Reviewerly/exHarmony huggingface dataset:
A Practical Approach to Forgetting in Description Logics with Nominals

This paper investigates the problem of forgetting in description logics with nominals. In particular, we develop a practical method for forgetting concept and role names from ontologies specified in the description logic ALCO, extending the basic ALC with nominals. The method always terminates, and is sound in the sense that the forgetting solution computed by the method has the same logical consequences with the original ontology. The method is so far the only approach to deductive forgetting in description logics with nominals. An evaluation of a prototype implementation shows that the method achieves a significant speed-up and notably better success rates than the Lethe tool which performs deductive forgetting for ALC-ontologies. Compared to Fame, a semantic forgetting tool for ALCOIH-ontologies, better success rates are attained. From the perspective of ontology engineering this is very useful, as it provides ontology curators with a powerful tool to produce views of ontologies.
Supervised adversarial networks for image saliency detection

In the past few years, Generative Adversarial Network (GAN) became a prevalent research topic. GAN has ability to generate good quality images that look like natural images from a random vector. In this paper, we follow the basic idea of GAN and propose a novel model for image saliency detection, which is called Supervised Adversarial Networks (SAN). However, different from GAN, the proposed method uses fully supervised learning to learn both G-Network and D-Network by applying class labels of the training set. Moreover, a novel kind of layer call conv-comparison layer is introduced into the D-Network to further improve the saliency performance. Experimental results on Pascal VOC 2012 database show that the SAN model can generate high quality saliency maps for many complicate natural images.
SWRL Parallel Reasoning Method with Spark SQL

With the rapid development of semantic Web and big data technology, ontology data has the characteristics of large scale, high speed growth and diversity which big data has. On one hand, the conventional ontology reasoners do not scale well for large amounts of ontologies because they are designed for run on a single machine. On the other hand, the existing scalable reasoners are not perfect enough, for example, to completely support the widely used Semantic Web Rule Language (SWRL) rules. This paper presents a method for SWRL scalable parallel reasoning using the Spark SQL programming model. Our method supports SWRL rules more completely, and because of the use of Spark SQL, our algorithm is simpler and can rely on Spark SQL's built-in optimizations to provide optimized execution efficiency. We also considered some optimizations to speed up the rule reasoning process.
Service identification by enhanced K-mean algorithm in service-oriented architecture

Service identification with right level of granularity is the most critical aspect in service oriented architecture. Service identification is challenging due to several reasons such as lack of reuse of services, lack of right decision to choose the appropriate service. SOA-based project uses large repository, where in services are stored randomly in the repository. This causes considerable amount of search time when service is searched from the database. In case of trigger-based application, current service storage process will not be much effective due to inadequacy in service level agreement. In this regard, the author(s) explored the possibilities of service identification using k-means clustering which will reduce search time, service replication and increase the performance and reliability of the service. Proposed model has been experimentally validated and author(s) found significant decrease in service search time. This model will be helpful in building the applications with minimum service level agreement.
An integrated ore prospecting model for the Nyasirori gold deposit in Tanzania

The Nyasirori gold deposit, located in the middle-western end of the Musoma-Mara Archean greenstone belt in Tanzania, is a tectonic altered rock type gold deposit controlled by shear tectonic zone. This work conducted high-precision ground magnetic measurements to delineate fault structures and favorable prospecting targets, utilized induced polarization (IP) intermediate gradient to roughly determine the distribution and extension of the tectonic altered zone and gold ore (mineralized) bodies, and further carried out IP sounding and magnetotelluric sounding to locate the tectonic altered zone and gold ore (mineralized) bodies. The anomalous gradient belt of the combination of positive and negative micromagnetic measurements reflects the detail of shallow surface tectonic alteration zone and gold mineralization body. Micromagnetic profile anomalies indicate the spatial location and occurrence of concealed tectonic alteration zone and gold (mineralized) ore bodies. Soil geochemical measurements indicate that the ore-forming element Au correlates well with As and Sb, and As and Sb anomalies have a good indication to gold orebodies. Based on the multi-source geological-geophysical-geochemical information of the Nyasirori gold deposit, this work established an integrated prospecting model and proposed a set of geophysical and geochemical methods for optimizing prospecting targets.
Full-Resilient Memory-Optimum Multi-Party Non-Interactive Key Exchange

