OpenRaiser/Intern-Atlas huggingface dataset:
Cluster-based MDS Algorithm for Nodes Localization in Wireless Sensor Networks with Irregular Topologies
Venue: arXiv cs.DC 2016
Year: 2016

Nodes localization in Wireless Sensor Networks (WSN) has arisen as a very challenging problem in the research community. Most of the applications for WSN are not useful without a priori known nodes positions. One solution to the problem is by adding GPS receivers to each node. Since this is an expensive approach and inapplicable for indoor environments, we need to find an alternative intelligent mechanism for determining nodes location. In this paper, we propose our cluster-based approach of multidimensional scaling (MDS) technique. Our initial experiments show that our algorithm outperforms MDS-MAP[8], particularly for irregular topologies in terms of accuracy.
Do players reason by forward induction in dynamic perfect information games?
Venue: arXiv cs.GT 2016
Year: 2016

We conducted an experiment where participants played a perfect-information game against a computer, which was programmed to deviate often from its backward induction strategy right at the beginning of the game. Participants knew that in each game, the computer was nevertheless optimizing against some belief about the participant's future strategy. It turned out that in the aggregate, participants were likely to respond in a way which is optimal with respect to their best-rationalization extensive form rationalizability conjecture - namely the conjecture that the computer is after a larger prize than the one it has foregone, even when this necessarily meant that the computer has attributed future irrationality to the participant when the computer made the first move in the game. Thus, it appeared that participants applied forward induction. However, there exist alternative explanations for the choices of most participants; for example, choices could be based on the extent of risk aversion that participants attributed to the computer in the remainder of the game, rather than to the sunk outside option that the computer has already foregone at the beginning of the game. For this reason, the results of the experiment do not yet provide conclusive evidence for Forward Induction reasoning on the part of the participants.
Interactive Semantic Featuring for Text Classification
Venue: arXiv cs.CL 2016
Year: 2016

In text classification, dictionaries can be used to define human-comprehensible features. We propose an improvement to dictionary features called smoothed dictionary features. These features recognize document contexts instead of n-grams. We describe a principled methodology to solicit dictionary features from a teacher, and present results showing that models built using these human-comprehensible features are competitive with models trained with Bag of Words features.
Kategorisasi dokumen web secara otomatis berdasarkan folksonomy menggunakan multinomial naive Bayes classifier
Venue: arXiv cs.IR 2016
Year: 2016

Folksonomy is a non-hierarchical document categorizing system, that treats every category in a flat manner, dan every category is entered freely by anyone who submitted a document in these categories. Categorization is done automatically at the time a document is submitted, by entering the list of categories that best fit the document. del.icio.us (http://del.icio.us) site is one of the most popular social bookmarking sites that uses folksonomy. Usage of folksonomy, although very easy, also has its weaknesses, such as use of different tags for the same concept, use of the same tag for different concepts, no quality control, etc. We try to provide a solution for some of these problems by analyzing Web documents' contents and categorizing them automatically using multinomial naive Bayes algorithm. Bayes classifier works by using a set of evidences and a set of classes. By training the system using sample data, we can determine the probability of an evidence given a particular class. Bayes classifier also uses prior probability of a class, which can be calculated from sample data. From these analysis, when given a new document which is formed by a set of evidences (words), the probabilities of each class given that document (posterior probabilities) can be determined. This system is implemented using PHP 5, Apache, and MySQL. The conclusion from building this system is that the Bayes method can be used to automatically categorize documents and also as an assistive tool for manual categorization. ----- Folksonomy merupakan metode kategorisasi dokumen yang tidak hierarkis, menyamaratakan kedudukan setiap kategori, dan judul kategori ditentukan secara bebas oleh siapa saja yang memasukkan sebuah dokumen di dalam kategori-kategori tersebut.
Neural Autoregressive Collaborative Filtering for Implicit Feedback
Venue: arXiv cs.IR 2016
Year: 2016

This paper proposes implicit CF-NADE, a neural autoregressive model for collaborative filtering tasks using implicit feedback ( e.g. click, watch, browse behaviors). We first convert a users implicit feedback into a like vector and a confidence vector, and then model the probability of the like vector, weighted by the confidence vector. The training objective of implicit CF-NADE is to maximize a weighted negative log-likelihood. We test the performance of implicit CF-NADE on a dataset collected from a popular digital TV streaming service. More specifically, in the experiments, we describe how to convert watch counts into implicit relative rating, and feed into implicit CF-NADE. Then we compare the performance of implicit CF-NADE model with the popular implicit matrix factorization approach. Experimental results show that implicit CF-NADE significantly outperforms the baseline.
Issues in evaluating semantic spaces using word analogies
Venue: arXiv cs.CL 2016
Year: 2016

