Reviewerly/exHarmony huggingface dataset:
Cognate Identification to Augment Lexical Resources for NLP

Cognates are words across different languages that are known to have a common ancestral origin. For example, the English word night and the German Nacht, both meaning night are cognates with a common ancestral (Proto-Germanic) origin. Cognates are not always revealingly similar and can change substantially over time such that they do not share form similarity. Automatic cognate identification determines whether a given word pair is cognate or not. A cognate pair may have diverged at the surface level over time, but it shares a common ancestor and is likely to have similar meanings. This is especially true in languages that are typologically closer to each other. Our system uses a character-level model with recurrent neural network architecture and attention. We test its performance on datasets drawn from three different language families. Our results show an improvement in performance as compared to existing models and highlight the usefulness of phonetic and conceptual features. Our model finds similar word pairs with high accuracy from a pair of closely related languages (Hindi and Marathi). One of the applications of our work is to project linguistic annotations from a high-resource language to a (typologically-related) low-resource language. This projection can be used to bootstrap lexical resource creation, e.g., predicate frame information, word sense annotations, etc. The bootstrapping of lexical resources is particularly relevant for languages in South Asia, which are diverse but share areal and typological properties. Apart from this application, cognate identification helps improve the performance of tasks like sentence alignment for machine translation.
Quadratic Kernel Learning for Interpolation Kernel Machine Based Graph Classification

Interpolating classifiers interpolate all the training data and thus have zero training error. Recent research shows their fundamental importance for high-performance ensemble techniques. Interpolation kernel machines belong to the class of interpolating classifiers and do generalize well. They have been demonstrated to be a good alternative to support vector machine for graph classification. In this work we further improve their performance by considering multiple kernel learning. We establish a general scheme for achieving this goal. The current experimental work is done using quadratic kernel combination. Our experimental results demonstrate the performance boosting potential of our approach against the use of individual graph kernels.
Behavioral Game Theory in Cyber Security: The Influence of Interdependent Information's Availability on Cyber-Strike and Patching Processes

Cyber-strikes are on the rise, and the availability of information on the actions of hackers and analysts has the potential to shape decision-making in these situations. However, previous literature has not adequately explored this relationship. This research investigates the impact of the availability of information about the opponent's actions on decision-making in cyber-strike situations. 100 participants, consisting of 50 pairs of hackers and analysts, were randomly assigned to two conditions (AV and Non-AV) in a repeated Markovian game. In AV, both players had complete information on the opponent's actions and payoffs, while in Non-AV, the information was missing. In the Markovian game, the hacker repeatedly chose between strike or not-strike actions, and the analyst repeatedly chose between patch or not-patch actions. In both AV and Non-AV conditions, after the analyst’s patch actions, the network was randomly in a non-susceptible (ns) state or a susceptible (s) state with each state occurring 50% of the time. The strike proportions were similar in ns and s states and similar in AV and Non-AV conditions. However, the patch proportions were lesser in the Non-AV condition than the AV condition and greater in the ns state than the s state.
Assessment of Various Deep Reinforcement Learning Techniques in Complex Virtual Search-and-Retrieve Environments Compared to Human Performance

Recently, the area of deep reinforcement learning (DRL) has seen remarkable advances in fields like medicine, robotics, and automation. Nonetheless, there remains a dearth of understanding about how cutting-edge DRL algorithms, such as Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC), match up against human proficiency in demanding search-and-retrieve operations. Furthermore, there is a scarcity of structured assessments of the efficacy of these algorithms in intricate and dynamic surroundings after hyperparameter adjustment. To tackle this discrepancy, the research at hand aims to evaluate and contrast the impact of the proportion of targets to distractions on human and machine agents’ performance in a complex search simulation using a professional gaming engine. Moreover, the influence of the amount of neurons and layers in DRL algorithms will be scrutinized in connection to the search-and-retrieve missions. The task requires an agent (whether human or model) to traverse an environment and gather target objects while sidestepping distractor objects. The results of the study exhibit that humans accomplished better in training scenarios, whereas model agents performed better in test scenarios. Moreover, SAC emerged as a superior performer compared to PPO across all test conditions. Furthermore, boosting the amount of units and layers was found to enhance the performance of DRL algorithms. These conclusions imply that similar hyperparameter configurations can be utilized when contrasting models are generated using DRL algorithms. The study also delves into the implications of utilizing AI models to direct human decisions.
Model-Based Operator Assistance: How to Match Engineering Models with Humans’ Cognitive Representations of Their Actions?