Multi-Party Non-Interactive Key Exchange (MP-NIKE) is a fundamental cryptographic primitive in which users register into a key generation centre and receive a public/private key pair each. After that, any subset of these users can compute a shared key without any interaction. Nowadays, IoT devices suffer from a high number and large size of messages exchanged in the Key Management Protocol (KMP). To overcome this, an MP-NIKE scheme can eliminate the airtime and latency of messages transferred between IoT devices. MP-NIKE schemes can be realized by using multilinear maps. There are several attempts for constructing multilinear maps based on indistinguishable obfuscation, lattices and the Chinese Remainder Theorem (CRT). Nevertheless, these schemes are inefficient in terms of computation cost and memory overhead. Besides, several attacks have been recently reported against CRT-based and lattice-based multilinear maps. There is only one modular exponentiation-based MP-NIKE scheme in the literature which has been claimed to be both secure and efficient. In this article, we present an attack on this scheme based on the Euclidean algorithm, in which two colluding users can obtain the shared key of any arbitrary subgroup of users. We also propose an efficient and secure MP-NIKE scheme. We show how our proposal is secure in the random oracle model assuming the hardness of the root extraction modulo a composite number.
Entanglement of two dipole-coupled qubits interacting with a detuned cavity thermal field

This paper studies the entanglement properties in a system of two dipole-coupled natural or artificial two-level atoms not-resonantly interacting with an intensive single-mode thermal field for coherent initial atomic states. The results show that the entanglement is weaken due to the initial atomic coherence for model with detuning in contrast to the model with resonant atom-field interaction. For non-dissipative model under consideration the detuning results also in decreasing of the atom-atom entanglement in time for separable initial atomic state and intensive cavity thermal field.
Errors may not cue recall of corrective feedback: Evidence against the mediation hypothesis of the testing effect.

Successfully retrieving information protects it against later forgetting. Failed retrieval attempts are also beneficial if followed by study of corrective feedback. To explain both of these findings, researchers have proposed the mediation hypothesis. In the case of learning from corrective feedback, initial errors may serve as mediators, becoming associated with the corrective information and assisting with recall of that information during later retrieval attempts. A simple prediction follows: reminding learners of their initial errors should improve their recall of the corrections. We tested this prediction with a set of 3 experiments. In Experiment 1, participants learned cue-target word pairs (e.g., cactus-point) by reading or by incorrectly guessing target words before viewing corrective feedback. Immediately afterward, participants were equally good at recalling targets from the cue alone, the cue plus their own guess, or the cue plus a guess made by another participant. Experiment 2 produced similar results when the retention test was delayed by 24 hr. In Experiment 3, participants instead learned word triplets having a mediational relationship through preexisting associations (e.g., film-star-galaxy). Here, reminding participants of a genuine mediator greatly improved target recall, supporting the validity of our cueing procedure. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
A Study on the Attributes of Travel Information Entropy: Focusing on Online Travel Product Reviews

Online reviews to obtain travel information by social network services have become more critical. Tourists tend to consider online reviews to be more believable than the information from tour operators and travel agencies. Online consumer reviews, in particular, are playing an increasingly significant role in travelers' purchase behavior. However, from the literature review, a wide range of information with different levels of certainty is available, and the different tourism information may result in different impacts on tourist behavior. This study employed “entropy” in the information theory that indicates the degree of uncertainty related to the information in the message. The information entropy theory assumes that high information entropy implies more uncertainty to customers. This study investigated and verified the entropy attribute of travel information through the integrated research methodology. As a result of the exploratory factor analysis for the validity test of the qualitative research, the study identified four factors as the entropy of travel information: asymmetry, discordance, distrust, and uncertainty. These findings hold significant implications for both travel agencies and marketers.
User Interaction with a Technology Evolution Model to Select Most Viable Enterprise Information Systems

One of alternative ways to achieve a competitive advantage by an enterprise in the ICT area is to select the most prospective information system (IS) as a seed technology. The latter is defined as a first technology from a given area implemented in an enterprise, that determines or strongly influences the choice of further technologies from the same area. Seed ICT selection is particularly relevant for enterprises whose core business activity relies on online service provision. In this paper we present a user-requirement-based interaction scheme of an application that models the evolution of enterprise ISs, taking into account the production patterns of software releases, the functionalities offered, the users' community growth trends, and other factors. The evolution is modeled by hybrid discrete-time/discrete-event systems, and multi-models built with them. The technology choice is supported by multicriteria ranking algorithms that apply IS innovativeness forecasts. Moreover, this evolution model of enterprise IS can handle different uncertainties and cyber security issues. We present the use cases and scenarios, model management, and the knowledge base that stores time series, IS characteristics and forecasts. The use of this application is illustrated by a real-life example of three popular open-source CMSs. We conclude that an informed choice of the first IS and the corresponding enterprise architecture as a seed technology is particularly relevant for start-ups and SMEs. An extended variant of the above application may also provide decision support to developer teams seeking software evolution models to discover and apply the best-possible technology development and market strategies.
Faster Update Time for Turnstile Streaming Algorithms

