OpenRaiser/Intern-Atlas huggingface dataset:
Synapse: Synthetic Application Profiler and Emulator
Venue: arXiv cs.DC 2018
Year: 2018

Motivated by the need to emulate workload execution characteristics on high-performance and distributed heterogeneous resources, we introduce Synapse. Synapse is used as a proxy application (or "representative application") for real workloads, with the advantage that it can be tuned in different ways and dimensions, and also at levels of granularity that are not possible with real applications. Synapse has a platform-independent application profiler, and has the ability to emulate profiled workloads on a variety of resources. Experiments show that the automated profiling performed using Synapse captures an application's characteristics with high fidelity. The emulation of an application using Synapse can reproduce the application's execution behavior in the original run-time environment, and can also reproduce those behaviors on different run-time environments.
Evaluating the Readability of Force Directed Graph Layouts: A Deep Learning Approach
Venue: arXiv cs.CV 2018
Year: 2018

Existing graph layout algorithms are usually not able to optimize all the aesthetic properties desired in a graph layout. To evaluate how well the desired visual features are reflected in a graph layout, many readability metrics have been proposed in the past decades. However, the calculation of these readability metrics often requires access to the node and edge coordinates and is usually computationally inefficient, especially for dense graphs. Importantly, when the node and edge coordinates are not accessible, it becomes impossible to evaluate the graph layouts quantitatively. In this paper, we present a novel deep learning-based approach to evaluate the readability of graph layouts by directly using graph images. A convolutional neural network architecture is proposed and trained on a benchmark dataset of graph images, which is composed of synthetically-generated graphs and graphs created by sampling from real large networks. Multiple representative readability metrics (including edge crossing, node spread, and group overlap) are considered in the proposed approach. We quantitatively compare our approach to traditional methods and qualitatively evaluate our approach using a case study and visualizing convolutional layers. This work is a first step towards using deep learning based methods to evaluate images from the visualization field quantitatively.
Inlining External Sources in Answer Set Programs
Venue: arXiv cs.AI 2018
Year: 2018

HEX-programs are an extension of answer set programs (ASP) with external sources. To this end, external atoms provide a bidirectional interface between the program and an external source. The traditional evaluation algorithm for HEX-programs is based on guessing truth values of external atoms and verifying them by explicit calls of the external source. The approach was optimized by techniques that reduce the number of necessary verification calls or speed them up, but the remaining external calls are still expensive. In this paper we present an alternative evaluation approach based on inlining of external atoms, motivated by existing but less general approaches for specialized formalisms such as DL-programs. External atoms are then compiled away such that no verification calls are necessary. The approach is implemented in the dlvhex reasoner. Experiments show a significant performance gain. Besides performance improvements, we further exploit inlining for extending previous (semantic) characterizations of program equivalence from ASP to HEX-programs, including those of strong equivalence, uniform equivalence and H, B -equivalence. Finally, based on these equivalence criteria, we characterize also inconsistency of programs wrt. extensions. Since well-known ASP extensions (such as constraint ASP) are special cases of HEX, the results are interesting beyond the particular formalism. Under consideration in Theory and Practice of Logic Programming (TPLP).
Secure Incentivization for Decentralized Content Delivery
Venue: arXiv cs.NI 2018
Year: 2018

Prior research has proposed technical solutions to use peer-to-peer (P2P) content delivery to serve Internet video, showing that it can reduce costs to content providers. Yet, such methods have not become widespread except for a few niche instances. An important challenge is incentivization: what tangible benefits does P2P content delivery offer users who bring resources to the table? In this paper, we ask whether monetary incentives can help attract peers in P2P content delivery systems. We commissioned a professional survey of people around theUnited States to answer several relevant questions. We found that 51% of the 876 respondents--substantially larger than our expectations--answered "yes" to whether they would participate for suitable financial incentives. Encouraged by the results of the survey, we propose Gringotts, a system to structure incentives and securely incorporate P2P delivery into content delivery systems. Gringotts provides a novel Proof of Delivery mechanism that allows content providers to verify correct delivery of their files, and shows how to use cryptocurrency to pay peers while guarding against liars and Sybil attacks.
Algorithms for Noisy Broadcast under Erasures
Venue: arXiv cs.DS 2018
Year: 2018

