Reviewerly/exHarmony huggingface dataset:
Probabilistic characterisation of models of first-order theories

We study probabilistic characterisation of a random model of a finite set of first order axioms. Given a set of first order axioms T and a structure M which we only know is a model of T, we are interested in the probability that M would satisfy a sentence ψ. Answering this question for all sentences in the language will give a probability distribution over the set of sentences which can be regarded as the probabilistic characterisation of the model M. We investigate defining these probabilistic characterisations as the limit of probability functions imposed on the set of finite models of T. We show how a symmetry axiom can uniquely specify the probability function over finite models and will study the existence of the limit in terms of the quantifier complexity of T.
Cryptanalysis Results on Spook

Spook [BBB+19] is one of the 32 candidates that has made it to the second round of the NIST Lightweight Cryptography Standardization process, and is particularly interesting since it proposes differential side channel resistance. In this paper, we present practical distinguishers of the full 6-step version of the underlying permutations of Spook, namely Shadow-512 and Shadow-384, solving challenges proposed by the designers on the permutation. We also propose practical forgeries with 4-step Shadow for the S1P mode of operation in the nonce misuse scenario, which is allowed by the CIML2 security game considered by the authors. All the results presented in this paper have been implemented.
ASIC-Resistant Proof of Work Based on Power Analysis of Low-End Microcontrollers

Application-Specific Integrated Circuit (ASIC)-resistant Proof-of-Work (PoW) is widely adopted in modern cryptocurrency. The operation of ASIC-resistant PoW on ASIC is designed to be inefficient due to its special features. In this paper, we firstly introduce a novel ASIC-resistant PoW for low-end microcontrollers. We utilized the measured power trace during the cryptographic function on certain input values. Afterward, the post-processing routine was performed on the power trace to remove the noise. The refined power trace is always constant information depending on input values. By performing the hash function with the power trace, the final output was obtained. This framework only works on microcontrollers and the power trace depends on certain input values, which is not predictable and computed by ASIC.
An Improved Shapley Value Benefit Distribution Mechanism in Cooperative Game of Cyber Threat Intelligence Sharing

Cyber threat intelligence sharing enables organizations to gain stronger cyber security defense capabilities with less security investment. However, how to motivate organizations to share more threat intelligence information is a challenge. In this paper, cyber threat intelligence sharing is modeled as a cooperative game model, and a reward mechanism based on the shapley value is proposed. In this mechanism, we first introduced the concept of risk coefficient in cyber threat intelligence sharing, and improved the basic Shapley value benefit distribution mechanism to make the distribution of benefits fairer and more reasonable. Finally, the validity of the proposed model is verified by a case.
3D RECONSTRUCTION AND IMAGE PROCESSING OF ANTHROPOLOGICAL ARCHAEOLOGICAL FINDINGS

Abstract. A wide variety of methods are used in archaeological research today, including 3D imaging techniques (photogrammetry) which are involved at different stages starting from explorations preceding excavation to multiple studies. The archaeologically obtained material includes anthropological findings, among which odontological (related to human teeth) are of interest as they are composed of resistible tissues (hence are preserved well) and can serve for biological as well as historical interpretations. However, among the methods employed in odontological studies some are destructive and bring to unwelcome irreversible changes or even complete loss of the analysed samples. However, the existing and rapidly-developing techniques, especially, referring to 3D imaging and prototyping, suggest different approaches which can facilitate avoiding undesirable consequences of invasive methods of research. Thus they can provide for either preservation of findings through development and application of non-invasive study techniques, or, at least, preserve data referring the findings which have to be destroyed in order to receive valuable, in terms of research, information. It is shown on the example of the studied mandibular fragment from the Early Bronze archaeological site of Shengavit how multidisciplinary cooperation and the described workflow contribute to preservation of information regarding the finding and possible restoration of its original features. An effective communication between different professionals was provided due to implementing non-contact measurements techniques, obtaining and processing 3D images and 3D printing.
Continuous-variable measurement-device-independent quantum key distribution with source-intensity errors

Continuous-variable measurement-device-independent quantum key distribution (CV-MDI-QKD) is immune to all detector side channel attacks, however its security requires trusted sources. We investigate the effects of source intensity errors, the most crucial source error, on the security of CV-MDI-QKD protocol with Gaussian-modulated coherent states. We establish a general source intensity errors model and derive the secret key rate based on different assumptions on the abilities of legal parties Alice and Bob. To avoid the adverse effects of the sources' intensity errors on CV-MDI-QKD, we present different data-processing schemes. We also assess the security of the protocol against collective Gaussian attacks in the finite-size scenario with composable security. Taking source intensity errors into consideration, we achieve improved security compared to previous demonstrations. Our results will provide a useful reference for practical implementations of CV-MDI-QKD.
A Simplified Structure of the Simplest Interval Type-2 Fuzzy Two-Term Controller

