Reviewerly/exHarmony huggingface dataset:
Simpler Completeness Proofs for Modal Logics with Intersection

There has been a significant interest in modal logics with intersection, prominent examples including epistemic and doxastic logics with distributed knowledge, propositional dynamic logic with intersection, and description logics with concept intersection. Completeness proofs for such logics tend to be complicated, in particular on model classes such as S5 used, e.g., in standard epistemic logic, mainly due to the undefinability of intersection of modalities in standard modal logic. A standard proof method for the S5 case uses an “unraveling-folding” technique to achieve a treelike model to deal with the problem of undefinability. This method, however, is not easily adapted to other logics, due to its reliance on S5 in a number of steps. In this paper we demonstrate a simpler and more general proof technique by building a treelike canonical model directly, which avoids the complications in the processes of unraveling and folding. We illustrate the technique by showing completeness of the normal modal logics K, D, T, B, S4 and S5 extended with intersection modalities. Furthermore, these treelike canonical models are compatible with Fischer-Ladner-style closures, and we combine the methods to show the completeness of the mentioned logics further extended with transitive closure of union modalities known from PDL or epistemic logic. Some of these completeness results are new.
Blockchain-Based Incentive Announcement In Vanet Using CreditCoin

Ad hoc networks for cars (VANET) otherwise referred to as systems for wise transportation. In order to improve road well-being and maximize highway efficiency, VANET guarantees auspicious and exact match between vehicle to vehicle (V2V) and infrastructural vehicle (V2I). VANET is ineffective against malicious networks that can get into the structures and trigger real Medium Access Controls (MACs). It involves denial of service attacks, modification of knowledge, pantomime attacks, Sybil attacks, and replay attacks. Great security issues emerge with the proliferation of Blockchain. At the same time, a sample remains open for protection and discernibility. Steps were made to address the problems, thus revealing them to specific events. We are considering an echo statement in CreditCoin automobile declaration convention. This guarantees flexibility and security for use in the submission of comments. In CreditCoin, we design a stimulus portion centered on the Blockchain. When they gain or invest coins when driving powers, they track legendary focuses. In the meantime, CreditCoin does jam protection and keeps black. However, CreditCoin foreshadows a number of security threats and allows dependent defense in view of Blockchain, as trace manager approaches malignant hubs in an awful case.
Web 6.0: Journey From Web 1.0 To Web 6.0

The fast track toward the improvement of Web is instituted to be as an out and out wonder in the present society with fused utilization of current creative innovation and rethinking the method for arranging, imparting and working together with person which in wording lead us to blend of terrific victories and disappointments. The motivation behind this paper is to comprehend and conceptualize the development of Web from the scratch to the up and coming patterns in the field of Web Technology. The World Wide Web as the biggest data build has had much progress since its approach. This paper gives a foundation of the development of the web from web 1.0 to web 4.0. Web 1.0 as a snare of data associations, Web 2.0 as a trap of individuals associations, Web 3.0 as a trap of learning associations and web 4.0 as a trap of knowledge associations are portrayed as four ages of the web in the paper. Additionally, in this work, we inquire about the advancement of things to come of the (Web 5.0 and Web 6.0). Likewise, we present the present status and worries about the Web as a data source and correspondence channel.
Ground state of a cross-cavity quantum Rabi model

Abstract We study a cross-cavity quantum Rabi model with ultrastrong coupling, and its analytical ground state is demonstrated to be approximately obtained by using the rotating-transformation method. Our method requires making three unitary transformations to the original Hamiltonian and ignoring the three-party cross interaction. In near-resonance and ultrastrong-coupling regimes, the ground-state fidelity is higher than 99% for a ±20% detuning range. We analytically demonstrate that the entanglement in ground state first quadratically increases with the qubit-oscillator coupling strength, then gets close to the maximum value 1. The oscillator’s nonclassical property in the ground state is well explained by analytically solving the Wigner function.
A relational tsetlin machine with applications to natural language understanding

Abstract Tsetlin machines (TMs) are a pattern recognition approach that uses finite state machines for learning and propositional logic to represent patterns. In addition to being natively interpretable, they have provided competitive accuracy for various tasks. In this paper, we increase the computing power of TMs by proposing a first-order logic-based framework with Herbrand semantics. The resulting TM is relational and can take advantage of logical structures appearing in natural language, to learn rules that represent how actions and consequences are related in the real world. The outcome is a logic program of Horn clauses, bringing in a structured view of unstructured data. In closed-domain question-answering, the first-order representation produces 10 × more compact KBs, along with an increase in answering accuracy from 94.83 % to 99.48 % . The approach is further robust towards erroneous, missing, and superfluous information, distilling the aspects of a text that are important for real-world understanding
Multi‐camera 3D ball tracking framework for sports video

