OpenRaiser/Intern-Atlas huggingface dataset:
Secure and Energy Efficient Transmission for IRS-Assisted Cognitive Radio Networks
Venue: arXiv cs.IT 2021
Year: 2021

The spectrum efficiency (SE) and security of the secondary users (SUs) in the cognitive radio networks (CRNs) have become two main issues due to the limitation interference to the primary users (PUs) and the shared spectrum with the PUs. Intelligent reflecting surface (IRS) has been recently proposed as a revolutionary technique which can help to enhance the SE and physical layer security of wireless communications. This paper investigates the application of IRS in an underlay CRN, where a multi-antenna cognitive base station (CBS) utilizes spectrum assigned to the PU to communicate with a SU via IRS in the presence of multiple coordinated eavesdroppers (Eves). To achieve the trade-off between the secrecy rate (SR) and energy consumption, we investigate the secrecy energy efficiency (SEE) maximization problem by jointly designing the transmit beamforming at the CBS and the reflect beamforming at the IRS. To solve the non-convex problem with coupled variables, we propose an iterative alternating optimization algorithm to solve the sub-problems alternately, by utilizing an iterative penalty function based algorithm for sub-problem 1 and the difference of two-convex functions method for sub-problem 2. Furthermore, we provide a second-order-cone-programming (SOCP) approximation approach to reduce the computational complexity. Finally, the simulation results demonstrate that IRS can help significantly improve the SE and enhance the physical layer security in the CRNs. Moreover, the effectiveness and superiority of our proposed algorithm in achieving the trade-off between the SR and energy consumption are verified.
Networked partisanship and framing: a socio-semantic network analysis of the Italian debate on migration
Venue: arXiv cs.SI 2021
Year: 2021

The huge amount of data made available by the massive usage of social media has opened up the unprecedented possibility to carry out a data-driven study of political processes. While particular attention has been paid to phenomena like elite and mass polarization during online debates and echo-chambers formation, the interplay between online partisanship and framing practices, jointly sustaining adversarial dynamics, still remains overlooked. With the present paper, we carry out a socio-semantic analysis of the debate about migration policies observed on the Italian Twittersphere, across the period May-November 2019. As regards the social analysis, our methodology allows us to extract relevant information about the political orientation of the communities of users - hereby called partisan communities - without resorting upon any external information. Remarkably, our community detection technique is sensitive enough to clearly highlight the dynamics characterizing the relationship among different political forces.As regards the semantic analysis, our networks of hashtags display a mesoscale structure organized in a core-periphery fashion, across the entire observation period. Taken altogether, our results point at different, yet overlapping, trajectories of conflict played out using migration issues as a backdrop. A first line opposes communities discussing substantively of migration to communities approaching this issue just to fuel hostility against political opponents; within the second line, a mechanism of distancing between partisan communities reflects shifting political alliances within the governmental coalition. Ultimately, our results contribute to shed light on the complexity of the Italian political context characterized by multiple poles of partisan alignment.
Will You Come Back to Contribute? Investigating the Inactivity of OSS Core Developers in GitHub
Venue: arXiv cs.SE 2021
Year: 2021

Several Open Source Software (OSS) projects depend on the continuity of their development communities to remain sustainable. Understanding how developers become inactive or why they take breaks can help communities prevent abandonment and incentivize developers to come back. In this paper, we propose a novel method to identify developers' inactive periods by analyzing the individual rhythm of contributions to the projects. Using this method, we quantitatively analyze the inactivity of core developers in 18 OSS organizations hosted on GitHub. We also survey core developers to receive their feedback about the identified breaks and transitions. Our results show that our method was effective for identifying developers' breaks. About 94% of the surveyed core developers agreed with our state model of inactivity; 71% and 79% of them acknowledged their breaks and state transition, respectively. We also show that all core developers take breaks (at least once) and about a half of them (~45%}) have completely disengaged from a project for at least one year. We also analyzed the probability of transitions to/from inactivity and found that developers who pause their activity have a ~35-55\% chance to return to an active state; yet, if the break lasts for a year or longer, then the probability of resuming activities drops to ~21-26%, with a ~54% chance of complete disengagement. These results may support the creation of policies and mechanisms to make OSS community managers aware of breaks and potential project abandonment.
Semiotically-grounded distant viewing of diagrams: insights from two multimodal corpora
Venue: arXiv cs.CL 2021
Year: 2021

