OpenRaiser/Intern-Atlas huggingface dataset:
A Lyapunov-Stable Adaptive Method to Approximate Sensorimotor Models for Sensor-Based Control
Venue: arXiv cs.RO 2020
Year: 2020

In this article, we present a new scheme that approximates unknown sensorimotor models of robots by using feedback signals only. The formulation of the uncalibrated sensor-based regulation problem is first formulated, then, we develop a computational method that distributes the model estimation problem amongst multiple adaptive units that specialise in a local sensorimotor map. Different from traditional estimation algorithms, the proposed method requires little data to train and constrain it (the number of required data points can be analytically determined) and has rigorous stability properties (the conditions to satisfy Lyapunov stability are derived). Numerical simulations and experimental results are presented to validate the proposed method.
Inter-sequence Enhanced Framework for Personalized Sequential Recommendation
Venue: arXiv cs.IR 2020
Year: 2020

Modeling the sequential correlation of users' historical interactions is essential in sequential recommendation. However, the majority of the approaches mainly focus on modeling the \emph{intra-sequence} item correlation within each individual sequence but neglect the \emph{inter-sequence} item correlation across different user interaction sequences. Though several studies have been aware of this issue, their method is either simple or implicit. To make better use of such information, we propose an inter-sequence enhanced framework for the Sequential Recommendation (ISSR). In ISSR, both inter-sequence and intra-sequence item correlation are considered. Firstly, we equip graph neural networks in the inter-sequence correlation encoder to capture the high-order item correlation from the user-item bipartite graph and the item-item graph. Then, based on the inter-sequence correlation encoder, we build GRU network and attention network in the intra-sequence correlation encoder to model the item sequential correlation within each individual sequence and temporal dynamics for predicting users' preferences over candidate items. Additionally, we conduct extensive experiments on three real-world datasets. The experimental results demonstrate the superiority of ISSR over many state-of-the-art methods and the effectiveness of the inter-sequence correlation encoder.
Algebraic Approach to Directed Rough Sets
Venue: arXiv cs.LO 2020
Year: 2020

In relational approach to general rough sets, ideas of directed relations are supplemented with additional conditions for multiple algebraic approaches in this research paper. The relations are also specialized to representations of general parthood that are upper-directed, reflexive and antisymmetric for a better behaved groupoidal semantics over the set of roughly equivalent objects by the first author. Another distinct algebraic semantics over the set of approximations, and a new knowledge interpretation are also invented in this research by her. Because of minimal conditions imposed on the relations, neighborhood granulations are used in the construction of all approximations (granular and pointwise). Necessary and sufficient conditions for the lattice of local upper approximations to be completely distributive are proved by the second author. These results are related to formal concept analysis. Applications to student centered learning and decision making are also outlined.
A Named Entity Based Approach to Model Recipes
Venue: arXiv cs.CL 2020
Year: 2020

Traditional cooking recipes follow a structure which can be modelled very well if the rules and semantics of the different sections of the recipe text are analyzed and represented accurately. We propose a structure that can accurately represent the recipe as well as a pipeline to infer the best representation of the recipe in this uniform structure. The Ingredients section in a recipe typically lists down the ingredients required and corresponding attributes such as quantity, temperature, and processing state. This can be modelled by defining these attributes and their values. The physical entities which make up a recipe can be broadly classified into utensils, ingredients and their combinations that are related by cooking techniques. The instruction section lists down a series of events in which a cooking technique or process is applied upon these utensils and ingredients. We model these relationships in the form of tuples. Thus, using a combination of these methods we model cooking recipe in the dataset RecipeDB to show the efficacy of our method. This mined information model can have several applications which include translating recipes between languages, determining similarity between recipes, generation of novel recipes and estimation of the nutritional profile of recipes. For the purpose of recognition of ingredient attributes, we train the Named Entity Relationship (NER) models and analyze the inferences with the help of K-Means clustering. Our model presented with an F1 score of 0.95 across all datasets. We use a similar NER tagging model for labelling cooking techniques (F1 score = 0.88) and utensils (F1 score = 0.90) within the instructions section. Finally, we determine the temporal sequence of relationships between ingredients, utensils and cooking techniques for modeling the instruction steps.
Local Graph Clustering with Network Lasso
Venue: arXiv cs.LG 2020
Year: 2020

We study the statistical and computational properties of a network Lasso method for local graph clustering. The clusters delivered by nLasso can be characterized elegantly via network flows between cluster boundary and seed nodes. While spectral clustering methods are guided by a minimization of the graph Laplacian quadratic form, nLasso minimizes the total variation of cluster indicator signals. As demonstrated theoretically and numerically, nLasso methods can handle very sparse clusters (chain-like) which are difficult for spectral clustering. We also verify that a primal-dual method for nonsmooth optimization allows to approximate nLasso solutions with optimal worst-case convergence rate.
A First Instagram Dataset on COVID-19
Venue: arXiv cs.SI 2020
Year: 2020

