Reviewerly/exHarmony huggingface dataset:
Mapping Factoid Adjective Constraints to Existential Restrictions over Knowledge Bases

The rapid progress of question answering (QA) systems over knowledge bases (KBs) enables end users to acquire knowledge with natural language questions. While mapping proper nouns and relational phrases to semantic constructs in KBs has been extensively studied, little attention has been devoted to adjectives, most of which play the role of factoid constraints on the modified nouns. In this paper, we study the problem of finding appropriate representations for adjectives over KBs. We propose a novel approach, called Adj2ER, to automatically map an adjective to several existential restrictions or their negation forms. Specifically, we leverage statistic measures for generating candidate existential restrictions and supervised learning for filtering the candidates, which largely reduce the search space and overcome the lexical gap. We create two question sets with adjectives from QALD and Yahoo! Answers, and conduct experiments over DBpedia. Our experimental results show that Adj2ER can generate high-quality mappings for most adjectives and significantly outperform several alternative approaches. Furthermore, current QA systems can gain a promising improvement when integrating our adjective mapping approach.
Natural Language Processing with DeepPavlov Library and Additional Semantic Features

In this paper some basic methods of NER task managing in DeepPavlov library along with new neural network modifications with additional semantic features are observed. Means of DeepPavlov library were slightly improved and applied to new dataset with unique additional features, which caused feasible improvement of the neural model.
Visual Perception for Autonomous Driving inspired by Convergence-Divergence Zones

Visual perception is, by large, the main source of information used by humans when driving. Therefore, it is natural and appropriate to rely heavily on vision analysis for autonomous driving, as done in most projects. However, there is a significant difference between the common approach of vision in autonomous driving, and visual perceptions in humans when driving. Essentially, image analysis is often regarded as an isolated and autonomous module, which high level output drives the control modules of the vehicle. The direction here presented is different, we try to take inspiration from the brain architecture that makes humans so effective in learning tasks as complex as the one of driving. There are two key theories about biological perception grounding our development. The first is the view of the thinking activity as a simulation of perceptions and action, as theorized by Hesslow. The second is the Convergence-Divergence Zones (CDZs) mechanism of mental simulation connecting the process of extracting features from a visual scene, to the inverse process of imagining a scene content by decoding features stored in memory. We will show how our model, based on semi-supervised variational autoencoder, is a rather faithful implementation of these two basic neurocognitive theories.
Optimal Partition Search

In the field of search algorithms, choosing an optimal partition that takes much less time to search a desired element in an array in comparison with other partitions is a daunting task. Traditional search algorithms such as binary search and quadratic search do not always perform better in the arrays having arbitrary size. This paper aims to develop an algorithm that gives the optimal partition to use in the array so that it fetches the desired element in an optimal time. The basic approach to implement this algorithm is taken from binary search but done these arch by considering partitions starting from two up to the size of the array and calculating these arching time taken by each and every partition to search the desired element that is further compared with searching times taken by all other partitions. As a result, the algorithm then finds the optimal partition. The results show that the proposed algorithm performs well by means of giving the optimal partition on the basis of an input array having arbitrary size and an item to be searched. The basic difference between traditional searching algorithms and the proposed algorithm is that those algorithms are used for searching the desired element, but this algorithm finds the partition that searches the desired element in minimum time which makes this algorithm a generalized version of all partition searching algorithms such as linear search, binary search, and quadratic search. Moreover,this algorithm can be used as a preprocessing step for finding optimal partition and then apply searching algorithm using that partition in order to search the desired element in minimum time. Overall, the conclusion is that the optimization concept applied in this work allows finding the most favorable partition for searching an element in the array. This work can provide beneficial guidance for using search algorithms correctly in actual search space.
Automatic Feature Extraction for CBIR and Image Annotation Applications

