OpenRaiser/Intern-Atlas huggingface dataset:
From D2D to Ds2D: Prolonging the Battery Life of Mobile Devices via Ds2D Communications
Venue: arXiv cs.NI 2017
Year: 2017

Emerging device centric systems (DCS) such as device-to-device (D2D) communications are considered as a standard part of future mobile networks, where operators/consumers involve the devices in direct communication to improve the cellular system throughput, latency, fairness, and energy efficiency. However, battery life of mobile devices involved in such communications is crucial for 5G smartphone users to explore the emerging applications in DCS. It is anticipated that the owners of 5G-enabled smartphones use their devices more extensively to talk, text, email, and surf the Web more often than do customers with 4G smartphones or traditional handsets, which puts a significantly higher demand on the battery life. Smartphones are currently equipped with multiple radio interfaces that enable them to access different types of wireless networks including LTE-direct and Wi-Fi-direct, besides cellular networks. Such a capability is not well explored within the context of DCS. This article proposes a new scheme to support the emerging features in DCS where a D2D-enabled mobile device (sink device or a file/content requester) aggregates the radio resources of multiple mobile devices (source devices or file/content providers) via its multiple radio interfaces such that the scheme is referred to as devices-to-device (Ds2D) communications. Ds2D communication scheme ensures an optimal packet split among the source mobile devices to improve the file/content transfer latency (FTL), energy efficiency, and battery life. Simulation results demonstrate that the proposed optimal packet split scheme among multiple source devices participating in Ds2D communication scheme guarantees an improvement in mobile battery life over wide range of data rate levels in comparison with the random packet split strategy and traditional D2D communication paradigm between the sink and source mobile devices.
On cyclic codes of composite length and the minimal distance
Venue: arXiv cs.IT 2017
Year: 2017

In an interesting paper Professor Cunsheng Ding provided three constructions of cyclic codes of length being a product of two primes. Numerical data shows that many codes from these constructions are best cyclic codes of the same length and dimension over the same finite field. However, not much is known about these codes. In this paper we explain some of the mysteries of the numerical data by developing a general method on cyclic codes of composite length and on estimating the minimal distance. Inspired by the new method, we also provide a general construction of cyclic codes of composite length. Numerical data shows that it produces many best cyclic codes as well. Finally, we point out how these cyclic codes can be used to construct convolutional codes with large free distance.
Active Expressions: Basic Building Blocks for Reactive Programming
Venue: arXiv cs.SE 2017
Year: 2017

Modern software development without reactive programming is hard to imagine. Reactive programming favors a wide class of contemporary software systems that respond to user input, network messages, and other events. While reactive programming is an active field of research, the implementation of reactive concepts remains challenging. In particular, change detection represents a hard but inevitable necessity when implementing reactive concepts. Typically, change detection mechanisms are not intended for reuse but are tightly coupled to the particular change resolution mechanism. As a result, developers often have to re-implement similar abstractions. A reusable primitive for change detection is still missing. To find a suitable primitive, we identify commonalities in existing reactive concepts. We discover a class of reactive concepts, state-based reactive concepts. All state-based reactive concepts share a common change detection mechanism: they detect changes in the evaluation result of an expression. On the basis of the identified common change detection mechanism, we propose active expressions as a reusable primitive. By abstracting the tedious implementation details of change detection, active expressions can ease the implementation of reactive programming concepts. We evaluate the design of active expressions by re-implementing a number of existing state-based reactive concepts using them. The resulting implementations highlight the expressiveness of active expressions. Active expressions enable the separation of essential from non-essential parts when reasoning about reactive programming concepts. By using active expressions as a primitive for change detection, developers of reactive language constructs and runtime support can now focus on the design of how application programmers should be able to react to change. Ultimately, we would like active expressions to encourage experiments with novel reactive programming concepts and with that to yield a wider variety of them to explore.
Edit Transactions: Dynamically Scoped Change Sets for Controlled Updates in Live Programming
Venue: arXiv cs.SE 2017
Year: 2017