The novel coronavirus (COVID-19) pandemic outbreak is drastically shaping and reshaping many aspects of our life, with a huge impact on our social life. In this era of lockdown policies in most of the major cities around the world, we see a huge increase in people and professional engagement in social media. Social media is playing an important role in news propagation as well as keeping people in contact. At the same time, this source is both a blessing and a curse as the coronavirus infodemic has become a major concern, and is already a topic that needs special attention and further research. In this paper, we provide a multilingual coronavirus (COVID-19) Instagram dataset that we have been continuously collected since March 30, 2020. We are making our dataset available to the research community at Github. We believe that this contribution will help the community to better understand the dynamics behind this phenomenon in Instagram, as one of the major social media. This dataset could also help study the propagation of misinformation related to this outbreak.
Citation Cascade and the Evolution of Topic Relevance
Venue: arXiv cs.SI 2020
Year: 2020

Citation analysis, as a tool for quantitative studies of science, has long emphasized direct citation relations, leaving indirect or high order citations overlooked. However, a series of early and recent studies demonstrate the existence of indirect and continuous citation impact across generations. Adding to the literature on high order citations, we introduce the concept of a citation cascade: the constitution of a series of subsequent citing events initiated by a certain publication. We investigate this citation structure by analyzing more than 450,000 articles and over 6 million citation relations. We show that citation impact exists not only within the three generations documented in prior research, but also in much further generations. Still, our experimental results indicate that two to four generations are generally adequate to trace a work's scientific impact. We also explore specific structural properties such as depth, width, structural virality, and size, which account for differences among individual citation cascades. Finally, we find evidence that it is more important for a scientific work to inspire trans domain (or indirectly related domain) works than to receive only intra domain recognition in order to achieve high impact. Our methods and findings can serve as a new tool for scientific evaluation and the modeling of scientific history.
Hierarchical Clustering of World Cuisines
Venue: arXiv cs.SI 2020
Year: 2020

Cultures across the world have evolved to have unique patterns despite shared ingredients and cooking techniques. Using data obtained from RecipeDB, an online resource for recipes, we extract patterns in 26 world cuisines and further probe for their inter-relatedness. By application of frequent itemset mining and ingredient authenticity we characterize the quintessential patterns in the cuisines and build a hierarchical tree of the world cuisines. This tree provides interesting insights into the evolution of cuisines and their geographical as well as historical relatedness.
Challenge Closed-book Science Exam: A Meta-learning Based Question Answering System
Venue: arXiv cs.AI 2020
Year: 2020

Prior work in standardized science exams requires support from large text corpus, such as targeted science corpus fromWikipedia or SimpleWikipedia. However, retrieving knowledge from the large corpus is time-consuming and questions embedded in complex semantic representation may interfere with retrieval. Inspired by the dual process theory in cognitive science, we propose a MetaQA framework, where system 1 is an intuitive meta-classifier and system 2 is a reasoning module. Specifically, our method based on meta-learning method and large language model BERT, which can efficiently solve science problems by learning from related example questions without relying on external knowledge bases. We evaluate our method on AI2 Reasoning Challenge (ARC), and the experimental results show that meta-classifier yields considerable classification performance on emerging question types. The information provided by meta-classifier significantly improves the accuracy of reasoning module from 46.6% to 64.2%, which has a competitive advantage over retrieval-based QA methods.
Signature-based Selection of IaaS Cloud Services
Venue: arXiv cs.DC 2020
Year: 2020

We propose a novel approach to select IaaS cloud services for a long-term period where the service providers offer limited QoS information. The proposed approach leverages free short-term trials to obtain the previously undisclosed QoS information. A new significance-based trial scheme is proposed using frequency distribution analysis to test a consumer's long-term workloads in a short trial. We introduce a novel IaaS signature technique to uniquely identify the variability of a provider's QoS performance. A Signature-based QoS Performance Discovery (SPD) algorithm is proposed which leverages the combination of free trials and IaaS signatures. A set of exhaustive experiments with real-world datasets is conducted to evaluate the proposed approach.
Reinforcement Learning Generalization with Surprise Minimization
Venue: arXiv cs.LG 2020
Year: 2020

Generalization remains a challenging problem for deep reinforcement learning algorithms, which are often trained and tested on the same set of deterministic game environments. When test environments are unseen and perturbed but the nature of the task remains the same, generalization gaps can arise. In this work, we propose and evaluate a surprise minimizing agent on a generalization benchmark to show an additional reward learned from a simple density model can show robustness in procedurally generated game environments that provide constant source of entropy and stochasticity.
A Distributed Ledger-Enabled Interworking Model for the Wireless Air Interface
Venue: arXiv cs.NI 2020
Year: 2020

