Reviewerly/exHarmony huggingface dataset:
The Gender Panopticon: Artificial Intelligence, Gender, and Design Justice

Using recent research from data scientists and technologists, this article argues that we are at a contradictory moment in history regarding the intersection of gender and technology, particularly as it affects LGBTQ+ communities. At the very same moment that we see the law embracing more and more visibility regarding gender identities and fluidity, we also see an even greater reliance on surveillance technologies that are flatly incapable of working beyond the binary of male and female. These technological limitations become even more fraught in today’s age, where we face an unprecedented degree of surveillance – gender-related, and otherwise--than we have ever seen in history. When a binary system of gender merges with the binary nature of code, the result fails to integrate LGBTQ communities, particularly nonbinary and transgender populations, erasing them from view.Using insights from a wide range of studies on artificial intelligence technologies – automated body scanners, facial recognition, and content filtering on social media, we argue in this Article, that we need to grapple with the reality that the relationship between technology and gender is far more complicated than the law currently suggests. Technology companies, along with multiple courts, colleges, and workplaces, must realize that the binary presumptions of male and female identity are largely outdated for some, and often fail to capture the contemporary complexity of gender identity formation. The question for legal scholars and legislatures is how the law – and technology -- can and should respond to this complexity. In the final sections, we discuss some of the legal implications of these technologies of surveillance, looking at both law and the design of technology, and turn to some of the normative possibilities to develop greater equality and gender self-determination.
Integrated modeling method of Cyber physical system based on extended AADL

Abstract The concept and connotation of CPS are analyzed firstly in the paper, then the challenges of system modeling brought by heterogeneous feature of the CPS system are pointed out. How to describe the CPS model are studied. We propose a description method of CPS integrated modeling framework based on extended AADL (architecture analysis and design language); and realize the unified description of the computing entity, the physical entity and interactive entity through extending the standard and appendix of AADL.
Leukocyte Counting and Reporting Using Densenet - Deep Learning

Leukocytes are cells that are present in immune system which protect our body from infectious disease causing viruses and foreign invaders. Having increasing or decreasing number of WBCs than normal may indicate an underlying condition. Establishing the count of white cells and categorization of leukocytes that is generally known as WBC (white blood cells) is pivotal in the appraisal and discovery of illness in an individual, which leads to complications in the immune system that result in various diseases such as blood clots, leukaemia, anaemia, HIV etc. To get the accurate results Densenet (Densely connected convolution network) Deep leaning approach is used to classify the white blood cells as polynuclear and mononuclear and to indicate the type of disease depending upon the percentage in WBC count.
Incident Detection based on Multimodal data from Social Media using Deep Learning Methods

Social media is one of the uses of crowdsourcing to gather vast amounts of information. The applications of incident detection using social media data are commonly focus on text analysis. Due to the ability of social media to capture variant data types such as text, voice, image, or video, the development of incident detection based on multimodal data is preferred. The use of multimodal data on incident detection is expected to improve the accuracy of prediction. This research aims to detect emergency incidents based on multimodal data streams from social media using deep learning methods. We compare several deep learning architectures that implement some neural network variants, namely Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). We crawled data from Twitter API and labeled into three incident categories i.e. flood, traffic jam, and wildfire. The CNN and C-LSTM are used for text prediction, and the best performance obtained by C-LSTM and achieved 99.09% in the accuracy. The compared CNN models for image prediction are AlexNet, VGG16, VGG19, and SqueezeNet. The best performance obtained by VGG16 with data augmentation and achieved 99.08% in the accuracy. The incident detection results of multimodal data are obtained from the highest confidence level of text or image.
AttCorr: A Novel Deep Learning Model for Flow Correlation Attacks on Tor

The proliferation of illegal information and criminal behavior on anonymous networks arouses the demand for deanonymization attacks on anonymous communication systems. Flow correlation is a common technique for deanonymization attacks, but most related works currently are based on statistical learning models, thus suffering from burdensome artificial feature engineering. In this paper, we design a novel deep learning model AttCorr for flow correlation attacks on Tor. AttCorr takes the raw traffic features of flow pair as input, including packet sizes, flow directions and inter-packet delays, and uses the multi-head attention mechanism to capture the flow information involved in features and the complex nature of noise in Tor. Experiment results show that AttCorr achieves the same level of accuracy as the stateof-the art method of DeepCorr with lower complexity, simpler feature processing and better interpretability.
Generation of Invertible High Order Matrix Keys for the Hill Cipher

