OpenRaiser/Intern-Atlas huggingface dataset:
On Quantifying and Understanding the Role of Ethics in AI Research: A Historical Account of Flagship Conferences and Journals
Venue: arXiv cs.CY 2018
Year: 2018

Recent developments in AI, Machine Learning and Robotics have raised concerns about the ethical consequences of both academic and industrial AI research. Leading academics, businessmen and politicians have voiced an increasing number of questions about the consequences of AI not only over people, but also on the large-scale consequences on the the future of work and employment, its social consequences and the sustainability of the planet. In this work, we analyse the use and the occurrence of ethics-related research in leading AI, machine learning and robotics venues. In order to do so we perform long term, historical corpus-based analyses on a large number of flagship conferences and journals. Our experiments identify the prominence of ethics-related terms in published papers and presents several statistics on related topics. Finally, this research provides quantitative evidence on the pressing ethical concerns of the AI community.
Relating Zipf's law to textual information
Venue: arXiv cs.CL 2018
Year: 2018

Zipf's law is the main regularity of quantitative linguistics. Despite of many works devoted to foundations of this law, it is still unclear whether it is only a statistical regularity, or it has deeper relations with information-carrying structures of the text. This question relates to that of distinguishing a meaningful text (written in an unknown system) from a meaningless set of symbols that mimics statistical features of a text. Here we contribute to resolving these questions by comparing features of the first half of a text (from the beginning to the middle) to its second half. This comparison can uncover hidden effects, because the halves have the same values of many parameters (style, genre, author's vocabulary {\it etc}). In all studied texts we saw that for the first half Zipf's law applies from smaller ranks than in the second half, i.e. the law applies better to the first half. Also, words that hold Zipf's law in the first half are distributed more homogeneously over the text. These features do allow to distinguish a meaningful text from a random sequence of words. Our findings correlate with a number of textual characteristics that hold in most cases we studied: the first half is lexically richer, has longer and less repetitive words, more and shorter sentences, more punctuation signs and more paragraphs. These differences between the halves indicate on a higher hierarchic level of text organization that so far went unnoticed in text linguistics. They relate the validity of Zipf's law to textual information. A complete description of this effect requires new models, though one existing model can account for some of its aspects.
Variational Collaborative Learning for User Probabilistic Representation
Venue: arXiv cs.LG 2018
Year: 2018

Collaborative filtering (CF) has been successfully employed by many modern recommender systems. Conventional CF-based methods use the user-item interaction data as the sole information source to recommend items to users. However, CF-based methods are known for suffering from cold start problems and data sparsity problems. Hybrid models that utilize auxiliary information on top of interaction data have increasingly gained attention. A few "collaborative learning"-based models, which tightly bridges two heterogeneous learners through mutual regularization, are recently proposed for the hybrid recommendation. However, the "collaboration" in the existing methods are actually asynchronous due to the alternative optimization of the two learners. Leveraging the recent advances in variational autoencoder~(VAE), we here propose a model consisting of two streams of mutual linked VAEs, named variational collaborative model (VCM). Unlike the mutual regularization used in previous works where two learners are optimized asynchronously, VCM enables a synchronous collaborative learning mechanism. Besides, the two stream VAEs setup allows VCM to fully leverages the Bayesian probabilistic representations in collaborative learning. Extensive experiments on three real-life datasets have shown that VCM outperforms several state-of-art methods.
Medical Knowledge Embedding Based on Recursive Neural Network for Multi-Disease Diagnosis
Venue: arXiv cs.AI 2018
Year: 2018

