Reviewerly/exHarmony huggingface dataset:
Splitting Epistemic Logic Programs

Epistemic logic programs constitute an extension of the stable models semantics to deal with new constructs called subjective literals. Informally speaking, a subjective literal allows checking whether some regular literal is true in all stable models or in some stable model. As it can be imagined, the associated semantics has proved to be non-trivial, as the truth of the subjective literal may interfere with the set of stable models it is supposed to query. As a consequence, no clear agreement has been reached and different semantic proposals have been made in the literature. Unfortunately, comparison among these proposals has been limited to a study of their effect on individual examples, rather than identifying general properties to be checked. In this paper, we propose an extension of the well-known splitting property for logic programs to the epistemic case. To this aim, we formally define when an arbitrary semantics satisfies the epistemic splitting property and examine some of the consequences that can be derived from that, including its relation to conformant planning and to epistemic constraints. Interestingly, we prove (through counterexamples) that most of the existing proposals fail to fulfill the epistemic splitting property, except the original semantics proposed by Gelfond in 1991.
Robust topological policy iteration for infinite horizon bounded Markov Decision Processes

Markov Decision Processes (mdps) are commonly used to solve sequential decision problems. A less restrictive model is the Bounded-parameter mdp (bmdp) that allows: (i) the transition function to be expressed in terms of probability intervals and (ii) reasoning about a robust solution, i.e., the best solution under the worst model. In this paper, we propose the Robust Topological Policy Iteration (rtpi) algorithm which is a new policy iteration algorithm for infinite horizon bmdps based on a partition of the state space. The empirical results show that the more structured the domain, the better is the performance of rtpi.
A minimal ring extension of a large finite local prime ring is probably ramified

Given any minimal ring extension [Formula: see text] of finite fields, several families of examples are constructed of a finite local (commutative unital) ring [Formula: see text] which is not a field, with a (necessarily finite) inert (minimal ring) extension [Formula: see text] (so that [Formula: see text] is a separable [Formula: see text]-algebra), such that [Formula: see text] is not a Galois extension and the residue field of [Formula: see text] (respectively, [Formula: see text]) is [Formula: see text] (respectively, [Formula: see text]). These results refute an assertion of G. Ganske and McDonald stating that if [Formula: see text] are finite local rings such that [Formula: see text] is a separable [Formula: see text]-algebra, then [Formula: see text] is a Galois ring extension. We identify the homological error in the published proof of that assertion. Let [Formula: see text] be a finite special principal ideal ring (SPIR), but not a field, such that [Formula: see text] has index of nilpotency [Formula: see text] ([Formula: see text]). Impose the uniform distribution on the (finite) set of ([Formula: see text]-algebra) isomorphism classes of the minimal ring extensions of [Formula: see text]. If [Formula: see text] (for instance, if [Formula: see text]), the probability that a random isomorphism class consists of ramified extensions of [Formula: see text] is at least [Formula: see text]; if [Formula: see text] (for instance, if [Formula: see text] for some odd prime [Formula: see text]), the corresponding probability is at least [Formula: see text]. Additional applications, examples and historical remarks are given.
Encoding sparse and competitive structures among tasks in multi-task learning

Multi-task learning (MTL) aims to enhance generalization performance by exploring the inherent structures across tasks. Most existing MTL methods are based on the assumption that the tasks are positively correlated, and utilize the shared structures among tasks to improve learning performance. By contrast, there also exist competitive structure (negative relationships) among tasks in some real-world applications, and conventional MTL methods which explore shared structures across tasks may lead to unsatisfactory performance in this setting. Another challenge, especially in a high dimensional setting, is to exclude irrelevant features (sparse structure) from the final model. For this purpose, this work propose a new method, which is referred to as Sparse Exclusive Lasso (SpEL) for multi-task learning. The proposed SpEL is able to capture the competitive relationship among tasks (competitive structure), while remove unimportant features which are common across the tasks from the final model (sparse structure). Experimental studies on synthetic and real data indicate that the proposed method can significantly improve learning performance by identifying sparse and task-competitive structures simultaneously.
Vector sentences representation for data selection in statisticalmachine translation

