OpenRaiser/Intern-Atlas huggingface dataset:
Social Behavior Analysis in Visual Human Monitoring System : A Survey and Perspective
Venue: arXiv cs.SI 2016
Year: 2016

A social behavior analysis is used to study how a group of people interacts with another group. The analysis helps to understand how social behavior leads to its consequences such as what business decision is made after a businessmen's meeting. In this paper, we focus on visual human motion analysis which is one important component of social behavior analysis. Human motion analysis in visual surveillance usually tracks the motion of an individual or a group of people, yet social behavior is usually neglected. This paper first delivers a literature survey of visual surveillance, with emphasis on aspects of human motion analysis and social behavior. Second, it offers a perspective for social behavior analysis in an intelligent visual human monitoring system. Third, a social interaction is induced by a social behavior between two persons, one person and one group, or two groups, hence three general scenarios of social interactions are outlined for future theoretical development. The proposed human monitoring system enables the generation of valuable quantified information of social interactions. It provides an objective approach to evaluate performance of a human organization such as a company or a school. Finally, it can raise the awareness of researchers to further explore the field of social behavior analysis.
A Perspective on Sentiment Analysis
Venue: arXiv cs.CL 2016
Year: 2016

Sentiment Analysis (SA) is indeed a fascinating area of research which has stolen the attention of researchers as it has many facets and more importantly it promises economic stakes in the corporate and governance sector. SA has been stemmed out of text analytics and established itself as a separate identity and a domain of research. The wide ranging results of SA have proved to influence the way some critical decisions are taken. Hence, it has become relevant in thorough understanding of the different dimensions of the input, output and the processes and approaches of SA.
A neurofeedback system to promote learner engagement
Venue: arXiv cs.HC 2016
Year: 2016

This report describes a series of experiments that track novice programmer's engagement during two attention based tasks. The tasks required participants to watch a tutorial video on introductory programming and to attend to a simple maze game whilst wearing an electroencephalogram (EEG)device called the Emotiv EPOC. The EPOC's proprietary software includes a system which tracks emotional state (specifically: engagement, excitement, meditation, frustration, valence and long-term excitement). Using this data, a software application written in the Processing language was developed to track user's engagement levels and implement a neurofeedback based intervention when engagement fell below an acceptable level. The aim of the intervention was to prompt learners who disengaged with the task to re-engage. The intervention used during the video tutorial was to pause the video if a participant disengaged significantly. However other interventions such as slowing the video down, playing a noise or darkening/brightening the screen could also be used. For the maze game, the caterpillar moving through the maze slowed in line with disengagement and moved more quickly once the learner re-engaged. The approach worked very well and successfully re-engaged participants, although a number of improvements could be made. A number of interesting findings on the comparative engagement levels of different groups e.g. by gender and by age etc. were identified and provide useful pointers for future research studies.
Dynamic Pose-Robust Facial Expression Recognition by Multi-View Pairwise Conditional Random Forests
Venue: arXiv cs.CV 2016
Year: 2016

Automatic facial expression classification (FER) from videos is a critical problem for the development of intelligent human-computer interaction systems. Still, it is a challenging problem that involves capturing high-dimensional spatio-temporal patterns describing the variation of one's appearance over time. Such representation undergoes great variability of the facial morphology and environmental factors as well as head pose variations. In this paper, we use Conditional Random Forests to capture low-level expression transition patterns. More specifically, heterogeneous derivative features (e.g. feature point movements or texture variations) are evaluated upon pairs of images. When testing on a video frame, pairs are created between this current frame and previous ones and predictions for each previous frame are used to draw trees from Pairwise Conditional Random Forests (PCRF) whose pairwise outputs are averaged over time to produce robust estimates. Moreover, PCRF collections can also be conditioned on head pose estimation for multi-view dynamic FER. As such, our approach appears as a natural extension of Random Forests for learning spatio-temporal patterns, potentially from multiple viewpoints. Experiments on popular datasets show that our method leads to significant improvements over standard Random Forests as well as state-of-the-art approaches on several scenarios, including a novel multi-view video corpus generated from a publicly available database.
Left/Right Hand Segmentation in Egocentric Videos
Venue: arXiv cs.HC 2016
Year: 2016

Wearable cameras allow people to record their daily activities from a user-centered (First Person Vision) perspective. Due to their favorable location, wearable cameras frequently capture the hands of the user, and may thus represent a promising user-machine interaction tool for different applications. Existent First Person Vision methods handle hand segmentation as a background-foreground problem, ignoring two important facts: i) hands are not a single "skin-like" moving element, but a pair of interacting cooperative entities, ii) close hand interactions may lead to hand-to-hand occlusions and, as a consequence, create a single hand-like segment. These facts complicate a proper understanding of hand movements and interactions. Our approach extends traditional background-foreground strategies, by including a hand-identification step (left-right) based on a Maxwell distribution of angle and position. Hand-to-hand occlusions are addressed by exploiting temporal superpixels. The experimental results show that, in addition to a reliable left/right hand-segmentation, our approach considerably improves the traditional background-foreground hand-segmentation.
Dataset and Neural Recurrent Sequence Labeling Model for Open-Domain Factoid Question Answering
Venue: arXiv cs.CL 2016
Year: 2016