The representation of knowledge based on first-order logic captures the richness of natural language and supports multiple probabilistic inference models. Although symbolic representation enables quantitative reasoning with statistical probability, it is difficult to utilize with machine learning models as they perform numerical operations. In contrast, knowledge embedding (i.e., high-dimensional and continuous vectors) is a feasible approach to complex reasoning that can not only retain the semantic information of knowledge but also establish the quantifiable relationship among them. In this paper, we propose recursive neural knowledge network (RNKN), which combines medical knowledge based on first-order logic with recursive neural network for multi-disease diagnosis. After RNKN is efficiently trained from manually annotated Chinese Electronic Medical Records (CEMRs), diagnosis-oriented knowledge embeddings and weight matrixes are learned. Experimental results verify that the diagnostic accuracy of RNKN is superior to that of some classical machine learning models and Markov logic network (MLN). The results also demonstrate that the more explicit the evidence extracted from CEMRs is, the better is the performance achieved. RNKN gradually exhibits the interpretation of knowledge embeddings as the number of training epochs increases.
Content-Based Multi-Source Encrypted Image Retrieval in Clouds with Privacy Preservation
Venue: arXiv cs.CR 2018
Year: 2018

Content-based image retrieval (CBIR) is one of the fundamental image retrieval primitives. Its applications can be found in various areas, such as art collections and medical diagnoses. With an increasing prevalence of cloud computing paradigm, image owners desire to outsource their images to cloud servers. In order to deal with the risk of privacy leakage of images, images are typically encrypted before they are outsourced to the cloud, which makes CBIR an extremely challenging task. Existing studies focus on the scenario with only a single image owner, leaving the problem of CBIR with multiple image sources (i.e., owners) unaddressed. In this paper, we propose a secure CBIR scheme that supports Multiple Image owners with Privacy Protection (MIPP). We encrypt image features with a secure multi-party computation technique, which allows image owners to encrypt image features with their own keys. This enables efficient image retrieval over images gathered from multiple sources, while guaranteeing that image privacy of an individual image owner will not be leaked to other image owners. We also propose a new method for similarity measurement of images that can avoid revealing image similarity information to the cloud. Theoretical analysis and experimental results demonstrate that MIPP achieves retrieval accuracy and efficiency simultaneously, while preserving image privacy.
A 2-Approximation Algorithm for Feedback Vertex Set in Tournaments
Venue: arXiv cs.DS 2018
Year: 2018

A {\em tournament} is a directed graph $T$ such that every pair of vertices is connected by an arc. A {\em feedback vertex set} is a set $S$ of vertices in $T$ such that $T - S$ is acyclic. We consider the {\sc Feedback Vertex Set} problem in tournaments. Here the input is a tournament $T$ and a weight function $w : V(T) \rightarrow \mathbb{N}$ and the task is to find a feedback vertex set $S$ in $T$ minimizing $w(S) = \sum_{v \in S} w(v)$. We give the first polynomial time factor $2$ approximation algorithm for this problem. Assuming the Unique Games conjecture, this is the best possible approximation ratio achievable in polynomial time.
On the Security of an Unconditionally Secure, Universally Composable Inner Product Protocol
Venue: arXiv cs.CR 2018
Year: 2018

In this paper we discuss the security of a distributed inner product (DIP) protocol [IEEE TIFS, 11(1), (2016), 59-73]. We show information leakage in this protocol that does not happen in an ideal execution of DIP functionality. In some scenarios, this information leakage enables one of the parties to completely learn the other party's input. We will give examples of such scenarios.
Permissioned Blockchain Technologies for Academic Publishing
Venue: arXiv cs.DC 2018
Year: 2018

Academic publishing is continuously evolving with the gradual adoption of new technologies. Blockchain is a new technology that promises to change how individuals and organizations interact across various boundaries. The adoption of blockchains is beginning to transform diverse industries such as finance, supply chain, international trade, as well as energy and resource management and many others. Through trust, data immutability, decentralized distribution of data, and facilitation of collaboration without the need for centralized management and authority, blockchains have the potential to transform the academic publishing domain and to address some of the current problems such as productivity and reputation management, predatory publishing, transparent peer-review processes and many others. In this paper, we outline the technologies available in the domain of permissioned blockchains with focus on Hyperledger Fabric and discuss how they can be leveraged in the domain of academic publishing.
DT-LET: Deep Transfer Learning by Exploring where to Transfer
Venue: arXiv cs.LG 2018
Year: 2018