Accurate ball tracking in sports is vital for automatic sports analysis yet it is challenging mainly due to the small size and occlusions. This study proposes a novel multi-camera 3D ball tracking (MBT) framework for sports video. The proposed framework consists of four parts: 2D ball detection, 2D ball tracking, 3D position fusion, and 3D ball tracking. In 2D aspect, the multi-scale features are introduced to enhance the 2D ball detection, and the 2D ball tracking is also improved by exploring cross-view information to handle the occlusion and timely updating tracking model with detection results to alleviate the problem of tracking drift. For 3D ball, a novel 3D position fusion method is proposed to optimise the ball position and the 3D ball tracking approach with improved Kalman filter is finally applied to ensure a smooth 3D ball trajectory. Moreover, compared to the existing products in commercial, the proposed framework does not require any special equipment and is thus low cost. Extensive experiments for 2D and 3D ball on a public dataset demonstrate that the proposed framework is robust to ball tracking in sports video, even in the presence of environmental interferences, substantial occlusions, and even calibration errors.
Tradeoffs in Data Augmentation: An Empirical Study

Though data augmentation has become a standard component of deep neural network training, the underlying mechanism behind the effectiveness of these techniques remains poorly understood. In practice, augmentation policies are often chosen using heuristics of distribution shift or augmentation diversity. Inspired by these, we conduct an empirical study to quantify how data augmentation improves model generalization. We introduce two interpretable and easy-to-compute measures: Affinity and Diversity. We find that augmentation performance is predicted not by either of these alone but by jointly optimizing the two.
A Chatbot for Training Employees in Industry 4.0

Within what is called the Fourth Industrial Revolution, one of the problems that companies frequently are experiencing, in order to ensure themselves, their products and services over time, is the need for continuous employee training. If continuing training is a problem, e-learning could represent a natural solution. However, to be effective, e-learning should be both a company’s instrument, which allows monitoring and provides reliable results and an easily accessible tool to the end-user. It should provide an agile, simplified and meaningful path on an educational, cognitive and relational level. Chatbots are a tool that is used both in the e-learning sector and in the industry 4.0 paradigm. The chatbots could allow to build individual learning paths and monitoring the learning phase. This paper aims to propose a system capable of providing a constant, reliable and friendly help through a practical and helpful bot, which takes advantage of NLP techniques. In particular, the proposed chatbot acts as a reminder following the user during his company training, ready to provide, when needed, useful teaching material to complete the tailored educational path. A prototype has been developed and tested in the real scenario with promising results.
Scalable Transfer Learning with Expert Models

Transfer of pre-trained representations can improve sample efficiency and reduce computational requirements for new tasks. However, representations used for transfer are usually generic, and are not tailored to a particular distribution of downstream tasks. We explore the use of expert representations for transfer with a simple, yet effective, strategy. We train a diverse set of experts by exploiting existing label structures, and use cheap-to-compute performance proxies to select the relevant expert for each target task. This strategy scales the process of transferring to new tasks, since it does not revisit the pre-training data during transfer. Accordingly, it requires little extra compute per target task, and results in a speed-up of 2-3 orders of magnitude compared to competing approaches. Further, we provide an adapter-based architecture able to compress many experts into a single model. We evaluate our approach on two different data sources and demonstrate that it outperforms baselines on over 20 diverse vision tasks in both cases.
Towards Cross-Lingual Generalization of Translation Gender Bias

Cross-lingual generalization issues for less explored languages have been broadly tackled in recent NLP studies. In this study, we apply the philosophy on the problem of translation gender bias, which necessarily involves multilingualism and socio-cultural diversity. Beyond the conventional evaluation criteria for the social bias, we aim to put together various aspects of linguistic viewpoints into the measuring process, to create a template that makes evaluation less tilted to specific types of language pairs. With a manually constructed set of content words and template, we check both the accuracy of gender inference and the fluency of translation, for German, Korean, Portuguese, and Tagalog. Inference accuracy and disparate impact, namely the biasedness factors associated with each other, show that the failure of bias mitigation threatens the delicacy of translation. Furthermore, our analyses on each system and language indicate that the translation fluency and inference accuracy are not necessarily correlated. The results implicitly suggest that the amount of available language resources that boost up the performance might amplify the bias cross-linguistically.
Creating an augmented reality orientation using learning and design theories

