OpenRaiser/Intern-Atlas huggingface dataset:
A Review of Link Aggregation Control Protocol (LACP) as a Link Redundancy in SDN Based Network Using Ryu-Controller
Venue: arXiv cs.NI 2020
Year: 2020

A reliable network is an absolute requirement for telecommunication networks at this time. This is in line with the growing number of users who need a reliable and uninterrupted connection to the server. Link Aggregation is one of the techniques that can be used to provide a reliable network. Link Aggregation System combines two or more links into one logical link, which is characterized by the use of a single IP address. During the communication between host and server, whenever the used link is broken or terminated the other active link can act as redundancy to resume that communication. This mechanism is called link redundancy. This mechanism will not run without the existence of another mechanism that monitors the condition of the links, which is either connected or disconnected. That mechanism is called link monitoring. Link monitoring used in this simulation is the Media Independent Interface (MII). In this simulation, we used Ryu-Controller and mininet Simulator to test the reliability of Link Aggregation-based SDN (Software Defined Network). During the simulation, we analyzed failover performance and how the Link Aggregation distributes the connection across the links for every which comes from a different source. From the simulations, we confirmed that link redundancy, the same data rate for every connection, to some degree, worked as intended.
SLAM-Inspired Simultaneous Contextualization and Interpreting for Incremental Conversation Sentences
Venue: arXiv cs.CL 2020
Year: 2020

Distributed representation of words has improved the performance for many natural language tasks. In many methods, however, only one meaning is considered for one label of a word, and multiple meanings of polysemous words depending on the context are rarely handled. Although research works have dealt with polysemous words, they determine the meanings of such words according to a batch of large documents. Hence, there are two problems with applying these methods to sequential sentences, as in a conversation that contains ambiguous expressions. The first problem is that the methods cannot sequentially deal with the interdependence between context and word interpretation, in which context is decided by word interpretations and the word interpretations are decided by the context. Context estimation must thus be performed in parallel to pursue multiple interpretations. The second problem is that the previous methods use large-scale sets of sentences for offline learning of new interpretations, and the steps of learning and inference are clearly separated. Such methods using offline learning cannot obtain new interpretations during a conversation. Hence, to dynamically estimate the conversation context and interpretations of polysemous words in sequential sentences, we propose a method of Simultaneous Contextualization And INterpreting (SCAIN) based on the traditional Simultaneous Localization And Mapping (SLAM) algorithm. By using the SCAIN algorithm, we can sequentially optimize the interdependence between context and word interpretation while obtaining new interpretations online. For experimental evaluation, we created two datasets: one from Wikipedia's disambiguation pages and the other from real conversations. For both datasets, the results confirmed that SCAIN could effectively achieve sequential optimization of the interdependence and acquisition of new interpretations.
A Process for the Evaluation of Node Embedding Methods in the Context of Node Classification
Venue: arXiv cs.SI 2020
Year: 2020

Node embedding methods find latent lower-dimensional representations which are used as features in machine learning models. In the last few years, these methods have become extremely popular as a replacement for manual feature engineering. Since authors use various approaches for the evaluation of node embedding methods, existing studies can rarely be efficiently and accurately compared. We address this issue by developing a process for a fair and objective evaluation of node embedding procedures w.r.t. node classification. This process supports researchers and practitioners to compare new and existing methods in a reproducible way. We apply this process to four popular node embedding methods and make valuable observations. With an appropriate combination of hyperparameters, good performance can be achieved even with embeddings of lower dimensions, which is positive for the run times of the downstream machine learning task and the embedding algorithm. Multiple hyperparameter combinations yield similar performance. Thus, no extensive, time-consuming search is required to achieve reasonable performance in most cases.
Epidemic contact tracing with smartphone sensors
Venue: arXiv cs.CY 2020
Year: 2020

Contact tracing is widely considered as an effective procedure in the fight against epidemic diseases. However, one of the challenges for technology based contact tracing is the high number of false positives, questioning its trust-worthiness and efficiency amongst the wider population for mass adoption. To this end, this paper proposes a novel, yet practical smartphone-based contact tracing approach, employing WiFi and acoustic sound for relative distance estimate, in addition to the air pressure and the magnetic field for ambient environment matching. We present a model combining 6 smartphone sensors, prioritising some of them when certain conditions are met. We empirically verified our approach in various realistic environments to demonstrate an achievement of up to 95% fewer false positives, and 62% more accurate than Bluetooth-only system. To the best of our knowledge, this paper was one of the first work to propose a combination of smartphone sensors for contact tracing.
Stance Prediction for Contemporary Issues: Data and Experiments
Venue: arXiv cs.CL 2020
Year: 2020

