Identifying crime for forensic investigating teams when crimes involve people of different nationals is challenging. This paper proposes a new method for ethnicity (nationality) identification based on Cloud of Line Distribution (COLD) features of handwriting components. The proposed method, at first, explores tangent angle for the contour pixels in each row and the mean of intensity values of each row in an image for segmenting text lines. For segmented text lines, we use tangent angle and direction of base lines to remove rule lines in the image. We use polygonal approximation for finding dominant points for contours of edge components. Then the proposed method connects the nearest dominant points of every dominant point, which results in line segments of dominant point pairs. For each line segment, the proposed method estimates angle and length, which gives a point in polar domain. For all the line segments, the proposed method generates dense points in polar domain, which results in COLD distribution. As character component shapes change, according to nationals, the shape of the distribution changes. This observation is extracted based on distance from pixels of distribution to Principal Axis of the distribution. Then the features are subjected to an SVM classifier for identifying nationals. Experiments are conducted on a complex dataset, which show the proposed method is effective and outperforms the existing method

Humans can ground natural language commands to tasks at both abstract and fine-grained levels of specificity. For instance, a human forklift operator can be instructed to perform a high-level action, like "grab a pallet" or a low-level action like "tilt back a little bit." While robots are also capable of grounding language commands to tasks, previous methods implicitly assume that all commands and tasks reside at a single, fixed level of abstraction. Additionally, methods that do not use multiple levels of abstraction encounter inefficient planning and execution times as they solve tasks at a single level of abstraction with large, intractable state-action spaces closely resembling real world complexity. In this work, by grounding commands to all the tasks or subtasks available in a hierarchical planning framework, we arrive at a model capable of interpreting language at multiple levels of specificity ranging from coarse to more granular. We show that the accuracy of the grounding procedure is improved when simultaneously inferring the degree of abstraction in language used to communicate the task. Leveraging hierarchy also improves efficiency: our proposed approach enables a robot to respond to a command within one second on 90% of our tasks, while baselines take over twenty seconds on half the tasks. Finally, we demonstrate that a real, physical robot can ground commands at multiple levels of abstraction allowing it to efficiently plan different subtasks within the same planning hierarchy.

One approach to achieving artificial general intelligence (AGI) is through the emergence of complex structures and dynamic properties arising from decentralized networks of interacting artificial intelligence (AI) agents. Understanding the principles of consensus in societies and finding ways to make consensus more reliable becomes critically important as connectivity and interaction speed increase in modern distributed systems of hybrid collective intelligences, which include both humans and computer systems. We propose a new form of reputation-based consensus with greater resistance to reputation gaming than current systems have. We discuss options for its implementation, and provide initial practical results.

We present a neural sequence model designed specifically for symbolic music. The model is based on a learned edit distance mechanism which generalises a classic recursion from computer science, leading to a neural dynamic program. Repeated motifs are detected by learning the transformations between them. We represent the arising computational dependencies using a novel data structure, the edit tree; this perspective suggests natural approximations which afford the scaling up of our otherwise cubic time algorithm. We demonstrate our model on real and synthetic data; in all cases it outperforms a strong stacked long short-term memory benchmark.

Sentence simplification aims to improve readability and understandability, based on several operations such as splitting, deletion, and paraphrasing. However, a valid simplified sentence should also be logically entailed by its input sentence. In this work, we first present a strong pointer-copy mechanism based sequence-to-sequence sentence simplification model, and then improve its entailment and paraphrasing capabilities via multi-task learning with related auxiliary tasks of entailment and paraphrase generation. Moreover, we propose a novel 'multi-level' layered soft sharing approach where each auxiliary task shares different (higher versus lower) level layers of the sentence simplification model, depending on the task's semantic versus lexico-syntactic nature. We also introduce a novel multi-armed bandit based training approach that dynamically learns how to effectively switch across tasks during multi-task learning. Experiments on multiple popular datasets demonstrate that our model outperforms competitive simplification systems in SARI and FKGL automatic metrics, and human evaluation. Further, we present several ablation analyses on alternative layer sharing methods, soft versus hard sharing, dynamic multi-armed bandit sampling approaches, and our model's learned entailment and paraphrasing skills.

