT03521310 (ClinicalTrials.gov).
Surgical management of a lateral luxation of the tendon of the superficial digital flexor muscle using an autologous fascia lata graft
Authors: Cox Thomas , Humphreys William , Tomlinson Andrew
Published: July 2020

An 18‐month‐old, 23‐kg, neutered male Staffordshire bull terrier was presented with acute left pelvic limb lameness. There was marked swelling at the level of the tuber calcanei, and lateral luxation of the tendon of the superficial digital flexor muscle (TSDFM) was palpable. Surgical correction included replacement of the TSDFM before apposition of the retinacular tissues. An autologous fascia lata graft was harvested from the ipsilateral limb and sutured over the primary suture line as augmentation. A modified Robert‐Jones bandage with caudally positioned splint was placed for 12 days, and 5‐minute lead exercise was started after 6 weeks. At 10 and 14 weeks postoperatively, the dog was exercising without evidence of lameness or clinical signs. Subjective grading by the attending veterinary surgeon (European College of Veterinary Surgeons boarded surgeon) revealed no lameness at either time point (Visual Analogue Scale score of 0/10). A clinical metrology questionnaire revealed normal mobility (Liverpool Osteoarthritis in Dogs Questionnaire score of 3).
Mathematical modelling of the formation and evolution of surface ice
Authors: Unduche Fisaha S. , Doering John C.
Published: July 2020

During the fall season in cold regions when the air temperature drops below freezing levels, it removes heat from the water surface and creates a supercooled surface layer. In weak turbulent flows, the supercooled surface layer initiates the formation of ice particles on the water surface, which could evolve into various types of surface ice runs. In this paper a mathematical model of the formation and evolution of surface ice is presented. A heat balance model at the water surface is applied to calculate the heat loss from the water. The turbulent kinetic energy and the energy dissipation rates are modelled to find the eddy viscosity that affects the mixing rate. The mathematical model is then calibrated and verified using experimental data collected at the Hydraulics Research and Testing Laboratory at the University of Manitoba. The model simulates the supercooling process reasonably well for all surface ice conditions.
Radiative cooling of carbon cluster anions C2n+1− (n = 3–5)
Authors: Stockett Mark H. , Bull James N. , Buntine Jack T. , Carrascosa Eduardo , Anderson Emma K. , Gatchell Michael , Kaminska Magdalena , Nascimento Rodrigo F. , Cederquist Henrik , Schmidt Henning T. , Zettergren Henning
Published: July 2020

Radiative cooling of carbon cluster anions C2n+1− (n = 3–5) is investigated using the cryogenic electrostatic ion storage ring DESIREE. Two different strategies are applied to infer infrared emission on slow (milliseconds to seconds) and ultraslow (seconds to minutes) timescales. Initial cooling of the ions over the millisecond timescale is probed indirectly by monitoring the decay in the yield of spontaneous neutralization by thermionic emission. The observed cooling rates are consistent with a statistical model of thermionic electron emission in competition with infrared photon emission due to vibrational de-excitation. Slower cooling over the seconds to minutes timescale associated with infrared emission from low-frequency vibrational modes is probed using time-dependent action spectroscopy. For C9− and C11−, cooling is evidenced by the time-evolution of the yield of photo-induced neutralization following resonant excitation of electronic transitions near the detachment threshold. The cross-section for resonant photo-excitation is at least two orders of magnitude greater than for direct photodetachment. In contrast, C7− lacks electronic transitions near the detachment threshold.

Graphical abstract
Decision-Making on the Supply Chain of Fresh Agricultural Products with Two-Period Price and Option Contract
Authors: Yan Bo , Liu Gaodi , Wu Xiaohua , Wu Jiwen
Published: July 2020

The price risk of fresh agricultural products has been a significant topic in recent years. Taking the two-level fresh agricultural product supply chain as the research object, this paper studies the optimal ordering and coordination of supply chain based on two-period price, wholesale price and option contract. The optimal order decision of the retailer at the single period price and the optimal decision corresponding to the supplier are obtained when the output of the supplier is uncertain under decentralized decision-making. The range of penalty cost parameter that avoids supplier default is also obtained. The effect of two-period price on the optimal order decision and supply chain profits is discussed when the production yield of the supplier is fixed. Cost-sharing contract is introduced to increase the order quantity and achieve coordination because the option contract cannot completely make the supply chain coordination with two-period price. This paper provides a low-cost approach that can be applied in fresh agricultural supply chain to solve financing and order problems.
Vibro-Acoustic Analysis and Topology Optimization of Anti-Tetra Chiral Auxetic Lattices Driven by Different Colored Noises
Authors: Hosseinkhani A. , Younesian D. , Ranjbar M.
Published: July 2020