The offset method for solving word analogies has become a standard evaluation tool for vector-space semantic models: it is considered desirable for a space to represent semantic relations as consistent vector offsets. We show that the method's reliance on cosine similarity conflates offset consistency with largely irrelevant neighborhood structure, and propose simple baselines that should be used to improve the utility of the method in vector space evaluation.
Harnessing the Power of the Crowd to Increase Capacity for Data Science in the Social Sector
Venue: arXiv cs.HC 2016
Year: 2016

We present three case studies of organizations using a data science competition to answer a pressing question. The first is in education where a nonprofit that creates smart school budgets wanted to automatically tag budget line items. The second is in public health, where a low-cost, nonprofit women's health care provider wanted to understand the effect of demographic and behavioral questions on predicting which services a woman would need. The third and final example is in government innovation: using online restaurant reviews from Yelp, competitors built models to forecast which restaurants were most likely to have hygiene violations when visited by health inspectors. Finally, we reflect on the unique benefits of the open, public competition model.
Modeling and Inferring Human Intents and Latent Functional Objects for Trajectory Prediction
Venue: arXiv cs.CV 2016
Year: 2016

This paper is about detecting functional objects and inferring human intentions in surveillance videos of public spaces. People in the videos are expected to intentionally take shortest paths toward functional objects subject to obstacles, where people can satisfy certain needs (e.g., a vending machine can quench thirst), by following one of three possible intent behaviors: reach a single functional object and stop, or sequentially visit several functional objects, or initially start moving toward one goal but then change the intent to move toward another. Since detecting functional objects in low-resolution surveillance videos is typically unreliable, we call them "dark matter" characterized by the functionality to attract people. We formulate the Agent-based Lagrangian Mechanics wherein human trajectories are probabilistically modeled as motions of agents in many layers of "dark-energy" fields, where each agent can select a particular force field to affect its motions, and thus define the minimum-energy Dijkstra path toward the corresponding source "dark matter". For evaluation, we compiled and annotated a new dataset. The results demonstrate our effectiveness in predicting human intent behaviors and trajectories, and localizing functional objects, as well as discovering distinct functional classes of objects by clustering human motion behavior in the vicinity of functional objects.
Corpus-level Fine-grained Entity Typing Using Contextual Information
Venue: arXiv cs.CL 2016
Year: 2016

This paper addresses the problem of corpus-level entity typing, i.e., inferring from a large corpus that an entity is a member of a class such as "food" or "artist". The application of entity typing we are interested in is knowledge base completion, specifically, to learn which classes an entity is a member of. We propose FIGMENT to tackle this problem. FIGMENT is embedding-based and combines (i) a global model that scores based on aggregated contextual information of an entity and (ii) a context model that first scores the individual occurrences of an entity and then aggregates the scores. In our evaluation, FIGMENT strongly outperforms an approach to entity typing that relies on relations obtained by an open information extraction system.
Going viral: Optimizing Discount Allocation in Social Networks for Influence Maximization
Venue: arXiv cs.SI 2016
Year: 2016

In this paper, we investigate the discount allocation problem in social networks. It has been reported that 40\% of consumers will share an email offer with their friend and 28\% of consumers will share deals via social media platforms. What does this mean for a business? Essentially discounts should not just be treated as short term solutions to attract individual customer, instead, allocating discounts to a small fraction of users (called seed users) may trigger a large cascade in a social network. This motivates us to study the influence maximization discount allocation problem: given a social network and budget, we need to decide to which initial set users should offer the discounts, and how much should the discounts be worth. Our goal is to maximize the number of customers who finally adopt the target product. We investigate this problem under both non-adaptive and adaptive settings. In the first setting, we have to commit the set of seed users and corresponding discounts all at once in advance. In the latter case, the decision process is performed in a sequential manner, and each seed user that is picked provides the feedback on the discount, or, in other words, reveals whether or not she will adopt the discount. We propose a simple greedy policy with an approximation ratio of $\frac{1}{2}(1 - 1/e)$ in non-adaptive setting. For the significantly more complex adaptive setting, we propose an adaptive greedy policy with bounded approximation ratio in terms of expected utility.
Wi-Fi Coexistence with Duty Cycled LTE-U
Venue: arXiv cs.IT 2016
Year: 2016

