Reviewerly/exHarmony huggingface dataset:
Job Descriptions Keyword Extraction using Attention based Deep Learning Models with BERT

In this paper, we focus on creating a keywords extractor especially for a given job description job-related text corpus for better search engine optimization using attention based deep learning techniques. Millions of jobs are posted but most of them end up not being located due to improper SEO and keyword management. We aim to make this as easy to use as possible and allow us to use this for a large number of job descriptions very easily. We also make use of these algorithms to screen or get insights from large number of resumes, summarize and create keywords for a general piece of text or scientific articles. We also investigate the modeling power of BERT (Bidirectional Encoder Representations from Transformers) for the task of keyword extraction from job descriptions. We further validate our results by providing a fully-functional API and testing out the model with real-time job descriptions.
Recommending Attack Patterns for Software Requirements Document

To develop secure software, software developers need to know the potential threats to the software. Knowledge captured in the Common Attack Pattern Enumeration and Classification (CAPEC) database can help software developers to understand how attackers target application weaknesses. In this paper, we present a method of recommending CAPEC attack patterns based on software requirement specification (SRS) documents. The method uses topic modelling to extract topics from each attack pattern and to extract topics from the software system description, user classes, use cases, and function requirements within the SRS documents. Attack patterns are recommended by calculating the distance measure of each attack pattern topic distribution and each SRS topic distribution using cosine similarity. Attack patterns are then ranked from maximum to minimum. The top attack patterns are then recommended to the software developers as the most relevant to the software system under development.
Recent Studies of XAI - Review

Over the past years, there has been an increasing concern regarding the risk of bias and discrimination in algorithmic systems, which received significant attention amongst the research communities. To ensure the system’s fairness, various methods and techniques have been developed to assess and mitigate potential biases. Such methods, also known as “Formal Fairness”, look at various aspects of the system’s advanced reasoning mechanism and outcomes, with techniques ranging from local explanations (at feature level) to visual explanations (saliency maps). Another aspect, equally important, represents the perception of the users regarding the system’s fairness. Despite a decision system being provably “Fair”, if the users find it difficult to understand how the decisions were made, they will refrain from trusting, accepting, and ultimately using the system altogether. This raised the issue of “Perceived Fairness” which looks at means to reassure users of a system’s trustworthiness. In that sense, providing users with some form of explanation on why and how certain outcomes resulted, is highly relevant, especially nowadays as the reasoning mechanisms increase in complexity and computational power. Recent studies suggest a plethora of explanation types. The current work aims to review the recent progress in explaining systems’ reasoning and outcome, categorize and present it as a reference for the state-of-the-art fairness-related explanations review.
Email Sentiment Classification Using Lexicon-Based Opinion Labeling

Sentiment analysis is the most common approach to analyzing the text data to identify sentiment content. Opinions are critical to businesses and organizations because they always want to find consumers from general ideas about their products and services. The range of text data is generated from social media platforms where users share information on everything anywhere. This information is about the user's emotions, tweets, and comments. Email is the most popular and effective way of communication overall on the Internet. Today, several applications are presented in mobiles, computers, and laptops for email messaging purposes. Some researchers have developed a lot of techniques to perform sentiment analysis. In this work, sentiment analysis for email message detection using the XGB classification method is performed. The sentiment features are extracted using count vectorization and TF-IDF transformer. Sentiments are classified using opinion labeling and three classifiers including XGBoost, gradient boosting, and random forest. The proposed work is compared with existing approaches.
SDOF-GAN: Symmetric Dense Optical Flow Estimation With Generative Adversarial Networks

There is a growing consensus in computer vision that symmetric optical flow estimation constitutes a better model than a generic asymmetric one for its independence of the selection of source/target image.Yet, convolutional neural networks (CNNs), that are considered the de facto standard vision model, deal with the asymmetric case only in most cutting-edge CNNs-based optical flow techniques.We bridge this gap by introducing a novel model named SDOF-GAN: symmetric dense optical flow with generative adversarial networks (GANs).SDOF-GAN realizes a consistency between the forward mapping (source-to-target) and the backward one (target-to-source) by ensuring that they are inverse of each other with an inverse network.In addition, SDOF-GAN leverages a GAN model for which the generator estimates symmetric optical flow fields while the discriminator differentiates the "real" ground-truth flow field from a "fake" estimation by assessing the flow warping error.Finally, SDOF-GAN is trained in a semi-supervised fashion to enable both the precious labeled data and large amounts of unlabeled data to be fully-exploited.We demonstrate significant
FADIA