We investigate whether pre-trained bidirectional transformers with sentiment and emotion information improve stance detection in long discussions of contemporary issues. As a part of this work, we create a novel stance detection dataset covering 419 different controversial issues and their related pros and cons collected by procon.org in nonpartisan format. Experimental results show that a shallow recurrent neural network with sentiment or emotion information can reach competitive results compared to fine-tuned BERT with 20x fewer parameters. We also use a simple approach that explains which input phrases contribute to stance detection.
A frame semantics based approach to comparative study of digitized corpus
Venue: arXiv cs.CL 2020
Year: 2020

in this paper, we present a corpus linguistics based approach applied to analyzing digitized classical multilingual novels and narrative texts, from a semantic point of view. Digitized novels such as "the hobbit (Tolkien J. R. R., 1937)" and "the hound of the Baskervilles (Doyle A. C. 1901-1902)", which were widely translated to dozens of languages, provide rich materials for analyzing languages differences from several perspectives and within a number of disciplines like linguistics, philosophy and cognitive science. Taking motion events conceptualization as a case study, this paper, focus on the morphologic, syntactic, and semantic annotation process of English-Arabic aligned corpus created from a digitized novels, in order to re-examine the linguistic encodings of motion events in English and Arabic in terms of Frame Semantics. The present study argues that differences in motion events conceptualization across languages can be described with frame structure and frame-to-frame relations.
Design and Implementation of a Virtual 3D Educational Environment to improve Deaf Education
Venue: arXiv cs.CL 2020
Year: 2020

Advances in NLP, knowledge representation and computer graphic technologies can provide us insights into the development of educational tool for Deaf people. Actual education materials and tools for deaf pupils present several problems, since textbooks are designed to support normal students in the classroom and most of them are not suitable for people with hearing disabilities. Virtual Reality (VR) technologies appear to be a good tool and a promising framework in the education of pupils with hearing disabilities. In this paper, we present a current research tasks surrounding the design and implementation of a virtual 3D educational environment based on X3D and H-Anim standards. The system generates and animates automatically Sign language sentence from a semantic representation that encode the whole meaning of the Arabic input text. Some aspects and issues in Sign language generation will be discussed, including the model of Sign representation that facilitate reuse and reduces the time of Sign generation, conversion of semantic components to sign features representation with regard to Sign language linguistics characteristics and how to generate realistic smooth gestural sequences using X3D content to performs transition between signs for natural-looking of animated avatar. Sign language sentences were evaluated by Algerian native Deaf people. The goal of the project is the development of a machine translation system from Arabic to Algerian Sign Language that can be used as educational tool for Deaf children in algerian primary schools.
Optical Flow based Visual Potential Field for Autonomous Driving
Venue: arXiv cs.RO 2020
Year: 2020

Monocular vision-based navigation for automated driving is a challenging task due to the lack of enough information to compute temporal relationships among objects on the road. Optical flow is an option to obtain temporal information from monocular camera images and has been used widely with the purpose of identifying objects and their relative motion. This work proposes to generate an artificial potential field, i.e. visual potential field, from a sequence of images using sparse optical flow, which is used together with a gradient tracking sliding mode controller to navigate the vehicle to destination without collision with obstacles. The angular reference for the vehicle is computed online. This work considers that the vehicle does not require to have a priori information from the map or obstacles to navigate successfully. The proposed technique is tested both in synthetic and real images.
On the Number of Almost Envy-Free Allocations
Venue: arXiv cs.GT 2020
Year: 2020

Envy-freeness is a standard benchmark of fairness in resource allocation. Since it cannot always be satisfied when the resource consists of indivisible items even when there are two agents, the relaxations envy-freeness up to one item (EF1) and envy-freeness up to any item (EFX) are often considered. We establish tight lower bounds on the number of allocations satisfying each of these benchmarks in the case of two agents. In particular, while there can be as few as two EFX allocations for any number of items, the number of EF1 allocations is always exponential in the number of items. Our results apply a version of the vertex isoperimetric inequality on the hypercube and help explain the large gap in terms of robustness between the two notions.
Attention-Guided Discriminative Region Localization and Label Distribution Learning for Bone Age Assessment
Venue: arXiv cs.CV 2020
Year: 2020

