OpenRaiser/Intern-Atlas huggingface dataset:
Constrained Type Families
Venue: arXiv cs.PL 2017
Year: 2017

We present an approach to support partiality in type-level computation without compromising expressiveness or type safety. Existing frameworks for type-level computation either require totality or implicitly assume it. For example, type families in Haskell provide a powerful, modular means of defining type-level computation. However, their current design implicitly assumes that type families are total, introducing nonsensical types and significantly complicating the metatheory of type families and their extensions. We propose an alternative design, using qualified types to pair type-level computations with predicates that capture their domains. Our approach naturally captures the intuitive partiality of type families, simplifying their metatheory. As evidence, we present the first complete proof of consistency for a language with closed type families.
AP17-OLR Challenge: Data, Plan, and Baseline
Venue: arXiv cs.CL 2017
Year: 2017

We present the data profile and the evaluation plan of the second oriental language recognition (OLR) challenge AP17-OLR. Compared to the event last year (AP16-OLR), the new challenge involves more languages and focuses more on short utterances. The data is offered by SpeechOcean and the NSFC M2ASR project. Two types of baselines are constructed to assist the participants, one is based on the i-vector model and the other is based on various neural networks. We report the baseline results evaluated with various metrics defined by the AP17-OLR evaluation plan and demonstrate that the combined database is a reasonable data resource for multilingual research. All the data is free for participants, and the Kaldi recipes for the baselines have been published online.
Speaker Identification in each of the Neutral and Shouted Talking Environments based on Gender-Dependent Approach Using SPHMMs
Venue: arXiv cs.AI 2017
Year: 2017

It is well known that speaker identification performs extremely well in the neutral talking environments; however, the identification performance is declined sharply in the shouted talking environments. This work aims at proposing, implementing and testing a new approach to enhance the declined performance in the shouted talking environments. The new proposed approach is based on gender-dependent speaker identification using Suprasegmental Hidden Markov Models (SPHMMs) as classifiers. This proposed approach has been tested on two different and separate speech databases: our collected database and the Speech Under Simulated and Actual Stress (SUSAS) database. The results of this work show that gender-dependent speaker identification based on SPHMMs outperforms gender-independent speaker identification based on the same models and gender-dependent speaker identification based on Hidden Markov Models (HMMs) by about 6% and 8%, respectively. The results obtained based on the proposed approach are close to those obtained in subjective evaluation by human judges.
The Security Assessment Domain: A Survey of Taxonomies and Ontologies
Venue: arXiv cs.SE 2017
Year: 2017

The use of ontologies and taxonomies contributes by providing means to define concepts, minimize the ambiguity, improve the interoperability and manage knowledge of the security domain. Thus, this paper presents a literature survey on ontologies and taxonomies concerning the Security Assessment domain. We carried out it to uncover initiatives that aim at formalizing concepts from the Information Security and Test and Assessment fields of research. We applied a systematic review approach in seven scientific databases. 138 papers were identified and divided into categories according to their main contributions, namely: Ontology, Taxonomy and Survey. Based on their contents, we selected 47 papers on ontologies, 22 papers on taxonomies, and 11 papers on surveys. A taxonomy has been devised to be used in the evaluation of the papers. Summaries, tables, and a preliminary analysis of the selected works are presented. Our main contributions are: 1) an updated literature review, describing key characteristics, results, research issues, and application domains of the papers; and 2) the taxonomy for the evaluation process. We have also detected gaps in the Security Assessment literature that could be the subject of further studies in the field. This work is meant to be useful for security researchers who wish to adopt a formal approach in their methods and techniques.
Opportunistic Scheduling as Restless Bandits
Venue: arXiv cs.SY 2017
Year: 2017

In this paper we consider energy efficient scheduling in a multiuser setting where each user has a finite sized queue and there is a cost associated with holding packets (jobs) in each queue (modeling the delay constraints). The packets of each user need to be sent over a common channel. The channel qualities seen by the users are time-varying and differ across users; also, the cost incurred, i.e., energy consumed, in packet transmission is a function of the channel quality. We pose the problem as an average cost Markov Decision Problem, and prove that this problem is Whittle Indexable. Based on this result, we propose an algorithm in which the Whittle index of each user is computed and the user who has the lowest value is selected for transmission. We evaluate the performance of this algorithm via simulations and show that it achieves a lower average cost than the Maximum Weight Scheduling and Weighted Fair Scheduling strategies.
Reliable and Efficient Access for Alarm-initiated and Regular M2M Traffic in IEEE 802.11ah Systems
Venue: arXiv cs.IT 2017
Year: 2017

