Reviewerly/exHarmony huggingface dataset:
Automatic Segmentation of Texts based on Stylistic Features

We report on an automatic method and program for text structure discovery and subsequent segmentation of texts. The method, previously presented and herein enhanced, is based on stylistic features. The segmentation was applied to two self-biographic works; the results are compared and conclusions are derived. The method can be used as a tool in text generation, as a tool in editorial offices, and in literary analysis.
Intelligent Software Web Agents: A Gap Analysis

Semantic web technologies have shown their effectiveness, especially when it comes to knowledge representation, reasoning , and data integration. However, the original semantic web vision, whereby machine readable web data could be automatically actioned upon by intelligent software web agents, has yet to be realised. In order to better understand the existing technological opportunities and challenges, in this paper we examine the status quo in terms of intelligent software web agents, guided by research with respect to requirements and architectural components, coming from the agents community. We use the identified requirements to both further elaborate on the semantic web agent motivating use case scenario, and to summarise different perspectives on the requirements from the semantic web agent literature. We subsequently propose a hybrid semantic web agent architecture, and use the various components and subcomponents in order to provide a focused discussion in relation to existing semantic web standards and community activities. Finally, we highlight open research opportunities and challenges and take a broader perspective of the research by discussing the potential for intelligent software web agents as an enabling technology for emerging domains, such as digital assistants, cloud computing, and the internet of things.
Anti-occluded Person Re-identification via Pose Restoration and Dual Channel Feature Distance Measurement

In real scenes, persons are often blocked by obstacles. The purpose of occluded person re-identification is to identify the occluded persons in the non-shared view camera. In this paper, we propose a new framework, anti-occluded person re-identification model via pose restoration and dual channel feature extraction (PRAO). The network is divided into two modules: person pose repair module (PPR) and dual channel feature extraction module (DCFE). (1) In the person pose repair module, the instance segmentation network is used to detect the occlusion in the person image, and then the pre-trained edge smoothing GAN (e-GAN) is used to repair the person image. (2) In the dual channel feature extraction module, we change the original single channel person prediction structure into a dual channel person matching structure, which can accurately align the person features of the two channels, reduce the noise generated by image generation, and improve the identification accuracy of persons in the case of occlusion. Finally, a large number of experiments on occluded and non-occluded datasets show the performance of the method.
The Effect of News Article Quality on Ad Consumption

Practical news feed platforms generate a hybrid list of news articles and advertising items (e.g., products, services, or information) and many platforms optimize the position of news articles and advertisements independently. However, they should be arranged with careful consideration of each other, as we show in this study, since user behaviors toward advertisements are significantly affected by the news articles. This paper investigates the effect of news articles on users' ad consumption and shows the dependency between news and ad effectiveness. We conducted a service log analysis and showed that sessions with high-quality news article exposure had more ad consumption than those with low-quality news article exposure. Based on this result, we hypothesized that exposure to high-quality articles will lead to a high ad consumption rate. Thus, we conducted million-scale A/B testing to investigate the effect of high-quality articles on ad consumption, in which we prioritized high-quality articles in the ranking for the treatment group. The A/B test showed that the treatment group's ad consumption, such as the number of clicks, conversions, and sales, increased significantly while the number of article clicks decreased. We also found that users who prefer a social or economic topic had more ad consumption by stratified analysis. These insights regarding news articles and advertisements will help optimize news and ad effectiveness in rankings considering their mutual influence.
Towards Disambiguating Quests as a Technical Term

Quests are a popular topic of study in many academic fields. However, literature has not settled on the elements in a quest, much less a specific definition or even debate between two or three definitions. The purpose of this paper is to take a preliminary quest definition from our previous work, and revise it to be more broadly applicable. To inform the revision, we analyze quests from a few published games. Then, we propose a few modifications to the definition which allows it to more fully explain some design patterns in games. Finally, we evaluate our definition against other definitions proposed in past research.
Data Drive Fuzzy Cognitive Map for Classification Problems

In recent years Fuzzy Cognitive Maps had become an important tool for expert knowledge representation due to the flexibility and interpretability of modeled maps. Its construction frequently requires an expert’s intervention but, there are situations when only the data is available or is required to extract the contained implicit knowledge for analysis or decision making proposes. Several studies have been developed to improve or find causal relation values between the map concepts but usually require a previous concept definition step carried out by experts. The frequent pattern mining techniques show a way for non-trivial relations extraction from datasets, and those relations may represent a causality degree. In this paper, a strategy to extract concepts from continuous and discrete features for supervised classification problems is proposed. Additionally, to estimate the causality degree between defined map concepts is proposed to use association rule mining techniques. Finally, the strategy is evaluated to show the interpretability and accuracy of generated Fuzzy Cognitive Maps.
Distributed Letter Jam: Designing Distributed Board Game Play for Social Connection

