Miracle-dz/UniFAR-FTUs huggingface dataset:
Automated Contactless Defect Analysis Technique Using Computer Vision

In this paper, an automated contactless defect analysis technique using Computer Vision (CV) algorithms is presented. The proposed method includes closed-loop control of optical tools for automated image collection, as well as advanced image analysis methods to improve image quality and detect potential defects. As an example, the technique was successfully used to identify delamination defects along the perimeter of a large test chip.
INTEGRATING RASTA-PLP INTO SPEECH RECOGNITION

In previous work, we and others have shown that bandpass filtering of temporal trajectories of simple functions of the cr.itica1 band spectrum can lead to more robust speech recognizers in the presenre of additive and convolutional error. In this study we report results on several mechanisms for incorporating this analysis technique into training, in a way that is consistent with on-line approaches to speech recognition. In particular, we show improved robustness to these forms of degradation for a system that maps the filtered spectral points using a linear regression computed from results of the different transformations.
AKM-IoV: Authenticated Key Management Protocol in Fog Computing-Based Internet of Vehicles Deployment

Internet of Vehicles (IoV) is an intelligent application of Internet of Things (IoT) in smart transportation that takes intelligent commitments to the passengers to improve traffic safety and efficiency, and generate a more enjoyable driving and riding environment. Fog cloud-based IoV is another variant of mobile cloud computing where vehicular cloud and Internet can co-operate in more effective way in IoV. However, more increasing dependence on wireless communication, control, and computing technology makes IoV more dangerous to prospective attacks. For secure communication among vehicles, road-side units, fog and cloud servers, we design a secure authenticated key management protocol in fog computing-based IoV deployment, called AKM-IoV. In the designed AKM-IoV, after mutual authentication between communicating entities in IoV they establish session keys for secure communications. AKM-IoV is tested for its security analysis using the formal security analysis under the widely accepted real-or-random (ROR) model, informal, and formal security verification using the broadly accepted automated validation of Internet security protocols and applications (AVISPAs) tool. The practical demonstration of AKM-IoV is shown using the NS2 simulation. In addition, a detailed comparative study is conducted to show the efficiency and functionality and security features supported by AKM-IoV as compared to other existing recent protocols.
Understanding the Role of Style in E-commerce Shopping

Aesthetic style is the crux of many purchasing decisions. When considering an item for purchase, buyers need to be aligned not only with the functional aspects (e.g. description, category, ratings) of an item's specification, but also its stylistic and aesthetic aspects (e.g. modern, classical, retro) as well. Style becomes increasingly important on e-commerce sites like Etsy, an online marketplace for handmade and vintage goods, where hundreds of thousands of items can differ by style and aesthetic alone. As such, it is important for industry recommender systems to properly model style when understanding shoppers' buying preference. In past work, because of its abstract nature, style is often approached in an unsupervised manner, represented by nameless latent factors or embeddings. As a result, there has been no previous work on predictive models nor analysis devoted to understanding how style, or even the presence of style, impacts a buyer's purchase decision. In this paper, we discuss a novel process by which we leverage 43 named styles given by merchandising experts in order to bootstrap large-scale style prediction and analysis of how style impacts purchase decision. We train a supervised, style-aware deep neural network that is shown to predict item style with high accuracy, while generating style-aware embeddings that can be used in downstream recommendation tasks. We share in our analysis, based on over a year's worth of transaction data and show that these findings are crucial to understanding how to more explicitly leverage style signal in industry-scale recommender systems.
The information value of energy labels: A quasi-experimental approach

Promoting energy eﬃciency in the residential housing sector is a policy objective of increasing importance. In this paper, I assess the eﬀectiveness of energy labels in the resi dential housing sector of Netherlands from 2008 to 2017. Hedonic pricing models, commonly used in the literature, ﬁnd large transaction price premiums for dwellings with better energy labels. However, this result is sensitive to model speciﬁcations. Hence, the premiums cannot be causally attributed to energy labels. Addressing the shortcomings of this methodology, I construct a Regression Discontinuity Design model (RDD). The energy index, which deter mines the energy labels, is used to compare similar dwellings with diﬀerent energy labels. The RDD estimates provide no evidence for signiﬁcant price premiums due to energy labels. These ﬁndings suggest that energy labels do not represent information valued in the market and question the need for this type of information policy.
An expanded critical incident approach for exploring information use and learning