The noisy broadcast model was first studied in [Gallager, TranInf'88] where an $n$-character input is distributed among $n$ processors, so that each processor receives one input bit. Computation proceeds in rounds, where in each round each processor broadcasts a single character, and each reception is corrupted independently at random with some probability $p$. [Gallager, TranInf'88] gave an algorithm for all processors to learn the input in $O(\log\log n)$ rounds with high probability. Later, a matching lower bound of $Ω(\log\log n)$ was given in [Goyal, Kindler, Saks; SICOMP'08]. We study a relaxed version of this model where each reception is erased and replaced with a `?' independently with probability $p$. In this relaxed model, we break past the lower bound of [Goyal, Kindler, Saks; SICOMP'08] and obtain an $O(\log^* n)$-round algorithm for all processors to learn the input with high probability. We also show an $O(1)$-round algorithm for the same problem when the alphabet size is $Ω(\mathrm{poly}(n))$.
Shepherd: Enabling Automatic and Large-Scale Login Security Studies
Venue: arXiv cs.CR 2018
Year: 2018

More and more parts of the internet are hidden behind a login field. This poses a barrier to any study predicated on scanning the internet. Moreover, the authentication process itself may be a weak point. To study authentication weaknesses at scale, automated login capabilities are needed. In this work we introduce Shepherd, a scanning framework to automatically log in on websites. The Shepherd framework enables us to perform large-scale scans of post-login aspects of websites. Shepherd scans a website for login fields, attempts to submit credentials and evaluates whether login was successful. We illustrate Shepherd's capabilities by means of a scan for session hijacking susceptibility. In this study, we use a set of unverified website credentials, some of which will be invalid. Using this set, Shepherd is able to fully automatically log in and verify that it is indeed logged in on 6,273 unknown sites, or 12.4% of the test set. We found that from our (biased) test set, 2,579 sites, i.e., 41.4%, are vulnerable to simple session hijacking attacks.
Optimal Stateless Model Checking under the Release-Acquire Semantics
Venue: arXiv cs.PL 2018
Year: 2018

We present a framework for the efficient application of stateless model checking (SMC) to concurrent programs running under the Release-Acquire (RA) fragment of the C/C++11 memory model. Our approach is based on exploring the possible program orders, which define the order in which instructions of a thread are executed, and read-from relations, which specify how reads obtain their values from writes. This is in contrast to previous approaches, which also explore the possible coherence orders, i.e., orderings between conflicting writes. Since unexpected test results such as program crashes or assertion violations depend only on the read-from relation, we avoid a potentially significant source of redundancy. Our framework is based on a novel technique for determining whether a particular read-from relation is feasible under the RA semantics. We define an SMC algorithm which is provably optimal in the sense that it explores each program order and read-from relation exactly once. This optimality result is strictly stronger than previous analogous optimality results, which also take coherence order into account. We have implemented our framework in the tool Tracer. Experiments show that Tracer can be significantly faster than state-of-the-art tools that can handle the RA semantics.
RGB Video Based Tennis Action Recognition Using a Deep Historical Long Short-Term Memory
Venue: arXiv cs.CV 2018
Year: 2018