Playing board games together is a fun way for people to connect with friends and family, but social board game play can be challenging when people can't get together in-person. Using a Research through Design approach, we explored possibilities for enabling social distributed board game play amongst friends and family. We developed a video prototype of game play for a distributed version of Letter Jam. Through reflecting on this prototype, we discuss challenges for the design of distributed board games, including incorporating tangible inputs and accommodating a variety of household configurations. We also identify a set of design qualities, including spatiality, tangibility, and presence, that can inform the design of systems for social distributed board game play.
Domain Adaptation for Synthesis of Hazy Images

Most existing image dehazing methods based learning are less able to perform well to real hazy images.An important reason is that they are trained on synthetic hazy images whose distribution is different from real hazy images.To relieve this issue, this paper proposes a new hazy scene generation model based on domain adaptation, which uses a variational autoencoder to encode the synthetic hazy image pairs and the real hazy images into the latent space to adapt.The synthetic hazy image pairs guide the model to learn the mapping of clear images to hazy images, the real hazy images are used to adapt the synthetic hazy images' latent space to real hazy images through generative adversarial loss, so as to make the generative hazy images' distribution as close to the real hazy images' distribution as possible.By comparing the results of the model with traditional physical scattering models and Adobe Lightroom CC software, the hazy images generated in this paper is more realistic.Our end-to-end domain adaptation model is also very convenient to synthesize hazy images without depth map.Using traditional method to dehaze the synthetic hazy images generated by this paper, both SSIM and PSNR have been improved, proved that the effectiveness of our method.The non-reference haze density evaluation algorithm and other quantitative evaluation also illustrate the advantages of our method in synthetic hazy images.
Affects and Emotions in <i>IEEE Signal Processing Magazine</i> [From the Editor]

IEEE Signal Processing Magazine ( SPM ) is a fully edited magazine. This means that, after the review process, the final files provided by the authors are processed by a team of editors under the supervision and the responsibility of the magazine’s managing editor, Jessica Welsh. The contributions of Jessica and the editorial and production teams are very important, since they transform all the final files, figures, and images, into aesthetically pleasing articles that can have colorful sidebars, catchy, short titles, etc. Even if text is not changed substantially, the articles become much more visually attractive after they have been edited. These changes are unanimously appreciated by the authors, who usually cannot recognize their final articles!
MechaTag: A Mechanical Fiducial Marker and the Detection Algorithm

Abstract Fiducial markers are fundamental components of many computer vision systems that help, through their unique features (e.g., shape, color), a fast localization of spatial objects in unstructured scenarios. They find applications in many scientific and industrial fields, such as augmented reality, human-robot interaction, and robot navigation. In order to overcome the limitations of traditional paper-printed fiducial markers (i.e. deformability of the paper surface, incompatibility with industrial and harsh environments, complexity of the shape to reproduce directly on the piece), we aim at exploiting existing, or additionally fabricated, structural features on rigid bodies (e.g., holes), developing a fiducial mechanical marker system called MechaTag. Our system, endowed with a dedicated algorithm, is able to minimize recognition errors and to improve repeatability also in case of ill boundary conditions (e.g., partial illumination). We assess MechaTag in a pilot study, achieving a robustness of fiducial marker recognition above 95% in different environment conditions and position configurations. The pilot study was conducted by guiding a robotic platform in different poses in order to experiment with a wide range of working conditions. Our results make MechaTag a reliable fiducial marker system for a wide range of robotic applications in harsh industrial environments without losing accuracy of recognition due to the shape and material.
Influence-Driven Explanations for Bayesian Network Classifiers

We propose a novel approach to building influence-driven explanations (IDXs) for (discrete) Bayesian network classifiers (BCs). IDXs feature two main advantages wrt other commonly adopted explanation methods First, IDXs may be generated using the (causal) influences between intermediate, in addition to merely input and output, variables within BCs, thus providing a deep, rather than shallow, account of the BCs’ behaviour. Second, IDXs are generated according to a configurable set of properties, specifying which influences between variables count towards explanations. Our approach is thus flexible and can be tailored to the requirements of particular contexts or users. Leveraging on this flexibility, we propose novel IDX instances as well as IDX instances capturing existing approaches. We demonstrate IDXs’ capability to explain various forms of BCs, and assess the advantages of our proposed IDX instances with both theoretical and empirical analyses.
Reducing the Width of a Feed-Forward Neural Network