One of the most popular approaches to machine translation consists in formulating the problem as a pattern recognition approach. Under this perspective, bilingual corpora are precious resources, as they allow for a proper estimation of the underlying models. In this framework, selecting the best possible corpus is critical, and data selection aims to find the best subset of the bilingual sentences from an available pool of sentences such that the final translation quality is improved. In this paper, we present a new data selection technique that leverages a continuous vector-space representation of sentences. Experimental results report improvements compared not only with a system trained only with in-domain data, but also compared with a system trained on all the available data. Finally, we compared our proposal with other state-of-the-art data selection techniques (Cross-entropy selection and Infrequent ngrams recovery) in two different scenarios, obtaining very promising results with our proposal: our data selection strategy is able to yield results that are at least as good as the best-performing strfategy for each scenario. The empirical results reported are coherent across different language pairs.
Unbalanced biclique cryptanalysis of a full round Midori

Midori is a family of lightweight block ciphers presented by Banik et al. at Asiacrypt 2015. Biclique cryptanalysis is a typical key-recovery attack that is proposed to attack the full AES by Bogdanov et al. in ASIACRYPT 2011. The method can attack a great deal of ciphers utilising the main idea of MITM attack and the basic principle of the biclique structure. In this study, the authors first provide an unbalanced biclique attack on full round Midori with partial matching and precomputation. They demonstrate that full round Midori64/128 are not secure against unbalanced biclique attacks. They construct a five-round unbalanced biclique on Midori64, with data complexity of and time complexity of by investigating the simple key schedule and the encryption structure. Furthermore, they present a four-round unbalanced biclique on Midori128 with data complexity of and computational complexity of . To the best of authors' knowledge, the result is the best single-key cryptanalytic result of Midori.
Deep Learning for Healthcare Biometrics

Mistakes in healthcare systems such as a mix-up of records or confusing medical charts lead to the wrong medications to patients. Major tasks such as administrative costs, legal expenses, and liabilities incur high cost to the healthcare industry using traditional, inaccurate patient identification processes. This can be resolved by biometric technology. Only physiological features can be used for patient identification to eliminate need of SSN, insurance card, or date of birth during registration. A biometric template can be directly mapped to an electronic health record to accurately authenticate individuals on subsequent visits. This technology ensures no medical records can be mimicked and the right care is provided to the right patient. Deep learning provides a platform to solve identification and diagnostic problems arising in medicine and can be used in healthcare biometrics to analyze clinical parameters and their combinations for disease prognosis (e.g., prediction of disease, extracting medical knowledge, therapy planning, and support).
Reduced quaternion matrix‐based sparse representation and its application to colour image processing

The traditional colour image sparse models ignore the relationship among the three separate colour channels. The authors propose a novel colour image sparse model by employing reduced quaternion matrix, which can treat independent colour channels as a whole. In addition, reduced quaternion matrix singular value decomposition is employed to design the corresponding dictionary learning algorithm. To make the proposed model robust and tractable, a reduced quaternion split Bregman iteration is developed to solve the minimisation problem. The proposed model cannot only preserve inherent colour structures but also avoid hue bias issue efficiently. Extensive experiments on colour image de-noising, in-painting, and super-resolution manifest that the proposed sparse representation model outperforms the state-of-the-art schemes.
Multi-Scale Guided Mask Refinement for Coarse-to-Fine RGB-D Perception

Pixel-level object segmentation is highly desired in many vision applications. Although segmentation methods purely based on visual input have achieved great success in the past decade, their further improvement is still hindered by the intrinsic drawback of color camouflage. With the rapid development and wide deployment of depth sensors, depth assisted methods are increasingly popular in visual perception systems. It is expected that RGB-D based methods can lead to significant performance improvements because color and depth are naturally complementary. However, how to merge color and depth modalities for segmentation with both high efficiency and high accuracy remains an open problem to be addressed. In this letter, we propose to divide the segmentation process into “coarse” and “refining” stages because a coarse segmentation can be easily obtained by various light-weight methods. In this way, we can tackle this problem by focusing on the refinement of coarse segmentations. In particular, we propose a multi-scale approach that selectively inherits the effective features of both edge-preserving filtering and deep neural networks. The proposed approach is evaluated on several benchmark datasets, respectively, using the coarse segmentations from background subtraction and object detection as the input. Numerous results indicate that our approach can achieve significant accuracy improvements compared to other alternatives, demonstrating superior edge-preserving capability. Besides an effective method for merging RGB-D information, our study on the capability of coarse-to-fine refinement also brings new inspirations for designing light-weight perception systems.
SortingHat: Wizardry on Software Project Members