Action recognition has attracted increasing attention from RGB input in computer vision partially due to potential applications on somatic simulation and statistics of sport such as virtual tennis game and tennis techniques and tactics analysis by video. Recently, deep learning based methods have achieved promising performance for action recognition. In this paper, we propose weighted Long Short-Term Memory adopted with convolutional neural network representations for three dimensional tennis shots recognition. First, the local two-dimensional convolutional neural network spatial representations are extracted from each video frame individually using a pre-trained Inception network. Then, a weighted Long Short-Term Memory decoder is introduced to take the output state at time t and the historical embedding feature at time t-1 to generate feature vector using a score weighting scheme. Finally, we use the adopted CNN and weighted LSTM to map the original visual features into a vector space to generate the spatial-temporal semantical description of visual sequences and classify the action video content. Experiments on the benchmark demonstrate that our method using only simple raw RGB video can achieve better performance than the state-of-the-art baselines for tennis shot recognition.
Developing Robot Driver Etiquette Based on Naturalistic Human Driving Behavior
Venue: arXiv cs.RO 2018
Year: 2018

Automated vehicles can change the society by improved safety, mobility and fuel efficiency. However, due to the higher cost and change in business model, over the coming decades, the highly automated vehicles likely will continue to interact with many human-driven vehicles. In the past, the control/design of the highly automated (robotic) vehicles mainly considers safety and efficiency but failed to address the "driving culture" of surrounding human-driven vehicles. Thus, the robotic vehicles may demonstrate behaviors very different from other vehicles. We study this "driving etiquette" problem in this paper. As the first step, we report the key behavior parameters of human driven vehicles derived from a large naturalistic driving database. The results can be used to guide future algorithm design of highly automated vehicles or to develop realistic human-driven vehicle behavior model in simulations.
We Hear Your Activities through Wi-Fi Signals
Venue: arXiv cs.NI 2018
Year: 2018

In this paper we focus on the problem of human activity recognition without identification of the individuals in a scene. We consider using Wi-Fi signals to detect certain human mobility behaviors such as stationary, walking, or running. The main objective is to successfully detect these behaviors for the individuals and based on that enable detection of the crowd's overall mobility behavior. We propose a method which infers mobility behaviors in two stages: from Wi-Fi signals to trajectories and from trajectories to the mobility behaviors. We evaluate the applicability of the proposed approach using the StudentLife dataset which contains Wi-Fi, GPS, and accelerometer measurements collected from smartphones of 49 students within a three-month period. The experimental results indicate that there is high correlation between stability of Wi-Fi signals and mobility activity. This unique characteristic provides sufficient evidences to extend the proposed idea to mobility analytics of groups of people in the future.
Right-to-left online construction of parameterized position heaps
Venue: arXiv cs.DS 2018
Year: 2018

Two strings of equal length are said to parameterized match if there is a bijection that maps the characters of one string to those of the other string, so that two strings become identical. The parameterized pattern matching problem is, given two strings $T$ and $P$, to find the occurrences of substrings in $T$ that parameterized match $P$. Diptarama et al. [Position Heaps for Parameterized Strings, CPM 2017] proposed an indexing data structure called parameterized position heaps, and gave a left-to-right online construction algorithm. In this paper, we present a right-to-left online construction algorithm for parameterized position heaps. For a text string $T$ of length $n$ over two kinds of alphabets $Σ$ and $Π$ of respective size $σ$ and $π$, our construction algorithm runs in $O(n \log(σ+ π))$ time with $O(n)$ space. Our right-to-left parameterized position heaps support pattern matching queries in $O(m \log (σ+ π) + m π+ \mathit{pocc}))$ time, where $m$ is the length of a query pattern $P$ and $\mathit{pocc}$ is the number of occurrences to report. Our construction and pattern matching algorithms are as efficient as Diptarama et al.'s algorithms.
Verbal Disinhibition towards Robots is Associated with General Antisociality
Venue: arXiv cs.CY 2018
Year: 2018