Live programming environments enable programmers to edit a running program and obtain immediate feedback on each individual change. The liveness quality is valued by programmers to help work in small steps and continuously add or correct small functionality while maintaining the impression of a direct connection between each edit and its manifestation at run-time. Such immediacy may conflict with the desire to perform a combined set of intermediate steps, such as a refactoring, without immediately taking effect after each individual edit. This becomes important when an incomplete sequence of small-scale changes can easily break the running program. State-of-the-art solutions focus on retroactive recovery mechanisms, such as debugging or version control. In contrast, we propose a proactive approach: Multiple individual changes to the program are collected in an Edit Transaction, which can be made effective if deemed complete. Upon activation, the combined steps become visible together. Edit Transactions are capable of dynamic scoping, allowing a set of changes to be tested in isolation before being extended to the running application. This enables a live programming workflow with full control over change granularity, immediate feedback on tests, delayed effect on the running application, and coarse-grained undos. We present an implementation of Edit Transactions along with Edit-Transaction-aware tools in Squeak/Smalltalk. We asses this implementation by conducting a case study with and without the new tool support, comparing programming activities, errors, and detours for implementing new functionality in a running simulation. We conclude that workflows using Edit Transactions have the potential to increase confidence in a change, reduce potential for run-time errors, and eventually make live programming more predictable and engaging.
Study of cost functionals for ptychographic phase retrieval to improve the robustness against noise, and a proposal for another noise-robust ptychographic phase retrieval scheme
Venue: arXiv cs.IT 2017
Year: 2017

Recently, efforts have been made to improve ptychography phase retrieval algorithms so that they are more robust against noise. Often the algorithm is adapted by changing the cost functional that needs to be minimized. In particular, it has been suggested that the cost functional should be obtained using a maximum-likelihood approach that takes the noise statistics into account. Here, we consider the different choices of cost functional, and to how they affect the reconstruction results. We find that seemingly the only consistently reliable way to improve reconstruction results in the presence of noise is to reduce the step size of the update function. In addition, a noise-robust ptychographic reconstruction method has been proposed that relies on adapting the intensity constraints
Diversity of preferences can increase collective welfare in sequential exploration problems
Venue: arXiv cs.AI 2017
Year: 2017

In search engines, online marketplaces and other human-computer interfaces large collectives of individuals sequentially interact with numerous alternatives of varying quality. In these contexts, trial and error (exploration) is crucial for uncovering novel high-quality items or solutions, but entails a high cost for individual users. Self-interested decision makers, are often better off imitating the choices of individuals who have already incurred the costs of exploration. Although imitation makes sense at the individual level, it deprives the group of additional information that could have been gleaned by individual explorers. In this paper we show that in such problems, preference diversity can function as a welfare enhancing mechanism. It leads to a consistent increase in the quality of the consumed alternatives that outweighs the increased cost of search for the users.
On the Impossibility of Supersized Machines
Venue: arXiv cs.CY 2017
Year: 2017

In recent years, a number of prominent computer scientists, along with academics in fields such as philosophy and physics, have lent credence to the notion that machines may one day become as large as humans. Many have further argued that machines could even come to exceed human size by a significant margin. However, there are at least seven distinct arguments that preclude this outcome. We show that it is not only implausible that machines will ever exceed human size, but in fact impossible.
An Outline of Separation Logic
Venue: arXiv cs.LO 2017
Year: 2017

Separation Logic is an effective Program Logic for proving programs that involve pointers. Reasoning with pointers becomes difficult especially when there is aliasing arising due to several pointers to a given cell location. In this paper, we try to explore the problems with aliasing through some simple examples and introduce the notion of separating conjunction as a tool to deal with it. We introduce Separation Logic as an extension of the standard Hoare Logic with the help pf a programming language that has four pointer manipulating commands. These commands perform the usual heap operations such as lookup, update, allocation and deallocation. The new set of assertions and axioms of Separation Logic is presented in a semi-formal style. Examples are given to illustrate the unique features of the new assertions and axioms. Finally the paper concludes with the proofs of some real programs using the axioms of Separation Logic.
Efficient Registration of Pathological Images: A Joint PCA/Image-Reconstruction Approach
Venue: arXiv cs.CV 2017
Year: 2017

Registration involving one or more images containing pathologies is challenging, as standard image similarity measures and spatial transforms cannot account for common changes due to pathologies. Low-rank/Sparse (LRS) decomposition removes pathologies prior to registration; however, LRS is memory-demanding and slow, which limits its use on larger data sets. Additionally, LRS blurs normal tissue regions, which may degrade registration performance. This paper proposes an efficient alternative to LRS: (1) normal tissue appearance is captured by principal component analysis (PCA) and (2) blurring is avoided by an integrated model for pathology removal and image reconstruction. Results on synthetic and BRATS 2015 data demonstrate its utility.
Ontological Multidimensional Data Models and Contextual Data Qality
Venue: arXiv cs.DB 2017
Year: 2017