Nowadays, software projects and in particular open source ones heavily rely on a plethora of tools (e.g., Git, GitHub) to support and coordinate development activities. Despite their paramount value, they foster to fragment members' contribution, since members can access them with different identities (e.g., email, username). Thus, researchers and practitioners willing to evaluate individual members contributions are often forced to develop ad-hoc scripts or perform manual work to merge identities. This comes at the risk of obtaining wrong results and hindering replication of their work. In this demo we present SortingHat, which helps to track unique identities of project members and their related information such as gender, country and organization enrollments. It allows to manipulate identities interactively as well as to load bulks of identities via batch files (useful for projects with large communities). SortingHat is a component of GrimoireLab, an industry strong free platform developed by Bitergia, which offers commercial software analytics and is part of the CHAOSS project of the Linux Foundation. A video showing SortingHat is available at https://youtu.be/724I1XcQV6c.
Fast multi-view image rendering method based on reverse search for matching

In a traditional multi-view image generation algorithm, partial image information might be lost at the pixel mapping step during the 3D image acquisition. A lower hardware cost and shorter operation time can be realized if an effective algorithm is employed. Therefore, a fast multi-view image rendering method based on reverse search is proposed in this paper, it uses pixel mapping information to derive a rendering image. Compared with the conventional depth-image-based rendering (DIBR) which using multiple images, the method only generates one image with reverse mapping in the image rendering step. First, the parallax range of the original image is calculated. Then, a warping image is generated through reverse mapping; this image has contained information for eight different viewpoints. Finally, the image modification can be made. Experimental results show that the proposed method effectively reduces rendering time and memory size by 58.82% and 83.78%, respectively. In addition, the mean structural similarity between the two images created by DIBR and our method is 1, which means that our method has the same effect as DIBR.
Multiple exposure fusion based on sharpness-controllable fuzzy feedback

Multiple exposure fusion (MEF) is attracting considerable attention in research on high dynamic range (HDR) imaging: Eliminating the need to generate an intermediate HDR image, MEF directly expands an image’s dynamic range and thus provides greater detail enhancement than traditional HDR techniques . However, in the fusion stage, the optimal weights of each pixel in the images input to the final synthesized image are challenging to determine and usually required manual tuning of parameters. In addition, many MEF algorithms have been proposed, but most have lacked a self-regulation mechanism. To tackle the above disadvantages, we apply fuzzy theory and present a novel MEF framework with a fuzzy feedback structure. In this work, over- and under-exposed images are generated from a single input image using local histogram stretching. This avoids the creation of ghost artifacts when multiple exposed images are fused in the dynamic scene containing object motion. In the fusion stage, fuzzy logic is used to determine pixel weights based on gradient and chrominance analysis, and a guided image filter is used to suppress noise and enhance edges in the weight maps. To ensure detail enhancement without excessive or insufficient sharpness, we developed a simple sharpness measure named the edge-map overlapping rate (EOR). With EOR and the feedback structure, users are allowed to manipulate the output synthesized image to their preferred sharpness level, and the above weights are appropriately redesigned by automatically regulating the magnitude of the fuzzy input. From experimental results, this work demonstrated excellent image quality and outperformed other existing HDR/MEF methods.
Adaptive BPEL Service Compositions via Variability Management

Service-Oriented Architectures are a popular development paradigm to enable distributed applications constructed from independent web services. When coordinated, web services are an infrastructure to fulfill dynamic and vertical integration of business. They may face frequent changes of both requirements and execution environments. Static and predefined service compositions using business process execution language (BPEL) are not able to cater for such rapid and unpredictable context shifts. The authors propose a variability management-based adaptive and configurable service composition approach that treats changes as first-class citizens and consists of identifying, expressing, realizing, and managing changes of service compositions. The proposed approach is realized with a language called VxBPEL to support variability in service compositions and a platform for design, execution, analysis, and maintenance of VxBPEL-based service compositions. Four case studies validate the feasibility of the proposed approach while exhibiting good performance of the supporting platform.
Dynamic embedding strategy of VQ-based information hiding approach

Information security is one of the most challenging issues. Cryptography and Steganography techniques are two popular methods for protecting data privacy. In this study, an information hiding method with dynamic embedding capacity based on vector quantization is proposed for protecting confidential data. To improve embedding capacity and image quality at the same time, dynamic-length secret bits are embedded into each pixel. Compared with previous approaches, the proposed method preforms better regarding the embedding capacity and image quality.
Knowledge visualization and nano-crystal modeling geometry