In this paper, we study vibro-acoustic behavior of auxetic sandwich panels subjected to different excitations and boundary conditions. The core of this panel has the auxetic feature (with negative Poisson’s ratio or NPR) with anti-tetrachiral honeycomb structure. Mechanical behavior of the core is formulated using theoretical relations presented for this kind of auxetic. Using the Finite Element Method, the modal analysis and spectral analysis of the structure are accomplished. Different random colored noises are applied as the system excitation. First, a parametric study is performed; and some interesting results are observed from investigating the effects of geometric parameters, boundary conditions, and noise color on the vibro-acoustic behavior of the structure. These parameters affect the natural frequencies, level of radiated sound, and mass of the structure. An optimization algorithm is applied to the geometrical parameters in order to simultaneously reduce the level of radiated sound and preserve the amount of total mass. By the use of the Genetic Algorithm (GA), we could achieve a remarkable noise attenuation gain. It is shown that the GA choses different optimized parameters for the structure according to the location of the load and frequency content of the load spectrum.
The Duflo non-commutative Fourier transform for the Lorentz group
Authors: Rosati Giacomo
Published: July 2020

For a quantum system whose phase space is the cotangent bundle of a Lie group, like for systems endowed with particular cases of curved geometry, one usually resorts to a description in terms of the irreducible representations of the Lie group, where the role of (non-commutative) phase space variables remains obscure. However, a non-commutative Fourier transform can be defined, intertwining the group and (non-commutative) algebra representation, depending on the specific quantization map. We discuss the construction of the non-commutative Fourier transform and the non-commutative algebra representation, via the Duflo quantization map, for a system whose phase space is the cotangent bundle of the Lorentz group.
Nonclassical symmetries and analytic solutions to Kawahara equation
Authors: Verma Pallavi , Kaur Lakhveer
Published: July 2020

The focus of this paper is on Kawahara equation (KE) which models diverse variety of waves enforced in many branches of applied sciences. Symmetries to the considered equation are successfully obtained by practicing nonclassical method. According to nonclassical symmetries, KE has been reduced to numerous ODEs. Further, these ODEs are treated such that new analytic solutions are determined by applying power series, ansätz and [Formula: see text]-expansion method.
Experimental Investigations of Hydraulic Surges Passing Over a Rectangular Canal
Authors: Elsheikh Nuri , Azimi Amir H. , Nistor Ioan , Mohammadian Abdolmajid
Published: July 2020

The purpose of this experimental study was to investigate the effects of a rectangular canal on the hydrodynamics of turbulent surges before and after the canal by implementing a series of physical experiments. A dam-break wave model was used to simulate the tsunami-like turbulent waves passing over a smooth and horizontal surface, in the presence and absence of a canal. Three canal depths of [Formula: see text], 0.10 and 0.15[Formula: see text]m were used to model shallow, moderate and deep conditions and three canal widths of [Formula: see text], 1.60 and 3.0[Formula: see text]m were selected to model narrow to wide canals. The front velocity of the dam-break induced surges were controlled by rapidly releasing upstream impounded set volumes of water with depths of [Formula: see text], 0.30 and 0.40[Formula: see text]m. The dam-break wave propagation over a horizontal, dry and smooth bed revealed four regimes describing the variations of surge height with time. The arrival time to reach the maximum surge height and the quasi steady-state regime was correlated with each impoundment depth and an empirical formulation was proposed to estimate the onset of the quasi steady-state flow. The maximum surge heights measured before and after the mitigation canal location were compared with those recorded in the corresponding tests without the presence of the canal. It was found that the peak surge height upstream of the canal could increase up to 40% compared to the test without the presence of the canal in relatively small impoundment depth and in presence of a narrow canal due to momentum dissipation. The wave height downstream of the canal increased between 10% and 50% of the wave height without the presence of the canal and the minimum change in the wave height occurred for the canal width to depth ratio of 20. The time-history of surge velocity after the mitigation canal indicated a significant decay of between 40% and 60% in the presence of a canal due to the bed friction changes and momentum dissipation.
Algorithmic aspects of k-part degree restricted domination in graphs
Authors: Kamath S. S. , Senthil Thilak A. , Rashmi M.
Published: July 2020