Internet of Things (IoT) technology promises to bring new value creation opportunities across all major industrial sectors. This will yield industries to deploy more devices into their networks. A key pillar to ensure the safety and security of the running services on these devices is remote attestation. Unfortunately,existing solutions fail to cope with the recent challenges raised by large IoT networks. In particular, the heterogeneity of the devices used in the network affects the performance of a remote attestation protocol. Another challenge in these networks is their dynamic nature: More IoT devices may be added gradually over time. This poses a problem in terms of key management in remote attestation.
TIED: A Cycle Consistent Encoder-Decoder Model for Text-to-Image Retrieval

Retrieving specific vehicle tracks by Natural Language (NL)-based descriptions is a convenient way to monitor vehicle movement patterns and traffic-related events. NL-based image retrieval has several applications in smart cities, traffic control, etc. In this work, we propose TIED, a text-to-image encoder-decoder model for the simultaneous extraction of visual and textual information for vehicle track retrieval. The model consists of an encoder network that enforces the two modalities into a common latent space and a decoder network that performs an inverse mapping to the text descriptions. The method exploits visual semantic attributes of a target vehicle along with a cycle-consistency loss. The proposed method employs both intra-modal and inter-modal relationships to improve retrieval performance. Our system yields competitive performance achieving the 7th position in the Natural Language-Based Vehicle Retrieval public track of the 2021 NVIDIA AI City Challenge. We demonstrate that the proposed TIED model obtains six times higher Mean Reciprocal Rank (MRR) than the baseline, achieving an MRR of 15.48. The code and models will be made publicly available.
A Pupil Segmentation Algorithm Based on Fuzzy Clustering of Distributed Information

Pupil segmentation is critical for line-of-sight estimation based on the pupil center method. Due to noise and individual differences in human eyes, the quality of eye images often varies, making pupil segmentation difficult. In this paper, we propose a pupil segmentation method based on fuzzy clustering of distributed information, which first preprocesses the original eye image to remove features such as eyebrows and shadows and highlight the pupil area; then the Gaussian model is introduced into global distribution information to enhance the classification fuzzy affiliation for the local neighborhood, and an adaptive local window filter that fuses local spatial and intensity information is proposed to suppress the noise in the image and preserve the edge information of the pupil details. Finally, the intensity histogram of the filtered image is used for fast clustering to obtain the clustering center of the pupil, and this binarization process is used to segment the pupil for the next pupil localization. Experimental results show that the method has high segmentation accuracy, sensitivity, and specificity. It can accurately segment the pupil when there are interference factors such as light spots, light reflection, and contrast difference at the edge of the pupil, which is an important contribution to improving the stability and accuracy of the line-of-sight tracking.
A photonic complex perceptron for ultrafast data processing

Abstract In photonic neural network a key building block is the perceptron. Here, we describe and demonstrate a complex-valued photonic perceptron that combines time and space multiplexing in a fully passive silicon photonics integrated circuit to process data in the optical domain. A time dependent input bit sequence is broadcasted into a few delay lines and detected by a photodiode. After detection, the phases are trained by a particle swarm algorithm to solve the given task. Since only the phases of the propagating optical modes are trained, signal attenuation in the perceptron due to amplitude modulation is avoided. The perceptron performs binary pattern recognition and few bit delayed XOR operations up to 16 Gbps (limited by the used electronics) with Bit Error Rates as low as $$10^{-6}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mn>10</mml:mn> <mml:mrow> <mml:mo>-</mml:mo> <mml:mn>6</mml:mn> </mml:mrow> </mml:msup> </mml:math> .
Karma - blockchain based charity foundation platform

Charity plays an essential role in our society, and often recognized as a type of social debt, leading to the circulation of a significant amount of money worldwide. We have witnessed increased growth of non-commercial organizations and public charity funds through recent years, collecting donations for various philanthropic needs. Unfortunately, charity funds frequently gain much traction from the unscrupulous organization, resulting in significant damage for industry's reputation, reducing trust level, affecting the ability to raise donations. We strongly believe that utilizing blockchain technology will boost trust,increase efficiency, and encourage more donations.The Karma-Token project, a blockchain-based charity foundation platform that facilitates the trustful network's formation and is accountable for collecting donation funds. The blockchain network would be comprised of publically known, trustful, and prestigious organizations. All organizations' operations within the platform will become fully transparent and visible, leveraging properties of immutability, provenance, and non-repudiation. Therefore the platform will alleviate the results of dishonest actions, revealing fraudulent organizations' activities.
Joint Optimization for Bandwidth Utilization and Delay Based on Particle Swarm Optimization