Interval Type-2 (IT2) fuzzy controllers are quite efficient in handling disturbances and uncertainties. This is mainly due to the Footprint of Uncertainty (FoU) in type-2 fuzzy sets. However the computational complexity also increases with FoU. Therefore we propose a simplified structure of IT2 Takagi-Sugeno (TS) fuzzy two-term controller in this paper. A novel TS rule base is introduced which incorporates uncertainty in the input type-2 fuzzy sets and also in the rule consequent parameters. The rule base consists of two rules which are logically connected with ‘Algebraic Product (AP)’ AND operator and ‘Bounded Sum (BS)’ OR operator. AP and BS operators enable the simplification of the input plane which in turn reduces the complexity of the controller structure. Further, the performance of the controller structure is evaluated through simulation study on a nonlinear (coupled-tank) system.
Siamese Cascaded Region Proposal Networks With Channel-Interconnection-Spatial Attention for Visual Tracking

Trackers based on Siamese networks show great potential in tracking accuracy and speed. However, it is still challenging to adapt offline training model to online tracking. In this paper, a Siamese based tracker (SCRPN-CISA) is proposed, which integrates three attention mechanisms and a novel Cascaded Region Proposal Networks (RPN) architecture, for improving the feature extraction ability, adaptability and discrimination ability in complex scenes. Firstly, the deep network VGG-Net-D is adopted as the backbone network in the Siamese framework to increase the feature extraction capability. Then, a Channel-Interconnection-Spatial Attention module is constructed to enhance the adaptive and discriminative capability of the model. Next, a Deconvolution Adjust Block is built to fusion cross-layer features. Finally, a Three-Layer Cascaded RPN is conceived to acquire the foreground-background classification and bounding box regression by correlation calculation, and moreover, a proposal region screening strategy is presented to obtain more accurate tracking results. Experiments on OTB-2015, UAV123, VOT2016, and VOT2019 benchmarks demonstrate that, the proposed tracker (SCRPN-CISA) achieves competitive performance compared with the state-of-the-art trackers.
Using Natural Language Processing to Identify Similar Patent Documents

The search for prior art documents is an important, but time-consuming task for the patent attorney. Today, these searches are carried out using keywords, which is problematic since inventions often are described using abstract and general terms in the patent applications. In addition, synonyms must be taken into account and formulated manually. This means a risk of relevant documents being overlooked. In this Master’s thesis, we investigated the use of natural language processing (NLP) on a huge database of patent applications. The aim was to create a tool that can find similar documents by comparing the title and abstract of a provided document with existing documents in the database, thus removing the need to manually extract keywords. We investigated several machine learning models that transform text into nu- merical representations, and applied them to the documents in the database. These models include a number of recent, pre-trained, word embeddings and sentence embeddings. We also developed a web application, which allows the user to perform a search using patent application number or a short text de- scribing an invention. Cosine similarity was used to compare the numerical representations of documents. We also investigated the use of clustering as a way to limit the search domain and speed up the process. Patent associates helped us to evaluate the different models on a set of test queries. Among the models, Sentence-BERT (SBERT) outperformed the others, reaching a mean average precision (MAP) of 0.7655 at finding relevant or very relevant documents. (Less)
New Normal Acceleration Strategy for Bali Tourism Destination Recovery with E-Tourism and Special Health Protocol for the Tourism Sector

Virtual tourism is accepted make an advantage for bosses small scale of travel industry through the offer of access administrations, however, all in large scale virtual tourism will affect essentially on the travel industry goals in the truth of the experience since they can't be provided by the supplier. Virtual tourism just give looks and impressions, yet unfit to give a taste, smell, feeling, fulfillment, dedication, and dependability. Arranging a traveler visit may utilize different proposals offered by different suppliers. For booking travel tickets inside the nation, traveloka will be their decision with different advantageous installment choices gave. For booking global travel tickets they may pick TripAdvisor. For the choice of goals to be visited, potential sightseers will no doubt utilize the suggestions offered by Indonesia Virtual Tour, and Tripadvisor may be for determination of facilities, imminent travelers get more suggestions, however, who can show evaluations that are probably going to be trailed by expected voyagers. Regardless of how straight forward virtual tourism is, they have a significant job as an extension between the travel industry makers and likely voyagers. E-tourism created in Indonesia right now has not addressed the most significant part of giving data and assurance to sightseers when they decide to visit vacationer goals. The vast majority of the suppliers that fabricate virtually the travel industry frameworks have the point of building up a business association or business affiliations with entrepreneurs, for example, convenience administrations, tagging administrations, eatery administrations, and different administrations focused on business. Another technique arranged to quicken the recuperation of Bali's travel industry is to make and set explicit wellbeing gauges. Explicit Health Standards arranged for the travel industry as expressed in Circular Letter Number 3355 of 2020 concerning the new ordinary Protocol for the Bali Tourism Sector.
A New Adaptive Spatial Filtering Method in the Wavelet Domain for Medical Images