In this article, we bring together theories of multimodal communication and computational methods to study how primary school science diagrams combine multiple expressive resources. We position our work within the field of digital humanities, and show how annotations informed by multimodality research, which target expressive resources and discourse structure, allow imposing structure on the output of computational methods. We illustrate our approach by analysing two multimodal diagram corpora: the first corpus is intended to support research on automatic diagram processing, whereas the second is oriented towards studying diagrams as a mode of communication. Our results show that multimodally-informed annotations can bring out structural patterns in the diagrams, which also extend across diagrams that deal with different topics.
On Implicit Attribute Localization for Generalized Zero-Shot Learning
Venue: arXiv cs.CV 2021
Year: 2021

Zero-shot learning (ZSL) aims to discriminate images from unseen classes by exploiting relations to seen classes via their attribute-based descriptions. Since attributes are often related to specific parts of objects, many recent works focus on discovering discriminative regions. However, these methods usually require additional complex part detection modules or attention mechanisms. In this paper, 1) we show that common ZSL backbones (without explicit attention nor part detection) can implicitly localize attributes, yet this property is not exploited. 2) Exploiting it, we then propose SELAR, a simple method that further encourages attribute localization, surprisingly achieving very competitive generalized ZSL (GZSL) performance when compared with more complex state-of-the-art methods. Our findings provide useful insight for designing future GZSL methods, and SELAR provides an easy to implement yet strong baseline.
A Dual-Memory Architecture for Reinforcement Learning on Neuromorphic Platforms
Venue: arXiv cs.LG 2021
Year: 2021

Reinforcement learning (RL) is a foundation of learning in biological systems and provides a framework to address numerous challenges with real-world artificial intelligence applications. Efficient implementations of RL techniques could allow for agents deployed in edge-use cases to gain novel abilities, such as improved navigation, understanding complex situations and critical decision making. Towards this goal, we describe a flexible architecture to carry out reinforcement learning on neuromorphic platforms. This architecture was implemented using an Intel neuromorphic processor and demonstrated solving a variety of tasks using spiking dynamics. Our study proposes a usable energy efficient solution for real-world RL applications and demonstrates applicability of the neuromorphic platforms for RL problems.
A Framework for Enabling Safe and Resilient Food Factories for the Public Feeding Programs
Venue: arXiv cs.CY 2021
Year: 2021

Public feeding programs continue to be a major source of nutrition to a large part of the population across the world. Any disruption to these activities, like the one during the Covid-19 pandemic, can lead to adverse health outcomes, especially among children. Policymakers and other stakeholders must balance the need for continuing the feeding programs while ensuring the health and safety of workers engaged in the operations. This has led to several innovations that leverage advanced technologies like AI and IOT to monitor the health and safety of workers and ensure hygienic operations. However, there are practical challenges in its implementation on a large scale. This paper presents an implementation framework to build resilient public feeding programs using a combination of intelligent technologies. The framework is a result of piloting the technology solution at a facility run as part of a large mid-day meal feeding program in India. Using existing resources like CCTV cameras and new technologies like AI and IOT, hygiene and safety compliance anomalies can be detected and reported in a resource-efficient manner. It will guide stakeholders running public feeding programs as they seek to restart suspended operations and build systems that better adapt to future crises.
Efficient Error Prediction for Differentially Private Algorithms
Venue: arXiv cs.CR 2021
Year: 2021

Differential privacy is a strong mathematical notion of privacy. Still, a prominent challenge when using differential privacy in real data collection is understanding and counteracting the accuracy loss that differential privacy imposes. As such, the accuracy/privacy trade-off of differential privacy needs to be balanced on a case-by-case basis. Applications in the literature tend to focus solely on analytical accuracy bounds, not include data in error prediction, or use arbitrary settings to measure error empirically. To fill the gap in the literature, we propose a novel application of factor experiments to create data aware error predictions. Basically, factor experiments provide a systematic approach to conducting empirical experiments. To demonstrate our methodology in action, we conduct a case study where error is dependent on arbitrarily complex tree structures. We first construct a tool to simulate poll data. Next, we use our simulated data to construct a least squares model to predict error. Last, we show how to validate the model. Consequently, our contribution is a method for constructing error prediction models that are data aware.
Relationship-based Neural Baby Talk
Venue: arXiv cs.CV 2021
Year: 2021