One of the most significant problems in large-scale applications is how to allocate requests from end-users to datacenters.Most existing studies focus only on high bandwidth utilization of datacenters or low request delay of end-users, lacking the consideration of both at the same time.To achieve the joint optimization for bandwidth utilization of datacenter providers and delay of end-users, this paper proposes a novel Particle Swarm Optimization (PSO) based request allocation algorithm, based on the construction of a mathematical model for joint optimization.The algorithm adapts the coding scheme and redefines the operator to make PSO suitable for discrete optimization problems and use fitness function to get a global optimal solution.Through extensive simulations, it is demonstrated that the PSO based request allocation algorithm proposed in this study can improve the bandwidth utilization and keep the average request delay within acceptable limits or even better.
Initialization and Transfer Learning of Stochastic Binary Networks from Real-Valued Ones

We consider the training of binary neural networks (BNNs) using the stochastic relaxation approach, which leads to stochastic binary networks (SBNs). We identify that a severe obstacle to training deep SBNs without skip connections is already the initialization phase. While smaller models can be trained from a random (possibly data-driven) initialization, for deeper models and large datasets, it becomes increasingly difficult to obtain non-vanishing and low variance gradients when initializing randomly.In this work, we initialize SBNs from real-valued networks with ReLU activations. Real valued networks are well established, easier to train and benefit from many techniques to improve their generalization properties. We propose that closely approximating their internal features can provide a good initialization for SBN. We transfer features incrementally, layer-by-layer, accounting for noises in the SBN, exploiting equivalent reparametrizations of ReLU networks and using a novel transfer loss formulation. We demonstrate experimentally that with the proposed initialization, binary networks can be trained faster and achieve a higher accuracy than when initialized randomly.
A Comparative Study of Various Filtering Techniques

Image processing is a fast growing area of active research. It comprises methods to perform several useful operations on images, to modify/enhance the image or to tease out useful information from it. A very basic application of image processing is image filtering. Filtering is a technique of image modification or enhancement. We filter an image to enhance some features or to get rid of other features - the techniques include smoothing, sharpening, edge enhancement. Here we apply different smoothing and edge enhancement filtering methods to an image and evaluate the quality of the image in both cases using an image quality assessment technique called BRISQUE and by calculating the PSNR ratio of images.
Unsupervised Discrete Hashing With Affinity Similarity

In recent years, supervised hashing has been validated to greatly boost the performance of image retrieval. However, the label-hungry property requires massive label collection, making it intractable in practical scenarios. To liberate the model training procedure from laborious manual annotations, some unsupervised methods are proposed. However, the following two factors make unsupervised algorithms inferior to their supervised counterparts: (1) Without manually-defined labels, it is difficult to capture the semantic information across data, which is of crucial importance to guide robust binary code learning. (2) The widely adopted relaxation on binary constraints results in quantization error accumulation in the optimization procedure. To address the above-mentioned problems, in this paper, we propose a novel Unsupervised Discrete Hashing method (UDH). Specifically, to capture the semantic information, we propose a balanced graph-based semantic loss which explores the affinity priors in the original feature space. Then, we propose a novel self-supervised loss, termed orthogonal consistent loss, which can leverage semantic loss of instance and impose independence of codes. Moreover, by integrating the discrete optimization into the proposed unsupervised framework, the binary constraints are consistently preserved, alleviating the influence of quantization errors. Extensive experiments demonstrate that UDH outperforms state-of-the-art unsupervised methods for image retrieval.
DSKRL: A dissimilarity-support-aware knowledge representation learning framework on noisy knowledge graph

Knowledge graphs are widely used in a variety of knowledge-driven applications. Due to the inefficiency of manual construction of knowledge graphs, automatic construction mechanism has become the dominant method, which introduces noise. However, most knowledge representation learning approaches assume that there is no noise in the knowledge graph and hence ignore noise detection. In this paper, we propose a dissimilarity-support-aware knowledge representation learning framework, which accomplishes knowledge representation learning and noise detection simultaneously. Specifically, we introduce triple dissimilarity and triple support to construct the model energy function which is based on translation-based methods. The triple dissimilarity measures the matching extent of entities and relations in triples and the triple support measures the credibility of the matching extent. In order to make triple dissimilarity and triple support estimation effective and comprehensive, we synthesize structural information and auxiliary information (entity hierarchical type and relation path information) in triple dissimilarity and triple support. We conduct experiments on three datasets for the knowledge graph noise detection task and the knowledge graph completion task. The experimental results show that our model achieves significant and consistent improvements compared to all baseline methods.
Adversarial Robustness Across Representation Spaces

