OpenRaiser/Intern-Atlas huggingface dataset:
On the Bias of Directed Information Estimators
Venue: arXiv cs.IT 2019
Year: 2019

When estimating the directed information between two jointly stationary Markov processes, it is typically assumed that the recipient of the directed information is itself Markov of the same order as the joint process. While this assumption is often made explicit in the presentation of such estimators, a characterization of when we can expect the assumption to hold is lacking. Using the concept of d-separation from Bayesian networks, we present sufficient conditions for which this assumption holds. We further show that the set of parameters for which the condition is not also necessary has Lebesgue measure zero. Given the strictness of these conditions, we introduce a notion of partial directed information, which can be used to bound the bias of directed information estimates when the directed information recipient is not itself Markov. Lastly we estimate this bound on simulations in a variety of settings to assess the extent to which the bias should be cause for concern.
An end-to-end Generative Retrieval Method for Sponsored Search Engine --Decoding Efficiently into a Closed Target Domain
Venue: arXiv cs.IR 2019
Year: 2019

In this paper, we present a generative retrieval method for sponsored search engine, which uses neural machine translation (NMT) to generate keywords directly from query. This method is completely end-to-end, which skips query rewriting and relevance judging phases in traditional retrieval systems. Different from standard machine translation, the target space in the retrieval setting is a constrained closed set, where only committed keywords should be generated. We present a Trie-based pruning technique in beam search to address this problem. The biggest challenge in deploying this method into a real industrial environment is the latency impact of running the decoder. Self-normalized training coupled with Trie-based dynamic pruning dramatically reduces the inference time, yielding a speedup of more than 20 times. We also devise an mixed online-offline serving architecture to reduce the latency and CPU consumption. To encourage the NMT to generate new keywords uncovered by the existing system, training data is carefully selected. This model has been successfully applied in Baidu's commercial search engine as a supplementary retrieval branch, which has brought a remarkable revenue improvement of more than 10 percents.
A Hybrid Control Design for Autonomous Vehicles at Uncontrolled Intersections
Venue: arXiv cs.RO 2019
Year: 2019

As autonomous vehicles (AVs) inch closer to reality, a central requirement for acceptance will be earning the trust of humans in everyday driving situations. In particular, the interaction between AVs and pedestrians is of high importance, as every human is a pedestrian at some point of the day. This paper considers the interaction of a pedestrian and an autonomous vehicle at a mid-block, unsignalized intersection where there is ambiguity over when the pedestrian should cross and when and how the vehicle should yield. By modeling pedestrian behavior through the concept of gap acceptance, the authors show that a hybrid controller with just four distinct modes allows an autonomous vehicle to successfully interact with a pedestrian across a continuous spectrum of possible crosswalk entry behaviors. The controller is validated through extensive simulation and compared to an alternate POMDP solution and experimental results are provided on a research vehicle for a virtual pedestrian.
Application Specific Drone Simulators: Recent Advances and Challenges
Venue: arXiv cs.RO 2019
Year: 2019

Over the past two decades, Unmanned Aerial Vehicles (UAVs), more commonly known as drones, have gained a lot of attention, and are rapidly becoming ubiquitous because of their diverse applications such as surveillance, disaster management, pollution monitoring, film-making, and military reconnaissance. However, incidents such as fatal system failures, malicious attacks, and disastrous misuses have raised concerns in the recent past. Security and viability concerns in drone-based applications are growing at an alarming rate. Besides, UAV networks (UAVNets) are distinctive from other ad-hoc networks. Therefore, it is necessary to address these issues to ensure proper functioning of these UAVs while keeping their uniqueness in mind. Furthermore, adequate security and functionality require the consideration of many parameters that may include an accurate cognizance of the working mechanism of vehicles, geographical and weather conditions, and UAVNet communication. This is achievable by creating a simulator that includes these aspects. A performance evaluation through relevant drone simulator becomes indispensable procedure to test features, configurations, and designs to demonstrate superiority to comparative schemes and suitability. Thus, it becomes of paramount importance to establish the credibility of simulation results by investigating the merits and limitations of each simulator prior to selection. Based on this motivation, we present a comprehensive survey of current drone simulators. In addition, open research issues and research challenges are discussed and presented.
Optimization of Project Scheduling Activities in Dynamic CPM and PERT Networks Using Genetic Algorithms
Venue: arXiv cs.AI 2019
Year: 2019