The operation of process plants can be supported by assistance systems that rely on engineering models to answer various questions. However, suitable answers can only be computed when the questions posed by the operators using the assistance are compatible with those considered by the engineers developing the models. Such compatibility cannot be taken for granted, because operator cognition and action rarely play a role during plant engineering. How can we determine whether a model is useful for answering particular operator questions? A promising strategy is to apply compatible frameworks for describing the domain concepts captured by the models and the cognitive action representations of operators. To this end, we introduce the concept of abstraction hierarchies that are used both by engineers to model the technical system and by humans to identify their actions. Thus, they provide an opportunity for matching the two. We argue that such matching cannot be performed algorithmically. If humans are to be responsible for the matching, this presupposes that models are equipped with understandable descriptions of their capabilities. Accordingly, we argue that model descriptions are a cornerstone of successful model-based operator assistance. We discuss the challenges of this approach and identify directions for future work.
Visually Enhanced Parallel Coordinates Plot With Two-Dimensional Kernel Density Scatter Plots

Parallel coordinates plots are popular tools for high-dimensional data visualization. To alleviate the difficulties caused by the inherent defects that arise when the dimensions are increased, this study attached two-dimensional kernel density scatter plots to parallel coordinates plot, which integrates the Cartesian and parallel coordinate systems, to combine their benefits and conveniently explore the relationships between paired attributes. The collaborative design combining the two visual types was realized through the corelative axis swap interaction method, which allows users to freely exchange parallel coordinate axes while also updating the associated kernel density scatter plots based on the Cartesian coordinate system. In this study, we obtained six cases of real datasets and performed a field trial study to assess the usefulness of our method in assisting users in evaluating correlations between paired attributes. The results show that our method is more useful for analyzing the relationships between paired attributes.
Lightweight Object Detection Algorithm Based on Grid Site Work Dress Code Detection Method

To realize the real-time and accurate detection of the dress code of the field operators of the power grid site, this paper uses the mobile deployment ball equipped with a lightweight object detection algorithm. Since the commonly used object detection algorithms are difficult to deploy on mobile devices, a lightweight object detection algorithm is proposed, which is consist of a lightweight convolutional neural network and an improved bi-direction feature pyramid network. Finally, a dataset of grid dress code detection is built, and the improved algorithm is experimentally validated on a mobile device equipped with artificial intelligence chips. The detection results show that the lightweight object detection algorithm used in this paper improves the real-time video detection speed by 22.00% to 15.50 FPS.
Velocity- and Error-Aware Switching of Motion Prediction Models for Cloud Virtual Reality

Offloading virtual reality (VR) computations to a cloud computing entity can enable support for VR services on low-end user devices but may result in increased latency, which will lead to mismatch between the user’s viewport and the received VR image, thus inducing motion sickness. Predicting future motion and rendering future images accordingly is a promising solution to the latency problem. In this paper, we develop velocity- and error-aware model switching schemes applicable to a wide range of existing motion prediction models. First, we consider the chattering problem of machine learning (ML)-based prediction models and the relationship between the velocity and the prediction error gap between an ML model and the case of no prediction (NOP). Accordingly, we propose a velocity-aware switching (VAS) scheme that combines the outputs from the ML model and the NOP case via a weight determined by the head motion velocity. Next, we develop an ensemble method combining a set of outputs from VAS and other models, called error-aware switching (EAS). EAS switches between model outputs based on the error statistics of those outputs under the parallel execution of multiple models, including VAS models. For EAS, schemes for both hard switching and soft integration of the model outputs are proposed. We evaluate the proposed schemes based on real VR motion traces for diverse ML-based prediction models.
Global-Local and Occlusion Awareness Network for Object Tracking in UAVs

Multi-object tracking in unmanned aerial vehicle (UAV) scenes is a crucial task with numerous applications across various domains.The goal of this task is to track multiple objects such as people and vehicles over time as they move through the scene, often captured by cameras mounted on UAV.However, tracking objects in UAV scenes can be challenging due to several factors, including the scale variance of objects as they move through the scene and the frequent occlusions caused by complex scenes.To address these issues, we propose a Global-Local and Occlusion Awareness (GLOA) tracking network for unmanned aerial vehicles.It comprises two main components: global-local awareness detector (GLA-D) and the occlusion awareness data association (OADA).The GLA-D uses our specially designed global-local awareness block to extract scale variance feature information from the input frames.It then outputs more discriminative identity information by adding identity embedding branches to the prediction head.The GLA-D is designed to better handle scale variance issues and improve object tracking accuracy.The OADA method used different metrics for high and low scoring detection frames was to alleviate occlusion problems in tracking scenarios.By combining these two components, the GLOA provides a more robust and effective solution for multi-object tracking in UAV scenes.Experiments on two public datasets may indicate the effectiveness of the proposed method.
Predicting Quality of Services Based on a Two-Stream Deep Learning Model With User and Service Graphs