In the area of information technology, organizing and indexing of the digital information is a primary concern. In CBIR system, one of the most significant issues is a semantic gap. Semantic gap refers to the difference between the features extracted from image and interpretation of features content in the image or within the regions by human. Hence, automatic image annotation has achieved momentum in the last few years. The objective of the automatic image annotation (AIA) is to allocate textual labels to the image that clearly describes content or objects in the image. Accuracy of the automated image annotation algorithm depends upon the feature extraction process. Therefore, effective feature extraction algorithm is essential. In this paper, feature extraction algorithm using Gabor filter is presented. Gabor filter through its multi-resolution capability successfully extracts effective features from images or regions obtained after segmentation. It is demonstrated that the Gabor filter generates low level, less number of features and accurate description of the image if filter with frequency response in the band of 50–75% of the total frequency is selected. These extracted features further reduce the complexity in the classification algorithms developed using statistical models or soft computing techniques.
Contextualized Word Representations with Effective Attention for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) aims at identifying sentiment polarities towards aspect in a sentence. Attention mechanism has played an important role in previous state-of-the-art neural models. However, existing attention mechanisms proposed for aspect based sentiment classification mostly focus on identifying the sentiment words, without considering the relevance of such words with respect to the given aspects in the sentence. To solve this problem, we propose a new architecture, self-attention with co-attention (SACA) for aspect-based sentiment analysis. Self-attention is capable of conducting direct connections between arbitrary two words in context from a global perspective, while co-attention can capture the word-level interaction between aspect and context. Moreover, previous works simply averaged aspect vector to learn the attention weights on the context words, which may bring information loss if the aspect has multiple words. To address the problem, we employ the pre-trained contextual word embeddings and character-level word embeddings as word representation. We evaluate the proposed approach on three datasets, experimental results demonstrate that our model outperforms the state-of-the-art on all three datasets.
Sequential Inverse Covariance Intersection Fusion Kalman Filter for Multi-sensor Systems with Packet Dropouts

This paper mainly studies the fusion estimation problem of multi-sensor systems with packet dropouts. Firstly, by the augmented state algorithm, the state space model is transformed into a new augmented system with fictitious noises. By Kalman filtering method and Inverse Covariance Intersection (ICI) method, Sequential Inverse Covariance Intersection (SICI) structure is designed and then the SICI fusion Kalman filter is presented, which avoids the computation of the cross-covariances among the local filters and has less conservativeness than the previous Sequential Covariance Intersection (SCI) filters. The accuracy relations among the local estimators and the fused estimators are proved. The validity and the consistency of the presented fuser are demonstrated by a simulation example, and its precision relation is shown.
Streamlined Decoder for Chinese Spoken Language Understanding

As a critical component of Spoken Dialog System (SDS), spoken language understanding (SLU) attracts a lot of attention, especially for methods based on unaligned data. Recently, a new approach has been proposed that utilizes the hierarchical relationship between act-slot-value triples. However, it ignores the transfer of internal information which may record the intermediate information of the upper level and contribute to the prediction of the lower level. So, we propose a novel streamlined decoding structure with attention mechanism, which uses three successively connected RNN to decode act, slot and value respectively. On the first Chinese Audio-Textual Spoken Language Understanding Challenge (CATSLU), our model exceeds state-of-the-art model on an unaligned multi-turn task-oriented Chinese spoken dialogue dataset provided by the contest.
Visual Democratisation: AR and the Underpass Festival

This paper discusses the opportunities Augmented Reality (AR) offers art experiences to merge underused public spaces with cultural programming, establish communities, and influence urban development.It focuses on the issues of usability and accessibility necessary for the democratisation of the technology and uses Etic Lab's current project in development, The Underpass Festival, as a model for speculatively testing these ideas.This structure exemplifies Etic Lab's bottom-up approach to R&D that aims to align emerging technologies with issues surrounding social and cultural development.The Underpass Festival in Milton Keynes will constitute a study taking cues from groups of artists, the local community and publicly available data to propagate the use of AR in the built environment.It is scheduled to take place in Summer 2020.Our research currently extends to an on-going feasibility study to establish a route to 'hanging' artworks on the walls of public walkways.The festival aims to transform Milton Keynes' iconic but underappreciated public spaces into the cultural real estate they were designed to be, and to develop a radical new democratic model for the role of art and galleries across the city, navigating the, "digital representations that reconfigure everyday experiences of place" (Feldman 2018), with little to no 'visible' footprint.We believe that software-based AR, the kind contingent on the existing technology people already carry around, should focus on accessible spaces repurposed as virtual sites for augmented reality implementation.It is our understanding that the suspension of disbelief that augmented and virtual reality play with is not limited to high definition visuality, and that the fidelity of the 'reality' presented should not be at the expense of its immediacy, accessibility and usefulness.These characteristics, for us, are the crux of visual democratisation and the basis of our research.Our feasibility study is, therefore, looking to the limitations of the existing technology available to the average member of the public: not as a route to developing the AR assets themselves, but instead as a means for the city and its inhabitants to begin mapping and reclaiming these particular sites.This paper will examine the issues of usability and democratisation of AR technology in relation to the Underpass Festival, and to what constitutes visual democratisation in the context of AR development.It will discuss the festival as speculative case study and work in progress, and its site-specificity.It will use the challenge of displaying artworks virtually in public spaces to consider the importance of visual quality relative to other attributes such as accurate mapping and 'hanging' on the underpass walls.It will conclude by comparing this project with some previous Etic Lab research around what we call the Oz Effect, and the impact of revealing 'the man behind the curtain', or the workings of the technology.It will examine this in relation to the aesthetics of the 'poor image'.Augmented reality.
Providing RS Participation for Geo-Distributed Data Centers Using Deep Learning-Based Power Prediction