EEE 802.11ah is a novel WiFi-based protocol, aiming to provide an access solution for the machine-to-machine (M2M) communications. In this paper, we propose an adaptive access mechanism that can be seamlessly incorporated into IEEE 802.11ah protocol operation and that supports all potential M2M reporting regimes, which are periodic, on-demand We show that it is possible to both efficiently and reliably resolve all reporting stations in the cell, within the limits of the allowed deadlines. As a side result, we also provide a rationale for modeling the inter-arrival time in alarm events by using the Beta distribution, a model that is considered in the 3GPP standardization.
On Conceptually Simple Algorithms for Variants of Online Bipartite Matching
Venue: arXiv cs.DS 2017
Year: 2017

We present a series of results regarding conceptually simple algorithms for bipartite matching in various online and related models. We first consider a deterministic adversarial model. The best approximation ratio possible for a one-pass deterministic online algorithm is $1/2$, which is achieved by any greedy algorithm. Dürr et al. recently presented a $2$-pass algorithm called Category-Advice that achieves approximation ratio $3/5$. We extend their algorithm to multiple passes. We prove the exact approximation ratio for the $k$-pass Category-Advice algorithm for all $k \ge 1$, and show that the approximation ratio converges to the inverse of the golden ratio $2/(1+\sqrt{5}) \approx 0.618$ as $k$ goes to infinity. The convergence is extremely fast --- the $5$-pass Category-Advice algorithm is already within $0.01\%$ of the inverse of the golden ratio. We then consider a natural greedy algorithm in the online stochastic IID model---MinDegree. This algorithm is an online version of a well-known and extensively studied offline algorithm MinGreedy. We show that MinDegree cannot achieve an approximation ratio better than $1-1/e$, which is guaranteed by any consistent greedy algorithm in the known IID model. Finally, following the work in Besser and Poloczek, we depart from an adversarial or stochastic ordering and investigate a natural randomized algorithm (MinRanking) in the priority model. Although the priority model allows the algorithm to choose the input ordering in a general but well defined way, this natural algorithm cannot obtain the approximation of the Ranking algorithm in the ROM model.
Pricing Social Goods
Venue: arXiv cs.GT 2017
Year: 2017

Social goods are goods that grant value not only to their owners but also to the owners' surroundings, be it their families, friends or office mates. The benefit a non-owner derives from the good is affected by many factors, including the type of the good, its availability, and the social status of the non-owner. Depending on the magnitude of the benefit and on the price of the good, a potential buyer might stay away from purchasing the good, hoping to free ride on others' purchases. A revenue-maximizing seller who sells social goods must take these considerations into account when setting prices for the good. The literature on optimal pricing has advanced considerably over the last decade, but little is known about optimal pricing schemes for selling social goods. In this paper, we conduct a systematic study of revenue-maximizing pricing schemes for social goods: we introduce a Bayesian model for this scenario, and devise nearly-optimal pricing schemes for various types of externalities, both for simultaneous sales and for sequential sales.
Improvement of training set structure in fusion data cleaning using Time-Domain Global Similarity method
Venue: arXiv cs.LG 2017
Year: 2017

Traditional data cleaning identifies dirty data by classifying original data sequences, which is a class$-$imbalanced problem since the proportion of incorrect data is much less than the proportion of correct ones for most diagnostic systems in Magnetic Confinement Fusion (MCF) devices. When using machine learning algorithms to classify diagnostic data based on class$-$imbalanced training set, most classifiers are biased towards the major class and show very poor classification rates on the minor class. By transforming the direct classification problem about original data sequences into a classification problem about the physical similarity between data sequences, the class$-$balanced effect of Time$-$Domain Global Similarity (TDGS) method on training set structure is investigated in this paper. Meanwhile, the impact of improved training set structure on data cleaning performance of TDGS method is demonstrated with an application example in EAST POlarimetry$-$INTerferometry (POINT) system.
$\mathcal{P}$-schemes and Deterministic Polynomial Factoring over Finite Fields
Venue: arXiv cs.CC 2017
Year: 2017

We introduce a family of mathematical objects called $\mathcal{P}$-schemes, where $\mathcal{P}$ is a poset of subgroups of a finite group $G$. A $\mathcal{P}$-scheme is a collection of partitions of the right coset spaces $H\backslash G$, indexed by $H\in\mathcal{P}$, that satisfies a list of axioms. These objects generalize the classical notion of association schemes as well as the notion of $m$-schemes (Ivanyos et al. 2009). Based on $\mathcal{P}$-schemes, we develop a unifying framework for the problem of deterministic factoring of univariate polynomials over finite fields under the generalized Riemann hypothesis (GRH).
Investigating the potential of social network data for transport demand models
Venue: arXiv cs.SI 2017
Year: 2017