Data quality assessment and data cleaning are context-dependent activities. Motivated by this observation, we propose the Ontological Multidimensional Data Model (OMD model), which can be used to model and represent contexts as logic-based ontologies. The data under assessment is mapped into the context, for additional analysis, processing, and quality data extraction. The resulting contexts allow for the representation of dimensions, and multidimensional data quality assessment becomes possible. At the core of a multidimensional context we include a generalized multidimensional data model and a Datalog+/- ontology with provably good properties in terms of query answering. These main components are used to represent dimension hierarchies, dimensional constraints, dimensional rules, and define predicates for quality data specification. Query answering relies upon and triggers navigation through dimension hierarchies, and becomes the basic tool for the extraction of quality data. The OMD model is interesting per se, beyond applications to data quality. It allows for a logic-based, and computationally tractable representation of multidimensional data, extending previous multidimensional data models with additional expressive power and functionalities.
Psychological and Personality Profiles of Political Extremists
Venue: arXiv cs.CL 2017
Year: 2017

Global recruitment into radical Islamic movements has spurred renewed interest in the appeal of political extremism. Is the appeal a rational response to material conditions or is it the expression of psychological and personality disorders associated with aggressive behavior, intolerance, conspiratorial imagination, and paranoia? Empirical answers using surveys have been limited by lack of access to extremist groups, while field studies have lacked psychological measures and failed to compare extremists with contrast groups. We revisit the debate over the appeal of extremism in the U.S. context by comparing publicly available Twitter messages written by over 355,000 political extremist followers with messages written by non-extremist U.S. users. Analysis of text-based psychological indicators supports the moral foundation theory which identifies emotion as a critical factor in determining political orientation of individuals. Extremist followers also differ from others in four of the Big Five personality traits.
Who Makes Trends? Understanding Demographic Biases in Crowdsourced Recommendations
Venue: arXiv cs.SI 2017
Year: 2017

Users of social media sites like Facebook and Twitter rely on crowdsourced content recommendation systems (e.g., Trending Topics) to retrieve important and useful information. Contents selected for recommendation indirectly give the initial users who promoted (by liking or posting) the content an opportunity to propagate their messages to a wider audience. Hence, it is important to understand the demographics of people who make a content worthy of recommendation, and explore whether they are representative of the media site's overall population. In this work, using extensive data collected from Twitter, we make the first attempt to quantify and explore the demographic biases in the crowdsourced recommendations. Our analysis, focusing on the selection of trending topics, finds that a large fraction of trends are promoted by crowds whose demographics are significantly different from the overall Twitter population. More worryingly, we find that certain demographic groups are systematically under-represented among the promoters of the trending topics. To make the demographic biases in Twitter trends more transparent, we developed and deployed a Web-based service 'Who-Makes-Trends' at twitter-app.mpi-sws.org/who-makes-trends.
Sentiment Analysis of Citations Using Word2vec
Venue: arXiv cs.CL 2017
Year: 2017

Citation sentiment analysis is an important task in scientific paper analysis. Existing machine learning techniques for citation sentiment analysis are focusing on labor-intensive feature engineering, which requires large annotated corpus. As an automatic feature extraction tool, word2vec has been successfully applied to sentiment analysis of short texts. In this work, I conducted empirical research with the question: how well does word2vec work on the sentiment analysis of citations? The proposed method constructed sentence vectors (sent2vec) by averaging the word embeddings, which were learned from Anthology Collections (ACL-Embeddings). I also investigated polarity-specific word embeddings (PS-Embeddings) for classifying positive and negative citations. The sentence vectors formed a feature space, to which the examined citation sentence was mapped to. Those features were input into classifiers (support vector machines) for supervised classification. Using 10-cross-validation scheme, evaluation was conducted on a set of annotated citations. The results showed that word embeddings are effective on classifying positive and negative citations. However, hand-crafted features performed better for the overall classification.
Complexity-Aware Assignment of Latent Values in Discriminative Models for Accurate Gesture Recognition
Venue: arXiv cs.CV 2017
Year: 2017

Many of the state-of-the-art algorithms for gesture recognition are based on Conditional Random Fields (CRFs). Successful approaches, such as the Latent-Dynamic CRFs, extend the CRF by incorporating latent variables, whose values are mapped to the values of the labels. In this paper we propose a novel methodology to set the latent values according to the gesture complexity. We use an heuristic that iterates through the samples associated with each label value, stimating their complexity. We then use it to assign the latent values to the label values. We evaluate our method on the task of recognizing human gestures from video streams. The experiments were performed in binary datasets, generated by grouping different labels. Our results demonstrate that our approach outperforms the arbitrary one in many cases, increasing the accuracy by up to 10%.
Transfer Learning for Improving Model Predictions in Highly Configurable Software
Venue: arXiv cs.SE 2017
Year: 2017