The concept of network is predominantly used in several applications of computer communication networks. It is also a fact that the dominating set acts as a virtual backbone in a communication network. These networks are vulnerable to breakdown due to various causes, including traffic congestion. In such an environment, it is necessary to regulate the traffic so that these vulnerabilities could be reasonably controlled. Motivated by this, [Formula: see text]-part degree restricted domination is defined as follows. For a positive integer [Formula: see text], a dominating set [Formula: see text] of a graph [Formula: see text] is said to be a [Formula: see text]-part degree restricted dominating set ([Formula: see text]-DRD set) if for all [Formula: see text], there exists a set [Formula: see text] such that [Formula: see text] and [Formula: see text]. The minimum cardinality of a [Formula: see text]-DRD set of a graph [Formula: see text] is called the [Formula: see text]-part degree restricted domination number of [Formula: see text] and is denoted by [Formula: see text]. In this paper, we present a polynomial time reduction that proves the NP -completeness of the [Formula: see text]-part degree restricted domination problem for bipartite graphs, chordal graphs, undirected path graphs, chordal bipartite graphs, circle graphs, planar graphs and split graphs. We propose a polynomial time algorithm to compute a minimum [Formula: see text]-DRD set of a tree and minimal [Formula: see text]-DRD set of a graph.
Taiwan’s 2020 Presidential, Vice Presidential and Legislative Election: Testing Election Theories
Authors: COPPER John F
Published: July 2020

This article assesses the status of theories of Taiwan’s election winners and losers, looking at the 2020 campaign and the election results. Both seemed to prove the pendulum (alternating party) theory. But the evidence was weak, mainly coincidental, while other constructs and ideas played a major role.
Turkey and China: Contemporary Political, Economic and Strategic Relations
Authors: DUERKOP Colin
Published: July 2020

Turkish–Chinese relations are of growing relevance for both countries as well as at a global level. This relationship is steadily becoming more significant and multifaceted in Turkish foreign policy, despite several thorny issues still remaining between the two countries. Nevertheless, many other aspects such as increasing trade and intensified intergovernmental dialogues characterise this bilateral relationship. This article analyses the broader status and fluctuations of Turkey–China relations in the areas of politics, economics and strategic outlook.
Behavioural variation in limulids (Xiphosurida) from the Middle Jurassic of the Cleveland Basin, Yorkshire, UK, as deduced from their traces: a review and reappraisal
Authors: Romano Mike
Published: July 2020

The paper is a study of horseshoe crab (limulid) behaviour, based on their ichnites from the Middle Jurassic rocks of the Cleveland Basin, Yorkshire, UK, from a succession spanning a period of
c
. 8 myr. Five named ichnospecies have been recognized from this sequence as having a limulid maker:
Selenichnites hundalensis
,
S. rossendalensis
,
Kouphichnium
aff.
variabilis
,
K.
cf.
arizonae
and
Crescentichnus tesiltus
, as well as unassigned ichnospecies (
K
. isp. indet.,
S
. isp.,
S
. isp. indet., and
C
. ispp. indet.). These traces have been interpreted as showing behavioural patterns during burial, feeding, locomotion and probing. The occurrence of these traces in both non-marine and marine strata together with their presumed lifestyle is compared to what is known of extant horseshoe crabs, and any difference in lifestyle is discussed.
A Tight I/O Lower Bound for Matrix Multiplication
Authors: Smith Tyler M , van de Geijn Robert A
Published: July 2020

A tight lower bound for required I/O when computing a matrix-matrix multiplication on a processor with two layers of memory is established. Prior work obtained weaker lower bounds by reasoning about the number of phases needed to perform C:=AB, where each phase is a series of operations involving S reads and writes to and from fast memory, and S is the size of fast memory. A lower bound on the number of phases was then determined by obtaining an upper bound on the number of scalar multiplications performed per phase. This paper follows the same high level approach, but improves the lower bound by considering C:=AB+C instead of C:=AB, and obtains the maximum number of scalar fused multiply-adds (FMAs) per phase instead of scalar additions. Key to obtaining the new result is the decoupling of the per-phase I/O from the size of fast memory. The new lower bound is 2mnk/ S - 2S where S is the size of fast memory. The constant for the leading term is an improvement of a factor 4/ 2. A theoretical algorithm that attains the lower bound is given, and how the state-of-the-art Goto's algorithm also in some sense meets the lower bound is discussed.
Machine Learning for Congestion Management and Routability Prediction within FPGA Placement
Authors: Szentimrey Hannah , Al-Hyari Abeer , Foxcroft Jeremy , Martin Timothy , Noel David , Grewal Gary , Areibi Shawki
Published: July 2020