Although there are many methods in the literature to eliminate noise from images, finding new methods remains a challenge in the field and, despite the complexity of existing methods, many of the methods do not reach a sufficient level of applicability, most often due to the relatively high calculation time. In addition, most existing methods perform well when the processed image is adapted to the algorithm, but otherwise fail or results in significant artifacts. The context of eliminating noise from images is similar to that of improving images and for this reason some notions necessary to understand the proposed method will be repeated. An adaptive spatial filter in the wavelet domain is proposed by soft truncation of the wavelet coefficients with threshold value adapted to the local statistics of the image and correction based on the hierarchical correlation map. The filter exploits, in a new way, both the inter-band and the bandwidth dependence of the wavelet coefficients, considering the minimization of computational resources.
Joint Demosaicing and Denoising Based on Interchannel Nonlocal Mean Weighted Moving Least Squares Method

Nowadays, the sizes of pixel sensors in digital cameras are decreasing as the resolution of the image sensor increases. Due to the decreased size, the pixel sensors receive less light energy, which makes it more sensitive to thermal noise. Even a small amount of noise in the color filter array (CFA) can have a significant effect on the reconstruction of the color image, as two-thirds of the missing data would have to be reconstructed from noisy data; because of this, direct denoising would need to be performed on the raw CFA to obtain a high-resolution color image. In this paper, we propose an interchannel nonlocal weighted moving least square method for the noise removal of the raw CFA. The proposed method is our first attempt of applying a two dimensional (2-D) polynomial approximation to denoising the CFA. Previous works make use of 2-D linear or directional 1-D polynomial approximations. The reason that 2-D polynomial approximation methods have not been applied to this problem is the difficulty of the weight control in the 2-D polynomial approximation method, as a small amount of noise can have a large effect on the approximated 2-D shape. This makes CFA denoising more important, as the approximated 2-D shape has to be reconstructed from only one-third of the original data. To address this problem, we propose a method that reconstructs the approximated 2-D shapes corresponding to the RGB color channels based on the measure of the similarities of the patches directly on the CFA. By doing so, the interchannel information is incorporated into the denoising scheme, which results in a well-controlled and higher order of polynomial approximation of the color channels. Compared to other nonlocal-mean-based denoising methods, the proposed method uses an extra reproducing constraint, which guarantees a certain degree of the approximation order; therefore, the proposed method can reduce the number of false reconstruction artifacts that often occur in nonlocal-mean-based denoising methods. Experimental results demonstrate the performance of the proposed algorithm.
RAPID VISUAL PRESENTATION TO SUPPORT GEOSPATIAL BIG DATA PROCESSING

Abstract. Given the limited number of human GIS/image analysts at any organization, use of their time and organizational resources is important, especially in light of Big Data application scenarios when organizations may be overwhelmed with vast amounts of geospatial data. The current manuscript is devoted to the description of experimental research outlining the concept of Human-Computer Symbiosis where computers perform tasks, such as classification on a large image dataset, and, in sequence, humans perform analysis with Brain-Computer Interfaces (BCIs) to classify those images that machine learning had difficulty with. The addition of the BCI analysis is to utilize the brain’s ability to better answer questions like: “Is the object in this image the object being sought?” In order to determine feasibility of such a system, a supervised multi-layer convolutional neural network (CNN) was trained to detect the difference between ‘ships’ and ‘no ships’ from satellite imagery data. A prediction layer was then added to the trained model to output the probability that a given image was within each of those two classifications. If the probabilities were within one standard deviation of the mean of a gaussian distribution centered at 0.5, they would be stored in a separate dataset for Rapid Serial Visual Presentations (RSVP), implemented with PsyhoPy, to a human analyst using a low cost EMOTIV “Insight” EEG BCI headset. During the RSVP phase, hundreds of images per minute can be sequentially demonstrated. At such a pace, human analysts are not capable of making any conscious decisions about what is in each image; however, the subliminal “aha-moment” still can be detected by the headset. The discovery of these moments are parsed out by exposition of Event Related Potentials (ERPs), specifically the P300 ERPs. If a P300 ERP is generated for detection of a ship, then the relevant image would be moved to its rightful designation dataset; otherwise, if the image classification is still unclear, it is set aside for another RSVP iteration where the time afforded to the analyst for observation of each image is increased each time. If classification is still uncertain after a respectable amount of RSVP iterations, the images in question would be located within the grid matrix of its larger image scene. The adjacent images to those of interest on the grid would then be added to the presentation to give an analyst more contextual information via the expanded field of view. If classification is still uncertain, one final expansion of the field of view is afforded. Lastly, if somehow the classification of the image is indeterminable, the image is stored in an archive dataset.
Point at the Triple: Generation of Text Summaries from Knowledge Base Triples

