Reviewerly/exHarmony huggingface dataset:
SecureAS: A Vulnerability Assessment System for Deep Neural Network Based on Adversarial Examples

Deep neural network (DNN) has been recently applied to many safety-critical environments. Unfortunately, recent research has proven that DNN can be vulnerable to well-designed examples, called adversarial examples. Adversarial examples can easily fool a well-performed deep learning model with little perturbations imperceptible to humans. In this paper, to tackle the DNN security issue, we propose a Model Adversarial Score (MAS) index to evaluate the vulnerability of a deep neural network, and introduce a deep learning vulnerability assessment system (SecureAS) using adversarial samples to assess the vulnerability and risk of a trained DNN in a blackbox way. We also present two adversary algorithms (FGNM and PINM) that provide better adversary images with the similar attack effect compared to existing approaches like FGSM and BIM. Our experimental results confirm the effectiveness of MAS algorithm, SecureAS, FGNM and PINM.
WIRE: An Automated Report Generation System using Topical and Temporal Summarization

The demand for a tool for summarizing emerging topics is increasing in modern life since the tool can deliver well-organized information to its users. Even though there are already a number of successful search systems, the system which automatically summarizes and organizes the content of emerging topics is still in its infancy. To fulfill such demand, we introduce an automated report generation system that generates a well-summarized human-readable report for emerging topics. In this report generation system, emerging topics are automatically discovered by a topic model and news articles are indexed by the discovered topics. Then, a topical summary and a timeline summary for each topic is generated by a topical multi-document summarizer and a timeline summarizer respectively. In order to enhance the apprehensibility of the users, the proposed report system provides two report modes. One is Today's Briefing which summarizes five discovered topics of every day, and the other is Full Report which shows a long-term view of each topic with a detailed topical summary and an important event timeline.
A Controlled Benchmark of Video Violence Detection Techniques

This benchmarking study aims to examine and discuss the current state-of-the-art techniques for in-video violence detection, and also provide benchmarking results as a reference for the future accuracy baseline of violence detection systems. In this paper, the authors review 11 techniques for in-video violence detection. They re-implement five carefully chosen state-of-the-art techniques over three different and publicly available violence datasets, using several classifiers, all in the same conditions. The main contribution of this work is to compare feature-based violence detection techniques and modern deep-learning techniques, such as Inception V3.
Membership Inference Against DNA Methylation Databases

Biomedical data sharing is one of the key elements fostering the advancement of biomedical research but poses severe risks towards the privacy of individuals contributing their data, as already demonstrated for genomic data. In this paper, we study whether and to which extent DNA methylation data, one of the most important epigenetic elements regulating human health, is prone to membership inference attacks, a critical type of attack that reveals an individual's participation in a given database. We design and evaluate three different attacks exploiting published summary statistics, among which one is based on machine learning and another is exploiting the dependencies between genome and methylation data. Our extensive evaluation on six datasets containing a diverse set of tissues and diseases collected from more than 1,300 individuals in total shows that such membership inference attacks are effective, even when the target's methylation profile is not accessible. It further shows that the machine-learning approach outperforms the statistical attacks, and that learned models are transferable across different datasets.
Predicting Cognitive Declines Using Longitudinally Enriched Representations for Imaging Biomarkers

With rapid progress in high-throughput genotyping and neuroimaging, researches of complex brain disorders, such as Alzheimer's Disease (AD), have gained significant attention in recent years. Many prediction models have been studied to relate neuroimaging measures to cognitive status over the progressions when these disease develops. Missing data is one of the biggest challenge in accurate cognitive score prediction of subjects in longitudinal neuroimaging studies. To tackle this problem, in this paper we propose a novel formulation to learn an enriched representation for imaging biomarkers that can simultaneously capture both the information conveyed by baseline neuroimaging records and that by progressive variations of varied counts of available follow-up records over time. While the numbers of the brain scans of the participants vary, the learned biomarker representation for every participant is a fixed-length vector, which enable us to use traditional learning models to study AD developments. Our new objective is formulated to maximize the ratio of the summations of a number of ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm distances for improved robustness, which, though, is difficult to efficiently solve in general. Thus we derive a new efficient iterative solution algorithm and rigorously prove its convergence. We have performed extensive experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. A performance gain has been achieved to predict four different cognitive scores, when we compare the original baseline representations against the learned representations with enrichments. These promising empirical results have demonstrated improved performances of our new method that validate its effectiveness.
VM Scaling and Load Balancing via Cost Optimal MDP Solution