Placement for Field Programmable Gate Arrays (FPGAs) is one of the most important but time-consuming steps for achieving design closure. This article proposes the integration of three unique machine learning models into the state-of-the-art analytic placement tool GPlace3.0 with the aim of significantly reducing placement runtimes. The first model, MLCong, is based on linear regression and replaces the computationally expensive global router currently used in GPlace3.0 to estimate switch-level congestion. The second model, DLManage, is a convolutional encoder-decoder that uses heat maps based on the switch-level congestion estimates produced by MLCong to dynamically determine the amount of inflation to apply to each switch to resolve congestion. The third model, DLRoute, is a convolutional neural network that uses the previous heat maps to predict whether or not a placement solution is routable. Once a placement solution is determined to be routable, further optimization may be avoided, leading to improved runtimes. Experimental results obtained using 372 benchmarks provided by Xilinx Inc. show that when all three models are integrated into GPlace3.0, placement runtimes decrease by an average of 48%.
Integrity Verification Mechanism of Sensor Data Based on Bilinear Map Accumulator
Authors: Ren Yongjun , Qi Jian , Liu Yepeng , Wang Jin , Kim Gwang-Jun
Published: July 2020

With the explosive growth in the number of IoT devices, ensuring the integrity of the massive data generated by these devices has become an important issue. Due to the limitation of hardware, most past data integrity verification schemes randomly select partial data blocks and then perform integrity validation on those blocks instead of examining the entire dataset. This will result in that unsampled data blocks cannot be detected even if they are tampered with. To solve this problem, we propose a new and effective integrity auditing mechanism of sensor data based on a bilinear map accumulator. Using the proposed approach will examine all the data blocks in the dataset, not just some of the data blocks, thus, eliminating the possibility of any cloud manipulation. Compared with other schemes, our proposed solution has been proved to be highly secure for all necessary security requirements, including tag forgery, data deletion, replacement, replay, and data leakage attacks. The solution reduces the computational and storage costs of cloud storage providers and verifiers, and also supports dynamic operations for data owners to insert, delete, and update data by using a tag index table (TIT). Compared with existing schemes based on RSA accumulator, our scheme has the advantages of fast verification and witness generation and no need to map data blocks to prime numbers. The new solution supports all the characteristics of a data integrity verification scheme.
Physiological Monitoring by Fail Proof Lifetime Enhancement Algorithm for Smart Digital Environments
Authors: S Malathy , Mallikarjuna Basetty , R Maheswar , GR Kanagachidambaresan , TVP Sundararajan , A Sampathkumar , Dhasarathan Vigneswaran
Published: July 2020

Physiological signals monitoring in smart digital environment became ease with miniaturized embedded systems. The node loses its energy due to continuous monitoring and becomes unavailable during critical conditions. The implanted node dissipated heat and causes tissue damage when overloaded. The problem is addressed through Fail Proof Lifetime Enhancement (FPLE) Algorithm by optimally scheduling the nodes in digital environment based on the energy level and through Threshold T* policy model proposed. The objective is achieved by classifying packets based on their status and packets are transmitted towards sink once it meets a threshold value T*. A part of the energy in the sensor node is utilized during emergencies to ensure the availability of monitoring the subject during critical conditions. The subject status is realized as a Finite State Machine and evaluated through the Markov model and threshold T* based framework is adopted to enhance the lifetime of the network. The FPLE algorithm performs better than the SingleHop, MultiHop and ATTEMPT routing scheme in terms of network lifetime and throughput. The proposed FPLE algorithm provides 1.91 times lifetime and 1.1 times throughput when compared with the ATTEMPT communication protocol. The false data generation is identified and alarmed through this approach, and the FPLE algorithm also ensures monitoring during critical conditions. The implanted node is loaded least to avoid thermal dissipation and tissue damage of the subject. The FPLE algorithm is tested in real-time digital environment for its novel working, provides better results when compared to ATTEMPT, SingleHop, and MultiHop protocol.
System-level Logical Execution Time
Authors: Gemlau Kai-Björn , KÖHLER Leonie , Ernst Rolf , Quinton Sophie
Published: July 2020

