Reviewerly/exHarmony huggingface dataset:
Topic Modeling on User Stories using Word Mover's Distance

Requirements elicitation has recently been complemented with crowd-based techniques, which continuously involve large, heterogeneous groups of users who express their feedback through a variety of media. Crowd-based elicitation has great potential for engaging with (potential) users early on but also results in large sets of raw and unstructured feedback. Consolidating and analyzing this feedback is a key challenge for turning it into sensible user requirements. In this paper, we focus on topic modeling as a means to identify topics within a large set of crowd-generated user stories and compare three approaches: (1) a traditional approach based on Latent Dirichlet Allocation, (2) a combination of word embeddings and principal component analysis, and (3) a combination of word embeddings and Word Mover's Distance. We evaluate the approaches on a publicly available set of 2,966 user stories written and categorized by crowd workers. We found that a combination of word embeddings and Word Mover's Distance is most promising. Depending on the word embeddings we use in our approaches, we manage to cluster the user stories in two ways: one that is closer to the original categorization and another that allows new insights into the dataset, e.g. to find potentially new categories. Unfortunately, no measure exists to rate the quality of our results objectively. Still, our findings provide a basis for future work towards analyzing crowd-sourced user stories.
Pairwise Relations Oriented Discriminative Regression

Linear Regression (LR) is a popular and effective technique in pattern recognition area, which aims to find a transform matrix between source data and target data (usually label matrix). However, a binary zero-one label matrix may be too strict and inappropriate for regression. Besides, directly projecting source data to target data by one transform matrix may lose some intrinsic data information. To address these issues, this paper proposes a novel Pairwise Relations oriented Discriminative Regression (PRDR) method. In PRDR, the source data is regressed into a latent space instead of label space. To supervise the discriminative projection learning, the pairwise relations in source data space and label space are exploited in the latent space simultaneously. The pairwise label relations are transferred into the latent subspace by solving a distance-distance difference minimization problem, and the intraclass instance relations are also preserved in latent space. These two constraints ensure the pairwise similarity of data points after transformation which is beneficial for classification. By further enlarging the margins between true and false classes, PRDR is extended to a robust version, i.e., R-PRDR. An efficient algorithm is presented to solve the PRDR model. Extensive experiments on several popular image datasets demonstrate the effectiveness and efficiency of the proposed method compared with some state-of-the-art regression approaches.
Hybrid Approach to Content-Based Image Retrieval Using Modified Multi-Scale LBP and Color Features

The objective of the content-based image retrieval (CBIR) system is to retrieve the visually identical images from the database efficiently and effectively. It is a broad research realm with the availability of numerous applications. Performance dependence of CBIR focuses on the extraction, reduction, and selection of the features along with the practice of classification technique. In this work, we have proposed the hybrid approach of two different feature descriptors: global color histogram and multi-scale local binary pattern (MS-LBP); furthermore, the use of PCA for dimension reduction and LDA for the selection of features. The proposed method is evaluated concerning various benchmark datasets, viz., Corel-1k, Corel-5k, Corel-10k, and Ghim-10k together with result comparison based on the precision and recall values at different thresholds. The classification purposes are satisfied with Euclidean and City Block distance. The performance study of the proposed work displays it as outperformer than the identified state-of-the-art literature.
Linear Canonical Wigner Distribution of Noisy LFM Signals via Multiobjective Optimization Analysis Involving Variance-SNR

The existing output signal-to-noise ratio (SNR) optimization model seems not accurate enough to characterize the closed-form instantaneous cross-correlation function type of Wigner distribution’s optimum detection performance, resulting in an unsatisfactory optimal linear canonical transform free parameters selection strategy. A more appropriate output SNR definition, the so-called variance-SNR, is then proposed to model a new kind of optimization, the variance-SNR optimization model. By integrating these two optimization models, it follows a multiobjective optimization, whose solutions to noisy single component linear frequency-modulated signals are also deduced. The newly derived strategy on determination of the optimal parameters implies the previous one so that there is a better detection performance in terms of noise suppression.
Verifiable database supporting keyword searches with forward security

The primitive of verifiable database (VDB) enables the integrity of database when a resource-limited client outsources a huge dynamic database on an untrusted cloud server. Meanwhile, the client could perform various types of queries on the database such as index-based queries and keyword-based queries. In the real-world applications, it is more suitable to consider VDB schemes supporting full updating operations (i.e., replacement, insertion, and deletion). Inherently, forward/backward-secure searchable encryption schemes should be adopted to incorporate VDB schemes. In this paper, we first attempt to address the problem of how to perform forward-secure keyword searching on VDB schemes. Moreover, we present a concrete VDB construction that can support forward-secure keyword search and full updating operations. Security and efficiency analysis demonstrate that the proposed VDB schemes can achieve the desired security properties with high efficiency.
An efficient secure data compression technique based on chaos and adaptive Huffman coding

