Reviewerly/exHarmony huggingface dataset:
A Metamorphic Testing Approach for Assessing Question Answering Systems

Question Answering (QA) enables the machine to understand and answer questions posed in natural language, which has emerged as a powerful tool in various domains. However, QA is a challenging task and there is an increasing concern about its quality. In this paper, we propose to apply the technique of metamorphic testing (MT) to evaluate QA systems from the users’ perspectives, in order to help the users to better understand the capabilities of these systems and then to select appropriate QA systems for their specific needs. Two typical categories of QA systems, namely, the textual QA (TQA) and visual QA (VQA), are studied, and a total number of 17 metamorphic relations (MRs) are identified for them. These MRs respectively focus on some characteristics of different aspects of QA. We further apply MT to four QA systems (including two APIs from the AllenNLP platform, one API from the Transformers platform, and one API from CloudCV) by using all of the MRs. Our experimental results demonstrate the capabilities of the four subject QA systems from various aspects, revealing their strengths and weaknesses. These results further suggest that MT can be an effective method for assessing QA systems.
Super-resolving blurry face images with identity preservation

Face images captured in unconstrained settings may suffer from one or multiple degradations, which would degrade the visual aesthetics of images and the performance of face recognition methods. However, many current methods only focus on a specific degradation or restoring the images without considering face identity. To address these problems, an identity-preservation-based deep learning method is proposed for super-resolving blurry face images. First, an extra recognition module is designed and integrated with the restoration module to extract different levels of identity-related and semantic features. Second, an assemble loss function is developed to use the identity preservation information as regularization and prior to guide the restoration and recognition process. Finally, qualitative and quantitative evaluations are conducted to demonstrate the effectiveness of the proposed method for face recovery and face recognition. The results indicate that facial identity can serve as an effective prior to face image restoration.
Integrated Internet of Things (IoT) technology device on smart home system with human posture recognition using kNN method

Abstract IoT device technology is currently developing rapidly, for example in smart home systems that have several features including lighting, surveillance security, temperature control, water sensors, and smart electricity. IoT device consists of smart electricity integrated with human action recognition using sensor vision are developed in this work. In smart electricity system, we build some relays controlled by smartphone applications and web-based platforms. We can control the relays and monitor the voltage, current, and power used from electricity appliances that are connected to our IoT device. In human action recognition, we use a single RGB camera to capture some human poses into spatiotemporal sequences to get data for training. There are six poses for testing scenario, these poses will be clustered using kNN (k-Nearest Neighbor) method. Each human action that is recognized will be connected to an IoT device for controlling the switching mode on the relays in smart electricity system. The result in this experiment shows that the system successfully reads every single posture with quite good accuracy performance using confusion matrix.
Addressing Data Sparsity in Collaborative Filtering Based Recommender Systems Using Clustering and Artificial Neural Network

Collaborative Filtering (CF) is fundamentally characterized by Recommender Systems (RSs), which have recently attracted researchers' attention. The ever-increasing data about users and items and the emergence of machine learning approaches have motivated the recent development of CF. The sparsity caused by the lack of recorded transactions and data makes it challenging for CF to distinguish between users' similar preferences. As a result of the data sparsity issue, CF ultimately lacks the ability to generate useful recommendations and suffers from poor performance. This paper proposes a novel model that uses clustering and artificial neural network to address the issue of data sparsity in CF. The proposed model CANNBCF, a short name for Clustering and Artificial Neural Network Based Collaborative Filtering, is evaluated using four different datasets from four popular domains (books, music, jokes, and movies). The proposed model shows its superiority to solve the sparsity issue that the traditional CF technique encounters. In this paper, eight experiments are conducted to evaluate the performance of CANNBCF. The evaluation criteria include accuracy, precision, recall, F1-score, and Receiver Operating Characteristics used to examine the proposed model. The results of the experiments show that CANNBCF effectively solves the sparsity issue, improves the quality of recommendations, and demonstrates promising prediction accuracy.
An Intelligent Three-Level Digital Watermarking Method for Document Protection