Logical Execution Time (LET) is a timed programming abstraction, which features predictable and composable timing. It has recently gained considerable attention in the automotive industry, where it was successfully applied to master the distribution of software applications on multi-core electronic control units. However, the LET abstraction in its conventional form is only valid within the scope of a single component. With the recent introduction of System-level Logical Execution Time (SL LET), the concept could be transferred to a system-wide scope. This article improves over a first paper on SL LET, by providing matured definitions and an extensive discussion of the concept. It also features a comprehensive evaluation exploring the impacts of SL LET with regard to design, verification, performance, and implementability. The evaluation goes far beyond the contexts in which LET was originally applied. Indeed, SL LET allows us to address many open challenges in the design and verification of complex embedded hardware/software systems addressing predictability, synchronization, composability, and extensibility. Furthermore, we investigate performance trade-offs, and we quantify implementation costs by providing an analysis of the additionally required buffers.
Fog in the Clouds
Authors: Faraci Giuseppe , Grasso Christian , Schembra Giovanni
Published: July 2020

Internet of Things (IoT) has emerged as a huge paradigm shift by connecting a versatile and massive collection of smart objects to the Internet, coming to play an important role in our daily lives. Data produced by IoT devices can generate a number of computational tasks that cannot be executed locally on the IoT devices. The most common solution is offloading these tasks to external devices with higher computational and storage capabilities, usually provided by centralized servers in remote clouds or on the edge by using the fog computing paradigm. Nevertheless, in some IoT scenarios there are remote or challenging areas where it is difficult to connect an IoT network to a fog platform with appropriate links, especially if IoT devices produce a lot of data that require processing in real-time. To this purpose, in this article, we propose to use unmanned aerial vehicles (UAVs) as fog nodes. Although this idea is not new, this is the first work that considers power consumption of the computing element installed on board UAVs, which is crucial, since it may influence flight mission duration. A System Controller (SC) is in charge of deciding the number of active CPUs at runtime by maximizing an objective function weighing power consumption, job loss probability, and processing latency. Reinforcement Learning (RL) is used to support SC in its decisions. A numerical analysis is carried out in a use case to show how to use the model introduced in the article to decide the computation power of the computing element in terms of number of available CPUs and CPU clock speed, and evaluate the achieved performance gain of the proposed framework.
The Security of Medical Data on Internet Based on Differential Privacy Technology
Authors: Lv Zhihan , Piccialli Francesco
Published: July 2020

The study aims at discussing the security of medical data in the Internet era. By using k-anonymity (K-A) and differential privacy (DP), an algorithm model combining K-A and DP was proposed, which was simulated through the experiments. In the Magic and EIA datasets, the algorithm constructed was compared with K-A and the L-diversity model to verify the performance of the model. The model constructed based on DP had the lowest privacy-leakage risks, which increased with the number of identifiers in the Magic and EIA datasets, and the information disclosure was the least. In addition, in its usability analysis, it was found that its value was the most obviously improved and its operation efficiency was the highest. The K-A-DP algorithm can effectively reduce the risk of privacy leakage and information loss, and has achieved excellent results. Despite the deficiencies in the process of the experiment, the study still provides a reference for solving the problem of medical data security.
Contextual Anomaly Detection in Solder Paste Inspection with Multi-Task Learning
Authors: Zheng Zimu , Pu Jie , Liu Linghui , Wang Dan , Mei Xiangming , Zhang Sen , Dai Quanyu
Published: July 2020

In this article, we study solder paste inspection (SPI), an important stage that is used in the semiconductor manufacturing industry, where abnormal boards should be detected. A highly accurate SPI can substantially reduce human expert involvement, as well as reduce the waste in disposing of the boards in good condition. A key difference today is that because of increasing demand in board customization, the number of board types increases substantially and quantity of the boards produced in each type decreases. Thus, the previous approaches where a fine-tuned model is developed for each board type are no longer viable.

Intrinsically, our problem is an anomaly detection problem. A major specialty in today’s SPI is that the target tasks for prediction cannot be fully pre-determined due to context changes during the solder paste printing stage. Our experiences show that a conventional approach to first define a set of tasks and train these tasks offline will lead to low accuracy. Here, we propose a novel multi-task approach, where the performance of all target tasks is ensured simultaneously. We note that the SPI process is streamlined and automatic, allowing the SPI time for only a few seconds. We propose a fast clustering algorithm that reuses existing models to avoid retraining and fine tune in the inference phase. We evaluate our approach using 3-month data collected from production lines. We show that we can reduce 81.28% of false alarms. This can translate to annual savings of $11.3 million.
Detection of Disaster-Affected Cultural Heritage Sites from Social Media Images Using Deep Learning Techniques
Authors: Kumar Pakhee , Ofli Ferda , Imran Muhammad , Castillo Carlos
Published: July 2020