Dynamic resource allocation mechanism is an essential building block in contemporary cloud computing environment, enabling the support of the large variability of incoming requests from an enormous number of applications utilizing such cloud infrastructure. In this article, we devise a dynamic resource allocation mechanism that optimizes the application’s profit under the set of costs and revenues while maintaining performance constraints. Specifically, we devise a decision-maker (DM) agent which formulates the joint admission control, scaling and load balancing problem as a stochastic process solvable by Markov decision process (MDP) which provides the optimal policy. Accordingly, at each time instance, the DM can determine based on the system’s current state, on the set of requirements and on the set of costs, whether to add or release a VM (scale-out or scale-in, respectively), whether to admit or reject an upcoming task and if admitting it, which VM to allocate it to. We explore the value function structure and provide insights with respect to the optimal policies produced from it. To address scalability issues of the detailed MDP solution we provide an alternative solution by abstract MDP which consolidates multiple system states into a single abstract state, hence can cope with much larger systems at the expense of slight performance degradation. To demonstrate the feasibility of the suggested scheme, we designed and implemented it, alongside with two traditional auto-scalers, on the Amazon Web Services (AWS) infrastructure. We ran numerous MATLAB simulations and AWS-based experiments which provided insights and demonstrated superiority against the traditional policies we compared with.
FPGA‐assisted state‐of‐polarisation generation for polarisation‐encoded optical communications

This study presents a theoretical model for polarisation manipulation using electronic polarisation controllers (EPCs) based on fibre squeezing. A method to calculate the EPC configuration in order to transform between two arbitrary states of polarisation (SOP) is presented. After, a technique to deterministically generate four SOPs for use in polarisation-encoded quantum communication systems is proposed. Moreover, the effectiveness of the proposed technique is experimentally assessed through the generation of two pairs of orthogonal SOPs. The experimental implementation used an field programmable gate array (FPGA) board to electrically control the four waveplates of the EPC, reaching a rate of 500 qubit/s. Results show that this polarisation generation process is intrinsically stable, demonstrating its potential for practical implementations of polarisation-encoding quantum key distribution systems using the BB84 and B92 protocols.
Appearance Shock Grammar for Fast Medial Axis Extraction From Real Images

We combine ideas from shock graph theory with more recent appearance-based methods for medial axis extraction from complex natural scenes, improving upon the present best unsupervised method, in terms of efficiency and performance. We make the following specific contributions: i) we extend the shock graph representation to the domain of real images, by generalizing the shock type definitions using local, appearance-based criteria; ii) we then use the rules of a Shock Grammar to guide our search for medial points, drastically reducing run time when compared to other methods, which exhaustively consider all points in the input image; iii) we remove the need for typical post-processing steps including thinning, non-maximum suppression, and grouping, by adhering to the Shock Grammar rules while deriving the medial axis solution; iv) finally, we raise some fundamental concerns with the evaluation scheme used in previous work and propose a more appropriate alternative for assessing the performance of medial axis extraction from scenes. Our experiments on the BMAX500 and SK-LARGE datasets demonstrate the effectiveness of our approach. We outperform the present state-of-the-art, excelling particularly in the high-precision regime, while running an order of magnitude faster and requiring no post-processing.
Proactive Suggestion Generation

Conversational systems such as digital assistants can help users per-form many simple tasks upon request. Looking to the future, these systems will also need to fully support more complex, multi-step tasks (e.g., following cooking instructions), and help users complete those tasks, e.g., via useful and relevant suggestions made during the process. This paper takes the first step towards automatic generation of task-related suggestions. We introduce proactive suggestion generation as a novel task of natural language generation, in which a decision is made to inject a suggestion into an ongoing user dialog and one is then automatically generated. We propose two types of stepwise suggestions: multiple-choice response generation and text generation. We provide several models for each type of suggestion, including binary and multi-class classification, and text generation.
LinCos-Softmax: Learning Angle-Discriminative Face Representations With Linearity-Enhanced Cosine Logits

In recent years, the angle-based softmax losses have significantly improved the performance of face recognition whereas these loss functions are all based on cosine logit. A potential weakness is that the nonlinearity of the cosine function may undesirably saturate the angular optimization between the features and the corresponding weight vectors, thereby preventing the network from fully learning to maximize the angular discriminability of features. As a result, the generalization of learned features may be compromised. To tackle this issue, we propose a Linear-Cosine Softmax Loss (LinCos-Softmax) to more effectively learn angle-discriminative facial features. The main characteristic of the loss function we propose is the use of an approximated linear logit. Compared with the conventional cosine logit, it has a stronger linear relationship with the angle on enhancing angular discrimination through Taylor expansion. We also propose an automatic scale parameter selection scheme, which can conveniently provide an appropriate scale for different logits without the need for exhaustive parameter search to improve performance. In addition, we propose a margin-enhanced Linear-Cosine Softmax Loss (m-LinCos-Softmax) to further enlarge inter-class distances and reduce intra-class variations. Experimental results on several face recognition benchmarks (LFW, AgeDB-30, CFP-FP, MegaFace Challenge 1) demonstrate the effectiveness of the proposed method and its superiority to existing angular softmax loss variants.
Financial globalization and institutions in Africa: the case of foreign direct investment, central bank independence and political institutions