Projects consist of interconnected dimensions such as objective, time, resource and environment. Use of these dimensions in a controlled way and their effective scheduling brings the project success. Project scheduling process includes defining project activities, and estimation of time and resources to be used for the activities. At this point, the project resource-scheduling problems have begun to attract more attention after Program Evaluation and Review Technique (PERT) and Critical Path Method (CPM) are developed one after the other. However, complexity and difficulty of CPM and PERT processes led to the use of these techniques through artificial intelligence methods such as Genetic Algorithm (GA). In this study, an algorithm was proposed and developed, which determines critical path, critical activities and project completion duration by using GA, instead of CPM and PERT techniques used for network analysis within the scope of project management. The purpose of using GA was that these algorithms are an effective method for solution of complex optimization problems. Therefore, correct decisions can be made for implemented project activities by using obtained results. Thus, optimum results were obtained in a shorter time than the CPM and PERT techniques by using the model based on the dynamic algorithm. It is expected that this study will contribute to the performance field (time, speed, low error etc.) of other studies.
Fine-Grain Checkpointing with In-Cache-Line Logging
Venue: arXiv cs.OS 2019
Year: 2019

Non-Volatile Memory offers the possibility of implementing high-performance, durable data structures. However, achieving performance comparable to well-designed data structures in non-persistent (transient) memory is difficult, primarily because of the cost of ensuring the order in which memory writes reach NVM. Often, this requires flushing data to NVM and waiting a full memory round-trip time. In this paper, we introduce two new techniques: Fine-Grained Checkpointing, which ensures a consistent, quickly recoverable data structure in NVM after a system failure, and In-Cache-Line Logging, an undo-logging technique that enables recovery of earlier state without requiring cache-line flushes in the normal case. We implemented these techniques in the Masstree data structure, making it persistent and demonstrating the ease of applying them to a highly optimized system and their low (5.9-15.4\%) runtime overhead cost.
Data Driven Analysis of Tiny Touchscreen Performance with MicroJam
Venue: arXiv cs.MM 2019
Year: 2019

The widespread adoption of mobile devices, such as smartphones and tablets, has made touchscreens a common interface for musical performance. New mobile musical instruments have been designed that embrace collaborative creation and that explore the affordances of mobile devices, as well as their constraints. While these have been investigated from design and user experience perspectives, there is little examination of the performers' musical outputs. In this work, we introduce a constrained touchscreen performance app, MicroJam, designed to enable collaboration between performers, and engage in a novel data-driven analysis of more than 1600 performances using the app. MicroJam constrains performances to five seconds, and emphasises frequent and casual music making through a social media-inspired interface. Performers collaborate by replying to performances, adding new musical layers that are played back at the same time. Our analysis shows that users tend to focus on the centre and diagonals of the touchscreen area, and tend to swirl or swipe rather than tap. We also observe that while long swipes dominate the visual appearance of performances, the majority of interactions are short with limited expressive possibilities. Our findings are summarised into a set of design recommendations for MicroJam and other touchscreen apps for social musical interaction.
Rank weights for arbitrary finite field extensions
Venue: arXiv cs.IT 2019
Year: 2019

In this paper, we study several definitions of generalized rank weights for arbitrary finite extensions of fields. We prove that all these definitions coincide, generalizing known results for extensions of finite fields.
From Commands to Goal-based Dialogs: A Roadmap to Achieve Natural Language Interaction in RoboCup@Home
Venue: arXiv cs.RO 2019
Year: 2019