While direct allocation of spectrum and evolved medium access protocols provide a base for ubiquitous wireless connectivity, the existing TCP/IP and OSI models were designed for wired networks and do not address open interconnection of air interfaces. Without an interconnection model for the air interface, existing network designs continue to tie wireless medium access to that of the backhaul provider for ownership of access and identity trust, resulting in limitations on functionality and coverage. In this paper, we propose a novel solution to access ownership and identity trust by extending the TCP network standard, under a new model we propose, named TCP-Air which integrates distributed ledger technologies directly at the air interface. Further, we present two use cases of the TCP-Air model, demonstrating applications not feasible under existing permissioned-access network designs.
Towards Blockchain for Decentralized Self-Organizing Wireless Networks
Venue: arXiv cs.CR 2020
Year: 2020

Distributed consensus mechanisms have been widely researched and made popular with a number of blockchain-based token applications, such as Bitcoin, and Ethereum. Although these general-purpose platforms have matured for scale and security, they are designed for human incentive and continue to require currency reward and contract functions that are not requisite in machine communications. Redes Chain is a custom designed blockchain, built to support fully decentralized self-organization in wireless networks-without a cryptocurrency or contract dependency.
Airmed: Efficient Self-Healing Network of Low-End Devices
Venue: arXiv cs.CR 2020
Year: 2020

The proliferation of application specific cyber-physical systems coupled with the emergence of a variety of attacks on such systems (malware such as Mirai and Hajime) underlines the need to secure such networks. Most existing security efforts have focused on only detection of the presence of malware. However given the ability of most attacks to spread through the network once they infect a few devices, it is important to contain the spread of a virus and at the same time systematically cleanse the impacted nodes using the communication capabilities of the network. Toward this end, we present Airmed - a method and system to not just detect corruption of the application software on a IoT node, but to self correct itself using its neighbors. Airmed's decentralized mechanisms prevent the spread of self-propagating malware and can also be used as a technique for updating application code on such IoT devices. Among the novelties of Airmed are a novel bloom-filter technique along with hardware support to identify position of the malware program from the benign application code, an adaptive self-check for computational efficiency, and a uniform random-backoff and stream signatures for secure and bandwidth efficient code exchange to correct corrupted devices. We assess the performance of Airmed, using the embedded systems security architecture of TrustLite in the OMNeT++ simulator. The results show that Airmed scales up to thousands of devices, ensures guaranteed update of the entire network, and can recover 95% of the nodes in 10 minutes in both internal and external propagation models. Moreover, we evaluate memory and communication costs and show that Airmed is efficient and incurs very low overhead.
Power of Artificial Intelligence to Diagnose and Prevent Further COVID-19 Outbreak: A Short Communication
Venue: arXiv cs.CY 2020
Year: 2020

Novel coronavirus-19 (2019-nCoV or COVID-19) is by far the most dangerous coronavirus ever identified for the third time in the three decades capable of infecting not only the animals but also the humans across the globe. Nearly 6000 deaths have been recorded due mainly to COVID-19 outbreak worldwide and more than 50% of these deaths appeared to have evolved from China where the virus was thought to originate. The endemicity of COVID-19 dramatically surpassed severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV) that were so far discovered in 2003 and 2012 respectively. Thus, the World Health Organization (WHO) has declared the 2019-nCoV outbreak not only a public health emergency but also pandemic in nature. Currently, over 120 countries including Nigeria were reported to have more than 157,844 confirmed cases and 5,846 deaths due mainly to COVID-19 outbreak as of March 15, 2020, 10:55 GMT. Artificial Intelligence (AI) is widely used to aid in the prediction, detection, response, recovery of disease and making clinical diagnosis. In this study, we highlighted the power of AI in the containment and mitigation of the spread of COVID-19 outbreak in African countries such as Nigeria where human to human contact is apparently inevitable.
Ensemble Deep Learning on Time-Series Representation of Tweets for Rumor Detection in Social Media
Venue: arXiv cs.LG 2020
Year: 2020

Social media is a popular platform for timely information sharing. One of the important challenges for social media platforms like Twitter is whether to trust news shared on them when there is no systematic news verification process. On the other hand, timely detection of rumors is a non-trivial task, given the fast-paced social media environment. In this work, we proposed an ensemble model, which performs majority-voting on a collection of predictions by deep neural networks using time-series vector representation of Twitter data for timely detection of rumors. By combining the proposed data pre-processing method with the ensemble model, better performance of rumor detection has been demonstrated in the experiments using PHEME dataset. Experimental results show that the classification performance has been improved by 7.9% in terms of micro F1 score compared to the baselines.
Is This the Right Time to Post My Task? An Empirical Analysis on a Task Similarity Arrival in TopCoder
Venue: arXiv cs.SE 2020
Year: 2020