A plant is operated on the basis of its manual usually; however, it is not realistic that a manual contains instructions for all cases, especially regarding abnormal ones. For obtaining appropriate operation procedures for a wide variety of cases, multilevel flow modeling (MFM) has been studied ([1]-[3]). MFM is a functional modeling framework, in which a plant structure is expressed as a directed graph. The framework also has a set of influence propagation rules, which consists of if-then rules regarding the states of related components. If the state of a component has changed, the resulting state of the other components can be obtained by applying the rules in the forward direction. Conversely, given a desired state of a component, we can obtain the states of other components to be satisfied for achieving the desired state by tracing back the propagation rules. This leads an action to a desired state. Our contributions are as follows: 1) We propose a method to translate MFM into an FOL. This enables the application of techniques used in the FOL to MFM, such as inference engines and abductive reasoners [6].

The World Health Organization says that compulsively playing video games now qualifies as a new mental health condition, in a move that some critics warn may risk stigmatizing too many young players. While it's hard for some investors to wrap their arms around the significance of gaming, the short answer is it is massive and expected to continue to exhibit huge growth for years to come. Can someone truly be addicted to video games? The World Health Organization thinks so – but a major professional organization for psychiatrists strongly disagrees. The World Health Organization on Monday classified "gaming disorder" as a diagnosable condition, giving mental health professionals a basis for setting up treatment and identifying risks for the addictive behavior.

In a bid to further strengthen its fintech portfolio, digital payments firm MobiKwik today announced a strategic investment of Rs. 2 crores in Pune based data science company, Pivotchain Solutions. Founded in February 2017 by Deepak Rao and Yogendra Pratap Singh, Pivotchain is a Predictive Analytics company with expertise in Machine Learning & Artificial Intelligence. It has built exclusive AI and deep learning models for Mobikwik. These models will be instrumental for MobiKwik as it rolls out lending products to address the credit requirements of its user base. Speaking on the strategic investment, Bipin Preet Singh, Founder and CEO, MobiKwik, said, "MobiKwik is transforming from a leading digital payments player, to India's largest digital financial services platform. Delivering high quality fintech products will require immense focus on data, and an in-depth understanding of the user requirements, across categories. Pivotchain is doing incredible work in alternate data scoring, predictive modeling & risk management and this investment will give us an edge over competition. We will continue to invest in companies that can add value to our business."

TWENTY years ago, the Open Source framework was published, delivering what would be the most significant trend in software development since that time. Whether you want to call it "free software" or "open source", ultimately, it's all about making application and system source codes widely available and putting the software under a license that favours user autonomy. According to Ovum, open source is already the default option across several big data categories ranging from storage, analytics and applications to machine learning. In the latest Black Duck Software and North Bridge's survey, 90% of respondents reported they rely on open source "for improved efficiency, innovation and interoperability," most commonly because of "freedom from vendor lock-in; competitive features and technical capabilities; ability to customise; and overall quality." There are now thousands of successful open source projects that companies must strategically choose from to stay competitive.

In a bid to strengthen its fintech portfolio, digital payments platform MobiKwik on Monday announced it has made a strategic investment of Rs 2 crore in Pune-based data science company Pivotchain Solutions. Pivotchain is a predictive analytics company with expertise in Machine Learning and Artificial Intelligence, and has built exclusive AI and deep learning models for MobiKwik. The Gurgaon-based payments firm said these models will be instrumental for MobiKwik as it rolls out numerous lending products to address the credit requirements of its user base. "MobiKwik is transforming from a leading digital payments player to India's largest digital financial services platform. Delivering high quality fintech products will require immense focus on data, and an in-depth understanding of the user requirements, across categories. Pivotchain is doing incredible work in alternate data scoring, predictive modeling and risk management and this investment will give us an edge over competition."