While question answering (QA) with neural network, i.e. neural QA, has achieved promising results in recent years, lacking of large scale real-word QA dataset is still a challenge for developing and evaluating neural QA system. To alleviate this problem, we propose a large scale human annotated real-world QA dataset WebQA with more than 42k questions and 556k evidences. As existing neural QA methods resolve QA either as sequence generation or classification/ranking problem, they face challenges of expensive softmax computation, unseen answers handling or separate candidate answer generation component. In this work, we cast neural QA as a sequence labeling problem and propose an end-to-end sequence labeling model, which overcomes all the above challenges. Experimental results on WebQA show that our model outperforms the baselines significantly with an F1 score of 74.69% with word-based input, and the performance drops only 3.72 F1 points with more challenging character-based input.
Confidence-Weighted Local Expression Predictions for Occlusion Handling in Expression Recognition and Action Unit detection
Venue: arXiv cs.CV 2016
Year: 2016

Fully-Automatic Facial Expression Recognition (FER) from still images is a challenging task as it involves handling large interpersonal morphological differences, and as partial occlusions can occasionally happen. Furthermore, labelling expressions is a time-consuming process that is prone to subjectivity, thus the variability may not be fully covered by the training data. In this work, we propose to train Random Forests upon spatially defined local subspaces of the face. The output local predictions form a categorical expression-driven high-level representation that we call Local Expression Predictions (LEPs). LEPs can be combined to describe categorical facial expressions as well as Action Units (AUs). Furthermore, LEPs can be weighted by confidence scores provided by an autoencoder network. Such network is trained to locally capture the manifold of the non-occluded training data in a hierarchical way. Extensive experiments show that the proposed LEP representation yields high descriptive power for categorical expressions and AU occurrence prediction, and leads to interesting perspectives towards the design of occlusion-robust and confidence-aware FER systems.
Opinion Mining in Online Reviews About Distance Education Programs
Venue: arXiv cs.CL 2016
Year: 2016

The popularity of distance education programs is increasing at a fast pace. En par with this development, online communication in fora, social media and reviewing platforms between students is increasing as well. Exploiting this information to support fellow students or institutions requires to extract the relevant opinions in order to automatically generate reports providing an overview of pros and cons of different distance education programs. We report on an experiment involving distance education experts with the goal to develop a dataset of reviews annotated with relevant categories and aspects in each category discussed in the specific review together with an indication of the sentiment. Based on this experiment, we present an approach to extract general categories and specific aspects under discussion in a review together with their sentiment. We frame this task as a multi-label hierarchical text classification problem and empirically investigate the performance of different classification architectures to couple the prediction of a category with the prediction of particular aspects in this category. We evaluate different architectures and show that a hierarchical approach leads to superior results in comparison to a flat model which makes decisions independently.
Modeling Software Development Methodologies: A Logic Based Approach
Venue: arXiv cs.SE 2016
Year: 2016

In the last two decades, the growing trend of software development industry has made different aspects of software engineering more interesting for the computer science research community. Software development life-cycle is one of these aspects that has a significant impact on the success/failure of software development projects. Since each software development methodology relatively provides its own software development life-cycle, encoding software development life-cycle in a workflow representation language can help developers to properly manage their software projects. In addition, such encoding can also be used in CASE tools. In this report, we consider the software development life-cycle as a workflow that can be represented by semantic web and rule based languages. Such consideration let one analyze the properties of the life-cycle. Specifically, we take a well-known agent oriented software development methodology and show that its corresponding life-cycle can be specified by Transaction Logic easily and concisely. Finally, the compact and clear representation of the life-cycle in Transaction Logic can be used in CASE tools to guide software developers.
Admissible Hierarchical Clustering Methods and Algorithms for Asymmetric Networks
Venue: arXiv cs.LG 2016
Year: 2016