Accurately predicting Quality of Service (QoS) is one of the main challenges in the area of service recommendation, and it hasthusattractedmuchattention in recent years. This field existsmanymethods, most of which are inspired bycollaborative filtering in service recommendation. They predict the missing QoS values of services by collecting the historical information of similar users/services, but their prediction accuracy needs further improvements. This work proposes user and service graphs are proposed for the first time in the field of QoS prediction by exploiting deep relationships among users and services. Based on the graphs, user/service feature vector sets are found via similar users/services. A two-stream deep learning-based prediction model is proposed for service QoS prediction. It has a deep convolutional neural network with two efficient deep convolutional units to deal with user/service feature vectors parallelly. Experiments are carried out to show that the proposed method can achieve better QoS prediction accuracy of services than the existing approaches such as non-negative matrix factorization, probabilistic matrix factorization, covering-based neighbor-hood-aware matrix factorization, neighbor integration deep matrix factorization, and several traditional methods.
TENET: Sublogarithmic Proof and Sublinear Verifier Inner Product Argument without a Trusted Setup

We propose a new inner product argument (IPA), called TENET, which features sublogarithmic proof size and sublinear verifier without a trusted setup. IPA is a core primitive for various advanced proof systems including range proofs, circuit satisfiability, and polynomial commitment, particularly where a trusted setup is hard to apply. At ASIACRYPT 2022, Kim, Lee, and Seo showed that pairings can be utilized to exceed the complexity barrier of the previous discrete logarithm-based IPA without a trusted setup. More precisely, they proposed two pairing-based IPAs, one with sublogarithmic proof size and the other one with sublinear verifier cost, but they left achieving both complexities simultaneously as an open problem. We investigate the obstacles for this open problem and then provide our solution TENET, which achieves both sublogarithmic proof size and sublinear verifier. We prove the soundness of TENET under the discrete logarithm assumption and double pairing assumption.
Improved Boomerang Attacks on Deoxys-BC

In this paper, we present two techniques to improve the previous attack against the tweakable block cipher Deoxys-BC. First, we apply the idea of “key bridging” to Deoxys-BC and get a better attack on the 9-round Deoxys-BC-256 whose time complexity is decreased from $$2^{168}$$ to $$2^{147}$$ and memory complexity is decreased from $$2^{129}$$ to $$2^{100}$$ . Second, we adjust the distinguisher to utilize the additional sieve to filter the data better. Then we apply this method to the 14-round attack on Deoxys-BC-384 and reduce the time complexity from $$2^{278.8}$$ to $$2^{260.4}$$ and the memory complexity from $$2^{129}$$ to $$2^{125.4}$$ .
Extractable Witness Encryption for the Homogeneous Linear Equations Problem

Witness encryption is a cryptographic primitive which encrypts a message under an instance of an NP language and decrypts the ciphertext using a witness associated with that instance. In the current state of the art, most of the witness encryption constructions are based on multilinear maps. Following the construction of Choi and Vaudenay based on RSA-related problems, we suggest a novel witness key encapsulation mechanism based on the hardness of solving homogeneous linear Diophantine equations (HLE problem). Our arithmetic-based construction aims to solve an issue raised by these authors where the security might be compromised if the adversary is able to find small solutions to a homogeneous linear Diophantine equation, while avoiding the inefficiency of multilinear maps. The security of our scheme is based on a hidden group order and a knowledge assumption.
Improved Hybrid Attack via Error-Splitting Method for Finding Quinary Short Lattice Vectors

Plenty of lattice-based cryptosystems use ternary or quinary sparse short vectors to accelerate the computing procedure. The hybrid attack, proposed by Howgrave-Graham, utilizes the sparsity of the short non-zero vector. Some improved hybrid attack presents a state-of-the-art performance in solving SVPs with ternary short vectors. However, the efficiency of the hybrid attack decreases when the short vector becomes quinary. Although there are several works for the ternary case, few of them focus on the situation that the infinite norm is larger than 1. In this paper, we propose a new attack for lattice-based schemes using quinary short vectors, called the ESHybrid attack. Our attack is based on the structure of the hybrid attack, with the Meet-LWE algorithm [25] that replaces the traditional meet-in-the-middle structure. To solve the open question that Meet-LWE can not be applied to the hybrid attack, we propose the Error-Splitting (ES) technique. This technique utilizes the structure of quinary vectors and also makes the Meet-LWE procedure faster. We give a complete analysis of the complexity of this algorithm. The evaluation from practical parameter sets ensures that our ESHybrid attack is faster than the ordinary lattice reduction approach and the existing hybrid attack.
Efficient Card-Based Millionaires’ Protocols via Non-binary Input Encoding