This study investigates the impact of an augmented reality library orientation on first-year undergraduate students’ feelings of anxiety toward the library and confidence using the library. This paper also reviews the best practices and theoretical foundation used for designing an augmented reality library orientation. The specific goals of the orientation are to increase students’ comfort and confidence with the library and librarians as measured by a pre- and post-orientation survey. After taking the orientation, students reported an increased belief in the librarians’ desire to help them and an increased confidence in knowing how to use an academic library but reported no change in viewing the library as a welcoming place, feeling overwhelmed by the library, or levels of comfort asking for help.
Representation Learning Based on Autoencoder and Deep Adaptive Clustering for Image Clustering

Image clustering is a complex procedure, which is significantly affected by the choice of image representation. Most of the existing image clustering methods treat representation learning and clustering separately, which usually bring two problems. On the one hand, image representations are difficult to select and the learned representations are not suitable for clustering. On the other hand, they inevitably involve some clustering step, which may bring some error and hurt the clustering results. To tackle these problems, we present a new clustering method that efficiently builds an image representation and precisely discovers cluster assignments. For this purpose, the image clustering task is regarded as a binary pairwise classification problem with local structure preservation. Specifically, we propose here such an approach for image clustering based on a fully convolutional autoencoder and deep adaptive clustering (DAC). To extract the essential representation and maintain the local structure, a fully convolutional autoencoder is applied. To manipulate feature to clustering space and obtain a suitable image representation, the DAC algorithm participates in the training of autoencoder. Our method can learn an image representation that is suitable for clustering and discover the precise clustering label for each image. A series of real-world image clustering experiments verify the effectiveness of the proposed algorithm.
Wikidata Centric Vocabularies and URIs for Linking Data in Semantic Web Driven Digital Curation

Wikidata is evolving as the hub of Linked Open Data (LOD), with its language-neutral URIs and close adherence to Wikipedia. Well defined URIs help the data to be interoperable and linkable. This paper examines the possibilities of utilizing Wikidata as the means of a vocabulary resource for promoting the use of linkable concepts. Digital curation projects are vibrant with varying demands and purposes, which makes them less suitable for adopting any common vocabularies or ontologies. Also, developing and maintaining custom vocabularies are expensive processes for smaller projects in terms of resources and skill requirements. In general, Wikidata entities are well documented with Wikipedia entries, and Wikipedia entries express the conceptual and hierarchical relations in detail with provisions to modify or create. The authors explain the concept of using Wikidata as a vocabulary source with a proof of concept module implementation for Omeka-S, a widely adapted open source digital curation platform. This paper is expected to show some practical insights on reliable an reasonable vocabulary development for social informatics as well as cultural heritage projects, with a notion to improve the quality and quantity of linkable data from digital curation projects.
Mitigating the impact of adversarial attacks in very deep networks

Deep Neural Network (DNN) models have vulnerabilities related to security concerns, with attackers usually employing complex hacking techniques to expose their structures. Data poisoning-enabled perturbation attacks are complex adversarial ones that inject false data into models. They negatively impact the learning process, with no benefit to deeper networks, as they degrade a model’s accuracy and convergence rates. In this paper, we propose an attack-agnostic-based defense method for mitigating their influence. In it, a Defensive Feature Layer (DFL) is integrated with a well-known DNN architecture which assists in neutralizing the effects of illegitimate perturbation samples in the feature space. To boost the robustness and trustworthiness of this method for correctly classifying attacked input samples, we regularize the hidden space of a trained model with a discriminative loss function called Polarized Contrastive Loss (PCL). It improves discrimination among samples in different classes and maintains the resemblance of those in the same class. Also, we integrate a DFL and PCL in a compact model for defending against data poisoning attacks. This method is trained and tested using the CIFAR-10 and MNIST datasets with data poisoning-enabled perturbation attacks, with the experimental results revealing its excellent performance compared with those of recent peer techniques.
MicroNets: Neural Network Architectures for Deploying TinyML Applications on Commodity Microcontrollers