This paper characterizes hierarchical clustering methods that abide by two previously introduced axioms -- thus, denominated admissible methods -- and proposes tractable algorithms for their implementation. We leverage the fact that, for asymmetric networks, every admissible method must be contained between reciprocal and nonreciprocal clustering, and describe three families of intermediate methods. Grafting methods exchange branches between dendrograms generated by different admissible methods. The convex combination family combines admissible methods through a convex operation in the space of dendrograms, and thirdly, the semi-reciprocal family clusters nodes that are related by strong cyclic influences in the network. Algorithms for the computation of hierarchical clusters generated by reciprocal and nonreciprocal clustering as well as the grafting, convex combination, and semi-reciprocal families are derived using matrix operations in a dioid algebra. Finally, the introduced clustering methods and algorithms are exemplified through their application to a network describing the interrelation between sectors of the United States (U.S.) economy.
Performance of Massive MIMO Uplink with Zero-Forcing receivers under Delayed Channels
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we analyze the performance of the uplink communication of massive multi-cell multiple-input multiple-output (MIMO) systems under the effects of pilot contamination and delayed channels because of terminal mobility. The base stations (BSs) estimate the channels through the uplink training, and then use zero-forcing processing to decode the transmit signals from the users. The probability density function (PDF) of the signal-to-interference-plus-noise ratio is derived for any finite number of antennas. From this PDF, we derive an achievable ergodic rate with a finite number of BS antennas in closed form. Insights of the impact of the Doppler shift (due to terminal mobility) at the low signal-to-noise ratio regimes are exposed. In addition, the effects on the outage probability are investigated. Furthermore, the power scaling law and the asymptotic performance result by infinitely increasing the numbers of antennas and terminals (while their ratio is fixed) are provided. The numerical results demonstrate the performance loss for various Doppler shifts. Among the interesting observations revealed is that massive MIMO is favorable even in channel aging conditions.
Interleaving Optimization with Sampling-Based Motion Planning (IOS-MP): Combining Local Optimization with Global Exploration
Venue: arXiv cs.RO 2016
Year: 2016

Computing globally optimal motion plans for a robot is challenging in part because it requires analyzing a robot's configuration space simultaneously from both a macroscopic viewpoint (i.e., considering paths in multiple homotopic classes) and a microscopic viewpoint (i.e., locally optimizing path quality). We introduce Interleaved Optimization with Sampling-based Motion Planning (IOS-MP), a new method that effectively combines global exploration and local optimization to quickly compute high quality motion plans. Our approach combines two paradigms: (1) asymptotically-optimal sampling-based motion planning, which is effective at global exploration but relatively slow at locally refining paths, and (2) optimization-based motion planning, which locally optimizes paths quickly but lacks a global view of the configuration space. IOS-MP iteratively alternates between global exploration and local optimization, sharing information between the two, to improve motion planning efficiency. We evaluate IOS-MP as it scales with respect to dimensionality and complexity, as well as demonstrate its effectiveness on a 7-DOF manipulator for tasks specified using goal configurations and workspace goal regions.
Aggregation Architecture for Data Reduction and Privacy in Advanced Metering Infrastructure
Venue: arXiv cs.CR 2016
Year: 2016

Advanced Metering Infrastructure (AMI) have rapidly become a topic of international interest as governments have sponsored their deployment for the purposes of utility service reliability and efficiency, e.g., water and electricity conservation. Two problems plague such deployments. First is the protection of consumer privacy. Second is the problem of huge amounts of data from such deployments. A new architecture is proposed to address these problems through the use of Aggregators, which incorporate temporary data buffering and the modularization of utility grid analysis. These Aggregators are used to deliver anonymized summary data to the central utility while preserving billing and automated connection services.
Metric Perturbation Resilience
Venue: arXiv cs.DS 2016
Year: 2016

We study the notion of perturbation resilience introduced by Bilu and Linial (2010) and Awasthi, Blum, and Sheffet (2012). A clustering problem is $α$-perturbation resilient if the optimal clustering does not change when we perturb all distances by a factor of at most $α$. We consider a class of clustering problems with center-based objectives, which includes such problems as k-means, k-median, and k-center, and give an exact algorithm for clustering 2-perturbation resilient instances. Our result improves upon the result of Balcan and Liang (2016), who gave an algorithm for clustering $1+\sqrt{2}\approx 2.41$ perturbation resilient instances. Our result is tight in the sense that no polynomial-time algorithm can solve $(2-\varepsilon)$-perturbation resilient instances unless NP = RP, as was shown by Balcan, Haghtalab, and White (2016). We show that the algorithm works on instances satisfying a slightly weaker and more natural condition than perturbation resilience, which we call metric perturbation resilience.
Novel Word Embedding and Translation-based Language Modeling for Extractive Speech Summarization
Venue: arXiv cs.CL 2016
Year: 2016