Nowadays, geo-distributed Data Centers (DCs) are very common, because of providing more energy efficiency, higher system availability as well as flexibility. In a geo-distributed cloud, each local DC responds to the specific portion of the incoming load which distributed based on different Geographically Load Balancing (GLB) policies. As a large yet flexible power consumer, the local DC has a great impact on the local power grid. From this point of view, a local DC is a good candidate to participate in the emerging power market such as Regulation Service (RS) opportunity, that brings monetary benefits both for the DC as well as the grid. However, a fruitful collaboration requires the DC to have the capability of forecasting its future power consumption. While, given the different GLB policies, the amount of delivered load toward each local DC is a function of the whole system’s conditions, rather than the local situation. Thereby, the problem of RS participation for local DCs in a geo-distributed cloud is challenging. Motivated by this fact, this paper benefits from deep learning to predict the local DCs’ power consumption. We consider two main GLB policies, including Power-aware as well as Cost-aware, to acquire training data and construct a prediction model accordingly. Afterward, we leverage the prediction results to provide the opportunity of RS participation for geo-distributed DCs. Results show that the proposed approach reduces the energy cost by 22% on average in compared with well-known GLB policies.
Aesop

The traditional storytelling experiences are often one dimensional, wherein they only contain a single channel of communication with the audience through narration. With the advancements in technology, storytelling experiences have been augmented with the help of digital media to be more engaging and immersive. Authoring these scenarios, however, is complicated as it requires technical knowledge to interface the means of engagement. In this work, we talk about Aesop, a system which assists the narrator to author engaging storytelling experiences. Aesop provides a block-based interface like Scratch and manifests words of a story, Cues, and Visualization (Outputs) as blocks that enable the user to create captivating stories. Our system also leverages physical actions performed by the user as Cues. These cues can trigger visualizations like robot actions, animations, environments simulation using sound and lighting effects.
Design and Simulation of Cooperative Parking Robot

The cooperative parking robot completes the parking work in the form of a group of four robots working together. In this paper, we do calculation on some significant parameters which can be used to choose power source. We calculate the torque, speed, and power required by the power source according to the load and speed of parking robot. In addition, we perform force analysis calculations on key mechanisms of the robot. The checking of the strength of key parts and the finite element analysis of the whole structure by using the software ABAQUS are also carried out in this paper. The software ADAMS is also used to analyze forces during the process of lifting and lowering the wheel.
Automatic Breast Cancer Diagnostics Based on Statistical Analysis of Shape and Texture Features of Individual Cell Nuclei

The automatic detectionGramacki, Artur of nuclei within cytological samples is crucial for quantitativeKowal, Marek analysis in medical applications. Fortunately, Mazurkiewicz, Małgorzata modern digital microscopy systems allow imagingGramacki, Jarosław of biological material with very highPławiak-Mowna, Anna accuracy. A typical cytological sample contains hundreds or thousands of cell nuclei that need to be examined for a particular type of cancer (or the exclusion of neoplastic lesions). Typically, this assessment is made by a qualified physician by visually analyzing a biological material. As the complexity of cellular structures is very high, automating this process is a big challenge. In this paper, we try to face this problem. Real cytological images of breast cancer patients were collected by pathologists from the University Hospital in Zielona Góra, Poland. The individual cell nuclei were automatically detected within cytological sample imagery. Then a couple of different shape and texture features were collected. Based on this data, an attempt was made to classify them in the context of the possibility of automatically identifying the type of cancer (malignant, benign). The results obtained are moderately promising.
Multithreading Programming for Feature Extraction in Digital Images