Modern software systems are built to be used in dynamic environments using configuration capabilities to adapt to changes and external uncertainties. In a self-adaptation context, we are often interested in reasoning about the performance of the systems under different configurations. Usually, we learn a black-box model based on real measurements to predict the performance of the system given a specific configuration. However, as modern systems become more complex, there are many configuration parameters that may interact and we end up learning an exponentially large configuration space. Naturally, this does not scale when relying on real measurements in the actual changing environment. We propose a different solution: Instead of taking the measurements from the real system, we learn the model using samples from other sources, such as simulators that approximate performance of the real system at low cost. We define a cost model that transform the traditional view of model learning into a multi-objective problem that not only takes into account model accuracy but also measurements effort as well. We evaluate our cost-aware transfer learning solution using real-world configurable software including (i) a robotic system, (ii) 3 different stream processing applications, and (iii) a NoSQL database system. The experimental results demonstrate that our approach can achieve (a) a high prediction accuracy, as well as (b) a high model reliability.
The Stixel world: A medium-level representation of traffic scenes
Venue: arXiv cs.CV 2017
Year: 2017

Recent progress in advanced driver assistance systems and the race towards autonomous vehicles is mainly driven by two factors: (1) increasingly sophisticated algorithms that interpret the environment around the vehicle and react accordingly, and (2) the continuous improvements of sensor technology itself. In terms of cameras, these improvements typically include higher spatial resolution, which as a consequence requires more data to be processed. The trend to add multiple cameras to cover the entire surrounding of the vehicle is not conducive in that matter. At the same time, an increasing number of special purpose algorithms need access to the sensor input data to correctly interpret the various complex situations that can occur, particularly in urban traffic. By observing those trends, it becomes clear that a key challenge for vision architectures in intelligent vehicles is to share computational resources. We believe this challenge should be faced by introducing a representation of the sensory data that provides compressed and structured access to all relevant visual content of the scene. The Stixel World discussed in this paper is such a representation. It is a medium-level model of the environment that is specifically designed to compress information about obstacles by leveraging the typical layout of outdoor traffic scenes. It has proven useful for a multitude of automotive vision applications, including object detection, tracking, segmentation, and mapping. In this paper, we summarize the ideas behind the model and generalize it to take into account multiple dense input streams: the image itself, stereo depth maps, and semantic class probability maps that can be generated, e.g., by CNNs. Our generalization is embedded into a novel mathematical formulation for the Stixel model. We further sketch how the free parameters of the model can be learned using structured SVMs.
Solving the clique cover problem on (bull, $C_4$)-free graphs
Venue: arXiv cs.DM 2017
Year: 2017

We give an $O(n^4)$ algorithm to find a minimum clique cover of a (bull, $C_4$)-free graph, or equivalently, a minimum colouring of a (bull, $2K_2$)-free graph, where $n$ is the number of vertices of the graphs.
Word-Alignment-Based Segment-Level Machine Translation Evaluation using Word Embeddings
Venue: arXiv cs.CL 2017
Year: 2017

One of the most important problems in machine translation (MT) evaluation is to evaluate the similarity between translation hypotheses with different surface forms from the reference, especially at the segment level. We propose to use word embeddings to perform word alignment for segment-level MT evaluation. We performed experiments with three types of alignment methods using word embeddings. We evaluated our proposed methods with various translation datasets. Experimental results show that our proposed methods outperform previous word embeddings-based methods.
Third time is the charm - Why the World just might be ready for the Internet of Things this time around
Venue: arXiv cs.CY 2017
Year: 2017

The technology to connect 'things' to the Internet has existed for more than 20 years, so if we take a look back at recent history we might well be tempted to ask the question why will IoT 'happen' this time around. In this paper we examine the origins of the Internet of Things, answer the question "Why Now?", and look forward to the next wave of disruptive technologies that will be coming to a device near you in the next few years.
Syntax Aware LSTM Model for Chinese Semantic Role Labeling
Venue: arXiv cs.CL 2017
Year: 2017

As for semantic role labeling (SRL) task, when it comes to utilizing parsing information, both traditional methods and recent recurrent neural network (RNN) based methods use the feature engineering way. In this paper, we propose Syntax Aware Long Short Time Memory(SA-LSTM). The structure of SA-LSTM modifies according to dependency parsing information in order to model parsing information directly in an architecture engineering way instead of feature engineering way. We experimentally demonstrate that SA-LSTM gains more improvement from the model architecture. Furthermore, SA-LSTM outperforms the state-of-the-art on CPB 1.0 significantly according to Student t-test ($p<0.05$).