Abstract Data stored in physical storage or transferred over a communication channel includes substantial redundancy. Compression techniques cut down the data redundancy to reduce space and communication time. Nevertheless, compression techniques lack proper security measures, e.g., secret key control, leaving the data susceptible to attack. Data encryption is therefore needed to achieve data security in keeping the data unreadable and unaltered through a secret key. This work concentrates on the problems of data compression and encryption collectively without negatively affecting each other. Towards this end, an efficient, secure data compression technique is introduced, which provides cryptographic capabilities for use in combination with an adaptive Huffman coding, pseudorandom keystream generator, and S-Box to achieve confusion and diffusion properties of cryptography into the compression process and overcome the performance issues. Thus, compression is carried out according to a secret key such that the output will be both encrypted and compressed in a single step. The proposed work demonstrated a congruent fit for real-time implementation, providing robust encryption quality and acceptable compression capability. Experiment results are provided to show that the proposed technique is efficient and produces similar space-saving (%) to standard techniques. Security analysis discloses that the proposed technique is susceptible to the secret key and plaintext. Moreover, the ciphertexts produced by the proposed technique successfully passed all NIST tests, which confirm that the 99% confidence level on the randomness of the ciphertext.
A class of skew cyclic codes and application in quantum codes construction

Let p be an odd prime, and k an integer such that k∣(p−1). This paper studies quantum codes from skew cyclic codes over the ring R=Fq[u]〈uk+1−u〉, where q is a power of the prime p. We construct a set of orthogonal idempotents of the ring R and using the set, skew cyclic codes over the ring R are decomposed as direct sum of skew cyclic codes over Fq. We obtain a necessary and sufficient condition for a skew cyclic code to contain its dual. As an application, we construct quantum codes from skew cyclic codes over Fq. It is observed that some quantum codes we constructed are MDS quantum codes.
Prediction of Age from Speech Features Using a Multi-Layer Perceptron Model

Age estimation from speech has recently received enormous interest for its plethora of applications such as personal voice assistants, user-profiling, targeted marketing, call-routing and criminal tracking. There is critical demand for a system that can estimate the age of the speaker quickly and accurately. In this paper we discuss the prediction of age group from speech features using a Multi-Layer Perceptron (MLP) architecture. Features like Mel Frequency Cepstral Coefficients (MFCC), formants, pitch, chroma values etc. are extracted from every speech sample and are then selected using Principal Component Analysis (PCA) and Redundant Feature Elimination (RFE) techniques. The training and testing are done on a repository from the Mozilla Speech database called Common Voice, consisting of around seventy-five thousand speech samples from different age groups. The implemented model gave us a training accuracy of 87.59% and testing accuracy of 85.68%. We have explained the approaches used in the existing solutions and their drawbacks. Having compared the performance from state-of-the-art solutions on this dataset, we have shown that our proposed model provides enhanced age group classification accuracy.
Semantic Search over Structured Data

Searching through large structured data is a crucial task in enterprise as well as online search. Existing keyword-based search methods were mostly designed for web document search, and support a limited variety of query types suitable for structured data. Their main underlying drawback is the lack of semantic understanding of the data and users' natural language queries, resulting in a fundamental disconnect. In this paper, we bridge that gap through effective semantic type discovery and indexing of structured data. We demonstrate S3D (Semantic Search over Structured Data), a novel system that supports queries such as finding related tables, rows or columns, amongst others, on a large scale of structured data from multiple heterogeneous sources.
Design of An Intelligent Wheelchair for Handicap People Conducting by Body Movement