Previous transfer learning methods based on deep network assume the knowledge should be transferred between the same hidden layers of the source domain and the target domains. This assumption doesn't always hold true, especially when the data from the two domains are heterogeneous with different resolutions. In such case, the most suitable numbers of layers for the source domain data and the target domain data would differ. As a result, the high level knowledge from the source domain would be transferred to the wrong layer of target domain. Based on this observation, "where to transfer" proposed in this paper should be a novel research frontier. We propose a new mathematic model named DT-LET to solve this heterogeneous transfer learning problem. In order to select the best matching of layers to transfer knowledge, we define specific loss function to estimate the corresponding relationship between high-level features of data in the source domain and the target domain. To verify this proposed cross-layer model, experiments for two cross-domain recognition/classification tasks are conducted, and the achieved superior results demonstrate the necessity of layer correspondence searching.
Gamifying the Escape from the Engineering Method Prison - An Innovative Board Game to Teach the Essence Theory to Future Project Managers and Software Engineers
Venue: arXiv cs.SE 2018
Year: 2018

Software Engineering is an engineering discipline but lacks a solid theoretical foundation. One effort in remedying this situation has been the SEMAT Essence specification. Essence consists of a language for modeling Software Engineering (SE) practices and methods and a kernel containing what its authors describe as being elements that are present in every software development project. In practice, it is a method agnostic project management tool for SE Projects. Using the language of the specification, Essence can be used to model any software development method or practice. Thus, the specification can potentially be applied to any software development context, making it a powerful tool. However, due to the manual work and the learning process involved in modeling practices with Essence, its initial adoption can be tasking for development teams. Due to the importance of project management in SE projects, new project management tools such as Essence are valuable, and facilitating their adoption is consequently important. To tackle this issue in the case of Essence, we present a game-based approach to teaching the use Essence. In this paper, we gamify the learning process by means of an innovative board game. The game is empirically validated in a study involving students from the IT faculty of University of Jyväskylä (n=61). Based on the results, we report the effectiveness of the game-based approach to teaching both Essence and SE project work.
Representation Theory of Compact Metric Spaces and Computational Complexity of Continuous Data
Venue: arXiv cs.LO 2018
Year: 2018

Choosing an encoding over binary strings for input/output to/by a Turing Machine is usually straightforward and/or inessential for discrete data (like graphs), but delicate -- heavily affecting computability and even more computational complexity -- already regarding real numbers, not to mention more advanced (e.g. Sobolev) spaces. For a general theory of computational complexity over continuous data we introduce and justify QUANTITATIVE admissibility as requirement for sensible encodings of arbitrary compact metric spaces, a refinement of qualitative 'admissibility' due to [Kreitz&Weihrauch'85]: An admissible representation of a T0 space $X$ is a (i) continuous partial surjective mapping from the Cantor space of infinite binary sequences which is (ii) maximal w.r.t. continuous reduction. By the Kreitz-Weihrauch (aka "Main") Theorem of computability over continuous data, for fixed spaces $X,Y$ equipped with admissible representations, a function $f:X\to Y$ is continuous iff it admits continuous a code-translating mapping on Cantor space, a so-called REALIZER. We define a QUANTITATIVELY admissible representation of a compact metric space $X$ to have (i) asymptotically optimal modulus of continuity, namely close to the entropy of $X$, and (ii) be maximal w.r.t. reduction having optimal modulus of continuity in a similar sense. Careful constructions show the category of such representations to be Cartesian closed, and non-empty: every compact $X$ admits a linearly-admissible representation. Moreover such representations give rise to a tight quantitative correspondence between the modulus of continuity of a function $f:X\to Y$ on the one hand and on the other hand that of its realizer: the MAIN THEOREM of computational complexity. This suggests (how) to take into account the entropies of the spaces under consideration when measuring algorithmic cost over continuous data.
Monolingual sentence matching for text simplification
Venue: arXiv cs.CL 2018
Year: 2018