Critical incidents offer a focus for exploratory research about human experiences, including information use and information literacy learning. This paper describes how critical incidents underpinned research about international students’ use of online information resources at two Australian universities. It outlines the development and application of an expanded critical incident approach (ECIA), explaining how ECIA built upon critical incident technique (CIT) and incorporated information literacy theory. It discusses points of expansion (differences) between CIT and ECIA. While CIT initially proved useful in structuring the research, the pilot study revealed methodological limitations. ECIA allowed more nuanced data analysis and the integration of reflection. The study produced a multifaceted word picture of international students’ experience of using online information resources to learn, and a set of critical findings about their information literacy learning needs. ECIA offers a fresh approach for researching information use, information experience, evidence-based practice, information literacy and informed learning.
Radio Fingerprinting Using Convolutional Neural Networks

of the Thesis Radio Fingerprinting Using Convolutional Neural Networks by Shamnaz Mohammed Riyaz Master of Science in Electrical and Computer Engineering Northeastern University, July 2018 Dr. Kaushik Chowdhury, Advisor In this thesis, we describe a method for uniquely identifying a specific radio among nominally similar devices using a combination of software defined radio (SDR) sensing capability and machine learning (ML) techniques. Our approach of radio fingerprinting applies ML over raw I/Q samples without specifically selecting features of interest. It distinguishes devices using only the transmitter hardware-induced signal modifications that serve as a unique signature for a particular device. No higher level decoding, feature engineering, or protocol knowledge is needed, further mitigating challenges of ID spoofing and coexistence of multiple protocols in a shared spectrum. Advances in SDR technology allows unprecedented control on the entire processing chain, allowing modification of each functional block as well as sampling the changes in the input waveform. We first demonstrate RF impairments by modifying the operational blocks in a typical wireless communications processing chain in a simulation study. We then generate over-the-air dataset compiled from an experimental testbed of SDRs such as B210 and X310 and train the data using an optimized deep convolutional neural network (CNN) architecture that gives good classification accuracy. We describe the parallel processing needs and choice of several hyper parameters to enable efficient training of the CNN model. We then compare the performance quantitatively with alternate techniques such as support vector machines and logistic regression. Overall our results show that we can achieve up to 90-99% experimental accuracy at transmitter-receiver distances varying between 2-50 feet over a noisy, multi-path wireless channel.
Connectivity Based on Multi-Voxel Patterns Can Selectively Identify Brain Networks Where Condition-based Functional Connectivity Does Not: Evidence from the Scene Network

With a major focus of neuroimaging research on mapping brain network connectivity, it is essential that researchers use the most effective methods for determining which regions comprise functional networks. Here, we compare the functional magnetic resonance imaging (fMRI) brain networks that can be identified through shared fluctuations in regions’ univariate responses to conditions (i.e., condition-based functional connectivity), with those identified by shared fluctuations in multivariate information. To do this, we compare brain networks generated by two approaches for measuring connectivity: psychophysiological interaction (PPI), which measures the effect of conditions on shared univariate responses, and informational connectivity (IC), which measures shared fluctuations in the discriminability of multi-voxel patterns. We compare the findings generated by applying these methods to data collected while people perceptually process scenes and control (pseudo) scenes. Prior work establishing the regions involved in scene processing give us an opportunity to compare the sensitivity and selectivity of these approaches to detect a stimulus-relevant network. We find that, while each measure produces useful information, the PPI method was less selective than IC in detecting scene-related regions. Using PPI led to identifying networks containing both scene and object regions, with little specificity in connections between scene regions. In contrast, the network identified by IC was more consistent with prior literature examining the brain’s scene network. We recommend that – for conditions known to be represented in multi-voxel patterns – researchers wishing to prioritize specificity in mapping networks should examine informational connectivity over univariate connectivity approaches such as PPI.
RELAY DEPLOYMENT ALGORITHMS IN WIRELESS SENSOR NETWORKS: A SURVEY

Wireless Sensor Networks (WSNs) are subject to failures. Even though reliable routing protocols for WSNs exist and are well-understood, the physical network topology must ensure that alternate routes with an acceptable length to the sinksare in fact available when failures occur. This requires a sensor network deployment to be planned with an objective of ensuring some measure of robustness in the topology, so that when failures do occur the protocols can continue to offer reliable delivery. To ensure that sensor nodes have sufficient paths, it may be necessary to add a number of additional relay nodes, which do not sense, but only forward data from other nodes. In this paper, we review a range of existing algorithms to deploy relay nodes for fault tolerance. We classify the state-of-the-art relay placement algorithms based on routing structures, connectivity requirements, deployment locations, and fault-tolerant requirements.
Design of a development platform to monitor and manage Low Power, Wide Area WSNs