Abstract In this paper, we examine the bi-directional relationship between financial globalization (proxied by foreign direct investment (FDI) flows) and economic institutions (proxied by central bank independence (CBI)) taking into consideration the role of political institutions. We test our argument on a sample of 48 African countries (1970–2012) using a two-step System Generalized Methods of Moments, with collapsed instruments and Windmeijer robust standard errors. Using two proxies for CBI, the study finds that while legal CBI does not have a significant impact on FDI, high central bank governor turnover rates have a significantly negative impact on FDI inflows. However, higher levels of political institutions significantly enhance the impact of legal CBI on FDI inflows, and dampen the impact of high central bank governor turnover rates on FDI inflows. The study also shows that, higher FDI inflows have a significantly positive impact on both legal and de facto CBI. This impact is accelerated in countries characterized by higher levels of political institutions.
A Survey: Machine Learning Approach for Personality Analysis and Writer Identification through Handwriting

Graphology is a discipline used in the determination, study, and interpretation of a person's personality with the aid of spaces, curves, strokes in a handwritten text. According to Graphology, handwriting is nothing but expression of the subconscious mind. It expresses various personality traits like how honest a person is, how afraid the person is etc. For the purpose of analysis, the services of professionals in this field called graphologists are employed. However, it results in increased cost and efficiency of analysis is completely dependent on the graphologist. This paper includes comparison of various Machine learning algorithms used for personality analysis and writer identification with the accuracy of the algorithm.
What Does Plate Glass Reveal About Camera Calibration?

This paper aims to calibrate the orientation of glass and the field of view of the camera from a single reflection-contaminated image. We show how a reflective amplitude coefficient map can be used as a calibration cue. Different from existing methods, the proposed solution is free from image contents. To reduce the impact of a noisy calibration cue estimated from a reflection-contaminated image, we propose two strategies: an optimization-based method that imposes part of though reliable entries on the map and a learning-based method that fully exploits all entries. We collect a dataset containing 320 samples as well as their camera parameters for evaluation. We demonstrate that our method not only facilitates a general single image camera calibration method that leverages image contents but also contributes to improving the performance of single image reflection removal. Furthermore, we show our byproduct output helps alleviate the ill-posed problem of estimating the panorama from a single image.
Deep Restoration of Invisible QR Code from TPVM Display

QR (Quick Response) codes are widely used in daily lives for their convenience in conveying information from offline to online. However, black-white QR code image takes up space and is not visually appealing when embedded into videos. Owing to the high resolution and high refresh rates of display screens, we are able to embed QR code to be invisible on displays based on temporal psychovisual modulation (TPVM). In this work, we proposed a method to improve the invisibility of QR code via TPVM. Furthermore, we propose a fast restoration-recognition method to scan invisible QR codes by smartphones using deep convolutional neural networks. Extensive experiment results show that our proposed methods effectively improve the indivisibility of QR code and can efficiently recognize the invisible QR code correctly and stably. Our methods can be applied in video copyright protection and Internet shopping without affecting the visual quality for audiences.
Detecting Concept Drift In Medical Triage

in their accompanying referral documents, which contain a mix of free text and structured data. By training a model to predict triage decisions from these referral documents, we can partially automate the triage process, resulting in more efficient and systematic triage decisions. One of the difficulties of this task is maintaining robustness against changes in triage priorities due to changes in policy, funding, staff, or other factors. This is reflected as changes in relationship between document features and triage labels, also known as concept drift. These changes must be detected so that the model can be retrained to reflect the new environment. We introduce a new concept drift detection algorithm for this domain called calibrated drift detection method (CDDM). We evaluated CDDM on benchmark and synthetic medical triage datasets, and find it competitive with state-of-the-art detectors, while also being less prone to false positives from feature drift.
Sortation Control Using Multi-Agent Deep Reinforcement Learning in N-Grid Sortation System