We study feed-forward neural networks with linear rectification in this paper. Inspired by the idea of "neuron coverage" and "equivalence decomposition", we show that the FFNN's have a natural equivalent relation on them, and can be applied to decide whether the width of the network can be reduced.
Deceptive Techniques to Hide a Compressed Video Stream for Information Security

With the recent development of video compression methods, video transmission on traditional devices and video distribution using networks has increased in various devices such as drones, IP cameras, and small IoT devices. As a result, the demand for encryption techniques such as MPEG-DASH for transmitting streams over networks is increasing. These video stream security methods guarantee stream confidentiality. However, they do not hide the fact that the encrypted stream is being transmitted over the network. Considering that sniffing attacks can analyze the entropy of the stream and scan huge amounts of traffic on the network, to solve this problem, the deception method is required, which appears unencrypted but a confidential stream. In this paper, we propose the new deception method that utilizes standard NAL unit rules of video codec, where the unpromised device shows the cover video and the promised device shows the secret video for deceptive security. This method allows a low encryption cost and the stream to dodge entropy-based sniffing scan attacks. The proposed stream shows that successful decoding using five standard decoders and processing performance was 61% faster than the conventional encryption method in the test signal conformance set. In addition, a network encrypted stream scan method the HEDGE showed classification results that our stream is similar to a compressed video.
CRAFT: Composable Randomness Beacons and Output-Independent Abort MPC From Time

Recently, time-based primitives such as time-lock puzzles (TLPs) and verifiable delay functions (VDFs) have received a lot of attention due to their power as building blocks for cryptographic protocols. However, even though exciting improvements on their efficiency and security (e.g. achieving non-malleability) have been made, most of the existing constructions do not offer general composability guarantees and thus have limited applicability. Baum et al. (EUROCRYPT 2021) presented in TARDIS the first (im)possibility results on constructing TLPs with Universally Composable (UC) security and an application to secure two-party computation with output-independent abort (OIA-2PC), where an adversary has to decide to abort before learning the output. While these results establish the feasibility of UC-secure TLPs and applications, they are limited to the two-party scenario and suffer from complexity overheads. In this paper, we introduce the first UC constructions of VDFs and of the related notion of publicly verifiable TLPs (PV-TLPs). We use our new UC VDF to prove a folklore result on VDF-based randomness beacons used in industry and build an improved randomness beacon from our new UC PV-TLPs. We moreover construct the first multiparty computation protocol with punishable output-independent aborts (POIA-MPC), i.e. MPC with OIA and financial punishment for cheating. Our novel POIA-MPC both establishes the feasibility of (non-punishable) OIA-MPC and significantly improves on the efficiency of state-of-the-art OIA-2PC and (non-OIA) MPC with punishable aborts.
A quantum algorithm for single parity check code

In coding theory, error-detecting and error-correcting codes are used to enable digital communication over a noisy communication channel. One of the most uncomplicated error-detecting codes is the single parity check code. The single parity check code detects the number of ones (1s) in a string of bits. If the number of ones is even, then the bit string is said to have even parity. On the other hand, if the number of ones is odd, then the string is said to have odd parity. The challenge with this approach is that it cannot be detected if there are an even number of errors. Also, it can not tell which bit is in error (the error may even be the parity bit itself); thus, it cannot fix the error. This means the single parity check has a one-bit error detection capability but no error correction capability. Therefore, in this work, we propose a novel quantum algorithm that can be used to implement a single parity check code. Quantum algorithms run on a quantum computer and achieve a speedup, or other efficiency improvements, over any possible classical algorithm. The critical advantage of this algorithm is that it maintains a constant circuit depth regardless of the length of the bit string. Thus, the circuit depth of our proposed algorithm does not increase with bit string length. This demonstrates the power and efficiency of quantum computing in performing a single parity check.
Devanagari Handwritten Character Recognition

This paper aims to classify images of handwritten characters in the Devanagari script. We implement a ResNet architecture with 85 convolution layers to classify images on the publicly available Devanagari Handwritten Character Dataset (DHCD), that holds 92,000 images divided into 46 different classes. Our network implements the bottleneck variant of the residual module and executes the pre-activation method where the activation function and batch normalization are placed before the convolutions. This model outperforms previous works done to date on DHCD by recording an accuracy of 99.72%.
Answering POI-recommendation Questions using Tourism Reviews