Currently, there is a great advance in the construction of processors with many cores, providing more computational power and resources to use. In the field of image processing, most of the algorithms use a sequential architecture that prevents from reaching the maximum performance of processors. In this work, we design and implement a set of low-level algorithms to optimize the processing of a two-dimensional convolution to obtain the best performance that a CPU can grant. Our approach uses parallel processing in four different cases of study based on multithreading. The computation time is compared in order to find which case achieves the best performance. In the same way, the computation time of the proposed algorithms is measured, and then, it is compared with general frameworks, in order to have a real metric of the proposed library with popular Application Programming Interfaces (API’s) like OpenMP.
Combining a context aware neural network with a denoising autoencoder for measuring string similarities

Measuring similarities between strings is central for many established and fast-growing research areas, including information retrieval, biology, and natural-language processing. The traditional approach to string similarity measurements is to define a metric with respect to a word space that quantifies and sums up the differences between characters in two strings; surprisingly, these metrics have not evolved a great deal over the past few decades. Indeed, the majority of them are still based on making a simple comparison between character and character distributions without considering the words context. This paper proposes a string metric that encompasses similarities between strings based on (1) the character similarities between the words, including non-standard and standard spellings of the same words, and (2) the context of these words. We propose a neural network composed of a denoising autoencoder and what we call a context encoder, both specifically designed to find similarities between the words based on their context. Experimental results show that the resulting metrics have succeeded in 85.4% of the cases in finding the correct version of a non-standard spelling among the closest words, compared to 63.2% using the established Normalised-Levenshtein distance. We also show that by employing our approach, the words used in similar context are calculated to be more similar than words with different contexts, which is a desirable property lacking in established string metrics.
Selected Challenges in Grammar-Based Text Generation from the Semantic Web

In this paper, based on the recent outcome of two shared tasks on structured data verbalisation, and examining one system in particular, we present some evidence why grammar-based systems are particularly relevant for the verbalisation of structured data as found in the Semantic Web. We then define possible future lines of research, centered around the FORGe system and the linguistic grounding of Semantic Web datasets.
A Phenomenological Analysis of the Distinction Between Structural Rules and Particle Rules in Dialogical Logic

As it is well-known, Husserl distinguishes between three levels of formal logic: pure morphology, consequence-logic and truth-logic. The distinction between the second level, which concerns the peculiarities of the derivation of propositions from each other, and the third level, which concerns the deduction or any kind of proof as a whole in which the truth of the premises and the consequences are at work, is very important to understand Husserl’s conception of pure logic. Such a distinction remains unnoticed for the truth-functional approaches toward logic, for they define the meaning of logical connectives on the basis of truth and falsity. Therefore, they cannot recognize the peculiarities of the derivation or consequence-relation as such. My aim in this paper is to show that the dialogical distinction between particle rules and structural rules, and also the distinction between play-level and strategy-level, may be considered as representing the distinction between the second and the third levels of logic; and thus the dialogical semantics provides us with a device to explore the phenomenological idea in a precise way. On the other hand, certain themes of the overall phenomenological analysis of logic help to show the philosophical significance of those dialogical features.
SeedsMiner: Accurate URL Blacklist-Generation Based on Efficient OSINT Seed Collection

Nowadays, increasing Internet use is plagued by malicious activity. Drive-by downloads are still a serious problem. Furthermore, new kinds of malicious sites are increasing. Published analysis has reported that 10% of Internet are malicious. To countermeasure these malicious URLs, blacklisting based on threat intelligence is widely used as a multilayer defense mechanism in modern Internet security techniques. Blacklisting on the network side is especially effective for protecting critical information infrastructure of various industries because it is not necessary to change the configuration or to use system resources. We propose a method of collecting efficiently malicious candidate URLs (seed) from open information and generating highly accurate blacklists based on information. This open information is called “Open Source Intelligence” (OSINT). OSINT information must be examined carefully to detect inaccuracies. Our seed collector that collects 50% or more truly malicious URLs, and we showed 75% of the blacklist generated by our proposed method was unknown to Google Safe Browsing. We also showed that malware collected by our method was rich in family variations, and 23% of the malware was unknown.