How to assess the potential of liking a city or a neighborhood before ever having been there. The concept of urban quality has until now pertained to global city ranking, where cities are evaluated under a grid of given parameters, or either to empirical and sociological approaches, often constrained by the amount of available information. Using state of the art machine learning techniques and thousands of geotagged satellite and perspective images from diverse urban cultures, this research characterizes personal preference in urban spaces and predicts a spectrum of unknown likeable places for a specific observer. Unlike most urban perception studies, our intention is not by any means to provide an objective measure of urban quality, but rather to portray personal views of the city or Cities of Indexes.

Bugs are prevalent in software development. To improve software quality, bugs are filed using a bug tracking system. Properties of a reported bug would consist of a headline, description, project, product, component that is affected by the bug and the severity of the bug. Duplicate bugs rate (% of duplicate bugs) are in the range from single digit (1 to 9%) to double digits (40%) based on the product maturity , size of the code and number of engineers working on the project. Duplicate bugs range are between 9% to 39% in some of the open source projects like Eclipse, Firefox etc. Detection of duplicity deals with identifying whether any two bugs convey the same meaning. This detection of duplicates helps in de-duplication. Detecting duplicate bugs help reduce triaging efforts and saves time for developers in fixing the issues. Traditional natural language processing techniques are less accurate in identifying similarity between sentences. Using the bug data present in a bug tracking system, various approaches were explored including several machine learning algorithms, to obtain a viable approach that can identify duplicate bugs, given a pair of sentences(i.e. the respective bug descriptions). This approach considers multiple sets of features viz. basic text statistical features, semantic features and contextual features. These features are extracted from the headline, description and component and are subsequently used to train a classification algorithm.

Towards a deep understanding of these languages in model theory, we establish model-theoretic characterizations for a number of existential rule languages such as (disjunctive) embedded dependencies, tuple-generating dependencies (TGDs), (frontier-)guarded TGDs and linear TGDs. All these characterizations hold for arbitrary structures, and most of them also work on the class of finite structures. As a natural application of these characterizations, complexity bounds for the rewritability of above languages are also identified. 1 Introduction Existential rule languages, a family of languages that extend Datalog by allowing existential quantifiers in the rule head, had been initially introduced in databases in 1970s to specify the semantics of data stored in a database [ Abiteboul et al., 1995] . Since then, existential rule languages such as tuple-generating dependencies (TGDs), embedded dependencies and equality-generating dependencies have been extensively studied. These language have been recently rediscovered as languages for data exchange [ Fagin et al., 2005 ], data integration [ Lenzerini, 2002 ] and ontology-mediated query answering [ Cal ı et al., 2010 ] .

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I am the CEO and Co-founder of Pricefx and I started the company because I saw a huge opportunity in the Pricing software industry. I had been in the software business for over 20 years already with 5 years in Pricing software. I knew that the industry had to change to meet the evolving needs of businesses. I wasn't only focused on what was important at that time, I knew I had to build something that would grow and change with the future. Existing pricing solutions at the time were expensive and only offered on-premise installations that did not meet the needs of a business needing to act fast – in fact, some of those first-generation solutions took years to implement.

The Remote Access Trojan (RAT) can almost be considered the "legacy" tool of hackers. The RAT is a malware program that uses a back door for administrative control over the targeted computer. As such, RATs are used for "low and slow", prolonged, stealthy operations such as APTs. Using this malicious technique, the attackers take their time to explore the victim's networks and assets, and then move around as quietly as possible to achieve their objectives without detection. Some APTs have been in operation for years and RATs play a crucial part in enabling attackers to access targets while avoiding detection.

Zinier, a company that uses artificial intelligence (AI) to automate field work, has raised $90 million in a Series C funding round, bringing its total amount raised to $120 million. The startup plays heavily in the telecom sector -- 80% of its existing customers are in the space, including network operators, equipment vendors and suppliers, contractors, and engineers, according to Zinier's co-founder and CEO Arka Dhar. That's also reflected by the firms that returned to invest in this latest round, including Nokia-backed NGP Capital and Qualcomm Ventures. New investor Iconiq Capital led the round with participation from Tiger Global Management, Accel, Founders Fund, and Newfund Capital. "Zinier is going to play a very, very important role there," Dhar said in a phone interview.

Humans learn language through several perceptive cues and an endless continuum of multimodal interactions. To learn the names of the objects around us, we need some kind of a supervision or a context. Either our parents explicitly point us the tangible, non-abstract objects in our first years, or we grab the meaning of the words from the peripheral context. Likewise, the movements of the objects are described by "verbs". We learn the meaning of the verbs by watching the objects in motion, or we grab a verb through a linguistic context without visually perceiving it. Then we use the learned objects and verbs in different unseen combinations, constitute novel sentences and generalize the verbs and nouns to new unseen instances or cases. There is an ongoing process of connecting, updating and renewing the inputs from different modalities [12]. In this work, we explore the possibilities of learning the verbs from multimodal cues in a similar way to humans and propose a neural network model that aims to jointly capture the visual and textual representation. The problem is to build a cross-modal joint space which will help retrieving a textual modal given a visual modal, or vice versa.

Next point-of-interest (POI) recommendation aims to offer suggestions on which POI to visit next, given a user's POI visit history. This problem has a wide application in the tourism industry, and it is gaining an increasing interest as more POI check-in data become available. The problem is often modeled as a sequential recommendation problem to take advantage of the sequential patterns of user check-ins, e.g., people tend to visit Central Park after The Metropolitan Museum of Art in New York City. Recently, self-attentive networks have been shown to be both effective and efficient in general sequential recommendation problems, e.g., to recommend products, video games, or movies. Directly adopting self-attentive networks for next POI recommendation, however, may produce sub-optimal recommendations. This is because vanilla self-attentive networks do not consider the spatial and temporal patterns of user check-ins, which are two critical features in next POI recommendation. To address this limitation, in this paper, we propose a model named SANST that incorporates spatio-temporal patterns of user check-ins into self-attentive networks. To incorporate the spatial patterns, we encode the relative positions of POIs into their embeddings before feeding the embeddings into the self-attentive network. To incorporate the temporal patterns, we discretize the time of POI check-ins and model the temporal relationship between POI check-ins by a relation-aware self-attention module. We evaluate the performance of our SANST model with three real-world datasets. The results show that SANST consistently outperforms the state-of-theart models, and the advantage in nDCG@10 is up to 13.65%.

Uncertainty quantification for deep learning is a challenging open problem. Bayesian statistics offer a mathematically grounded framework to reason about uncertainties; however, approximate posteriors for modern neural networks still require prohibitive computational costs. We propose a family of algorithms which split the classification task into two stages: representation learning and uncertainty estimation. We compare four specific instances, where uncertainty estimation is performed via either an ensemble of Stochastic Gradient Descent or Stochastic Gradient Langevin Dynamics snapshots, an ensemble of bootstrapped logistic regressions, or via a number of Monte Carlo Dropout passes. We evaluate their performance in terms of \emph{selective} classification (risk-coverage), and their ability to detect out-of-distribution samples. Our experiments suggest there is limited value in adding multiple uncertainty layers to deep classifiers, and we observe that these simple methods strongly outperform a vanilla point-estimate SGD in some complex benchmarks like ImageNet.