Intralogistics is a technology that optimizes, integrates, automates, and manages the logistics flow of goods within a logistics transportation and sortation center. As the demand for parcel transportation increases, many sortation systems have been developed. In general, the goal of sortation systems is to route (or sort) parcels correctly and quickly. We design an n-grid sortation system that can be flexibly deployed and used at intralogistics warehouse and develop a collaborative multi-agent reinforcement learning (RL) algorithm to control the behavior of emitters or sorters in the system. We present two types of RL agents, emission agents and routing agents, and they are trained to achieve the given sortation goals together. For the verification of the proposed system and algorithm, we implement them in a full-fledged cyber-physical system simulator and describe the RL agents' learning performance. From the learning results, we present that the well-trained collaborative RL agents can optimize their performance effectively. In particular, the routing agents finally learn to route the parcels through their optimal paths, while the emission agents finally learn to balance the inflow and outflow of parcels.
Explainable Artificial Intelligence for Aviation Safety Applications

NASA's aviation research envisions future concepts for air transportation that will significantly expand the current nature of airspace and vehicle management with an increasing reliance on autonomy technologies. Indeed, increased reliance on autonomy technologies is a key enabler of these future concepts. Artificial Intelligence (AI) algorithms, which are at the heart of emerging autonomy technologies, are generally perceived as black boxes whose decisions are a result of complex rules learned on-the-fly. Unless these decisions are explained in a human understandable form, the end-users are less likely to accept them and certification personnel are less likely to clear these systems for field operation. Explainable AI (XAI) is an AI algorithm whose actions can be easily understood by humans. The research effort described in this paper developed EXplained Process and Logic of Artificial INtelligence Decisions (EXPLAIND)—a prototype tool for verification and validation of AI-based aviation systems. We provided a proof-of-concept for EXPLAIND by applying it to generate reliable, human-understandable explanations for decisions made by a NASA-developed aircraft trajectory anomaly detection AI algorithm. Cognitive walkthroughs of EXPLAIND’s explanation interface with controller subject matter experts demonstrated that EXPLAIND represents an important step towards user acceptance and certification of AI-based decision support tools (DSTs).
Innovative Deep Neural Network Modeling for Fine-Grained Chinese Entity Recognition

Named entity recognition (NER) is a basic but crucial task in the field of natural language processing (NLP) and big data analysis. The recognition of named entities based on Chinese is more complicated and difficult than English, which makes the task of NER in Chinese more challenging. In particular, fine-grained named entity recognition is more challenging than traditional named entity recognition tasks, mainly because fine-grained tasks have higher requirements for the ability of automatic feature extraction and information representation of deep neural models. In this paper, we propose an innovative neural network model named En2BiLSTM-CRF to improve the effect of fine-grained Chinese entity recognition tasks. This proposed model including the initial encoding layer, the enhanced encoding layer, and the decoding layer combines the advantages of pre-training model encoding, dual bidirectional long short-term memory (BiLSTM) networks, and a residual connection mechanism. Hence, it can encode information multiple times and extract contextual features hierarchically. We conducted sufficient experiments on two representative datasets using multiple important metrics and compared them with other advanced baselines. We present promising results showing that our proposed En2BiLSTM-CRF has better performance as well as better generalization ability in both fine-grained and coarse-grained Chinese entity recognition tasks.
Content Based Image Retrieval - Inspired by Computer Vision &amp; Deep Learning Techniques

With the growing popularity of digital services and internet technology billions of people are prone to information sharing and uploading photos. For accurate retrieval of images from huge digital image databases, Content Based Image Retrieval (CBIR) method are emerging as an influential next generation tools, with wide range of applications in fields like criminal investigation, shape recognition, medical diagnosis, remote sensing, digital forensic, radar engineering and robotics. The key challenge of CBIR systems lies in building the “semantic gap” that exists between the differences in the way of perceiving things from basic to complex image features. The intention behind our paper is to provide deeper analysis and impact of continuous advancements in Image Retrieval techniques since its origin. An empirical examination of most popular and upcoming techniques in this survey paved the way in selecting the suitable computer vision and Deep Learning Techniques to improve the performances of retrieval systems. Several Benchmark datasets are considered to validate and study the robustness of the techniques influencing feature extraction and classification.
Examining the Relationship between the Bitcoin and Cybercrime

In the past decade, online crime has begun to emerge, owing to Bitcoin what was released in 2009 [1], which was an excellent tool for cybercriminals to launder their income from illegal sources. In our research, we investigate the relationship between Bitcoin as cryptocurrency and cybercrime through theoretical examples and real crimes. Besides blockchain - registry and inventory system for the recording, tracking, monitoring, and transacting - various cyber-attack modes are presented, and solution and protection suggestions are also introduced that make the cyberspace more protected.