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2020 ACM-SIAM Symposium on Discrete Algorithms (SODA)Faster Update Time for Turnstile Streaming AlgorithmsJosh Alman and Huacheng YuJosh Alman and Huacheng Yupp.1803 - 1813Chapter DOI:https://doi.org/10.1137/1.9781611975994.110PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract In this paper, we present a new algorithm for maintaining linear sketches in turnstile streams with faster update time. As an application, we show that log n Count sketches or CountMin sketches with a constant number of columns (i.e., buckets) can be implicitly maintained in worst-case O(log0.582 n) update time using O(log n) words of space, on a standard word RAM with word-size w = Θ(log n). The exponent 0.582 ≈ 2ω/3 – 1, where ω is the current matrix multiplication exponent. Due to the numerous applications of linear sketches, our algorithm improves the update time for many streaming problems in turnstile streams, in the high success probability setting, without using more space, including ℓ2 norm estimation, ℓ2 heavy hitters, point query with ℓ1 or ℓ2 error, etc. Our algorithm generalizes, with the same update time and space, to maintaining log n linear sketches, where each sketch partitions the coordinates into k < logo(l) n buckets using a c-wise independent hash function for constant c, maintains the sum of coordinates for each bucket. Moreover, if arbitrary word operations are allowed, the update time can be further improved to O(log0.187 n), where 0.187 ≈ ω/2 – 1. Our update algorithm is adaptive, and it circumvents the non-adaptive cell-probe lower bounds for turnstile streaming algorithms by Larsen, Nelson and Nguyên (STOC’15). On the other hand, our result also shows that proving unconditional cell-probe lower bound for the update time seems very difficult, even if the space is restricted to be (nearly) the optimum. If ω = 2, the cell-probe update time of our algorithm would be logo(l) n. Hence, proving any higher lower bound would imply ω > 2. Previous chapter Next chapter RelatedDetails Published:2020eISBN:978-1-61197-599-4 https://doi.org/10.1137/1.9781611975994Book Series Name:ProceedingsBook Code:PRDA20Book Pages:xxii + 3011
Unsupervised Anomaly Detection via DBSCAN for KPIs Jitters in Network Managements

For many Internet companies, a huge amount of KPIs (e.g., server CPU usage, network usage, business monitoring data) will be generated every day. How to closely monitor various KPIs, and then quickly and accurately detect anomalies in such huge data for troubleshooting and recovering business is a great challenge, especially for unlabeled data. The generated KPIs can be detected by supervised learning with labeled data, but the current problem is that most KPIs are unlabeled. That is a time-consuming and laborious work to label anomaly for company engineers. Build an unsupervised model to detect unlabeled data is an urgent need at present. In this paper, unsupervised learning DBSCAN combined with feature extraction of data has been used, and for some KPIs, its best F-Score can reach about 0.9, which is quite good for solving the current problem.
A millimeter-wave Bell Test using a ferrite parametric amplifier and a homodyne interferometer

A combined ferrite parametric amplifier and millimeter-wave homodyne interferometer are proposed as an ambient temperature Bell Test. It is shown that the non-linear magnetic susceptibility of the yttrium iron garnet (YIG) ferrite, on account of its narrow line-width Larmor precessional resonance, make it an ideal material for the creation of entangled photons. These can be measured using a homodyne interferometer, as the much larger number of thermally generated photons associated with ambient temperature emission can be screened out. The proposed architecture may enable YIG quantum technology-based sensors to be developed, mimicking in the millimeter-wave band the large number of quantum optical experiments in the near-infrared and visible regions which had been made possible by use of the nonlinear beta barium borate ferroelectric, an analogue of YIG. It is illustrated here how the YIG parametric amplifier can reproduce quantum optical Type I and Type II wave interactions, which can be used to create entangled photons in the millimeter-wave band. It is estimated that when half a cubic centimeter of YIG crystal is placed in a magnetic field of a few Tesla and pumped with 5 Watts of millimeter-wave radiation, approximately 0.5x10^12 entangled millimeter-wave photon pairs per second are generated by the spin-wave interaction. This means an integration time of only a few tens of seconds is needed for a successful Bell Test. A successful demonstration of this will lead to novel architectures of entanglement-based quantum technology room temperature sensors, re-envisioning YIG as a modern quantum material.
Quantifying Quality of Actions Using Wearable Sensor