Coexistence of Wi-Fi and LTE-Unlicensed (LTE-U) technologies has drawn significant concern in industry. In this paper, we investigate the Wi-Fi performance in the presence of duty cycle based LTE-U transmission on the same channel. More specifically, one LTE-U cell and one Wi-Fi basic service set (BSS) coexist by allowing LTE-U devices transmit their signals only in predetermined duty cycles. Wi-Fi stations, on the other hand, simply contend the shared channel using the distributed coordination function (DCF) protocol without cooperation with the LTE-U system or prior knowledge about the duty cycle period or duty cycle of LTE-U transmission. We define the fairness of the above scheme as the difference between Wi-Fi performance loss ratio (considering a defined reference performance) and the LTE-U duty cycle (or function of LTE-U duty cycle). Depending on the interference to noise ratio (INR) being above or below -62dbm, we classify the LTE-U interference as strong or weak and establish mathematical models accordingly. The average throughput and average service time of Wi-Fi are both formulated as functions of Wi-Fi and LTE-U system parameters using probability theory. Lastly, we use the Monte Carlo analysis to demonstrate the fairness of Wi-Fi and LTE-U air time sharing.
Functional Distributional Semantics
Venue: arXiv cs.CL 2016
Year: 2016

Vector space models have become popular in distributional semantics, despite the challenges they face in capturing various semantic phenomena. We propose a novel probabilistic framework which draws on both formal semantics and recent advances in machine learning. In particular, we separate predicates from the entities they refer to, allowing us to perform Bayesian inference based on logical forms. We describe an implementation of this framework using a combination of Restricted Boltzmann Machines and feedforward neural networks. Finally, we demonstrate the feasibility of this approach by training it on a parsed corpus and evaluating it on established similarity datasets.
Fast Incremental Learning for Off-Road Robot Navigation
Venue: arXiv cs.RO 2016
Year: 2016

A promising approach to autonomous driving is machine learning. In such systems, training datasets are created that capture the sensory input to a vehicle as well as the desired response. A disadvantage of using a learned navigation system is that the learning process itself may require a huge number of training examples and a large amount of computing. To avoid the need to collect a large training set of driving examples, we describe a system that takes advantage of the huge number of training examples provided by ImageNet, but is able to adapt quickly using a small training set for the specific driving environment.
Framework for state and unknown input estimation of linear time-varying systems
Venue: arXiv cs.SY 2016
Year: 2016

The design of unknown-input decoupled observers and filters requires the assumption of an existence condition in the literature. This paper addresses an unknown input filtering problem where the existence condition is not satisfied. Instead of designing a traditional unknown input decoupled filter, a Double-Model Adaptive Estimation approach is extended to solve the unknown input filtering problem. It is proved that the state and the unknown inputs can be estimated and decoupled using the extended Double-Model Adaptive Estimation approach without satisfying the existence condition. Numerical examples are presented in which the performance of the proposed approach is compared to methods from literature.
Can Turing machine be curious about its Turing test results? Three informal lectures on physics of intelligence
Venue: arXiv cs.AI 2016
Year: 2016

What is the nature of curiosity? Is there any scientific way to understand the origin of this mysterious force that drives the behavior of even the stupidest naturally intelligent systems and is completely absent in their smartest artificial analogs? Can we build AI systems that could be curious about something, systems that would have an intrinsic motivation to learn? Is such a motivation quantifiable? Is it implementable? I will discuss this problem from the standpoint of physics. The relationship between physics and intelligence is a consequence of the fact that correctly predicted information is nothing but an energy resource, and the process of thinking can be viewed as a process of accumulating and spending this resource through the acts of perception and, respectively, decision making. The natural motivation of any autonomous system to keep this accumulation/spending balance as high as possible allows one to treat the problem of describing the dynamics of thinking processes as a resource optimization problem. Here I will propose and discuss a simple theoretical model of such an autonomous system which I call the Autonomous Turing Machine (ATM). The potential attractiveness of ATM lies in the fact that it is the model of a self-propelled AI for which the only available energy resource is the information itself. For ATM, the problem of optimal thinking, learning, and decision-making becomes conceptually simple and mathematically well tractable. This circumstance makes the ATM an ideal playground for studying the dynamics of intelligent behavior and allows one to quantify many seemingly unquantifiable features of genuine intelligence.
Improved Recurrent Neural Networks for Session-based Recommendations
Venue: arXiv cs.LG 2016
Year: 2016

Recurrent neural networks (RNNs) were recently proposed for the session-based recommendation task. The models showed promising improvements over traditional recommendation approaches. In this work, we further study RNN-based models for session-based recommendations. We propose the application of two techniques to improve model performance, namely, data augmentation, and a method to account for shifts in the input data distribution. We also empirically study the use of generalised distillation, and a novel alternative model that directly predicts item embeddings. Experiments on the RecSys Challenge 2015 dataset demonstrate relative improvements of 12.8% and 14.8% over previously reported results on the Recall@20 and Mean Reciprocal Rank@20 metrics respectively.
Beamforming for Full-Duplex Multiuser MIMO Systems
Venue: arXiv cs.IT 2016
Year: 2016