On the one hand, speech is a key aspect to people's communication. On the other, it is widely acknowledged that language proficiency is related to intelligence. Therefore, intelligent robots should be able to understand, at least, people's orders within their application domain. These insights are not new in RoboCup@Home, but we lack of a long-term plan to evaluate this approach. In this paper we conduct a brief review of the achievements on automated speech recognition and natural language understanding in RoboCup@Home. Furthermore, we discuss main challenges to tackle in spoken human-robot interaction within the scope of this competition. Finally, we contribute by presenting a pipelined road map to engender research in the area of natural language understanding applied to domestic service robotics.
Domain invariant hierarchical embedding for grocery products recognition
Venue: arXiv cs.CV 2019
Year: 2019

Recognizing packaged grocery products based solely on appearance is still an open issue for modern computer vision systems due to peculiar challenges. Firstly, the number of different items to be recognized is huge (i.e., in the order of thousands) and rapidly changing over time. Moreover, there exist a significant domain shift between the images that should be recognized at test time, taken in stores by cheap cameras, and those available for training, usually just one or a few studio-quality images per product. We propose an end-to-end architecture comprising a GAN to address the domain shift at training time and a deep CNN trained on the samples generated by the GAN to learn an embedding of product images that enforces a hierarchy between product categories. At test time, we perform recognition by means of K-NN search against a database consisting of just one reference image per product. Experiments addressing recognition of products present in the training datasets as well as different ones unseen at training time show that our approach compares favourably to state-of-the-art methods on the grocery recognition task and generalize fairly well to similar ones.
Eternal domination on prisms of graphs
Venue: arXiv cs.DM 2019
Year: 2019

An eternal dominating set of a graph $G$ is a set of vertices (or "guards") which dominates $G$ and which can defend any infinite series of vertex attacks, where an attack is defended by moving one guard along an edge from its current position to the attacked vertex. The size of the smallest eternal dominating set is denoted $γ^\infty(G)$ and is called the eternal domination number of $G$. In this paper, we answer a conjecture of Klostermeyer and Mynhardt [Discussiones Mathematicae Graph Theory, vol. 35, pp. 283-300], showing that there exist there are infinitely many graphs $G$ such that $γ^\infty(G)=θ(G)$ and $γ^\infty(G \Box K_2)<θ(G \Box K_2)$, where $θ(G)$ denotes the clique cover number of $G$.
Review Conversational Reading Comprehension
Venue: arXiv cs.CL 2019
Year: 2019

Inspired by conversational reading comprehension (CRC), this paper studies a novel task of leveraging reviews as a source to build an agent that can answer multi-turn questions from potential consumers of online businesses. We first build a review CRC dataset and then propose a novel task-aware pre-tuning step running between language model (e.g., BERT) pre-training and domain-specific fine-tuning. The proposed pre-tuning requires no data annotation, but can greatly enhance the performance on our end task. Experimental results show that the proposed approach is highly effective and has competitive performance as the supervised approach. The dataset is available at \url{https://github.com/howardhsu/RCRC}
Incremental Learning with Maximum Entropy Regularization: Rethinking Forgetting and Intransigence
Venue: arXiv cs.LG 2019
Year: 2019