This work improves monolingual sentence alignment for text simplification, specifically for text in standard and simple Wikipedia. We introduce a convolutional neural network structure to model similarity between two sentences. Due to the limitation of available parallel corpora, the model is trained in a semi-supervised way, by using the output of a knowledge-based high performance aligning system. We apply the resulting similarity score to rescore the knowledge-based output, and adapt the model by a small hand-aligned dataset. Experiments show that both rescoring and adaptation improve the performance of knowledge-based method.
Recognizing Film Entities in Podcasts
Venue: arXiv cs.IR 2018
Year: 2018

In this paper, we propose a Named Entity Recognition (NER) system to identify film titles in podcast audio. Taking inspiration from NER systems for noisy text in social media, we implement a two-stage approach that is robust to computer transcription errors and does not require significant computational expense to accommodate new film titles/releases. Evaluating on a diverse set of podcasts, we demonstrate more than a 20% increase in F1 score across three baseline approaches when combining fuzzy-matching with a linear model aware of film-specific metadata.
The Sorry State of TLS Security in Enterprise Interception Appliances
Venue: arXiv cs.CR 2018
Year: 2018

Network traffic inspection, including TLS traffic, in enterprise environments is widely practiced. Reasons for doing so are primarily related to improving enterprise security (e.g., malware detection) and meeting legal requirements. To analyze TLS-encrypted data, network appliances implement a Man-in-the-Middle TLS proxy, by acting as the intended web server to a requesting client (e.g., a browser), and acting as the client to the outside web server. As such, the TLS proxy must implement both a TLS client and a server, and handle a large amount of traffic, preferably, in real-time. However, as protocol and implementation layer vulnerabilities in TLS/HTTPS are quite frequent, these proxies must be, at least, as secure as a modern, up-to-date web browser, and a properly configured web server. As opposed to client-end TLS proxies (e.g., as in several anti-virus products), the proxies in network appliances may serve hundreds to thousands of clients, and any vulnerability in their TLS implementations can significantly downgrade enterprise security. To analyze TLS security of network appliances, we develop a comprehensive framework, by combining and extending tests from existing work on client-end and network-based interception studies. We analyze thirteen representative network appliances over a period of more than a year (including versions before and after notifying affected vendors, a total of 17 versions), and uncover several security issues. For instance, we found that four appliances perform no certificate validation at all, three use pre-generated certificates, and eleven accept certificates signed using MD5, exposing their clients to MITM attacks. Our goal is to highlight the risks introduced by widely-used TLS proxies in enterprise and government environments, potentially affecting many systems hosting security, privacy, and financially sensitive data.
MobileFace: 3D Face Reconstruction with Efficient CNN Regression
Venue: arXiv cs.CV 2018
Year: 2018

Estimation of facial shapes plays a central role for face transfer and animation. Accurate 3D face reconstruction, however, often deploys iterative and costly methods preventing real-time applications. In this work we design a compact and fast CNN model enabling real-time face reconstruction on mobile devices. For this purpose, we first study more traditional but slow morphable face models and use them to automatically annotate a large set of images for CNN training. We then investigate a class of efficient MobileNet CNNs and adapt such models for the task of shape regression. Our evaluation on three datasets demonstrates significant improvements in the speed and the size of our model while maintaining state-of-the-art reconstruction accuracy.
Oscillation Damping Control of Pendulum-like Manipulation Platform using Moving Masses
Venue: arXiv cs.RO 2018
Year: 2018

This paper presents an approach to damp out the oscillatory motion of the pendulum-like hanging platform on which a robotic manipulator is mounted. To this end, moving masses were installed on top of the platform. In this paper, asymptotic stability of the platform (which implies oscillation damping) is achieved by designing reference acceleration of the moving masses properly. A main feature of this work is that we can achieve asymptotic stability of not only the platform, but also the moving masses, which may be challenging due to the under-actuation nature. The proposed scheme is validated by the simulation studies.
Representing Sets as Summed Semantic Vectors
Venue: arXiv cs.AI 2018
Year: 2018