Bone age assessment (BAA) is clinically important as it can be used to diagnose endocrine and metabolic disorders during child development. Existing deep learning based methods for classifying bone age use the global image as input, or exploit local information by annotating extra bounding boxes or key points. However, training with the global image underutilizes discriminative local information, while providing extra annotations is expensive and subjective. In this paper, we propose an attention-guided approach to automatically localize the discriminative regions for BAA without any extra annotations. Specifically, we first train a classification model to learn the attention maps of the discriminative regions, finding the hand region, the most discriminative region (the carpal bones), and the next most discriminative region (the metacarpal bones). Guided by those attention maps, we then crop the informative local regions from the original image and aggregate different regions for BAA. Instead of taking BAA as a general regression task, which is suboptimal due to the label ambiguity problem in the age label space, we propose using joint age distribution learning and expectation regression, which makes use of the ordinal relationship among hand images with different individual ages and leads to more robust age estimation. Extensive experiments are conducted on the RSNA pediatric bone age data set. Using no training annotations, our method achieves competitive results compared with existing state-of-the-art semi-automatic deep learning-based methods that require manual annotation. Code is available at https: //github.com/chenchao666/Bone-Age-Assessment.
Opportunities of Optical Spectrum for Future Wireless Communications
Venue: arXiv cs.NI 2020
Year: 2020

The requirements in terms of service quality such as data rate, latency, power consumption, number of connectivity of future fifth-generation (5G) communication is very high. Moreover, in Internet of Things (IoT) requires massive connectivity. Optical wireless communication (OWC) technologies such as visible light communication, light fidelity, optical camera communication, and free space optical communication can effectively serve for the successful deployment of 5G and IoT. This paper clearly presents the contributions of OWC networks for 5G and IoT solutions.
Longest Square Subsequence Problem Revisited
Venue: arXiv cs.DS 2020
Year: 2020

The longest square subsequence (LSS) problem consists of computing a longest subsequence of a given string $S$ that is a square, i.e., a longest subsequence of form $XX$ appearing in $S$. It is known that an LSS of a string $S$ of length $n$ can be computed using $O(n^2)$ time [Kosowski 2004], or with (model-dependent) polylogarithmic speed-ups using $O(n^2 (\log \log n)^2 / \log^2 n)$ time [Tiskin 2013]. We present the first algorithm for LSS whose running time depends on other parameters, i.e., we show that an LSS of $S$ can be computed in $O(r \min\{n, M\}\log \frac{n}{r} + n + M \log n)$ time with $O(M)$ space, where $r$ is the length of an LSS of $S$ and $M$ is the number of matching points on $S$.
Manipulating the Distributions of Experience used for Self-Play Learning in Expert Iteration
Venue: arXiv cs.LG 2020
Year: 2020

Expert Iteration (ExIt) is an effective framework for learning game-playing policies from self-play. ExIt involves training a policy to mimic the search behaviour of a tree search algorithm - such as Monte-Carlo tree search - and using the trained policy to guide it. The policy and the tree search can then iteratively improve each other, through experience gathered in self-play between instances of the guided tree search algorithm. This paper outlines three different approaches for manipulating the distribution of data collected from self-play, and the procedure that samples batches for learning updates from the collected data. Firstly, samples in batches are weighted based on the durations of the episodes in which they were originally experienced. Secondly, Prioritized Experience Replay is applied within the ExIt framework, to prioritise sampling experience from which we expect to obtain valuable training signals. Thirdly, a trained exploratory policy is used to diversify the trajectories experienced in self-play. This paper summarises the effects of these manipulations on training performance evaluated in fourteen different board games. We find major improvements in early training performance in some games, and minor improvements averaged over fourteen games.
Bi-Criteria Multiple Knapsack Problem with Grouped Items
Venue: arXiv cs.DS 2020
Year: 2020

The multiple knapsack problem with grouped items aims to maximize rewards by assigning groups of items among multiple knapsacks, considering knapsack capacities. Either all items in a group are assigned or none at all. We propose algorithms which guarantee that rewards are not less than the optimal solution, with a bound on exceeded knapsack capacities. To obtain capacity-feasible solutions, we propose a binary-search heuristic combined with these algorithms. We test the performance of the algorithms and heuristics in an extensive set of experiments on randomly generated instances and show they are efficient and effective, i.e., they run reasonably fast and generate good quality solutions.
Wireless Power Transfer Under Kullback-Leibler Distribution Uncertainty: A Mathematical Framework
Venue: arXiv cs.IT 2020
Year: 2020

In this letter, we study the performance of a wireless power transfer system under energy harvesting distribution uncertainty. The uncertainty captures practical nonidealities of the rectification process and is modelled as the maximum Kullback-Leibler distance of the actual distribution from a nominal distribution. The case where symmetrized divergence is considered for the statistical distance between distributions is also considered. By formulating a convex optimization problem, we investigate a mathematical framework that provides closed form expressions for the minimum average harvested energy (the worst performance) and the associated statistical distribution. Theoretical results show that the energy harvesting performance is significantly degraded as the level of the uncertainty increases.
The $c$-differential behavior of the inverse function under the $EA$-equivalence
Venue: arXiv cs.IT 2020
Year: 2020

