Knowledge base (KB) is an important aspect in artificial intelligence. One significant challenge faced by KB construction is that it contains many noises, which prevents its effective usage. Even though some KB cleansing algorithms have been proposed, they focus on the structure of the knowledge graph and neglect the relation between the concepts, which could be helpful to discover wrong relations in KB. Motived by this, we measure the relation of two concepts by the distance between their corresponding instances and detect errors within the intersection of the conflicting concept sets. For efficient and effective knowledge base cleansing, we first apply a distance-based Model to determine the conflicting concept sets using two different methods. Then, we propose and analyze several algorithms on how to detect and repairing the errors based on our model, where we use hash method for an efficient way to calculate distance. Experimental results demonstrate that the proposed approaches could cleanse the knowledge bases efficiently and effectively.

Tel: 962 777 260 802 Recent advances in smart cities applications enforce security threads such as node replication attacks. Such attack is take place when the attacker plants a replicated network node within the network. Vehicular Ad hoc networks are connecting sensors that have limited resources and required the response time to be as low as possible. In this type networks, traditional detection algorithms of node replication attacks are not efficient. In this paper, we propose an initial idea to apply a newly adapted statistical methodology that can detect node replication attacks with high performance as compared to state-of-the-art techniques. We provide a sufficient description of this methodology and a road-map for testing and experiment its performance.

When generating adversarial examples to attack deep neural networks (DNNs), $\ell_p$ norm of the added perturbation is usually used to measure the similarity between original image and adversarial example. However, such adversarial attacks may fail to capture key infomation hidden in the input. This work develops a more general attack model i.e., the structured attack that explores group sparsity in adversarial perturbations by sliding a mask through images aiming for extracting key structures. An ADMM (alternating direction method of multipliers)-based framework is proposed that can split the original problem into a sequence of analytically solvable subproblems and can be generalized to implement other state-of-the-art attacks. Strong group sparsity is achieved in adversarial perturbations even with the same level of distortion in terms of $\ell_p$ norm as the state-of-the-art attacks. Extensive experimental results on MNIST, CIFAR-10 and ImageNet show that our attack could be much stronger (in terms of smaller $\ell_0$ distortion) than the existing ones, and its better interpretability from group sparse structures aids in uncovering the origins of adversarial examples.

The spectroscopy measurement is one of main pathways for exploring and understanding the nature. Today, it seems that racing artificial intelligence will remould its styles. The algorithms contained in huge neural networks are capable of substituting many of expensive and complex components of spectrum instruments. In this work, we presented a smart machine learning strategy on the measurement of absorbance curves, and also initially verified that an exceedingly-simplified equipment is sufficient to meet the needs for this strategy. Further, with its simplicity, the setup is expected to infiltrate into many scientific areas in versatile forms.

The ever-growing datasets published on Linked Open Data mainly contain encyclopedic information. However, there is a lack of quality structured and semantically annotated datasets extracted from unstructured real-time sources. In this paper, we present principles for developing a knowledge graph of interlinked events using the case study of news headlines published on Twitter which is a real-time and eventful source of fresh information. We represent the essential pipeline containing the required tasks ranging from choosing background data model, event annotation (i.e., event recognition and classification), entity annotation and eventually interlinking events. The state-of-the-art is limited to domain-specific scenarios for recognizing and classifying events, whereas this paper plays the role of a domain-agnostic road-map for developing a knowledge graph of interlinked events.

Recurrent neural networks (RNNs) are temporal networks and cumulative in nature that have shown promising results in various natural language processing tasks. Despite their success, it still remains a challenge to understand their hidden behavior. In this work, we analyze and interpret the cumulative nature of RNN via a proposed technique named as Layer-wIse-Semantic-Accumulation (LISA) for explaining decisions and detecting the most likely (i.e., saliency) patterns that the network relies on while decision making. We demonstrate (1) LISA: "How an RNN accumulates or builds semantics during its sequential processing for a given text example and expected response" (2) Example2pattern: "How the saliency patterns look like for each category in the data according to the network in decision making". We analyse the sensitiveness of RNNs about different inputs to check the increase or decrease in prediction scores and further extract the saliency patterns learned by the network. We employ two relation classification datasets: SemEval 10 Task 8 and TAC KBP Slot Filling to explain RNN predictions via the LISA and example2pattern.

Answer triggering is the task of selecting the best-suited answer for a given question from a set of candidate answers if exists. In this paper, we present a hybrid deep learning model for answer triggering, which combines several dependency graph based alignment features, namely graph edit distance, graph-based similarity and dependency graph coverage, with dense vector embeddings from a Convolutional Neural Network (CNN). Our experiments on the WikiQA dataset show that such a combination can more accurately trigger a candidate answer compared to the previous state-of-the-art models. Comparative study on WikiQA dataset shows 5.86% absolute F-score improvement at the question level.

Neuroinform manuscript No. (will be inserted by the editor) Abstract In order to decode human brain, Multivariate Pattern (MVP) classification generates cognitive models by using functional Magnetic Resonance Imaging (fMRI) datasets. As a standard pipeline in the MVP analysis, brain patterns in multi-subject fMRI dataset must be mapped to a shared space and then a classification model is generated by employing the mapped patterns. However, the MVP models may not provide stable performance on a new fMRI dataset because the standard pipeline uses disjoint steps for generating these models. Indeed, each step in the pipeline includes an objective function with independent optimization approach, where the best solution of each step may not be optimum for the next steps. For tackling the mentioned issue, this paper introduces Multi-Objective Cognitive Model (MOCM) that utilizes an integrated objective function for MVP analysis rather than just using those disjoint steps. For solving the integrated problem, we proposed a customized multi-objective optimization approach, where all possible solutions are firstly generated, and then our method ranks and selects the robust solutions as the final results. Empirical studies confirm that the proposed method can generate superior performance in comparison with other techniques. Keywords Multi-Objective Cognitive Model · fMRI Analysis · Multivariate Pattern · Multi-Objective Optimization 1 Introduction One of the primary goals in neuroscience is to understand how the neural activities in the human brain can be mapped to different cognitive tasks. The authors are with the College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China. Magnetic Resonance Imaging (fMRI) data is an interdisciplinary technique.

The universe of digital analytics is massive and can seem as complex as the cosmic universe. With such big, complicated subjects, we can get lost in the vast wilderness or become trapped in a silo. We can wander aimlessly, or feel a false sense of either accomplishment or frustration. Consequently, we lose sight of where we are, how we are doing and which direction is true north. I have experienced these challenges on numerous occasions myself. Even simple questions like "How effective is our analytics strategy?" That's because we have to talk about tools (so many!), work (collection, processing, reporting, analysis), processes, org structure, governance models, last-mile gaps, metrics ladders of awesomeness, and… so… much… more. There is another critical framework to figure out how you can take your analytics sophistication from wherever it is at the moment to nirvanaland. It is important to stress that none of these frameworks/answers exist in a vacuum.

Facial-recognition technology is not new, but it has progressed immensely in the past few years, mainly because of advances in artificial intelligence. Naturally, this has drawn the interest of Silicon Valley, advertising agencies, hardware manufacturers, and the government. But not everyone is thrilled. The American Civil Liberties Union (ACLU) and 35 other advocacy groups, for example, sent a letter to Amazon CEO Jeff Bezos demanding that his company stop providing advanced facial-recognition technology to law enforcement, warning that it could be misused against immigrants and protesters. Early iterations of the technology, which dates back to the 1960s, were clunky. Police had to create a facial-recognition database, which required a human user to specify key points on a photo of each subject's face, such as the center of pupils and corners of the eyes, mouth, and nose.