This article describes a method for early detection of disaster-related damage to cultural heritage. It is based on data from social media, a timely and large-scale data source that is nevertheless quite noisy. First, we collect images posted on social media that may refer to a cultural heritage site. Then, we automatically categorize these images according to two dimensions: whether they are indeed a photo in which a cultural heritage resource is the main subject, and whether they represent damage. Both categorizations are challenging image classification tasks, given the ambiguity of these visual categories; we tackle both tasks using a convolutional neural network. We test our methodology on a large collection of thousands of images from the web and social media, which exhibit the diversity and noise that is typical of these sources, and contain buildings and other architectural elements, heritage and not-heritage, damaged by disasters as well as intact. Our results show that while the automatic classification is not perfect, it can greatly reduce the manual effort required to find photos of damaged cultural heritage by accurately detecting relevant candidates to be examined by a cultural heritage professional.
Entropy Slicing Extraction and Transfer Learning Classification for Early Diagnosis of Alzheimer Diseases with sMRI
Authors: Kumar S. Sambath , Nandhini M.
Published: July 2020

Alzheimer’s Disease (AD) is an irreversible neurogenerative disorder that undergoes progressive decline in memory and cognitive function and is characterized by structural brain Magnetic Resonance Images (sMRI). In recent years, sMRI data has played a vital role in the evaluation of brain anatomical changes, leading to early detection of AD through deep networks. The existing AD problems such as preprocessing complexity and unreliability are major concerns at present. To overcome these, a model (

FE
ES
C
TL

) has been proposed with an entropy slicing for feature extraction and Transfer Learning for classification. In the present study, the entropy image slicing method is attempted for selecting the most informative MRI slices during training stages. The ADNI dataset is trained on Transfer Learning adopted by VGG-16 network for classifying the AD with normal individuals. The experimental results reveal that the proposed model has achieved an accuracy level of 93.05%, 86.39%, 92.00% for binary classifications (AD/MCI, MCI/CN, AD/CN) and 93.12% for ternary classification (AD/MCI/CN), respectively, and henceforth the efficiency in diagnosing AD is proved through comparative analysis.
From the Engraved Tablet to the Digital Tablet, History of a 15th-Century Music Score
Authors: Gaugne Ronan , Labaune Françoise , Fontaine Dominique , Cloirec Gaétan Le , Gouranton Valérie
Published: July 2020

This work illustrates the use of three different digitization techniques to study and valorize a 15th-century engraved tablet discovered during a preventive archaeological excavation in the area of a former convent. The tablet is covered with engraved inscriptions on both sides and includes a musical score. Digitization allowed for an advanced analysis of the inscriptions, and to generate a complete and precise 3D model of the artifact that was used to produce an interactive application deployed both on tactile tablets and websites. The interactive application integrates a musical interpretation of the score that gives access to a testimony of intangible heritage. This interdisciplinary work gathered archaeologists, researchers from computer science and physics, and a professional musician.
HandiText
Authors: Fang Liming , Zhu Hongwei , Lv Boqing , Liu Zhe , Meng Weizhi , Yu Yu , Ji Shouling , Cao Zehong
Published: July 2020

The Internet of Things (IoT) is a new manifestation of data science. To ensure the credibility of data about IoT devices, authentication has gradually become an important research topic in the IoT ecosystem. However, traditional graphical passwords and text passwords can cause user’s serious memory burdens. Therefore, a convenient method for determining user identity is needed. In this article, we propose a handwriting recognition authentication scheme named HandiText based on behavior and biometrics features. When people write a word by hand, HandiText captures their static biological features and dynamic behavior features during the writing process (writing speed, pressure, etc.). The features are related to habits, which make it difficult for attackers to imitate. We also carry out algorithms comparisons and experiments evaluation to prove the reliability of our scheme. The experiment results show that the Long Short-Term Memory has the best classification accuracy, reaching 99% while keeping relatively low false-positive rate and false-negative rate. We also test other datasets, the average accuracy of HandiText reach 98%, with strong generalization ability. Besides, the 324 users we investigated indicated that they are willing to use this scheme on IoT devices.
Composable Finite State Machine-based Modeling for Quality-of-Information-aware Cyber-physical Systems
Authors: Rosales Rafael , Paulitsch Michael
Published: July 2020

Time plays a major role in the
specification of Cyber-physical Systems (CPS) behavior
with concurrency, timeliness, asynchrony, and resource limits as their main characteristics. In addition to
timeliness
, the specification of CPS needs to assess and unambiguously define its behavior with respect to the other Quality-of-Information (QoI) properties: (1) Correctness, (2) Completeness, (3) Consistency, and (4) Accuracy. Very often, CPS need to handle these QoI properties, and any combination thereof, multiple times when performing computation and communication processes. However, a model-driven and systematic approach to specify CPS behavior that jointly considers combined QoI aspects is possible but missing in existing methodologies.