Location-based social network data offers the promise of collecting the data from a large base of users over a longer span of time at negligible cost. While several studies have applied social network data to activity and mobility analysis, a comparison with travel diaries and general statistics has been lacking. In this paper, we analysed geo-referenced Twitter activities from a large number of users in Singapore and neighbouring countries. By combining this data, population statistics and travel diaries and applying clustering techniques, we addressed detection of activity locations, as well as spatial separation and transitions between these locations. Kernel density estimation performs best to detect activity locations due to the scattered nature of the twitter data; more activity locations are detected per user than reported in the travel survey. The descriptive analysis shows that determining home locations is more difficult than detecting work locations for most planning zones. Spatial separations between detected activity locations from Twitter data - as reported in a travel survey and captured by public transport smart card data - are mostly similarly distributed, but also show relevant differences for very short and very long distances. This also holds for the transitions between zones. Whether the differences between Twitter data and other data sources stem from differences in the population sub-sample, clustering methodology, or whether social networks are being used significantly more at specific locations must be determined by further research. Despite these shortcomings, location-based social network data offers a promising data source for insights into activity locations and mobility patterns, especially for regions where travel survey data is not readily available.
Distribution-based bisimulation for labelled Markov processes
Venue: arXiv cs.LO 2017
Year: 2017

In this paper we propose a (sub)distribution-based bisimulation for labelled Markov processes and compare it with earlier definitions of state and event bisimulation, which both only compare states. In contrast to those state-based bisimulations, our distribution bisimulation is weaker, but corresponds more closely to linear properties. We construct a logic and a metric to describe our distribution bisimulation and discuss linearity, continuity and compositional properties.
Turned 70? It is time to start editing Wikipedia
Venue: arXiv cs.HC 2017
Year: 2017

Success of Wikipedia would not be possible without the contributions of millions of anonymous Internet users who edit articles, correct mistakes, add links or pictures. At the same time Wikipedia editors are currently overworked and there is always more tasks waiting to be completed than people willing to volunteer. The paper explores the possibility of involving the elderly in the Wikipedia editing process. Older adults were asked to complete various tasks on Wikipedia. Based on the observations made during these activities as well as in-depth interviews, a list of recommendation has been crafted. It turned out that older adults are willing to contribute to Wikiepdia but substantial changes have to be made in the Wikipedia editor.
Novel Logical Method for Security Analysis of Electronic Payment Protocols
Venue: arXiv cs.LO 2017
Year: 2017

Electronic payment protocols play a vital role in electronic commerce security, which is essential for secure operation of electronic commerce activities. Formal method is an effective way to verify the security of protocols. But current formal method lacks the description and analysis of timeliness in electronic payment protocols. In order to improve analysis ability, a novel approach to analyze security properties such as accountability, fairness and timeliness in electronic payment protocols is proposed in this paper. This approach extends an existing logical method by adding a concise time expression and analysis method. It enables to describe the event time, and extends the time characteristics of logical inference rules. We analyzed the Netbill protocol with the new approach and found that the fairness of the protocol is not satisfied, due to timeliness problem. The result illustrates the new approach is able to analyze the key properties of electronic payment protocols. Furthermore, the new approach can be introduced to analyze other time properties of cryptographic protocols.
Design Activism for Minimum Wage Crowd Work
Venue: arXiv cs.HC 2017
Year: 2017

Entry-level crowd work is often reported to pay less than minimum wage. While this may be appropriate or even necessary, due to various legal, economic, and pragmatic factors, some Requesters and workers continue to question this status quo. To promote further discussion on the issue, we survey Requesters and workers whether they would support restricting tasks to require minimum wage pay. As a form of design activism, we confronted workers with this dilemma directly by posting a dummy Mechanical Turk task which told them that they could not work on it because it paid less than their local minimum wage, and we invited their feedback. Strikingly, for those workers expressing an opinion, two-thirds of Indians favored the policy while two-thirds of Americans opposed it. Though a majority of Requesters supported minimum wage pay, only 20\% would enforce it. To further empower Requesters, and to ensure that effort or ignorance are not barriers to change, we provide a simple public API to make it easy to find a worker's local minimum wage by his/her IP address.
Tableaux for Policy Synthesis for MDPs with PCTL* Constraints
Venue: arXiv cs.LO 2017
Year: 2017

Markov decision processes (MDPs) are the standard formalism for modelling sequential decision making in stochastic environments. Policy synthesis addresses the problem of how to control or limit the decisions an agent makes so that a given specification is met. In this paper we consider PCTL*, the probabilistic counterpart of CTL*, as the specification language. Because in general the policy synthesis problem for PCTL* is undecidable, we restrict to policies whose execution history memory is finitely bounded a priori. Surprisingly, no algorithm for policy synthesis for this natural and expressive framework has been developed so far. We close this gap and describe a tableau-based algorithm that, given an MDP and a PCTL* specification, derives in a non-deterministic way a system of (possibly nonlinear) equalities and inequalities. The solutions of this system, if any, describe the desired (stochastic) policies. Our main result in this paper is the correctness of our method, i.e., soundness, completeness and termination.
Sums of Palindromes: an Approach via Automata
Venue: arXiv cs.FL 2017
Year: 2017