Existed studies have shown that crowd workers are more interested in taking similar tasks in terms of context, field, and required technology, rather than tasks from the same project. Therefore, it is important for task owners to not only be able to plan 'when the new task should arrive?' but also, to justify 'what the strategic task arrival plan should be?' in order to receive a valid submission for the posted task. To address these questions this research reports an empirical analysis on the impact of similar task arrival in the platform, on both tasks' success level and workers' performance. Our study supports that 1- A higher number of arrival tasks with similarity level greater than 70% will negatively impact on task competition level, 2- A bigger pool of similar open and arrival tasks would lead to lower worker attraction and elasticity, and 3- Workers who register for tasks with lower similarity level are more reliable to make a valid submission and 4- arriving task to the pool of 60% similar task will provide the highest chance of receiving a valid submission.
In-Place Parallel-Partition Algorithms using Exclusive-Read-and-Write Memory
Venue: arXiv cs.DS 2020
Year: 2020

We present an in-place algorithm for the partition problem that has linear work and polylogarithmic span. The algorithm uses only exclusive read/write shared variables, and can be implemented using parallel-for-loops without any additional concurrency considerations (i.e., the algorithm is EREW). A key feature of the algorithm is that it exhibits provably optimal cache behavior, up to small-order factors. We also present a second in-place EREW algorithm for the partition problem that has linear work and span $O(\log n \cdot \log \log n)$, which is within an $O(\log\log n)$ factor of the optimal span. By using this low-span algorithm as a subroutine within the cache-friendly algorithm, we are able to obtain a single EREW algorithm that combines their theoretical guarantees: the algorithm achieves span $O(\log n \cdot \log \log n)$ and optimal cache behavior. As an immediate consequence, we also get an in-place EREW quicksort algorithm with work $O(n \log n)$, span $O(\log^2 n \cdot \log \log n)$. Whereas the standard EREW algorithm for parallel partitioning is memory-bandwidth bound on large numbers of cores, our cache-friendly algorithm is able to achieve near-ideal scaling in practice by avoiding the memory-bandwidth bottleneck. The algorithm's performance is comparable to that of the Blocked Strided Algorithm of Francis, Pannan, Frias, and Petit, which is the previous state-of-the art for parallel EREW sorting algorithms, but which lacks theoretical guarantees on its span and cache behavior.
From Theory to Behaviour: Towards a General Model of Engagement
Venue: arXiv cs.LG 2020
Year: 2020

Engagement is a fuzzy concept. In the present work we operationalize engagement mechanistically by linking it directly to human behaviour and show that the construct of engagement can be used for shaping and interpreting data-driven methods. First we outline a formal framework for engagement modelling. Second we expanded on our previous work on theory-inspired data-driven approaches to better model the engagement process by proposing a new modelling technique, the Melchoir Model. Third, we illustrate how, through model comparison and inspection, we can link machine-learned models and underlying theoretical frameworks. Finally we discuss our results in light of a theory-driven hypothesis and highlight potential application of our work in industry.
Requirements engineering in global scaled agile software development environment: a multi-vocal literature review protocol
Venue: arXiv cs.SE 2020
Year: 2020

Context: Requirements engineering in global scaled agile software development and the planning phase for a multi-vocal literature review. Objective: Develop a protocol to specify the plan which will be followed to conduct a multi-vocal literature review study on requirements engineering in global scaled agile software development. Method: Kitchenham and Charters (2007), and Garousi et al. (2019) guidelines were followed to develop a protocol for multi-vocal literature review. Result: A validated protocol to conduct a multi-vocal literature review. Conclusion: The review protocol consist of five phases enumerated as follows: research questions, search strategies, validation of review process, reporting the review, and making changes to the protocol.
Robust Algorithms under Adversarial Injections
Venue: arXiv cs.DS 2020
Year: 2020

In this paper, we study streaming and online algorithms in the context of randomness in the input. For several problems, a random order of the input sequence---as opposed to the worst-case order---appears to be a necessary evil in order to prove satisfying guarantees. However, algorithmic techniques that work under this assumption tend to be vulnerable to even small changes in the distribution. For this reason, we propose a new \emph{adversarial injections} model, in which the input is ordered randomly, but an adversary may inject misleading elements at arbitrary positions. We believe that studying algorithms under this much weaker assumption can lead to new insights and, in particular, more robust algorithms. We investigate two classical combinatorial-optimization problems in this model: Maximum matching and cardinality constrained monotone submodular function maximization. Our main technical contribution is a novel streaming algorithm for the latter that computes a $0.55$-approximation. While the algorithm itself is clean and simple, an involved analysis shows that it emulates a subdivision of the input stream which can be used to greatly limit the power of the adversary.