As the first contribution of this work, we provide an extension to an established model of computation (MoC) based on “Functions driven by Finite State Machine” (FunState) to enable a model-driven
composition mechanism
to create CPS behavior specifications from reusable components.

Second, we present a novel set of
design patterns
to illustrate the modeling of
QoI-aware CPS specifications
that can be applied in several state-of-the-art Electronic System Level (ESL) methodologies. The time semantics of the MoC are formalized using the tagged-signal-model, and the presented model-driven approach enables the composition of multiple design patterns.

The main benefits of the presented model-driven approach and design patterns to create CPS specifications are as follows: (a) reduce modeling effort, errors, and time through the reuse of known recipes to re-incurring tasks and allow to automatically generate repetitive control flows based on extended Finite State Machines; (b) increase system robustness and facilitate the creation of holistic QoI management allowing to unambiguously define system behavior for scenarios with single/multiple QoI requirement violations in different models of computation; (c) dynamically validate timing behavior of system implementations to enable a multi-objective optimization of nonfunctional properties that influence CPS timing.

We demonstrate the aforementioned benefits through the modeling and evaluation of an infrastructure-assisted automated driving case study using Infrastructure-to-Vehicle (I2V) communications to distribute QoI critical road environment information.
Malware Triage for Early Identification of Advanced Persistent Threat Activities
Authors: Laurenza Giuseppe , Lazzeretti Riccardo , Mazzotti Luca
Published: July 2020

In the past decade, a new class of cyber-threats, known as “Advanced Persistent Threat” (APT), has emerged and has been used by different organizations to perform dangerous and effective attacks against financial and politic entities, critical infrastructures, and so on. To identify APT related malware early, a semi-automatic approach for malware samples analysis is needed. Recently, a
malware triage
step for a semi-automatic malware analysis architecture has been introduced. This step identifies incoming APT samples early, among all the malware delivered per day in the cyber-space, to immediately dispatch them to deeper analysis. In the article, the authors have built the knowledge base on known APTs obtained from publicly available reports. For efficiency reasons, they rely on static malware features, extracted with negligible delay, and use machine learning techniques for the identification. Unfortunately, the proposed solution has the disadvantage of requiring a long training time and needs to be completely retrained each time new APT samples or even a new APT class are discovered. In this article, we move from multi-class classification to a group of one-class classifiers, which significantly decreases runtime and allows higher modularity, while still guaranteeing precision and accuracy over 90%.
Semi-supervised Thai Sentence Segmentation Using Local and Distant Word Representations
Authors: Saetia Chanatip , Chalothorn Tawunrat , Chuangsuwanich Ekapol , Vateekul Peerapon
Published: July 2020

A sentence is typically treated as the minimal syntactic unit used for extracting valuable information from a longer piece of text. However, in written Thai, there are no explicit sentence markers. We proposed a deep learning model for the task of sentence segmentation that includes three main contributions. First, we integrate n-gram embedding as a local representation to capture word groups near sentence boundaries. Second, to focus on the keywords of dependent clauses, we combine the model with a distant representation obtained from self-attention modules. Finally, due to the scarcity of labeled data, for which annotation is difficult and time-consuming, we also investigate and adapt Cross-View Training (CVT) as a semi-supervised learning technique, allowing us to utilize unlabeled data to improve the model representations. In the Thai sentence segmentation experiments, our model reduced the relative error by 7.4% and 10.5% compared with the baseline models on the Orchid and UGWC datasets, respectively. We also applied our model to the task of pronunciation recovery on the IWSLT English dataset. Our model outperformed the prior sequence tagging models, achieving a relative error reduction of 2.5%. Ablation studies revealed that utilizing n-gram presentations was the main contributing factor for Thai, while the semi-supervised training helped the most for English.
Demystifying Energy Consumption Dynamics in Transiently powered Computers
Authors: Ahmed Saad , Nawaz Muhammad , Bakar Abu , Bhatti Naveed Anwar , Alizai Muhammad Hamad , Siddiqui Junaid Haroon , Mottola Luca
Published: July 2020

Transiently powered computers (TPCs) form the foundation of the battery-less Internet of Things, using energy harvesting and small capacitors to power their operation. This kind of power supply is characterized by extreme variations in supply voltage, as capacitors charge when harvesting energy and discharge when computing. We experimentally find that these variations cause marked fluctuations in
clock speed
and
power consumption
. Such a deceptively minor observation is overlooked in existing literature. Systems are thus designed and parameterized in overly conservative ways, missing on a number of optimizations.