Digital text is the most frequent interchange form of data that could hold sensitive information such as audit firms, banks, and educational institutes. This sensitive information needs to preserve its integrity and originality so that it could not only secure the data but also helps to identify ownership of text documents. This paper presents a novel and invisible digital watermarking approach for the secure exchange of text documents over the internet. Digital watermarking serves from the last decade for detection of forgery and tempering from digital text documents and maintained the copyright and authentication successfully. Many states of the art watermark techniques achieve high imperceptibility, robustness, and high hidden capacity; unfortunately failed to maintain the balance among these three conflicting parameters. As resolvent, we propose an intelligent Three-Level Digital Watermarking (3LDW) system for text documents copyright protection. 3LDW system can be applied to Microsoft Word objects, document open spaces, and text feature coding without affecting the content of the original document. Experimental results reveal that our proposed 3LDW system strongly resist against formatting attacks and efficiently preserves the imperceptibility. Additionally, embedding capacity analysis demonstrates a prominent improvement of the proposed system as compared to other similar approaches.
A nationwide census on wifi security threats

Carrying over 75% of the last-mile mobile Internet traffic, WiFi has inevitably become an enticing target for various security threats. In this work, we characterize a wide variety of real-world WiFi threats at an unprecedented scale, involving 19 million WiFi access points (APs) mostly located in China, by deploying a crowdsourced security checking system on 14 million mobile devices in the wild. Leveraging the collected data, we reveal the landscape of nationwide WiFi threats for the first time. We find that the prevalence, riskiness, and breakdown of WiFi threats deviate significantly from common understandings and prior studies. In particular, we detect attacks at around 4% of all WiFi APs, uncover that most WiFi attacks are driven by an underground economy, and provide strong evidence of web analytics platforms being the bottleneck of its monetization chain. Further, we provide insightful guidance for defending against WiFi attacks at scale, and some of our efforts have already yielded real-world impact---effectively disrupted the WiFi attack ecosystem.
Applications and extensions of context-sensitive rewriting

Context-sensitive rewriting is a restriction of term rewriting which is obtained by imposing replacement restrictions on the arguments of function symbols. It has proven useful to analyze computational properties of programs written in sophisticated rewriting-based programming languages such as CafeOBJ, Haskell, Maude, OBJ*, etc. Also, a number of extensions (e.g., to conditional rewriting or constrained equational systems) and generalizations (e.g., controlled rewriting or forbidden patterns) of context-sensitive rewriting have been proposed. In this paper, we provide an overview of these applications and related issues.
Emotional Attention Detection and Correlation Exploration for Image Emotion Distribution Learning

Current works on image emotion distribution learning typically extract visual representations from the holistic image or explore emotion-related regions in the image from a global-wise perspective. However, different regions of an image contribute differently to the arousal of each emotion. Existing works do not deeply explore corresponding emotion-aware regions of each emotion in the image, nor do they fully capture the relationship between each emotion-aware region and the emotion labels. In this article, we propose a novel attention based emotion distribution learning method, which can explore the emotion-related regions of images from the perspective of each emotion category, and can conduct region relationship learning. Specifically, we introduce a semantic guided attention detection network to generate class-wise attention maps for each emotion and a global-wise attention map for the holistic image. Meanwhile, an emotional graph-based network is adopted to capture the correlation between each region and the emotion distribution. Experiments on several benchmark datasets demonstrate the superiority of the proposed method compared to related works.
TOAD-GAN: A Flexible Framework for Few-Shot Level Generation in Token-Based Games

This work presents <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</i> oken-based <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> ne-shot <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</i> rbitrary <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</i> imension <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</i> enerative <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</i> dversarial <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> etwork ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TOAD-GAN</i> ), a novel procedural content generation algorithm that generates token-based video game levels from only one example. We show that the created levels can be of arbitrary size, and the patterns of the training levels are well captured. The method can be extended with user interaction during the generation process to achieve certain token layouts and interpretations of the same base level by different generators. Our method is further evaluated with an extensive ablation study and level similarity metrics on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Super Mario Bros.</i> benchmark. Finally, we extend our method to mix the style of multiple input levels, turning it into a framework for few-shot level generation.
Reinforcement Learning in Anylogic Simulation Models: A Guiding Example Using Pathmind

Reinforcement Learning has recently gained a lot of exposure in the simulation industry. In this paper, we demonstrate the use of reinforcement learning in AnyLogic software models using Pathmind. A coffee shop simulation is built to train a barista to make correct operational decisions and improve efficiency that directly affects customer service time. The trained policy outperforms rule-based functions in terms of customer service time and throughput.
Learning emotional word embeddings for sentiment analysis