Developers are often limited to the use of low order matrix keys when the Hill cipher is embraced. This is due to the complexity of determining matrix inverses when high order matrices are involved. Matrix inverses are required during decryption. This paper proposes an approach for generating invertible high order matrix keys for the Hill cipher. The Hill cipher uses a matrix key of order n x n for encryption, and its inverse is required for decryption. Increasing the order of the matrices used as keys in the Hill cipher undoubtedly strengthen the Hill encryption algorithm. When we increase the order of the matrices, the Hill cipher is more complex to decode as it would be hard to find the inverse matrices, hence hard to break. The proposed approach generates invertible high order matrix keys sequentially within a chosen order. However, the choice of an order is flexible. The actual key to be used is dynamically selected from a pool of candidate matrix keys. Our system for generating high order matrix keys was repeatedly run for several times, generating invertible high order matrices that are successfully pooled. The system also successfully randomly selected one matrix key from the pool. This approach eliminates repeated use of the same matrix key over and over. It strengthens the Hill cipher algorithm by complicating obvious brute force attacks. The algorithm's performances are anticipated to outclass those of the original Hill model.
Ordinal-Content VAE: Isolating Ordinal-Valued Content Factors in Deep Latent Variable Models

In deep representational learning, it is often desired to isolate a particular factor (termed content) from other factors (referred to as style). What constitutes the content is typically specified by users through explicit labels in the data, while all unlabeled/unknown factors are regarded as style. Recently, it has been shown that such content-labeled data can be effectively exploited by modifying the deep latent factor models (e.g., VAE) such that the style and content are well separated in the latent representations. However, the approach assumes that the content factor is categorical-valued (e.g., subject ID in face image data, or digit class in the MNIST dataset). In certain situations, the content is ordinal-valued, that is, the values the content factor takes are ordered rather than categorical, making content-labeled VAEs, including the latent space they infer, suboptimal. In this paper, we propose a novel extension of VAE that imposes a partially ordered set (poset) structure in the content latent space, while simultaneously making it aligned with the ordinal content values. To this end, instead of the iid Gaussian latent prior adopted in prior approaches, we introduce a conditional Gaussian spacing prior model. This model admits a tractable joint Gaussian prior, but also effectively places negligible density values on the content latent configurations that violate the poset constraint. To evaluate this model, we consider two specific ordinal structured problems: estimating a subject's age in a face image and elucidating the calorie amount in a food meal image. We demonstrate significant improvements in content-style separation over previous non-ordinal approaches.
KeystoneDepth: History in 3D

This paper introduces KeystoneDepth, the largest and most diverse collection of rectified historical stereo image pairs to date, consisting of tens of thousands of stereographs of people, events, objects, and scenes recorded between 1864 and 1966. Leveraging the Keystone-Mast Collection of stereographs from the California Museum of Photography, we apply multiple processing steps to produce clean stereo image pairs, complete with calibration data, rectification transforms, and disparity maps. We introduce a novel stereo rectification technique based on the unique properties of antique stereo cameras. To better visualize the results on 2D displays, we also introduce a self-supervised deep view synthesis technique trained on historical imagery. Our dataset is available at http://keystonedepth.cs.washington.edu/.
Lifting symmetry breaking constraints with inductive logic programming

Efficient omission of symmetric solution candidates is essential for combinatorial problem-solving. Most of the existing approaches are instance-specific and focus on the automatic computation of Symmetry Breaking Constraints (SBCs) for each given problem instance. However, the application of such approaches to large-scale instances or advanced problem encodings might be problematic since the computed SBCs are propositional and, therefore, can neither be meaningfully interpreted nor transferred to other instances. As a result, a time-consuming recomputation of SBCs must be done before every invocation of a solver. To overcome these limitations, we introduce a new model-oriented approach for Answer Set Programming that lifts the SBCs of small problem instances into a set of interpretable first-order constraints using the Inductive Logic Programming paradigm. Experiments demonstrate the ability of our framework to learn general constraints from instance-specific SBCs for a collection of combinatorial problems. The obtained results indicate that our approach significantly outperforms a state-of-the-art instance-specific method as well as the direct application of a solver.
Cache-Aware Dynamic Skewed Tree for Fast Memory Authentication