Incremental learning suffers from two challenging problems; forgetting of old knowledge and intransigence on learning new knowledge. Prediction by the model incrementally learned with a subset of the dataset are thus uncertain and the uncertainty accumulates through the tasks by knowledge transfer. To prevent overfitting to the uncertain knowledge, we propose to penalize confident fitting to the uncertain knowledge by the Maximum Entropy Regularizer (MER). Additionally, to reduce class imbalance and induce a self-paced curriculum on new classes, we exclude a few samples from the new classes in every mini-batch, which we call DropOut Sampling (DOS). We further rethink evaluation metrics for forgetting and intransigence in incremental learning by tracking each sample's confusion at the transition of a task since the existing metrics that compute the difference in accuracy are often misleading. We show that the proposed method, named 'MEDIC', outperforms the state-of-the-art incremental learning algorithms in accuracy, forgetting, and intransigence measured by both the existing and the proposed metrics by a large margin in extensive empirical validations on CIFAR100 and a popular subset of ImageNet dataset (TinyImageNet).
GA-Novo: De Novo Peptide Sequencing via Tandem Mass Spectrometry using Genetic Algorithm
Venue: arXiv cs.CE 2019
Year: 2019

Proteomics is the large-scale analysis of the proteins. The common method for identifying proteins and characterising their amino acid sequences is to digest the proteins into peptides, analyse the peptides using mass spectrometry and assign the resulting tandem mass spectra (MS/MS) to peptides using database search tools. However, database search algorithms are highly dependent on a reference protein database and they cannot identify peptides and proteins not included in the database. Therefore, de novo sequencing algorithms are developed to overcome the problem by directly reconstructing the peptide sequence of an MS/MS spectrum without using any protein database. Current de novo sequencing algorithms often fail to construct the completely matched sequences, and produce partial matches. In this study, we propose a genetic algorithm based method, GA-Novo, to solve the complex optimisation task of de novo peptide sequencing, aiming at constructing full length sequences. Given an MS/MS spectrum, GA-Novo optimises the amino acid sequences to best fit the input spectrum. On the testing dataset, GA-Novo outperforms PEAKS, the most commonly used software for this task, by constructing 8% higher number of fully matched peptide sequences, and 4% higher recall at partially matched sequences.
A Relational Tucker Decomposition for Multi-Relational Link Prediction
Venue: arXiv cs.LG 2019
Year: 2019

We propose the Relational Tucker3 (RT) decomposition for multi-relational link prediction in knowledge graphs. We show that many existing knowledge graph embedding models are special cases of the RT decomposition with certain predefined sparsity patterns in its components. In contrast to these prior models, RT decouples the sizes of entity and relation embeddings, allows parameter sharing across relations, and does not make use of a predefined sparsity pattern. We use the RT decomposition as a tool to explore whether it is possible and beneficial to automatically learn sparsity patterns, and whether dense models can outperform sparse models (using the same number of parameters). Our experiments indicate that---depending on the dataset--both questions can be answered affirmatively.
Smart Web Services (SmartWS) -- The Future of Services on the Web
Venue: arXiv cs.CY 2019
Year: 2019

The past few years have been marked by an increased use of sensor technologies, abundant availability of mobile devices, and growing popularity of wearables, which enable the direct integration of their data as part of rich client applications. Despite the potential and added value that such aggregate applications bring, the implementations are usually custom solutions for particular use cases and do not support easy integration of further devices. To this end, the vision of the Web of Things (WoT) is to leverage Web standards in order to interconnect all types of devices and real-world objects, and thus to make them a part of the World Wide Web (WWW) and provide overall interoperability. In this context we introduce Smart Web Services (SmartWS) that not only provide remote access to resources and functionalities, by relying on standard communication protocols, but also encapsulate `intelligence'. Smartness features can include, for instance, context-based adaptation, cognition, inference and rules that implement autonomous decision logic in order to realize services that automatically perform tasks on behalf of the users, without requiring their explicit involvement. In this paper, we present the key characteristics of SmartWS, and introduce a reference implementation framework. Furthermore, we describe a specific use case for implementing SmartWS in the medical domain and specify a maturity model for determining the quality and usability of SmartWS.
Outage Performance in Secure Cooperative NOMA
Venue: arXiv cs.IT 2019
Year: 2019