Word embeddings have been successfully applied in many natural language processing tasks due to its their effectiveness. However, the state-of-the-art algorithms for learning word representations from large amounts of text documents ignore emotional information, which is a significant research problem that must be addressed. To solve the above problem, we propose an emotional word embedding (EWE) model for sentiment analysis in this paper. This method first applies pre-trained word vectors to represent document features using two different linear weighting methods. Then, the resulting document vectors are input to a classification model and used to train a text sentiment classifier, which is based on a neural network. In this way, the emotional polarity of the text is propagated into the word vectors. The experimental results on three kinds of real-world data sets demonstrate that the proposed EWE model achieves superior performances on text sentiment prediction, text similarity calculation, and word emotional expression tasks compared to other state-of-the-art models.
PennSyn2Real: Training Object Recognition Models Without Human Labeling

Scalable training data generation is a critical problem in deep learning. We propose PennSyn2Real - a photo-realistic synthetic dataset consisting of more than 100 000 4K images of more than 20 types of micro aerial vehicles (MAVs). The dataset can be used to generate arbitrary numbers of training images for high-level computer vision tasks such as MAV detection and classification. Our data generation framework bootstraps chroma-keying, a mature cinematography technique, with a motion tracking system providing artifact-free and curated annotated images. Our system, therefore, allows object orientations and lighting to be controlled. This framework is easy to set up and can be applied to a broad range of objects, reducing the gap between synthetic and real-world data. We show that synthetic data generated using this framework can be directly used to train CNN models for common object recognition tasks such as detection and segmentation. We demonstrate competitive performance in comparison with training using only real images. Furthermore, bootstrapping the generated synthetic data in few-shot learning can significantly improve the overall performance, reducing the number of required training data samples to achieve the desired accuracy.
Velocity Obstacle Based Conflict Avoidance in Urban Environment with Variable Speed Limit

Current investigations into urban aerial mobility, as well as the continuing growth of global air transportation, have renewed interest in conflict detection and resolution (CD&amp;R) methods. The use of drones for applications such as package delivery, would result in traffic densities that are orders of magnitude higher than those currently observed in manned aviation. Such densities do not only make automated conflict detection and resolution a necessity, but will also force a re-evaluation of aspects such as coordination vs. priority, or state vs. intent. This paper looks into enabling a safe introduction of drones into urban airspace by setting travelling rules in the operating airspace which benefit tactical conflict resolution. First, conflicts resulting from changes of direction are added to conflict resolution with intent trajectory propagation. Second, the likelihood of aircraft with opposing headings meeting in conflict is reduced by separating traffic into different layers per heading–altitude rules. Guidelines are set in place to make sure aircraft respect the heading ranges allowed at every crossed layer. Finally, we use a reinforcement learning agent to implement variable speed limits towards creating a more homogeneous traffic situation between cruising and climbing/descending aircraft. The effects of all of these variables were tested through fast-time simulations on an open source airspace simulation platform. Results showed that we were able to improve the operational safety of several scenarios.
Revocation Statuses on the Internet

The modern Internet is highly dependent on the trust communicated via X.509 certificates. However, in some cases certificates become untrusted and it is necessary to revoke them. In practice, the problem of secure certificate revocation has not yet been solved, and today no revocation procedure (similar to Certificate Transparency w.r.t. certificate issuance) has been adopted to provide transparent and immutable history of all revocations. Instead, the status of most certificates can only be checked with Online Certificate Status Protocol (OCSP) and/or Certificate Revocation Lists (CRLs). In this paper, we present the first longitudinal characterization of the revocation statuses delivered by CRLs and OCSP servers from the time of certificate expiration to status disappearance. The analysis captures the status history of over 1 million revoked certificates, including 773K certificates mass-revoked by Let’s Encrypt. Our characterization provides a new perspective on the Internet’s revocation rates, quantifies how short-lived the revocation statuses are, highlights differences in revocation practices within and between different CAs, and captures biases and oddities in the handling of revoked certificates. Combined, the findings motivate the development and adoption of a revocation transparency standard.
A corrected and improved symbiotic organisms search algorithm for continuous optimization