We solve a sum rate maximization problem of full-duplex (FD) multiuser multiple-input multiple-output (MU-MIMO) systems. Since additional self-interference (SI) in the uplink channel and co-channel interference (CCI) in the downlink channel are coupled in FD communication, the downlink and uplink multiuser beamforming vectors are required to be jointly designed. However, the joint optimization problem is non-convex and hard to solve due to the coupled effect. To properly address the coupled design issue, we reformulate the problem into an equivalent uplink channel problem, using the uplink and downlink channel duality known as MAC-BC duality. Then, using minorization maximization (MM) algorithm based on an affine approximation, we obtain a solution for the reformulated problem. In addition, without any approximation and thus performance degradation, we develop an alternating algorithm based on iterative water-filling (IWF) to solve the non-convex problem. The proposed algorithms warrant fast convergence and low computational complexity.
Compiler-Assisted Workload Consolidation For Efficient Dynamic Parallelism on GPU
Venue: arXiv cs.DC 2016
Year: 2016

GPUs have been widely used to accelerate computations exhibiting simple patterns of parallelism - such as flat or two-level parallelism - and a degree of parallelism that can be statically determined based on the size of the input dataset. However, the effective use of GPUs for algorithms exhibiting complex patterns of parallelism, possibly known only at runtime, is still an open problem. Recently, Nvidia has introduced Dynamic Parallelism (DP) in its GPUs. By making it possible to launch kernels directly from GPU threads, this feature enables nested parallelism at runtime. However, the effective use of DP must still be understood: a naive use of this feature may suffer from significant runtime overhead and lead to GPU underutilization, resulting in poor performance. In this work, we target this problem. First, we demonstrate how a naive use of DP can result in poor performance. Second, we propose three workload consolidation schemes to improve performance and hardware utilization of DP-based codes, and we implement these code transformations in a directive-based compiler. Finally, we evaluate our framework on two categories of applications: algorithms including irregular loops and algorithms exhibiting parallel recursion. Our experiments show that our approach significantly reduces runtime overhead and improves GPU utilization, leading to speedup factors from 90x to 3300x over basic DP-based solutions and speedups from 2x to 6x over flat implementations.
Efficient Dynamic Pinning of Parallelized Applications by Distributed Reinforcement Learning
Venue: arXiv cs.DC 2016
Year: 2016

This paper introduces a resource allocation framework specifically tailored for addressing the problem of dynamic placement (or pinning) of parallelized applications to processing units. Under the proposed setup each thread of the parallelized application constitutes an independent decision maker (or agent), which (based on its own prior performance measurements and its own prior CPU-affinities) decides on which processing unit to run next. Decisions are updated recursively for each thread by a resource manager/scheduler which runs in parallel to the application's threads and periodically records their performances and assigns to them new CPU affinities. For updating the CPU-affinities, the scheduler uses a distributed reinforcement-learning algorithm, each branch of which is responsible for assigning a new placement strategy to each thread. According to this algorithm, prior allocations are going to be reinforced in the future proportionally to their prior performance. The proposed resource allocation framework is flexible enough to address alternative optimization criteria, such as maximum average processing speed and minimum speed variance among threads. We demonstrate analytically that convergence to locally-optimal placements is achieved asymptotically. Finally, we validate these results through experiments in Linux platforms.
Near-Optimal Computation of Runs over General Alphabet via Non-Crossing LCE Queries
Venue: arXiv cs.DS 2016
Year: 2016

Longest common extension queries (LCE queries) and runs are ubiquitous in algorithmic stringology. Linear-time algorithms computing runs and preprocessing for constant-time LCE queries have been known for over a decade. However, these algorithms assume a linearly-sortable integer alphabet. A recent breakthrough paper by Bannai et.\ al.\ (SODA 2015) showed a link between the two notions: all the runs in a string can be computed via a linear number of LCE queries. The first to consider these problems over a general ordered alphabet was Kosolobov (\emph{Inf.\ Process.\ Lett.}, 2016), who presented an $O(n (\log n)^{2/3})$-time algorithm for answering $O(n)$ LCE queries. This result was improved by Gawrychowski et.\ al.\ (accepted to CPM 2016) to $O(n \log \log n)$ time. In this work we note a special \emph{non-crossing} property of LCE queries asked in the runs computation. We show that any $n$ such non-crossing queries can be answered on-line in $O(n α(n))$ time, which yields an $O(n α(n))$-time algorithm for computing runs.
On the Statistical Model of Source Localization based on Range Difference Measurements
Venue: arXiv cs.IT 2016
Year: 2016

In this work we study the statistical model of source localization based on Range Difference measurements. We investigate the case of planar localization of a source using a minimal configuration of three non aligned receivers. Our analysis is based on a previous work of the same authors concerning the localization in a noiseless scenario. As the set of feasible measurements is a semialgebraic variety, this investigation makes use of techniques from Algebraic Statistics and Information Geometry.