The increasing development of the biomedical system and smart technology has a major impact on smart devices. A smart wheelchair is one of them to be improved with the blessings of this modern technology. In this paper, a smart wheelchair topology is proposed which is operated by a hand movement device and a smartphone. It comes with a lot of advanced features for people with disabilities who cannot walk or travel without the help of others. It is a hand-held wheelchair in which the gyro sensor and accelerometer are used and the Bluetooth phone control module is used to make it automatic. Users will wear a gesture system in their hands, and by moving the hand, the wheelchair will move forward, backward, left, and right. Arduino Mega and Arduino Nano are used as controllers. In this paper, the minimum threshold angle is compared with a microcontroller-based wheelchair where this proposed wheelchair started working with 5° fewer angles for forwarding and backward movement and 3° fewer angles for the left and right movement. Moreover, the linearity of this proposed wheelchair is -0.7, 0.045 & -0.03 when the sensitivity is 0.6102, 0.5214 & 0.55 for X, Y, Z axis respectively. The Sonar sensor is used here to prevent a safe movement. Using this design dimension and configuration, a prototype was eventually built and evaluated at various stages for performance evaluation.
Concept Drift Detection Using Autoencoders in Data Streams Processing

In this paper, the problem of concept drift detection in data stream mining algorithms is considered. The autoencoder is proposed to be applied as a drift detector. The autoencoders are neural networks that are learned how to reconstruct input data. As a side effect, they are able to learn compact nonlinear codes, which summarize the most important features of input data. We suspect that the properly learned autoencoder on one part of the data stream can then be used to monitor possible changes in the following stream parts. The changes are analyzed by monitoring variations of the autoencoder cost function. Two cost functions are applied in this paper: the cross-entropy and the reconstruction error. Preliminary experimental results show that the proposed autoencoder-based detector is able to handle different types of concept drift, e.g. the sudden or the gradual.
Are You Human?

Phishing is one of the most common cyberattacks these days. Attackers constantly look for new techniques to make their campaigns more lucrative by extending the lifespan of phishing pages. To achieve this goal, they leverage different anti-analysis (i.e., evasion) techniques to conceal the malicious content from anti-phishing bots and only reveal the payload to potential victims. In this paper, we study the resilience of anti-phishing entities to three advanced anti-analysis techniques based on human verification: Google re-CAPTCHA, alert box, and session-based evasion. We have designed a framework for performing our testing experiments, deployed 105 phishing websites, and provided each of them with one of the three evasion techniques. In the experiments, we report phishing URLs to major server-side anti-phishing entities (e.g., Google Safe Browsing, NetCraft, APWG) and monitor their occurrence in the blacklists. Our results show that Google Safe Browsing was the only engine that detected all the reported URLs protected by alert boxes. However, none of the anti-phishing engines could detect phishing URLs armed with Google re-CAPTCHA, making it so far the most effective protection solution of phishing content available to malicious actors. Our experiments show that all the major serverside anti-phishing bots only detected 8 out of 105 phishing websites protected by human verification systems. As a mitigation plan, we intend to disclose our findings to the impacted anti-phishing entities before phishers exploit human verification techniques on a massive scale.
Examining K-12 educator experiences from digital citizenship professional development

Digital citizenship refers to exhibiting responsible digital habits to function in a digital world. The purpose of this mixed methods study is to examine K-12 educators' experiences based on a digital citizenship graduate-level course that they participated in for professional development. Forty-five educators participated in this course. In addition to the knowledge and attitudinal data collected from assessment and survey, ten educators also participated in follow-up interviews at the end of the course. Results indicated that educators’ digital citizenship knowledge increased significantly over the course period. Qualitative data indicated educators could transfer the course content to their school environments and enrolled in the course due to personal, student-related, curriculum-related, and school-related reasons. Needs and challenges regarding digital citizenship were also identified.
Employing Transfer-Learning based CNN architectures to Enhance the Generalizability of Deepfake Detection

Advancements in machine learning have given rise to technologies and methodologies that are being put to use for immoral purposes, especially after the inception of Generative Adversarial Networks in 2014. Generative Adversarial Networks are capable of synthesizing hyper-realistic fake images and even videos. Various sophisticated machine learning techniques capable of creating ultra-realistic Deep Fake videos are being used to harass, blackmail women and children, induce political instability by spreading false, malicious propaganda which could in turn lead to social, political conflicts and outbursts with dire consequences. This poses a serious threat to personal safety and also endangers national security which calls for automated ways to detect deep fake videos. This paper proposes a method to expose such fake videos by using a CNN architecture and leveraging the technique of Transfer Learning. The proposed model implements an algorithm that uses a CNN for feature extraction from every frame in a video to train a binary classifier that learns to efficiently differentiate between real and manipulated videos. The method is evaluated against an extensive set of deepfake videos gathered from various datasets.
A BDD for Linux?