The emergence of agentic technologies (e.g., robots) in increasingly public realms (e.g., social media) has revealed surprising antisocial tendencies in human-agent interactions. In particular, there is growing indication of people's propensity to act aggressively towards such systems - without provocation and unabashedly so. Towards understanding whether this aggressive behavior is anomalous or whether it is associated with general antisocial tendencies in people's broader interactions, we examined people's verbal disinhibition towards two artificial agents. Using Twitter as a corpus of free-form, unsupervised interactions, we identified 40 independent Twitter users who tweeted abusively or non-abusively at one of two high-profile robots with Twitter accounts (TMI's Bina48 and Hanson Robotics' Sophia). Analysis of 50 of each user's tweets most proximate to their tweet at the respective robot (N=2,000) shows people's aggression towards the robots to be associated with more frequent abuse in their general tweeting. The findings thus suggest that disinhibition towards robots is not necessarily a pervasive tendency, but rather one driven by individual differences in antisociality. Nevertheless, such unprovoked abuse highlights a need for attention to the reception of agentic technologies in society, as well as the necessity of corresponding capacities to recognize and respond to antisocial dynamics.
Helix: Accelerating Human-in-the-loop Machine Learning
Venue: arXiv cs.LG 2018
Year: 2018

Data application developers and data scientists spend an inordinate amount of time iterating on machine learning (ML) workflows -- by modifying the data pre-processing, model training, and post-processing steps -- via trial-and-error to achieve the desired model performance. Existing work on accelerating machine learning focuses on speeding up one-shot execution of workflows, failing to address the incremental and dynamic nature of typical ML development. We propose Helix, a declarative machine learning system that accelerates iterative development by optimizing workflow execution end-to-end and across iterations. Helix minimizes the runtime per iteration via program analysis and intelligent reuse of previous results, which are selectively materialized -- trading off the cost of materialization for potential future benefits -- to speed up future iterations. Additionally, Helix offers a graphical interface to visualize workflow DAGs and compare versions to facilitate iterative development. Through two ML applications, in classification and in structured prediction, attendees will experience the succinctness of Helix programming interface and the speed and ease of iterative development using Helix. In our evaluations, Helix achieved up to an order of magnitude reduction in cumulative run time compared to state-of-the-art machine learning tools.
Security protocols for distributed wireless sensor networks
Venue: arXiv cs.NI 2018
Year: 2018

Sensor Networks technologies had proved their great practicability in the real world, being just a matter of time until this kind of networks will be standardized and used in the field. We focus on security issues in Distributed Sensor Networks like useful cryptosystems, attacks, preventing and detecting possible attacks, describing state of the art in domain and proposing new methods for further investigation
Cities of the Future: Employing Wireless Sensor Networks for Efficient Decision Making in Complex Environments
Venue: arXiv cs.MA 2018
Year: 2018

Decision making in large scale urban environments is critical for many applications involving continuous distribution of resources and utilization of infrastructure, such as ambient lighting control and traffic management. Traditional decision making methods involve extensive human participation, are expensive, and inefficient and unreliable for hard-to-predict situations. Modern technology, including ubiquitous data collection though sensors, automated analysis and prognosis, and online optimization, offers new capabilities for developing flexible, autonomous, scalable, efficient, and predictable control methods. This paper presents a new decision making concept in which a hierarchy of semantically more abstract models are utilized to perform online scalable and predictable control. The lower semantic levels perform localized decisions based on sampled data from the environment, while the higher semantic levels provide more global, time invariant results based on aggregated data from the lower levels. There is a continuous feedback between the levels of the semantic hierarchy, in which the upper levels set performance guaranteeing constraints for the lower levels, while the lower levels indicate whether these constraints are feasible or not. Even though the semantic hierarchy is not tied to a particular set of description models, the paper illustrates a hierarchy used for traffic management applications and composed of Finite State Machines, Conditional Task Graphs, Markov Decision Processes, and functional graphs. The paper also summarizes some of the main research problems that must be addressed as part of the proposed concept
Stimulation and Detection of Android Repackaged Malware with Active Learning
Venue: arXiv cs.CR 2018
Year: 2018