The recent explosion of Low Power Wide Area (LPWA) WSN devices has raised interest in perceiving the Quality of Service (QoS) provided to and by such applications. Current QoS solutions do not respect LPWA-specific considerations, such as limited resources and extreme scale. This study has set out to research an appropriate solution to QoS monitoring and management that does concern these considerations. This is achieved by establishing a development platform focused on LPWA QoS. The platform consists of two chief concepts. The first of which is a distributed stream processing architecture. The architecture back-bone is based on Apache Storm and provides scaffolding for different classes of stream transformations, which guides users in implementing their monitoring applications. The second artefact is a model capable of captivating resources and calculating the performance of a system, considering different modes of operation of that system. The proposed development platform is validated by implementing an instantiation of it, based on an actual, commercial on-street parking application. Though the study shows some deficiencies still present in the solution, its results demonstrate it as an applicable and feasible aid in constructing scalable applications capable of QoS monitoring in LPWA WSNs.
Exploring An Easy Way for Imbalanced Data Sets in Semantic Image Segmentation

In recent years, deep convolutional neural networks have gradually become the preferred method for image processing. After the development of Classification, Detection and Segmentation, a large variety of state-of-the-art models and algorithms have emerged in the field. However, for some specific data sets or tasks, not all methods are applicable, which is inconvenient to researchers. This paper took the data set provided in the airbus ship detection challenge in Kaggle as an example to explore an easy and effective method for segmentation tasks of data sets with class imbalance. This paper used U-Net with a pre-trained ResNets model, and tried different methods to explore the feature of the set. In the process of training ResNets, this paper proposed a new convolutional block structure which is inspired by Fibonacci sequence, but the effect is not good. In the end, the mF2 values of the models this paper trained achieved good results, which is better than the model of the combined training of ResNets and ordinary U-Net34. Moreover, the training parameters are less than that. This paper believe that this simple and effective training method will bring convenience to researchers in related fields.
Plant Disease Identification using Deep Neural Networks

Automatic identification of disease in plants from their leaves is one of the most challenging task to researchers. Diseases among plants degrade their performance and results into a huge reduction of agricultural products. Therefore, early and accurate diagnosis of such disease is of the utmost importance. The advancement in deep Convolutional Neural Network (CNN) has change the way of processing images as compared to traditional image processing techniques. Deep learning architectures are composed of multiple processing layers that learn the representations of data with multiple levels of abstraction. Therefore, proved highly effective in comparison to many state-of-the-art works. In this paper, we present a plant disease identification methodology from their leaves using deep CNNs. For this, we have adopted GoogLeNet that is considered a powerful architecture of deep learning to identify the disease types. Transfer learning has been used to fine tune the pre-trained model. An accuracy of 85.04% has been recorded in the identification of four disease class in Apple plant leaves. Finally, a comparison with other models has been performed to show the effectiveness of the approach.
Analyzing and Mitigating Gender Bias in Languages with Grammatical Gender and Bilingual Word Embeddings

Word embeddings have been shown to contain gender bias that is inherited from their training corpora. However, existing work focuses on quantifying and mitigating such bias in English, and the analysis cannot be directly applied in language with grammatical gender, such as Spanish. In this paper, we propose new definitions of gender bias for languages with grammatical gender and apply bilingual word embeddings to analyze and mitigate the bias. Experimental results on crosslingual analogy test and Word Embedding Association Test show that the proposed methods can effectively mitigate the multifaceted gender bias.
Complementary Perspectives in the Value Chain on ERP System Implementation

Philip Holst, ph.caict@cbs.dk, Center for Applied ICT (CAICT), CBS Janni Nielsen, jn.caict@cbs.dk, Center for Applied ICT (CAICT), CBS Pernille Hedegaard, Center for Applied ICT (CAICT), CBS Anne Sofie Halskov Bentsen, Center for Applied ICT (CAICT), CBS ! Abstract: In this paper we report on a explorative empirical study on implementing ERP systems in SMEs. Representatives from the different links in the value chain; user, customer, consultant and vendor were interviewed to investigate the process. The function of the qualitative interviews is probing for information. The interviews were conducted to shed light on the experiences of the different links across the value chain, and to uncover potential challenges in the implementation process.
A blockchain‐based framework to secure vehicular social networks