Adversarial robustness corresponds to the susceptibility of deep neural networks to imperceptible perturbations made at test time. In the context of image tasks, many algorithms have been proposed to make neural networks robust to adversarial perturbations made to the input pixels. These perturbations are typically measured in an ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</inf> norm. However, robustness often holds only for the specific attack used for training. In this work we extend the above setting to consider the problem of training of deep neural networks that can be made simultaneously robust to perturbations applied in multiple natural representations spaces. For the case of image data, examples include the standard pixel representation as well as the representation in the discrete cosine transform (DCT) basis. We design a theoretically sound algorithm with formal guarantees for the above problem. Furthermore, our guarantees also hold when the goal is to require robustness with respect to multiple ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</inf> norm based attacks. We then derive an efficient practical implementation and demonstrate the effectiveness of our approach on standard datasets for image classification. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Unified Analysis Specific to the Medical Field in the Interpretation of Medical Images through the Use of Deep Learning

Deep learning (DL) has seen an exponential development in recent years, with major impact in many medical fields, especially in the field of medical image.The purpose of the work converges in determining the importance of each component, describing the specificity and correlations of these elements involved in achieving the precision of interpretation of medical images using DL.The major contribution of this work is primarily to the updated characterisation of the characteristics of the constituent elements of the deep learning process, scientific data, methods of knowledge incorporation, DL models according to the objectives for which they were designed and the presentation of medical applications in accordance with these tasks.Secondly, it describes the specific correlations between the quality, type and volume of data, the deep learning patterns used in the interpretation of diagnostic medical images and their applications in medicine.Finally presents problems and directions of future research.Data quality and volume, annotations and labels, identification and automatic extraction of specific medical terms can help deep learning models perform image analysis tasks.Moreover, the development of models capable of extracting unattended features and easily incorporated into the architecture of DL networks and the development of techniques to search for a certain network architecture according to the objectives set lead to performance in the interpretation of medical images.
On-demand Personalized Explanation for Transparent Recommendation

The literature on explainable recommendations is already rich. In this paper, we aim to shed light on an aspect that remains under-explored in this area of research, namely providing personalized explanations. To address this gap, we developed a transparent Recommendation and Interest Modeling Application (RIMA) that provides on-demand personalized explanations with varying levels of detail to meet the demands of different types of end-users. The results of a preliminary qualitative user study demonstrated potential benefits in terms of user satisfaction with the explainable recommender system. Our work would contribute to the literature on explainable recommendation by exploring the potential of on-demand personalized explanations, and contribute to the practice by offering suggestions for the design and appropriate use of personalized explanation interfaces in recommender systems.
Encrypted Traffic Classification Based on Traffic Reconstruction

Network traffic classification is to classify network traffic into related traffic types, which plays a significant role in network management and network security. It can guarantee the quality of service of the network, and guarantee network security by intercepting malware traffic. The extensive usage of encryption techniques and the continuous update of encryption protocols make encrypted traffic classification become a new challenge. In this paper, we propose an encrypted traffic classification method based on traffic reconstruction, which can achieve encrypted network traffic with high precision. The main idea of this method is to extract the first 500 bytes of the payload as key data, and insert the length threshold identifier in the payload header, thenan one-dimensional convolutional neural network is used to classify the reconstructed traffic. We use the public dataset ISCX VPN-nonVPN and self-collected dataset to verify our proposed method. The experiment results show that our proposed method can achieve encrypted traffic characterization by F1 score of 98.5%, and achieve encrypted traffic application classification by F1 score of 98.91%.
Analyzing Stereotypes in Generative Text Inference Tasks

Stereotypes are inferences drawn about people based on their demographic attributes, which may result in harms to users when a system is deployed.In generative language-inference tasks, given a premise, a model produces plausible hypotheses that follow either logically (natural language inference) or commonsensically (commonsense inference).Such tasks are therefore a fruitful setting in which to explore the degree to which NLP systems encode stereotypes.In our work, we study how stereotypes manifest when the potential targets of stereotypes are situated in real-life, neutral contexts.We collect human judgments on the presence of stereotypes in generated inferences, and compare how perceptions of stereotypes vary due to annotator positionality.