With the advancement of computer and image-based technologies, imaging has come to be a pervasive medium by way of which we access, interpret and communicate information. Applying Knowledge Visualization framework principles to the study of a mineral at the unit cell level provides a fertile ground from which to extract significant representations. Using data collected from rigorous scientific analysis, the author extracted meaningful information from the crystal’s geometry and physical properties to create new representations according to the Knowledge-Visualization methodology. The outcome was assessed in several interactive platforms and opened a new perspective on joint collaboration between Science, Humanities, and the Arts. Additionally, the experiment provided a new ground of investigation for unexpected connections between geometry, earth sciences, and local cultures.
Anuvaadika: Implementation of Sanskrit to Hindi Translation Tool Using Rule-Based Approach

Background: Sanskrit is claimed as the second oldest language of the world and in ancient days, Sanskrit was considered as mother tongue in the larger part of India. But now, it is struggling for its acceptance in common people. Objective: Objective of the work is to develop algorithms for stemming of Sanskrit words, implement semantic analysis, discourse integration and pragmatic analysis. Another objective is to implement a translation tool that is able to translate Sanskrit text into Hindi. Results: Rule Based Method is used to prepare the corpora and implement the proposed work. Results: An interface is made available through which step by step translation can be seen and understood. Conclusion: This tool will be helpful to those who are familiar in Hindi language but unable to learn Sanskrit due to the scarcity of language experts. More than 60 million people from India and abroad who are active Hindi users and work on computer, can get themselves connected with Sanskrit and also can learn the Sanskrit fundamentals by self.
Design and evaluation of 3D CAPTCHAs

Most current 2D CAPTCHAs are vulnerable to automated character recognition attacks and the latest attacks can successfully break the 2D text CAPTCHAs at a rate of more than 90%. In this work, we present two novel 3D CAPTCHAs, which are more secure than current 2D text CAPTCHAs against automated character recognition attacks. Our approach is to display CAPTCHA characters on 3D objects. We exploit the difficulty that machines have in rotating 3D objects to find the correct viewpoint and in further recognizing characters in 3D, while we believe humans can easily perform these tasks. Using an offline automated character recognition attack, we find that 82% of new text reCAPTCHAs are broken, while approximately 60% of our 3D CAPTCHAs are broken and only if characters are focused on and zoomed in from a direct viewpoint. When CAPTCHAs are presented in slightly different views, the attack success rate is rapidly diminished to 0%. In addition, we use commercial Deep Neural Networks-based text and object detection classifiers to attack our systems, and demonstrate that our approach is extremely difficult to break with these classifiers, even if CAPTCHA characters are presented in direct, 2D view. With emulated relay attacks, fewer than 16% of our CAPTCHAs are accurately solved by human solvers, while more than 90% of current 2D text-based CAPTCHAs are solved. Also, we performed an IRB-approved user study to evaluate the usability of our approach. Participants agreed that our approach was usable in spite of the extra time required for 3D model rotation.
Grounding knowledge acquisition with ontology explanation:A case study

Knowledge validation is still a challenge when constructing knowledge-based systems. It is one of the major reasons for user rejection and disagreement between project participants. Systematic and periodic reviews of the domain ontology, with a formal agreement of the whole development team (including the experts) are a recommended good practice. Nevertheless, these reviews do not guarantee system success. This paper presents a case study of the construction process of a knowledge-based system. The process involved a group of experts with varied work experience. A great deal of negotiation happened during knowledge acquisition meetings, which took place during a 6-month period. After each meeting, changes in the ontology were verified through a web-based questionnaire, from which either consensual agreement was reached (and changes implemented) or the need for a new meeting was ascertained. An explanatory review at the beginning of each meeting further solidified the understanding of all participants. This cyclic process led to a final version of the ontology, ratified by all participants. This model supports diagnosis and prediction of failures in mechanical drilling rigs in oil exploration sites. Unexpectedly, during system trials, experts disagreed with results, which raised questions about the validity of the domain ontology. The system's explanation module provided a cornerstone for a reflective process that helped identify inconsistencies and corrections needed. These reflections led to adjustments to the ontology, and a reflection about previous decisions and element definitions. Explanations, derived from the ontology and instantiated using real scenarios, shed light on knowledge gaps and semantic inconsistencies of the domain model. In this paper we have three main goals: (1) to present our ontology construction process; (2) to highlight a particular situation where results were inadequate; and (3) to show how the explanation system helped experts and knowledge engineers identify gaps. We also present lessons learned from the whole process, that may apply in other situations.