We introduce the novel and challenging task of answering Points-of-interest (POI) recommendation questions, using a collection of reviews that describe candidate answer entities (POIs). We harvest a QA dataset that contains 47,124 paragraph-sized user questions from travelers seeking POI recommendations for hotels, attractions and restaurants. Each question can have thousands of candidate entities to choose from and each candidate is associated with a collection of unstructured reviews. Questions can include requirements based on physical location, budget, timings as well as other subjective considerations related to ambience, quality of service etc. Our dataset requires reasoning over a large number of candidate answer entities (over 5300 per question on average) and we find that running commonly used neural architectures for QA is prohibitively expensive. Further, commonly used retriever-ranker based methods also do not work well for our task due to the nature of review-documents. Thus, as a first attempt at addressing some of the novel challenges of reasoning-at-scale posed by our task, we present a task specific baseline model that uses a three-stage cluster-select-rerank architecture. The model first clusters text for each entity to identify exemplar sentences describing an entity. It then uses a neural information retrieval (IR) module to select a set of potential entities from the large candidate set. A reranker uses a deeper attention-based architecture to pick the best answers from the selected entities. This strategy performs better than a pure retrieval or a pure attention-based reasoning approach yielding nearly 25% relative improvement in [email protected] over both approaches. To the best of our knowledge we are the first to present an unstructured QA-style task for POI-recommendation, using real-world tourism questions and POI-reviews.
FlushBlocker: Lightweight mitigating mechanism for CPU cache flush instruction based attacks

CPU cache flush instruction based attacks (cache instruction attacks) such as FLUSH+RELOAD can function in many environments. Meltdown and Spectre adopt FLUSH+RELOAD with cache instructions to access secret data. Additionally, Rowhammer employs cache instructions to modify data in physical memory. An adversary can read and write arbitrary data using these attacks. The deployment of corresponding hardware to combat these attacks is difficult for users, and existing software-based countermeasures incur high overheads, or cannot be applied to a variety of machines. In this study, we propose a novel mitigation mechanism for cache instruction attacks called FlushBlocker, which employs an effective approach that focuses on restricting cache flush instructions. We implemented FlushBlocker on the latest Linux kernel to conduct experiments. The experimental results indicate that FlushBlocker prevents existing cache instruction attacks and runtime overhead is negligible.
Landmark-embedded Gaussian process with applications for functional data modeling

In practice, we often need to infer the value of a target variable from functional observation data. A challenge in this task is that the relationship between the functional data and the target variable is very complex: the target variable not only influences the shape but also the location of the functional data. In addition, due to the uncertainties in the environment, the relationship is probabilistic, that is, for a given fixed target variable value, we still see variations in the shape and location of the functional data. To address this challenge, we present a landmark-embedded Gaussian process model that describes the relationship between the functional data and the target variable. A unique feature of the model is that landmark information is embedded in the Gaussian process model so that both the shape and location information of the functional data are considered simultaneously in a unified manner. Gibbs–Metropolis–Hasting algorithm is used for model parameters estimation and target variable inference. The performance of the proposed framework is evaluated by extensive numerical studies and a case study of nano-sensor calibration.
Integrating research tools with library websites

Purpose The present investigation aims to measure the extent of the adoption of research tools among libraries of higher education (HE) institutes across the world based on the types of research tools and their diverse purposes of use. This study also intends to identify the current practices followed by the HE libraries to implement research tools. Design/methodology/approach First, a stratified sampling method was used to select 130 HE libraries from four continents – Asia, Oceania, Europe and North America. Second, a two-step Web content analysis was followed to collect data from the selected libraries along the parameters chosen for the survey. Findings This study gives an overview of the recent advances in the use of research tools by the libraries with numerous examples. Highlighting the differential rate of adoption of research tools across the regions, this study illustrates the degree of acceptance of research tools among the libraries. Research limitations/implications This study limits itself to a handful of libraries with English websites in four regions only to avoid the language limitation of the researcher. Inaccessible websites of libraries of HE institutes were also excluded from the survey. Originality/value Future researchers may use the evaluation instruments as basic tools to develop advanced research instruments to carry out Web content analysis in diverse spheres. This study guides librarians to develop an improved understanding of the requirements of an immersive online environment with enhanced accessibility to a multiplicity of research tools and facilities to provide improved research support throughout the entire research life cycle.