Vehicular social network is emerging as a new promising concept, combining two types of network paradigms, namely, vehicular networks and social networks. In order to manage efficiently the security and the control of the network, this paper proposes a new framework based on the emerging concepts of software‐defined vehicular network (SDVN) and blockchain. Using the SDVN makes the network more programmable, virtualized, and partitionable. However, on the other hand, it also creates a well‐known vulnerability of a single point of failure. Hence, we propose to introduce the blockchain paradigm that will enable the certification of transactions and ensure data anonymity in a fully distributed manner. To this end, three levels of controllers are needed: a principal controller (PC), roadside units (RSUs), and a local controller. In order to dynamically select miners, a distributed miners connected dominating set algorithm (DM‐CDS) has been proposed. The DM‐CDS is a single‐phase distributed algorithm that supports a dynamic topology based on a trust model and some other network parameters, such as the connectivity degree, the average link quality indicator, and the rank. The performance of the proposed DM‐CDS is evaluated throughout multiple scenarios using different parameters, such as trust metric, node density, node mobility, and radio range. The obtained results highlight the importance of such proposed architecture, especially in terms of number of required miners. For instance, when the density of nodes increases, the number of selected miners increases similarly to when the network length increases. The node mobility impacts also on the stability of the selected miners, in terms of withdrawing and joining, showing a variation between 0% and 10%. The trust metric has also an important impact on the selection of miners, as only nodes with a higher trust level are selected to endorse the roles of miners.
Incorporating Linguistic Constraints into Keyphrase Generation

Keyphrases, that concisely describe the high-level topics discussed in a document, are very useful for a wide range of natural language processing tasks. Though existing keyphrase generation methods have achieved remarkable performance on this task, they generate many overlapping phrases (including sub-phrases or super-phrases) of keyphrases. In this paper, we propose the parallel Seq2Seq network with the coverage attention to alleviate the overlapping phrase problem. Specifically, we integrate the linguistic constraints of keyphrase into the basic Seq2Seq network on the source side, and employ the multi-task learning framework on the target side. In addition, in order to prevent from generating overlapping phrases of keyphrases with correct syntax, we introduce the coverage vector to keep track of the attention history and to decide whether the parts of source text have been covered by existing generated keyphrases. Experimental results show that our method can outperform the state-of-the-art CopyRNN on scientific datasets, and is also more effective in news domain.
A NONPARAMETRIC BAYESIAN FRAMEWORK FOR MOBILE DEVICE SECURITY AND LOCATION BASED SERVICES

In June 2013, it was reported that, for the first time, more than half of American adults have smartphones. Smartphones are carried with the users most of the time and used to access all types of personal sensitive information, from email, Facebook to banking, files server, etc. With the excessive usage of the smartphones, there emerges two major problems, namely, how to protect the devices from cyber-attacks, and how to improve mobile users' experiences based on their locations.
Divide, Conquer and Combine: Hierarchical Feature Fusion Network with Local and Global Perspectives for Multimodal Affective Computing

We propose a general strategy named ‘divide, conquer and combine’ for multimodal fusion. Instead of directly fusing features at holistic level, we conduct fusion hierarchically so that both local and global interactions are considered for a comprehensive interpretation of multimodal embeddings. In the ‘divide’ and ‘conquer’ stages, we conduct local fusion by exploring the interaction of a portion of the aligned feature vectors across various modalities lying within a sliding window, which ensures that each part of multimodal embeddings are explored sufficiently. On its basis, global fusion is conducted in the ‘combine’ stage to explore the interconnection across local interactions, via an Attentive Bi-directional Skip-connected LSTM that directly connects distant local interactions and integrates two levels of attention mechanism. In this way, local interactions can exchange information sufficiently and thus obtain an overall view of multimodal information. Our method achieves state-of-the-art performance on multimodal affective computing with higher efficiency.
GPU computing with OpenCL to model 2D elastic wave propagation: exploring memory usage

Graphics processing units (GPUs) have become increasingly powerful in recent years. Programs exploring the advantages of this architecture could achieve large performance gains and this is the aim of new initiatives in high performance computing. The objective of this work is to develop an efficient tool to model 2D elastic wave propagation on parallel computing devices. To this end, we implement the elastodynamic finite integration technique, using the industry open standard open computing language (OpenCL) for cross-platform, parallel programming of modern processors, and an open-source toolkit called [Py]OpenCL. The code written with [Py]OpenCL can run on a wide variety of platforms; it can be used on AMD or NVIDIA GPUs as well as classical multicore CPUs, adapting to the underlying architecture. Our main contribution is its implementation with local and global memory and the performance analysis using five different computing devices (including Kepler, one of the fastest and most efficient high performance computing technologies) with various operating systems.