Enabling cooperation in a NOMA system is a promising approach to improve its performance. In this paper, we study the cooperation in a secure NOMA system, where the legitimate users are distributed uniformly in the network and the eavesdroppers are distributed according to a homogeneous Poisson point process. We consider a cooperative NOMA scheme (two users are paired as strong and weak users) in two phases: 1) Direct transmission phase, in which the base station broadcasts a superposition of the messages, 2) Cooperation phase, in which the strong user acts as a relay to help in forwarding the messages of the weak user. We study the secrecy outage performance in two cases: (i) security of the strong user, (ii) security of both users, are guaranteed. In the first case, we derive the exact secrecy outage probability of the system for some regions of power allocation coefficients and a lower bound on the secrecy outage probability is derived for the other regions. In the second case, the strong user is a relay or a friendly jammer (as well as a relay), where an upper bound on the secrecy outage probability is derived at high signal-to-noise-ratio regimes. For both cases, the cooperation in a two-user paired NOMA system necessitate to utilize the joint distribution of the distance between two random users. Numerical results shows the superiority of the secure cooperative NOMA for a range of the cooperation power compared to secure non-cooperative NOMA systems.
Assessing the Usages of LMS at KAU and Proposing FORCE Strategy for the Diffusion
Venue: arXiv cs.CY 2019
Year: 2019

Since the beginning of the Saudi Arabian academic year 1435 (Sept 2014), the web-based learning management system Blackboard has been introduced and made available to all instructors and students for all courses at King Abdulaziz University (KAU). The current study takes place to assess the current usages of the Blackboard usages at KAU. The data collected from the 923 students of the foundation year which represent about one-third of the total number of the male students for the academic 2016-2017. Based on statistical evidence gained from the students responses to the survey questions, 78% of the students are inactive users of the Blackboard. The study follows up with interviewing five instructors who teach first-year students in order to seek explanations of the Blackboard low usages by the students. The outcomes point significant processes at an individual level and as well as an organizational level. The Diffusion of Innovation (DOI) was used to study the case because it is believed to be the best explain such adoption of innovation at individual and organizational levels. Based on the current outcomes and the author's experience in teaching a computer course using Blackboard, a strategy called 'FORCE' is proposed for the diffusion process.
Resource Allocation in Green Dense Cellular Networks: Complexity and Algorithms
Venue: arXiv cs.IT 2019
Year: 2019

This paper studies the problem of user association, scheduling and channel allocation in dense cellular networks with energy harvesting base stations (EBSs). In this problem, the EBSs are powered solely by renewable energy and each user has a request for downloading data of certain size with a deadline constraint. The objective is to maximize the number of associated and scheduled users while allocating the available channels to the users and respecting the energy and deadline constraints. First, the computational complexity of this problem is characterized by studying its NP-hardness in different cases. Next, efficient algorithms are proposed in each case. The case of a single channel and a single EBS is solved using two polynomial-time optimal algorithms---one for arbitrary deadlines and a less-complex one for common deadlines. The case of a single channel and multiple EBSs is solved by proposing an efficient constant-factor approximation algorithm. The case of multiple channels is efficiently solved using a heuristic algorithm. Finally, our theoretical analysis is supplemented by simulation results to illustrate the performance of the proposed algorithms.
Making Quantum Computing Open: Lessons from Open-Source Projects
Venue: arXiv cs.SE 2019
Year: 2019

Quantum computing (QC) is an emerging computing paradigm with potential to revolutionize the field of computing. QC is a field that is quickly developing globally and has high barriers of entry. In this paper we explore both successful contributors to the field as well as wider QC community with the goal of understanding the backgrounds and training that helped them succeed. We gather data on 148 contributors to open-source quantum computing projects hosted on GitHub and survey 46 members of QC community. Our findings show that QC practitioners and enthusiasts have diverse backgrounds, with most of them having a PhD and trained in physics or computer science. We observe a lack of educational resources on quantum computing. Our goal for these findings is to start a conversation about how best to prepare the next generation of QC researchers and practitioners.