We rather demonstrate that
it is possible
to accurately model and concretely capitalize on these fluctuations. We derive an energy model as a function of supply voltage and prove its use in two settings. First, we develop EPIC, a compile-time energy analysis tool. We use it to substitute for the constant power assumption in existing analysis techniques, giving programmers accurate information on worst-case energy consumption of programs. When using EPIC with existing TPC system support, run-time energy efficiency drastically improves, eventually leading up to a 350% speedup in the time to complete a fixed workload. Further, when using EPIC with existing debugging tools, it avoids unnecessary program changes that hurt energy efficiency. Next, we extend the MSPsim emulator and explore its use in parameterizing a different TPC system support. The improvements in energy efficiency yield up to more than 1000% time speedup to complete a fixed workload.
Enabling Reference Verifiability for the World Wide Web with Webchain
Authors: Rohrer Elias , Heidel Steffen , Tschorsch Florian
Published: July 2020

As online sources are becoming more prevalent in journalism and scientific literature, the ephemeral nature of the World Wide Web is becoming an increasingly serious issue for their verifiability, replicability, and reproducibility. The architecture of Webchain, a new system enabling source and reference verifiability on the Web, is combining distributed ledger technologies with secure timestamping to ensure the history of creation, ownership, and referential integrity of online resources. We present the architecture and system extensions, conduct a security analysis, and evaluate the Webchain system based on a comprehensive prototype implementation. The results confirm the feasibility and robustness of our approach.
Efficient Discovery of Matching Dependencies
Authors: Schirmer Philipp , Papenbrock Thorsten , Koumarelas Ioannis , Naumann Felix
Published: July 2020

Matching dependencies (MDs) are data profiling results that are often used for data integration, data cleaning, and entity matching. They are a generalization of functional dependencies (FDs) matching
similar
rather than same elements. As their discovery is very difficult, existing profiling algorithms find either only small subsets of all MDs or their scope is limited to only small datasets.

We focus on the
efficient
discovery of all
interesting
MDs in real-world datasets. For this purpose, we propose HyMD, a novel MD discovery algorithm that finds all minimal, non-trivial MDs within given similarity boundaries. The algorithm extracts the exact similarity thresholds for the individual MDs from the data instead of using predefined similarity thresholds. For this reason, it is the first approach to solve the MD discovery problem in an exact and truly complete way. If needed, the algorithm can, however, enforce certain properties on the reported MDs, such as disjointness and minimum support, to focus the discovery on such results that are actually required by downstream use cases. HyMD is technically a hybrid approach that combines the two most popular dependency discovery strategies in related work: lattice traversal and inference from record pairs. Despite the additional effort of finding exact similarity thresholds for all MD candidates, the algorithm is still able to efficiently process large datasets, e.g., datasets larger than 3 GB.
ROLE OF ADVANCED WEB BASED CONTENT MANAGEMENT SYSTEM AND ITS SIGNIFICANCE IN LIBRARIES MANAGEMENT SYSTEM
Authors: Liu Bin , Lu Yao
Published: July 2020

Libraries are vaults of learning and the enormous development in computerized assets has constrained library experts to utilize different data innovation tools to oversee and render management to the clients in Chinese education and research sector. To accomplish more noteworthy proficiency in the quickly evolving economic condition, libraries are progressively searching for new standards to convey management to clients in their work areas to innovate their knowledge. Library gateways assume a significant job by making a web domain where clients can without much of a stretch access data. It offers benefits as well as improving insightful correspondence and research among the supporters of the library. This paper discuses the entry of Advanced Web based Content Management System (AW-CMS) using linked list based iterations for database management has been made here to clarify the different supervisionclients, so it mayimprove the successful data searchers and assess accurate data proficiently and reliability in an effective and efficient manner. It likewise features, how it attempts to meet client desires for data on interest in library management and its experimental validation in accordance with existing software systems.