This paper introduces a novel approach to quantify the quality of human actions. The presented approach uses expert action data to define the space in order to gauge the performance of any user to identify expertise level. The proposed approach uses pose estimation model to identify different body attributes (legs, shoulders, head ...) status (left, right, bend, curl ...), which is further passed to autoencoder to have a latent representation encoding all the relevant information. This encoded representation is further passed to OneClass SVM to estimate the boundaries based on latent representation of expert data. These learned boundaries are used to gauge the quality of any questioned user with respect to the selected expert. The proposed approach enables identifying any critical situations in real work environment to avoid risky positions.
SimpleAR: Augmented Reality high-level content design framework using visual programming

This research work proposes a new augmented reality high-level content design framework, this is, a tool to create augmented reality applications aimed at staff with basic or no programming knowledge. The proposed framework summarizes the experience gathered from a systematic mapping study carried out on similar frameworks. Our proposal has a flexible approach regarding its ability to be adapted to any augmented reality programming framework. We demonstrate its viability with an implementation of our framework with Vuforia for the creation of marker-based augmented reality applications. The results of usability tests based on ISO 9241–11 show that our proposal is effective because all users were able to complete all tasks, it is efficient because users were able to create augmented reality applications in less than 5 minutes and it satisfies the user by qualifying as “Highly acceptable” according to the average score of the System Usability Scale questionnaire.
Bridging the Worlds of MARC and Linked Data: Transition, Transformation, Accountability

In a Vision session at the NASIG 35th annual meeting, Dr. Philip Schreur presented the advantages for libraries to move away from MARC and towards linked data at this time. He concentrated on four themes: the advantages of new communication technologies and the open Web; new approaches to authority work and workflows; increased social justice through broader access and multiple vocabularies in a wider range of results; and possible gains through human-centered artificial intelligence. As initiatives expand from the Library of Congress and new conversion products appear on our horizon, embracing the transition from MARC to linked data may be easier, and more necessary, than it currently appears for many libraries.
Learning-based Object Detection in High Resolution UAV Images: An Empirical Study

Deep learning-based methods are continuously boosting the performance of detecting objects in natural images. On the contrary, detecting objects in Unmanned Aerial Vehicle (UAV) images remains to be a difficult task in the field of computer vision, due to the challenge of training a well-performed detection model working on UAV images which usually contain instances with varied orientations, scales, and contours, etc. Furthermore, only a few researchers have focused on this field, probably because of difficulties in UAV data acquisition and labelling. Inspired by this, we collected a large-scale dataset with multi-scale and high-resolution UAV images, named MOHR, which contains 10,631 images captured by a UAV affixed with three kinds of cameras. Since these images were captured in a suburban environment, we manually annotated five classes of objects, including car, truck, building, collapse and flood damage. An empirical study was then conducted by adopting six advanced object detection methods all of which are based on deep learning technologies. The results indicate the great potential of these evaluated object detection models, but also reveal that the research on such a challenging UAV dataset using current deep learning techniques is far reaching.
Integrating Coordinates with Context for Information Extraction in Document Images

Information extraction from document collections is a fundamental and important step to understand, structure and analyze data. Many approaches with rules and deep learning based techniques have been applied on plain text, however, when it comes to document images, such demand still exists but becomes quite challenging without linguistic knowledge. In this paper, we propose an approach to extract required named entities (NEs) from document images by integrating the coordinate information from the detection and recognition stage into the contextual information of the BiLSTM-CRF model with an attention mechanism. We test this method on two real-world datasets. One is a Contract Dataset of Listed Companies, and the other is an Insurance Policy Dataset of our own. The result shows a combination of coordinates and context with attention leverages extraction in document images, opening up potential applications on such tasks.
Motivation of personal names in Donji Ljubeš near Aleksinac

The subject of this paper is the motivation of personal names in village Donji Ljubeš, which is located in the northwestern part of Aleksinačko Pomoravlje. The research is based on a corpus that makes both male and female personal names excerpted from birth registers born in that village. The personal names of people are analysed from the semantic aspect. There are 72 male names registered for 172 people and 81 females names registered for 181 people. A total of 153 personal names of Slovenian origin were listed, by which 353 persons were named. This paper contributes to the onomastic research of Aleksinačko Pomoravlje, since the papers of this type in the given area are in deficit.