The symbiotic organisms search (SOS) algorithm is investigated and corrected by removing the benefit factors due to their biased search around the original dot. However, the removal of these benefit factors results in performance that is far inferior to the outstanding performance of the basic SOS algorithm. Accordingly, this paper suggests adopting combination schemes for the mutualistic equations in order to prevent premature convergence, and further recommends adopting lower combination rates for parasitic equations. Combination schemes are found to be not applicable to the commensal equation, as this equation is not greedy. Therefore, this paper proposes two types of combination schemes to improve the corrected SOS version in terms of achieving high early convergence speed, attaining convergence precision at a lower cost, arriving at the convergence plateau at either a lower cost or a higher level of precision, handling tests of composition functions well, and achieving competitive performance on CEC2015 test problems.
Weakly-Supervised Open-Retrieval Conversational Question Answering

Recent studies on Question Answering (QA) and Conversational QA (ConvQA) emphasize the role of retrieval: a system first retrieves evidence from a large collection and then extracts answers. This open-retrieval ConvQA setting typically assumes that each question is answerable by a single span of text within a particular passage (a span answer). The supervision signal is thus derived from whether or not the system can recover an exact match of this ground-truth answer span from the retrieved passages. This method is referred to as span-match weak supervision. However, information-seeking conversations are challenging for this span-match method since long answers, especially freeform answers, are not necessarily strict spans of any passage. Therefore, we introduce a learned weak supervision approach that can identify a paraphrased span of the known answer in a passage. Our experiments on QuAC and CoQA datasets show that the span-match weak supervisor can only handle conversations with span answers, and has less satisfactory results for freeform answers generated by people. Our method is more flexible as it can handle both span answers and freeform answers. Moreover, our method can be more powerful when combined with the span-match method which shows it is complementary to the span-match method. We also conduct in-depth analyses to show more insights on open-retrieval ConvQA under a weak supervision setting.
Novel Blind Signcryption Scheme for E-Voting System Based on Elliptic Curves

To make the electoral process more secure, comfortable, and universal, it is essential to use modern cryptographic techniques for ensuring the anonymity of information in the electronic voting system. In many emerging applications like electronic voting data anonymity as well as un-traceability are the most essential security properties. To ensure these properties we present here in this paper a more secure and comparatively efficient blind signcryption scheme using the Elliptic Curve Cryptosystem (ECC). The existing e-voting schemes are based on El-Gamal and the Rivest-Shamir-Adleman(RSA) cryptosystems which are not only expensive approaches but also lack the security features like unlinkability and forward secrecy. In our proposed scheme we use a low-cost elliptic curve cryptosystem with 160 bits key as compared to El-Gamal 2048 bits key and RSA 1024 bits key. In this scheme signer signs the message blindly without knowing the original contents then the voter forward signcrypted vote to polling server. The polling server is the actual voter data verifier or validator. The polling server checks the validity/authenticity of the voter and has the right to accept or reject the vote. Moreover, this scheme offers forward secrecy, unlinkability, and non-repudiation in addition to the basic security features like confidentiality, authenticity, integrity, and unforgeability. Overall performance evaluation proves that our scheme is comparatively more efficient in terms of computational and communicational costs. Furthermore, this scheme is suitable for the e-voting system due to its lower cost and extra security features.
Digitally Mediated Communication

In this chapter, we outline the pragmatic issues involved in the analysis of digitally mediated communication. We start with several discursive qualities and implications regarding the range of options for contextualization that the different interfaces exhibit and how users face and overcome digital cues--filtered channels. Then, we move on to more interactive features of digitally mediated communication. Finally, some social aspects of digitally mediated communication are addressed. In the three mentioned areas (discursive, interactive, social), we also seek to explain the role that non-propositional constraints and effects play in the eventual satisfaction with digitally mediated communication, compared to the one that can be found in situations of physical co-presence.
Fully Decoupled Neural Network Learning Using Delayed Gradients

Training neural networks with backpropagation (BP) requires a sequential passing of activations and gradients. This has been recognized as the lockings (i.e., the forward, backward, and update lockings) among modules (each module contains a stack of layers) inherited from the BP. In this brief, we propose a fully decoupled training scheme using delayed gradients (FDG) to break all these lockings. The FDG splits a neural network into multiple modules and trains them independently and asynchronously using different workers (e.g., GPUs). We also introduce a gradient shrinking process to reduce the stale gradient effect caused by the delayed gradients. Our theoretical proofs show that the FDG can converge to critical points under certain conditions. Experiments are conducted by training deep convolutional neural networks to perform classification tasks on several benchmark data sets. These experiments show comparable or better results of our approach compared with the state-of-the-art methods in terms of generalization and acceleration. We also show that the FDG is able to train various networks, including extremely deep ones (e.g., ResNet-1202), in a decoupled fashion.