While the classical differential uniformity ($c=1$) is invariant under the CCZ-equivalence, the newly defined \cite{EFRST20} concept of $c$-differential uniformity, in general is not invariant under EA or CCZ-equivalence, as was observed in \cite{SPRS20}. In this paper, we find an intriguing behavior of the inverse function, namely, that adding some appropriate linearized monomials increases the $c$-differential uniformity significantly, for some~$c$. For example, adding the linearized monomial $x^{p^d}$, where $d$ is the largest nontrivial divisor of $n$, increases the mentioned $c$-differential uniformity from~$2$ or $3$ (for $c\neq 0$) to $\geq p^{d}+2$, which in the case of AES' inverse function on $\F_{2^8}$ is a significant value of~$18$.
Variational Reward Estimator Bottleneck: Learning Robust Reward Estimator for Multi-Domain Task-Oriented Dialog
Venue: arXiv cs.AI 2020
Year: 2020

Despite its notable success in adversarial learning approaches to multi-domain task-oriented dialog system, training the dialog policy via adversarial inverse reinforcement learning often fails to balance the performance of the policy generator and reward estimator. During optimization, the reward estimator often overwhelms the policy generator and produces excessively uninformative gradients. We proposes the Variational Reward estimator Bottleneck (VRB), which is an effective regularization method that aims to constrain unproductive information flows between inputs and the reward estimator. The VRB focuses on capturing discriminative features, by exploiting information bottleneck on mutual information. Empirical results on a multi-domain task-oriented dialog dataset demonstrate that the VRB significantly outperforms previous methods.
Participatory Design to build better contact- and proximity-tracing apps
Venue: arXiv cs.CY 2020
Year: 2020

With the push for contact- and proximity-tracing solutions as a means to manage the spread of the pandemic, there is a distrust between the citizens and authorities that are deploying these solutions. The efficacy of the solutions relies on meeting a minimum uptake threshold which is hitting a barrier because of a lack of trust and transparency in how these solutions are being developed. We propose participatory design as a mechanism to evoke trust and explore how it might be applied to co-create technological solutions that not only meet the needs of the users better but also expand their reach to underserved and high-risk communities. We also highlight the role of the bazaar model of development and complement that with quantitative and qualitative metrics for evaluating the solutions and convincing policymakers and other stakeholders in the value of this approach with empirical evidence.
Graph Learning with Loss-Guided Training
Venue: arXiv cs.LG 2020
Year: 2020

Classically, ML models trained with stochastic gradient descent (SGD) are designed to minimize the average loss per example and use a distribution of training examples that remains {\em static} in the course of training. Research in recent years demonstrated, empirically and theoretically, that significant acceleration is possible by methods that dynamically adjust the training distribution in the course of training so that training is more focused on examples with higher loss. We explore {\em loss-guided training} in a new domain of node embedding methods pioneered by {\sc DeepWalk}. These methods work with implicit and large set of positive training examples that are generated using random walks on the input graph and therefore are not amenable for typical example selection methods. We propose computationally efficient methods that allow for loss-guided training in this framework. Our empirical evaluation on a rich collection of datasets shows significant acceleration over the baseline static methods, both in terms of total training performed and overall computation.
Benchmarking BioRelEx for Entity Tagging and Relation Extraction
Venue: arXiv cs.CL 2020
Year: 2020

Extracting relationships and interactions between different biological entities is still an extremely challenging problem but has not received much attention as much as extraction in other generic domains. In addition to the lack of annotated data, low benchmarking is still a major reason for slow progress. In order to fill this gap, we compare multiple existing entity and relation extraction models over a recently introduced public dataset, BioRelEx of sentences annotated with biological entities and relations. Our straightforward benchmarking shows that span-based multi-task architectures like DYGIE show 4.9% and 6% absolute improvements in entity tagging and relation extraction respectively over the previous state-of-art and that incorporating domain-specific information like embeddings pre-trained over related domains boosts performance.
Shared value economics: an axiomatic approach
Venue: arXiv cs.GT 2020
Year: 2020

The concept of shared value was introduced by Porter and Kramer as a new conception of capitalism. Shared value describes the strategy of organizations that simultaneously enhance their competitiveness and the social conditions of related stakeholders such as employees, suppliers and the natural environment. The idea has generated strong interest, but also some controversy due to a lack of a precise definition, measurement techniques and difficulties to connect theory to practice. We overcome these drawbacks by proposing an economic framework based on three key aspects: coalition formation, sustainability and consistency, meaning that conclusions can be tested by means of logical deductions and empirical applications. The presence of multiple agents to create shared value and the optimization of both social and economic criteria in decision making represent the core of our quantitative definition of shared value. We also show how economic models can be characterized as shared value models by means of logical deductions. Summarizing, our proposal builds on the foundations of shared value to improve its understanding and to facilitate the suggestion of economic hypotheses, hence accommodating the concept of shared value within modern economic theory.