What is the number of valid configurations for Linux? How to generate uniform random samples for Linux? Can we create a binary decision diagram for Linux? It seems that the product-line community tries hard to answer such questions for Linux and other configurable systems. However, attempts are often not published due to the publication bias (i.e., unsuccessful attempts are not published). As a consequence, researchers keep trying by potentially spending redundant effort. The goal of this challenge is to guide research on these computationally complex problems and to foster the exchange between researchers and practitioners.
Episodic Self-Imitation Learning with Hindsight

Episodic self-imitation learning, a novel self-imitation algorithm with a trajectory selection module and an adaptive loss function, is proposed to speed up reinforcement learning. Compared to the original self-imitation learning algorithm, which samples good state–action pairs from the experience replay buffer, our agent leverages entire episodes with hindsight to aid self-imitation learning. A selection module is introduced to filter uninformative samples from each episode of the update. The proposed method overcomes the limitations of the standard self-imitation learning algorithm, a transitions-based method which performs poorly in handling continuous control environments with sparse rewards. From the experiments, episodic self-imitation learning is shown to perform better than baseline on-policy algorithms, achieving comparable performance to state-of-the-art off-policy algorithms in several simulated robot control tasks. The trajectory selection module is shown to prevent the agent learning undesirable hindsight experiences. With the capability of solving sparse reward problems in continuous control settings, episodic self-imitation learning has the potential to be applied to real-world problems that have continuous action spaces, such as robot guidance and manipulation.
A Cyclical Learning Rate Method in Deep Learning Training

The learning rate is an important hyperparameter for training deep neural networks. The traditional learning rate method has the problems of instability of accuracy. Aiming at these problems, we proposed a new learning rate method with different cyclical changes in each training cycle instead of a fixed value. It achieves higher accuracy in less iterations and faster convergence. Through the experiment on CIFAR-10 and CIFAR-100 datasets based on VGG network and RESNET network, the final results show that the proposed method has better results on stability and accuracy than cyclical learning rate method.
Learn to Navigate Maplessly With Varied LiDAR Configurations: A Support Point-Based Approach

Deep reinforcement learning (DRL) demonstrates great potential in mapless navigation domain. However, such a navigation model is normally restricted to a fixed configuration of the range sensor because its input format is fixed. In this paper, we propose a DRL model that can address range data obtained from different range sensors with different installation positions. Our model first extracts the goal-directed features from each obstacle point. Subsequently, it chooses global obstacle features from all point-feature candidates and uses these features for the final decision. As only a few points are used to support the final decision, we refer to these points as support points and our approach as support point-based navigation (SPN). Our model can handle data from different LiDAR setups and demonstrates good performance in simulation and real-world experiments. Moreover, it shows great potential in crowded scenarios with small obstacles when using a high-resolution LiDAR.
Query Understanding via Intent Description Generation

Query understanding is a fundamental problem in information retrieval (IR), which has attracted continuous attention through the past decades. Many different tasks have been proposed for understanding users' search queries, e.g., query classification or query clustering. However, it is not that precise to understand a search query at the intent class/cluster level due to the loss of many detailed information. As we may find in many benchmark datasets, e.g., TREC and SemEval, queries are often associated with a detailed description provided by human annotators which clearly describes its intent to help evaluate the relevance of the documents. If a system could automatically generate a detailed and precise intent description for a search query, like human annotators, that would indicate much better query understanding has been achieved. In this paper, therefore, we propose a novel Query-to-Intent-Description (Q2ID) task for query understanding. Unlike those existing ranking tasks which leverage the query and its description to compute the relevance of documents, Q2ID is a reverse task which aims to generate a natural language intent description based on both relevant and irrelevant documents of a given query. To address this new task, we propose a novel Contrastive Generation model, namely CtrsGen for short, to generate the intent description by contrasting the relevant documents with the irrelevant documents given a query. We demonstrate the effectiveness of our model by comparing with several state-of-the-art generation models on the Q2ID task. We discuss the potential usage of such Q2ID technique through an example application.
High-quality holographic stereogram generation using four RGBD images

To computer-generate high-quality holographic stereograms, a huge number of images must be provided: several hundred for a horizontal parallax and the square of this number for a full parallax. In this paper, we propose to drastically reduce this number to four input images with depth maps (or equivalently, four groups of neighboring images used to compute a depth map) in any pose, in order to create the missing images with depth image-based rendering. We evaluate the view synthesis method objectively before providing visual results of the corresponding holographic stereograms. We believe this method outperforms shearlet-based approaches in objective view synthesis quality metrics and in the number of required input images ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mn>7</mml:mn><mml:mo>×<!-- × --></mml:mo><mml:mn>7</mml:mn></mml:math> ).