Executing machine learning workloads locally on resource constrained microcontrollers (MCUs) promises to drastically expand the application space of IoT. However, so-called TinyML presents severe technical challenges, as deep neural network inference demands a large compute and memory budget. To address this challenge, neural architecture search (NAS) promises to help design accurate ML models that meet the tight MCU memory, latency and energy constraints. A key component of NAS algorithms is their latency/energy model, i.e., the mapping from a given neural network architecture to its inference latency/energy on an MCU. In this paper, we observe an intriguing property of NAS search spaces for MCU model design: on average, model latency varies linearly with model operation (op) count under a uniform prior over models in the search space. Exploiting this insight, we employ differentiable NAS (DNAS) to search for models with low memory usage and low op count, where op count is treated as a viable proxy to latency. Experimental results validate our methodology, yielding our MicroNet models, which we deploy on MCUs using Tensorflow Lite Micro, a standard open-source NN inference runtime widely used in the TinyML community. MicroNets demonstrate state-of-the-art results for all three TinyMLperf industry-standard benchmark tasks: visual wake words, audio keyword spotting, and anomaly detection.
Augmenting Decision Competence in Healthcare Using AI-based Cognitive Models

In many critical decisions, such as medicine, transparency of the underlying decision process is critical. This extends to decision processes that are supported by artificial intelligence. Rather than using a post-hoc explainability approach from explainable AI research (SHAP or LIME), we develop and test an intrinsically transparent and intuitively interpretable model developed from cognitive science, fast-and-frugal trees, in a comparative analysis with state-of-the-art machine learning models. The resultant decision support can be easily implemented as laminated pocket card, augmenting the decision competence of physicians rather than replacing it.
Acti-DM1: Monitoring the Activity Level of People With Myotonic Dystrophy Type 1 Through Activity and Exercise Recognition

Myotonic dystrophy type 1 (DM1) is a rare disease where the highest prevalence is found in the small geographical region of Saguenay-Lac-St-Jean in Quebec, Canada. This disease impacts the quality of life and the ability of the affected people to pursue their normal day to day activities. To attenuate the impact of DM1, one could suggest that physiotherapists or other health professionals prescribe adapted physical exercise programs to carry out at home. It is, however, unpractical for the professionals to monitor every patient during their program and prohibitive in terms of cost. One alternative solution is to rely on the use of ambient technologies. To do so, in this research, our team developed an assistive system able to recognize simple mobility related activities (MA) and monitor each exercise performed during training sessions. The system can help the person by providing guidance and motivation throughout the training. The system was tested on 10 persons affected by the disease in their home for 10 weeks. The results obtained are encouraging and we discuss them in comparison with our previous work conducted in lab settings. Finally, to help further advances the research, the datasets are openly available online to the community.
On the Accuracy of Tor Bandwidth Estimation

The Tor network estimates its relays’ bandwidths using relay self-measurements of client traffic speeds. These estimates largely determine how existing traffic load is balanced across relays, and they are used to evaluate the network’s capacity to handle future traffic load increases. Thus, their accuracy is important to optimize Tor’s performance and strategize for growth. However, their accuracy has never been measured. We investigate the accuracy of Tor’s capacity estimation with an analysis of public network data and an active experiment run over the entire live network. Our results suggest that the bandwidth estimates underestimate the total network capacity by at least 50% and that the errors are larger for high-bandwidth and low-uptime relays. Our work suggests that improving Tor’s bandwidth measurement system could improve the network’s performance and better inform plans to handle future growth.
Structure Preserving Non-Photorealistic Rendering Framework for Image Abstraction and Stylization of Low-Illuminated and Underexposed Images

The proposed abstraction framework manipulates the visual-features from low-illuminated and underexposed images while retaining the prominent structural, medium scale details, tonal information, and suppresses the superfluous details like noise, complexity, and irregular gradient. The significant image features are refined at every stage of the work by comprehensively integrating a series of AnshuTMO and NPR filters through rigorous experiments. The work effectively preserves the structural features in the foreground of an image and diminishes the background content of an image. Effectiveness of the work has been validated by conducting experiments on the standard datasets such as Mould, Wang, and many other interesting datasets and the obtained results are compared with similar contemporary work cited in the literature. In addition, user visual feedback and the quality assessment techniques were used to evaluate the work. Image abstraction and stylization applications, constraints, challenges, and future work in the fields of NPR domain are also envisaged in this paper.
Reference-frame-independent quantum key distribution of wavelength division multiplexing with multiple quantum channels*

Reference-frame-independent quantum key distribution (RFI-QKD) can allow a quantum key distribution system to obtain the ideal key rate and transmission distance without reference system calibration, which has attracted much attention. Here, we propose an RFI-QKD protocol based on wavelength division multiplexing (WDM) considering finite-key analysis and crosstalk. The finite-key bound for RFI-QKD with decoy states is derived under the crosstalk of WDM. The resulting secret key rate of RFI-QKD, which is more rigorous, is obtained. Simulation results reveal that the secret key rate of RFI-QKD based on WDM is affected by the multiplexing channel number, as well as crosstalk between adjacent channels.