Repackaging is a technique that has been increasingly adopted by authors of Android malware. The main problem facing the research community working on devising techniques to detect this breed of malware is the lack of ground truth that pinpoints the malicious segments grafted within benign apps. Without this crucial knowledge, it is difficult to train reliable classifiers able to effectively classify novel, out-of-sample repackaged malware. To circumvent this problem, we argue that reliable classifiers can be trained to detect repackaged malware, if they are allowed to request new, more accurate representations of an app's behavior. This learning technique is referred to as active learning. In this paper, we propose the usage of active learning to train classifiers able to cope with the ambiguous nature of repackaged malware. We implemented an architecture, Aion, that connects the processes of stimulating and detecting repackaged malware using a feedback loop depicting active learning. Our evaluation of a sample implementation of Aion using two malware datasets (Malgenome and Piggybacking) shows that active learning can outperform conventional detection techniques and, hence, has great potential to detect Android repackaged malware.
Generation Meets Recommendation: Proposing Novel Items for Groups of Users
Venue: arXiv cs.IR 2018
Year: 2018

Consider a movie studio aiming to produce a set of new movies for summer release: What types of movies it should produce? Who would the movies appeal to? How many movies should it make? Similar issues are encountered by a variety of organizations, e.g., mobile-phone manufacturers and online magazines, who have to create new (non-existent) items to satisfy groups of users with different preferences. In this paper, we present a joint problem formalization of these interrelated issues, and propose generative methods that address these questions simultaneously. Specifically, we leverage the latent space obtained by training a deep generative model---the Variational Autoencoder (VAE)---via a loss function that incorporates both rating performance and item reconstruction terms. We then apply a greedy search algorithm that utilizes this learned latent space to jointly obtain K plausible new items, and user groups that would find the items appealing. An evaluation of our methods on a synthetic dataset indicates that our approach is able to generate novel items similar to highly-desirable unobserved items. As case studies on real-world data, we applied our method on the MART abstract art and Movielens Tag Genome dataset, which resulted in promising results: small and diverse sets of novel items.
Non-Reciprocity Compensation Combined with Turbo Codes for Secret Key Generation in Vehicular Ad Hoc Social IoT Networks
Venue: arXiv cs.CR 2018
Year: 2018

The physical attributes of the dynamic vehicle-to-vehicle (V2V) propagation channel can be utilised for the generation of highly random and symmetric cryptographic keys. However, in a physical-layer key agreement scheme, non-reciprocity due to inherent channel noise and hardware impairments can propagate bit disagreements. This has to be addressed prior to the symmetric key generation which is inherently important in social Internet of Things (IoT) networks, including in adversarial settings (e.g. battlefields). In this paper, we parametrically incorporate temporal variability attributes, such as three-dimensional (3D) scattering and scatterers mobility. Accordingly, this is the first work to incorporate such features into the key generation process by combining non-reciprocity compensation with turbo codes. Preliminary results indicate a significant improvement when using Turbo Codes in bit mismatch rate (BMR) and key generation rate (KGR) in comparison to sample indexing techniques.
A Multi-task Ensemble Framework for Emotion, Sentiment and Intensity Prediction
Venue: arXiv cs.CL 2018
Year: 2018

In this paper, through multi-task ensemble framework we address three problems of emotion and sentiment analysis i.e. "emotion classification & intensity", "valence, arousal & dominance for emotion" and "valence & arousal} for sentiment". The underlying problems cover two granularities (i.e. coarse-grained and fine-grained) and a diverse range of domains (i.e. tweets, Facebook posts, news headlines, blogs, letters etc.). The ensemble model aims to leverage the learned representations of three deep learning models (i.e. CNN, LSTM and GRU) and a hand-crafted feature representation for the predictions. Experimental results on the benchmark datasets show the efficacy of our proposed multi-task ensemble frameworks. We obtain the performance improvement of 2-3 points on an average over single-task systems for most of the problems and domains.
Remarks on an article by Rabern et al
Venue: arXiv cs.LO 2018
Year: 2018

We show that conjecture 15 in the article by Rabern et al. is wrong, comment on theorem 24 there, and conclude with some remarks on structures similar to the Yablo construction.