Memory integrity trees are widely-used to protect external memories in embedded systems against bus attacks. However, existing methods often result in high performance overheads incurred during memory authentication. To reduce memory accesses during authentication, the tree nodes are cached on-chip. In this paper, we propose a cacheaware technique to dynamically skew the integrity tree based on the application workloads in order to reduce the performance overhead. The tree is initialized using Van-Emde Boas (vEB) organization to take advantage of locality of reference. At run time, the nodes of the integrity tree are dynamically positioned based on their memory access patterns. In particular, frequently accessed nodes are placed closer to the root to reduce the memory access overheads. The proposed technique is compared with existing methods on Multi2Sim using benchmarks from SPEC-CPU2006, SPLASH-2 and PARSEC to demonstrate its performance benefits.
Revisiting the Threat Space for Vision-Based Keystroke Inference Attacks

A vision-based keystroke inference attack is a side-channel attack in which an attacker uses an optical device to record users on their mobile devices and infer their keystrokes. The threat space for these attacks has been studied in the past, but we argue that the defining characteristics for this threat space, namely the strength of the attacker, are outdated. Previous works do not study adversaries with vision systems that have been trained with deep neural networks because these models require large amounts of training data and curating such a dataset is expensive. To address this, we create a large-scale synthetic dataset to simulate the attack scenario for a keystroke inference attack. We show that first pre-training on synthetic data, followed by adopting transfer learning techniques on real-life data, increases the performance of our deep learning models. This indicates that these models are able to learn rich, meaningful representations from our synthetic data and that training on the synthetic data can help overcome the issue of having small, real-life datasets for vision-based key stroke inference attacks. For this work, we focus on single keypress classification where the input is a frame of a keypress and the output is a predicted key. We are able to get an accuracy of 95.6% after pre-training a CNN on our synthetic data and training on a small set of real-life data in an adversarial domain adaptation framework.
MOWDOC: A Dataset of Documents From Taking the Measure of Work for Building a Latent Semantic Analysis Space

DATA REPORT article Front. Psychol., 03 February 2021 | https://doi.org/10.3389/fpsyg.2020.523494
A Study of T’nT and CRF Based Approach for POS Tagging in Assamese Language

Assamese is one of the official languages of India which is officially recognized among 21 other official languages which is verbalized by, around 20 million people in the Northeastern part of India. In the recent time, a large number of works related to Natural Language Processing is going on for the language. Different types of models and algorithms have been employed for tagging the part of speech in a sentence, which is done by tagging each word against a predicted tag by an algorithm or by a model based on some annotated trained corpus. Assamese is a morphologically rich language. The lexical ambiguity of the words in the language is extensive. In this paper, performances of two existing tagging techniques for Assamese language have been compared, that is, Conditional Random Field and Trigrams'nTag to study the efficiency of the models in tagging of Parts-of-speeches for the language and the study might also help the Natural Language Processing (NLP) researchers in getting an understanding and analyzing the performance standard and effectiveness of this two existent models for POS tagging task for other morphologically rich Indian languages.
Multi-analysis surveillance and dynamic distribution of computational resources: Towards extensible, robust, and efficient monitoring of environments

Intelligent surveillance has been a hot area of research for the past two decades. However, the integral security of environments remains a challenge due to the presence of new types of threats and the very dynamism and complexity of these environments. To make further progress, new proposals are needed to facilitate the design and deployment of multi-analysis surveillance systems where different types of analysis are simultaneously performed and the available computation resources are limited. Such systems need to provide three main characteristics: extensibility, robustness and efficiency. Extensibility to add new analysis components when new events of interest must be monitored. Robustness to avoid that failures in one analysis component affect the rest. Efficiency to analyze environments in real-time and to support decision-making processes that address the detected anomalies. This paper proposes a formal model for the multi-analysis surveillance of environments by means of the named components of normality, designed to deploy surveillance systems that satisfies the three previously mentioned characteristics. Extensibility thanks to the activation and deactivation of components of normality based on the monitoring needs of the analyzed environment. Robustness as a result of the isolation of these components such that a failure in one of them does not spread to the rest. Finally, efficiency due to the dynamic allocation of resources that benefits the components that detect anomalies at a given time. The experimental results prove that such dynamic allocation improves execution times and reduces waiting times.
Abnormal identification of lubricating oil parameters and evaluation of physical and chemical properties based on machine learning

Abstract This paper firstly proposes an abnormality identification model for online oil monitoring parameters. The SVR model was established to predict the output power, and the information entropy of the deviation between the predicted value of the regression model and the actual monitoring value was used to identify the abnormal parameters of the oil. The physical and chemical properties of the oil was evaluated and predicted comprehensively. Principal component analysis (PCA) was used to eliminate the correlation between parameters, and then the deviation degree of each principal component is calculated after feature transformation. After adding the deviation degrees of each principal component according to their weights, the performance evaluation index of the oil is obtained. At the same time, a prediction model of oil performance trend based on Gated Recurrent Unit is proposed. The feasibility and applicability of this model are verified by comparing with the results of LSTM neural network and ARIMA model. The evaluation of the physical and chemical properties of the oil can detect the deterioration of the oil condition at the early stage, avoiding or reducing accidents, so as to ensure the safe and reliable operation of the unit.
Structural Similarity Measurement using Graph Edit Distance-Greedy on State chart Diagrams