Comparison of integers, a traditional topic in secure multiparty computation since Yao’s pioneering work on “Millionaires’ Problem” (FOCS 1982), is also well studied in card-based cryptography. For the problem, Miyahara et al. (Theoretical Computer Science, 2020) proposed a protocol using binary cards (i.e., cards with two kinds of symbols) that is highly efficient in terms of numbers of cards and shuffles, and its extension to number cards (i.e., cards with distinct symbols). In this paper, with a different design strategy which we name “Tug-of-War Technique”, we propose new protocols based on binary cards and on number cards. For binary cards, our protocol improves the previous protocol asymptotically (in bit lengths of input integers) in terms of numbers of cards and shuffles when adopting ternary encoding of input integers. For number cards, at the cost of increasing the number of cards, our protocol improves the number of shuffles of the previous protocol even with binary encoding, and more with q-ary encoding where $$q > 2$$ .
PMAC$$r$$x: A Vector-Input MAC for High-Dimensional Vectors with BBB Security

At Eurocrypt 2006, Rogaway and Shrimpton presented the idea of vector-input MAC that accepts a vector consisting of variable-length bitstrings. They proposed S2V as a concrete instantiation of the vector-input MAC and S2V is more efficient than the classical method, encoding a vector into a single bitstring and then applying a conventional MAC such as CMAC. However, S2V severely limits the maximum number of elements in a vector. Moreover, the security is up to the birthday bound with respect to the block length of the underlying block cipher (i.e., n/2-bit security for n-bit block). To overcome these drawbacks, we use tweakable block ciphers (TBCs) and present a new vector-input MAC, called PMAC $$r$$ x, taking PMAC2x by List and Nandi (CT-RSA 2017) as the baseline scheme. Our proposal allows a significantly larger number of elements than S2V and enjoys the beyond-the-birthday-bound (BBB) security. PMAC $$r$$ x is more efficient than the encode-then-PMAC2x method with respect to the number of primitive calls, as in the case of S2V (where the comparison is made with CMAC).
DEANONYMIZATION OF TOR NETWORK USERS

Tor is a virtual network that implements onion routing and aims to provide the user withthe greatest possible anonymity on the Internet. Due to ensuring a high level ofanonymity, the Tor network attracts more and more criminals, mainly pedophiles,thieves and hackers, who use the opportunities associated with it for illegal activities.This paper presents the most popular attacks on the Tor network, aimed at establishingthe identity of its users. This paper will explore ways to de-anonymize by compromisingor having enough relays, and manipulating basic network communication to end up onthe Tor traffic forwarding path. The paper will also present traffic analysis techniquesthat allow an outside observer to infer which nodes are used to transmit traffic in thecircuit. Finally, the use of artificial intelligence to recognize what pages and services theTor user is visiting will be discussed. The work aims to present the vulnerability of theTor network, which should be considered an opportunity in the fight against cybercrime,and to encourage further research on the vulnerabilities of the Tor network, aimed atidentifying cybercriminals.
APPLICATION OF BLOCKCHAIN TECHNOLOGY IN SECURITY ANDDEFENSE

Blockchain technology is a relatively new phenomenon that we can associate withvirtual, decentralized money that is Bitcoin. However, it is wrong to identify thistechnology with virtual money, because Bitcoin is only an example of one of the firstuses of technology. Its properties and the guarantee of high security have found manysupporters of using this technology in everyday life to facilitate work in variousindustries of society.The paper presents a number of examples of its possible applications. Its potential usein the military sphere only reinforces our belief that it can significantly affect themaintenance of security among the public or adequately protect the sensitive resourcesof states.
Bayesian (cross stitch) sampler

&lt;i&gt; &lt;/i&gt; Downloads Jump to the downloads and get your own free pattern! For my latest pandemic art medium (on this, the three hundred and nineteenth day of sheltering in place), I decided to teach myself how to cross stitch. I did a ton of needlepoint as a kid and teen, but was always afraid of cross stitch because it was so much smaller and more delicate.
Digital health funding for COVID-19 vaccine deployment across four major donor agencies

The international response to the COVID-19 pandemic provided opportunities for countries to use digital technologies for vaccine deployment and associated activities, but misaligned digital investments could weaken or fragment national systems. In this review of 311 funding applications from 120 country governments to four donor agencies (UNICEF; Gavi, the Vaccine Alliance; the Global Fund to Fight AIDS, Tuberculosis and Malaria; and the World Bank) up to May 1, 2022, we found that 272 (87%) of the applications included at least one digital aspect and that substantial funding has been dedicated towards digital aspects from donors. The majority of digital aspects concerned immunisation information systems, vaccine acceptance and uptake, and COVID-19 surveillance. As the global community sets its sights on a COVID-19-free world, continued coordinated investments in digital health and health information systems for pandemic preparedness and response will be key to strengthening the resilience of health systems.