We investigate the problem of generating natural language summaries from knowledge base triples. Our approach is based on a pointer-generator network, which, in addition to generating regular words from a fixed target vocabulary, is able to verbalise triples in several ways. We undertake an automatic and a human evaluation on single and open-domain summaries generation tasks. Both show that our approach significantly outperforms other data-driven baselines.
MHOLline 2.0: Workflow for automatic large-scale modeling and analysis of proteins

Genomes and their proteins can be analyzed by different perspectives, with different goals and applicability of results in several areas of research. In bioinformatics and computational biology, it is common the use of multiple combinations of programs and databases to extract information from the raw data. Choosing the proper program, setting its parameters, filtering the input files, extracting information from output files, and creating scripts for automating tasks can be challenging. This work describes the MHOLline, an online scientific workflow that provides a set of modules that enables a broad large-scale analysis of proteins (e.g. whole genomes) in a few hours. The version 1.0 (released in 2010), was wholly reformulated, and the new version 2.0 is available at www.mholline2.lncc.br. This version presents new features, modules, interface, code optimization, runtime reduction, more security, a new results visualization interface, and an automatic and user-friendly refinement page
GRID: GRID Resample by Information Distribution

This paper exploits a concise yet efficient initialization strategy to optimize grid sampling-based superpixel segmentation algorithms. Rather than straight distributing all initial seeds evenly, it adopts a context-aware approach to modify their positions and total number via a coarse-to-fine manner. Firstly, half the expected number of seeds are regularly sampled on the image grid, thereby creating a rough distribution of color information for all rectangular cells. A series of fission is then performed on cells that contain excessive color information recursively. In each cell, the local color uniformity is balanced by a dichotomy on one original seed, which generates two new seeds and settles them to spatially symmetrical sub-regions. Therefore, the local concentration of seeds is adaptive to the complexity of regional information. In addition, by calculating the amount of color via a summed area table (SAT), the informative regions can be located at a very low time cost. As a result, superpixels are produced from ideal original seeds with an exact number and exhibit better boundary adherence. Experiments demonstrate that the proposed strategy effectively promotes the performance of simple linear iterative clustering (SLIC) and its variants in terms of several quality measures.
Avoiding Negative Side Effects Due to Incomplete Knowledge of AI Systems

Autonomous agents acting in the real-world often operate based on models that ignore certain aspects of the environment. The incompleteness of any given model – handcrafted or machine acquired – is inevitable due to practical limitations of any modeling technique for complex real-world settings. Due to the limited fidelity of its model, an agent’s actions may have unexpected, undesirable consequences during execution. Learning to recognize and avoid such negative side effects (NSEs) of an agent’s actions is critical to improve the safety and reliability of autonomous systems. Mitigating NSEs is an emerging research topic that is attracting increased attention due to the rapid growth in the deployment of AI systems and their broad societal impacts. This article provides a comprehensive overview of different forms of NSEs and the recent research efforts to address them. We identify key characteristics of NSEs, highlight the challenges in avoiding NSEs, and discuss recently developed approaches, contrasting their benefits and limitations. The article concludes with a discussion of open questions and suggestions for future research directions.
Stochastic Estimations of the Total Number of Classes for a Clustering having Extremely Large Samples to be Included in the Clustering Engine

We considered the problem how to handle the exploding number of possibilities to be sorted into irreducible classes by using a clustering tool when its input capacity cannot accommodate the total number of the possibility. Concrete situations are explained taking examples of atomic substitutions in the supercell modeling of alloys. The number of the possibility sometimes amounts to $\sim$ trillion being too large to be accommodate. It is hence not practically feasible to identify how many irreducible classes exist by straightforward manner even though there are several tools available to perform the clustering. We have developed a stochastic framework to avoid the shortage of capacity, providing a method to estimate the total number of irreducible classes (the order of the classes) as a statistical estimate. A prominent conclusion derived here is that the statistical variation of the number of classes at each sampling trial is working as a promising measure to estimate the order.