With the emergence of the need for online learning, the automatic grading system is inevitable requirements in an e-learning system. The automatic grading system in software design courses requires a method for measuring similarity between the key-answer design and student-answer designs. There have been several efforts to develop methods for measuring the design similarity. The similarity measurement that has been developed based on semantic or structural aspects of the design. Nevertheless, the purpose of those methods is to reuse software designs. This study proposes a graph representation of the State chart diagram. The graph models the structural aspects of the State chart diagram. This study also proposes the use of Graph Edit Distance (GED) greedy for calculating the structural similarity between two graphs. Graph representation of the State chart diagram is used as input to the GED-greedy method. The results show that all parameters used can determine the structural similarity between two State chart diagrams with graph representation of the State chart diagram as input. State chart similarity results obtained were 0.83.
Multicriteria Methods for Group Decision Processes: An Overview

Important decisions are often taken by groups of decision-makers who need to choose among several alternatives in view of multiple objectives, in recognition of the interests of stakeholders. Such decision problems can be supported with the methods and processes of multicriteria decision analysis (MCDA) which foster collaboration, lend structure to the decision process, and help in managing problem complexity. In this chapter, we examine rationales for using MCDA methods in group decision processes, outline typical phases of these processes, summarize widely used MCDA methods and discuss some of their recent methodological extensions. We also provide guidelines for the design and implementation of MCDA-assisted group decision processes, based on an examination of behavioral factors and a structured review of selected case studies. We conclude with an outlook for the future in light of recent developments and trends.
NCQ: Code Reuse Support for Node.js Developers

Code reuse is an important part of software development. The adoption of code reuse practices is especially common among Node.js developers. The Node.js package manager, NPM, indexes over 1 Million packages and developers often seek out packages to solve programming tasks. Due to the vast number of packages, selecting the right package is difficult and time consuming. With the goal of improving productivity of developers that heavily reuse code through third-party packages, we present <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Node Code Query</i> (NCQ), a Read-Eval-Print-Loop environment that allows developers to 1) search for NPM packages using natural language queries, 2) search for code snippets related to those packages, 3) automatically correct errors in these code snippets, 4) quickly setup new environments for testing those snippets, and 5) transition between search and editing modes. In two user studies with a total of 20 participants, we find that participants begin programming faster and conclude tasks faster with NCQ than with baseline approaches, and that they like, among other features, the search for code snippets and packages. Our results suggest that NCQ makes Node.js developers more efficient in reusing code.
A mean shift segmentation scheme using several pixel characteristics

In this paper, a new segmentation method based on the mean shift (MS) algorithm is presented. The proposed approach divides the image into two sets of pixels: operative elements and inactive elements. In its first phase, the MS scheme considers only the operative elements. In its second stage, the results obtained by the MS method with the operative data are used to include the inactive data. During this stage, each inactive pixel is assigned to the cluster corresponding to the nearest operative pixel. As a final operation, clusters that maintain the minimal number of elements are blended with other nearby clusters. Our method has been tested against other current segmentation methods using test images extracted from the Berkley dataset. Numerical experiments demonstrate that our approach exhibits better performance in terms of consistency, quality, velocity and accuracy.
Should Variation Be Encoded Explicitly in Databases?

Variation occurs in databases in many different forms and contexts. For example, a single database schema evolves over time, data from different sources may be combined, and the various configurations of a software product line (SPL) may have different data needs. While approaches have been developed to deal with many such scenarios, particularly in the fields of database evolution and data integration, there is no solution that treats variation as a general and orthogonal concern in databases. This is a problem when various kinds of variation intersect, such as during the evolution of a SPL. Previously, we have proposed variational databases (VDB) as a general way to represent variation in both the structure and content of databases. Although the model underlying VDB is simple, encoding variation explicitly in databases introduces complexity akin to using preprocessing directives in software. In this paper, we explore the feasibility of VDB and its associated variational query language for encoding different kinds of database variability. We develop two use cases that illustrate how different kinds of variation can be encoded and integrated in VDB, and how the corresponding information needs can be expressed as variational queries. We then use these use cases to discuss the benefits and drawbacks of such a direct encoding of variation in data and queries.