Recently, Cilleruelo, Luca, & Baxter proved, for all bases b >= 5, that every natural number is the sum of at most 3 natural numbers whose base-b representation is a palindrome. However, the cases b = 2, 3, 4 were left unresolved. We prove, using a decision procedure based on automata, that every natural number is the sum of at most 4 natural numbers whose base-2 representation is a palindrome. Here the constant 4 is optimal. We obtain similar results for bases 3 and 4, thus completely resolving the problem. We consider some other variations on this problem, and prove similar results. We argue that heavily case-based proofs are a good signal that a decision procedure may help to automate the proof.
Formation of Stable Strategic Networks with Desired Topologies
Venue: arXiv cs.SI 2017
Year: 2017

Many real-world networks such as social networks consist of strategic agents. The topology of these networks often plays a crucial role in determining the ease and speed with which certain information driven tasks can be accomplished. Consequently, growing a stable network having a certain desired topology is of interest. Motivated by this, we study the following important problem: given a certain desired topology, under what conditions would best response link alteration strategies adopted by strategic agents, uniquely lead to formation of a stable network having the given topology. This problem is the inverse of the classical network formation problem where we are concerned with determining stable topologies, given the conditions on the network parameters. We study this interesting inverse problem by proposing (1) a recursive model of network formation and (2) a utility model that captures key determinants of network formation. Building upon these models, we explore relevant topologies such as star graph, complete graph, bipartite Turan graph, and multiple stars with interconnected centers. We derive a set of sufficient conditions under which these topologies uniquely emerge, study their social welfare properties, and investigate the effects of deviating from the derived conditions.
6thSense: A Context-aware Sensor-based Attack Detector for Smart Devices
Venue: arXiv cs.CR 2017
Year: 2017

Sensors (e.g., light, gyroscope, accelerotmeter) and sensing enabled applications on a smart device make the applications more user-friendly and efficient. However, the current permission-based sensor management systems of smart devices only focus on certain sensors and any App can get access to other sensors by just accessing the generic sensor API. In this way, attackers can exploit these sensors in numerous ways: they can extract or leak users' sensitive information, transfer malware, or record or steal sensitive information from other nearby devices. In this paper, we propose 6thSense, a context-aware intrusion detection system which enhances the security of smart devices by observing changes in sensor data for different tasks of users and creating a contextual model to distinguish benign and malicious behavior of sensors. 6thSense utilizes three different Machine Learning-based detection mechanisms (i.e., Markov Chain, Naive Bayes, and LMT) to detect malicious behavior associated with sensors. We implemented 6thSense on a sensor-rich Android smart device (i.e., smartphone) and collected data from typical daily activities of 50 real users. Furthermore, we evaluated the performance of 6thSense against three sensor-based threats: (1) a malicious App that can be triggered via a sensor (e.g., light), (2) a malicious App that can leak information via a sensor, and (3) a malicious App that can steal data using sensors. Our extensive evaluations show that the 6thSense framework is an effective and practical approach to defeat growing sensor-based threats with an accuracy above 96% without compromising the normal functionality of the device. Moreover, our framework costs minimal overhead.
Synthesizing Deep Neural Network Architectures using Biological Synaptic Strength Distributions
Venue: arXiv cs.NE 2017
Year: 2017

In this work, we perform an exploratory study on synthesizing deep neural networks using biological synaptic strength distributions, and the potential influence of different distributions on modelling performance particularly for the scenario associated with small data sets. Surprisingly, a CNN with convolutional layer synaptic strengths drawn from biologically-inspired distributions such as log-normal or correlated center-surround distributions performed relatively well suggesting a possibility for designing deep neural network architectures that do not require many data samples to learn, and can sidestep current training procedures while maintaining or boosting modelling performance.
SAM: Semantic Attribute Modulation for Language Modeling and Style Variation
Venue: arXiv cs.CL 2017
Year: 2017

This paper presents a Semantic Attribute Modulation (SAM) for language modeling and style variation. The semantic attribute modulation includes various document attributes, such as titles, authors, and document categories. We consider two types of attributes, (title attributes and category attributes), and a flexible attribute selection scheme by automatically scoring them via an attribute attention mechanism. The semantic attributes are embedded into the hidden semantic space as the generation inputs. With the attributes properly harnessed, our proposed SAM can generate interpretable texts with regard to the input attributes. Qualitative analysis, including word semantic analysis and attention values, shows the interpretability of SAM. On several typical text datasets, we empirically demonstrate the superiority of the Semantic Attribute Modulated language model with different combinations of document attributes. Moreover, we present a style variation for the lyric generation using SAM, which shows a strong connection between the style variation and the semantic attributes.