Word embedding methods revolve around learning continuous distributed vector representations of words with neural networks, which can capture semantic and/or syntactic cues, and in turn be used to induce similarity measures among words, sentences and documents in context. Celebrated methods can be categorized as prediction-based and count-based methods according to the training objectives and model architectures. Their pros and cons have been extensively analyzed and evaluated in recent studies, but there is relatively less work continuing the line of research to develop an enhanced learning method that brings together the advantages of the two model families. In addition, the interpretation of the learned word representations still remains somewhat opaque. Motivated by the observations and considering the pressing need, this paper presents a novel method for learning the word representations, which not only inherits the advantages of classic word embedding methods but also offers a clearer and more rigorous interpretation of the learned word representations. Built upon the proposed word embedding method, we further formulate a translation-based language modeling framework for the extractive speech summarization task. A series of empirical evaluations demonstrate the effectiveness of the proposed word representation learning and language modeling techniques in extractive speech summarization.
User Load Analysis and Pilot Sequence Design for Multi-Cell Massive MIMO Networks
Venue: arXiv cs.IT 2016
Year: 2016

We propose a novel algorithm to design user load-achieving pilot sequences that mitigate pilot contamination in multi-cell massive multiple-input multiple-output (MIMO) networks. To this end, we first derive expressions for the user load and the load region of the network considering both small-scale and large-scale propagation effects. We then develop the pilot sequence algorithm for multi-cell massive MIMO networks as per the rules of generalized Welch bound equality design. Notably, we find that our algorithm and the corresponding downlink power allocation ensure that the user load is achieved when the signal-to-interference-plus-noise ratio (SINR) requirements for the users lie within the load region. Furthermore, we demonstrate the performance advantage of our proposed design relative to the existing designs, in terms of a larger load region and a higher maximum permitted SINR. Finally, we show that our proposed design can satisfy the pre-defined SINR requirements for users with a finite number of antennas at the base station (BS), while the existing designs cannot satisfy the same requirements even with an infinite number of antennas at the BS.
Automated Prediction of Temporal Relations
Venue: arXiv cs.CL 2016
Year: 2016

Background: There has been growing research interest in automated answering of questions or generation of summary of free form text such as news article. In order to implement this task, the computer should be able to identify the sequence of events, duration of events, time at which event occurred and the relationship type between event pairs, time pairs or event-time pairs. Specific Problem: It is important to accurately identify the relationship type between combinations of event and time before the temporal ordering of events can be defined. The machine learning approach taken in Mani et. al (2006) provides an accuracy of only 62.5 on the baseline data from TimeBank. The researchers used maximum entropy classifier in their methodology. TimeML uses the TLINK annotation to tag a relationship type between events and time. The time complexity is quadratic when it comes to tagging documents with TLINK using human annotation. This research proposes using decision tree and parsing to improve the relationship type tagging. This research attempts to solve the gaps in human annotation by automating the task of relationship type tagging in an attempt to improve the accuracy of event and time relationship in annotated documents. Scope information: The documents from the domain of news will be used. The tagging will be performed within the same document and not across documents. The relationship types will be identified only for a pair of event and time and not a chain of events. The research focuses on documents tagged using the TimeML specification which contains tags such as EVENT, TLINK, and TIMEX. Each tag has attributes such as identifier, relation, POS, time etc.
ARTist: The Android Runtime Instrumentation and Security Toolkit
Venue: arXiv cs.CR 2016
Year: 2016

We present ARTist, a compiler-based application instrumentation solution for Android. ARTist is based on the new ART runtime and the on-device dex2oat compiler of Android, which replaced the interpreter-based managed runtime (DVM) from Android version 5 onwards. Since dex2oat is yet uncharted, our approach required first and foremost a thorough study of the compiler suite's internals and in particular of the new default compiler backend Optimizing. We document the results of this study in this paper to facilitate independent research on this topic and exemplify the viability of ARTist by realizing two use cases. Moreover, given that seminal works like TaintDroid hitherto depend on the now abandoned DVM, we conduct a case study on whether taint tracking can be re-instantiated using a compiler-based instrumentation framework. Overall, our results provide compelling arguments for preferring compiler-based instrumentation over alternative bytecode or binary rewriting approaches.
Participation and Privacy perception in virtual environments: the role of sense of community, culture and sex between Italian and Turkish
Venue: arXiv cs.CY 2016
Year: 2016

Advancements in information and communication technologies have enhanced our possibilities to communicate worldwide, eliminating borders and making it possible to interact with people coming from other cultures like never happened before. Such powerful tools have brought us to reconsider our concept of privacy and social involvement in order to make them fit into this wider environment. It is possible to claim that the ICT revolution is changing our world and is having a core role as a mediating factor for social movements (e.g., Arab spring) and political decisions (e.g., Brexit), shaping the world in a faster and shared brand new way. It is then interesting to explore how the perception of this brand new environment (in terms of social engagement, privacy perception and sense of belonging to a community) differs even in similar cultures separated by recent historical reasons, as for example in Italian and Turkish cultures.