Representing meaning in the form of high dimensional vectors is a common and powerful tool in biologically inspired architectures. While the meaning of a set of concepts can be summarized by taking a (possibly weighted) sum of their associated vectors, this has generally been treated as a one-way operation. In this paper we show how a technique built to aid sparse vector decomposition allows in many cases the exact recovery of the inputs and weights to such a sum, allowing a single vector to represent an entire set of vectors from a dictionary. We characterize the number of vectors that can be recovered under various conditions, and explore several ways such a tool can be used for vector-based reasoning.
Robotics Rights and Ethics Rules
Venue: arXiv cs.RO 2018
Year: 2018

It is very important to adhere strictly to ethical and social influences when delivering most of our life to artificial intelligence systems. With industry 4.0, the internet of things, data analysis and automation have begun to be of great importance in our lives. With the Yapanese version of Industry 5.0, it has come to our attention that machine-human interaction and human intelligence are working in harmony with the cognitive computer. In this context, robots working on artificial intelligence algorithms co-ordinated with the development of technology have begun to enter our lives. But the consequences of the recent complaints of the Robots have been that important issues have arisen about how to be followed in terms of intellectual property and ethics. Although there are no laws regulating robots in our country at present, laws on robot ethics and rights abroad have entered into force. This means that it is important that we organize the necessary arrangements in the way that robots and artificial intelligence are so important in the new world order. In this study, it was aimed to examine the existing rules of machine and robot ethics and to set an example for the arrangements to be made in our country, and various discussions were given in this context.
Empirical Methodology for Crowdsourcing Ground Truth
Venue: arXiv cs.HC 2018
Year: 2018

The process of gathering ground truth data through human annotation is a major bottleneck in the use of information extraction methods for populating the Semantic Web. Crowdsourcing-based approaches are gaining popularity in the attempt to solve the issues related to volume of data and lack of annotators. Typically these practices use inter-annotator agreement as a measure of quality. However, in many domains, such as event detection, there is ambiguity in the data, as well as a multitude of perspectives of the information examples. We present an empirically derived methodology for efficiently gathering of ground truth data in a diverse set of use cases covering a variety of domains and annotation tasks. Central to our approach is the use of CrowdTruth metrics that capture inter-annotator disagreement. We show that measuring disagreement is essential for acquiring a high quality ground truth. We achieve this by comparing the quality of the data aggregated with CrowdTruth metrics with majority vote, over a set of diverse crowdsourcing tasks: Medical Relation Extraction, Twitter Event Identification, News Event Extraction and Sound Interpretation. We also show that an increased number of crowd workers leads to growth and stabilization in the quality of annotations, going against the usual practice of employing a small number of annotators.
Rectilinear Shortest Paths Among Transient Obstacles
Venue: arXiv cs.CG 2018
Year: 2018

This paper presents an optimal $Θ(n \log n)$ algorithm for determining time-minimal rectilinear paths among $n$ transient rectilinear obstacles. An obstacle is transient if it exists in the scene only for a specific time interval, i.e., it appears and then disappears at specific times. Given a point robot moving with bounded speed among transient rectilinear obstacles and a pair of points $s$, $d$, we determine a time-minimal, obstacle-avoiding path from $s$ to $d$. The main challenge in solving this problem arises as the robot may be required to wait for an obstacle to disappear, before it can continue moving toward the destination. Our algorithm builds on the continuous Dijkstra paradigm, which simulates propagating a wavefront from the source point. We also solve a query version of this problem. For this, we build a planar subdivision with respect to a fixed source point, so that minimum arrival time to any query point can be reported in $O(\log n)$ time, using point location for the query point in this subdivision.