Understanding interactions between objects in an image is an important element for generating captions. In this paper, we propose a relationship-based neural baby talk (R-NBT) model to comprehensively investigate several types of pairwise object interactions by encoding each image via three different relationship-based graph attention networks (GATs). We study three main relationships: \textit{spatial relationships} to explore geometric interactions, \textit{semantic relationships} to extract semantic interactions, and \textit{implicit relationships} to capture hidden information that could not be modelled explicitly as above. We construct three relationship graphs with the objects in an image as nodes, and the mutual relationships of pairwise objects as edges. By exploring features of neighbouring regions individually via GATs, we integrate different types of relationships into visual features of each node. Experiments on COCO dataset show that our proposed R-NBT model outperforms state-of-the-art models trained on COCO dataset in three image caption generation tasks.
Adversarial Reinforcement Learning for Procedural Content Generation
Venue: arXiv cs.LG 2021
Year: 2021

We present a new approach ARLPCG: Adversarial Reinforcement Learning for Procedural Content Generation, which procedurally generates and tests previously unseen environments with an auxiliary input as a control variable. Training RL agents over novel environments is a notoriously difficult task. One popular approach is to procedurally generate different environments to increase the generalizability of the trained agents. ARLPCG instead deploys an adversarial model with one PCG RL agent (called Generator) and one solving RL agent (called Solver). The Generator receives a reward signal based on the Solver's performance, which encourages the environment design to be challenging but not impossible. To further drive diversity and control of the environment generation, we propose using auxiliary inputs for the Generator. The benefit is two-fold: Firstly, the Solver achieves better generalization through the Generator's generated challenges. Secondly, the trained Generator can be used as a creator of novel environments that, together with the Solver, can be shown to be solvable. We create two types of 3D environments to validate our model, representing two popular game genres: a third-person platformer and a racing game. In these cases, we shows that ARLPCG has a significantly better solve ratio, and that the auxiliary inputs renders the levels creation controllable to a certain degree. For a video compilation of the results please visit https://youtu.be/z7q2PtVsT0I.
Exploiting Spherical Projections To Generate Human-Like Wrist Pointing Movements
Venue: arXiv cs.RO 2021
Year: 2021

The mechanism behind the generation of human movements is of great interest in many fields (e.g. robotics and neuroscience) to improve therapies and technologies. Optimal Feedback Control (OFC) and Passive Motion Paradigm (PMP) are currently two leading theories capable of effectively producing human-like motions, but they require solving nonlinear inverse problems to find a solution. The main benefit of using PMP is the possibility of generating path-independent movements consistent with the stereotypical behaviour observed in humans, while the equivalent OFC formulation is path-dependent. Our results demonstrate how the path-independent behaviour observed for the wrist pointing task can be explained by spherical projections of the planar tasks. The combination of the projections with the fractal impedance controller eliminates the nonlinear inverse problem, which reduces the computational cost compared to previous methodologies. The motion exploits a recently proposed PMP architecture that replaces the nonlinear inverse optimisation with a nonlinear anisotropic stiffness impedance profile generated by the Fractal Impedance Controller, reducing the computational cost and not requiring a task-dependent optimisation.
AVEC: Accelerator Virtualization in Cloud-Edge Computing for Deep Learning Libraries
Venue: arXiv cs.DC 2021
Year: 2021

Edge computing offers the distinct advantage of harnessing compute capabilities on resources located at the edge of the network to run workloads of relatively weak user devices. This is achieved by offloading computationally intensive workloads, such as deep learning from user devices to the edge. Using the edge reduces the overall communication latency of applications as workloads can be processed closer to where data is generated on user devices rather than sending them to geographically distant clouds. Specialised hardware accelerators, such as Graphics Processing Units (GPUs) available in the cloud-edge network can enhance the performance of computationally intensive workloads that are offloaded from devices on to the edge. The underlying approach required to facilitate this is virtualization of GPUs. This paper therefore sets out to investigate the potential of GPU accelerator virtualization to improve the performance of deep learning workloads in a cloud-edge environment. The AVEC accelerator virtualization framework is proposed that incurs minimum overheads and requires no source-code modification of the workload. AVEC intercepts local calls to a GPU on a device and forwards them to an edge resource seamlessly. The feasibility of AVEC is demonstrated on a real-world application, namely OpenPose using the Caffe deep learning library. It is observed that on a lab-based experimental test-bed AVEC delivers up to 7.48x speedup despite communication overheads incurred due to data transfers.
DepGraph: Localizing Performance Bottlenecks in Multi-Core Applications Using Waiting Dependency Graphs and Software Tracing
Venue: arXiv cs.SE 2021
Year: 2021

This paper addresses the challenge of understanding the waiting dependencies between the threads and hardware resources required to complete a task. The objective is to improve software performance by detecting the underlying bottlenecks caused by system-level blocking dependencies. In this paper, we use a system level tracing approach to extract a Waiting Dependency Graph that shows the breakdown of a task execution among all the interleaving threads and resources. The method allows developers and system administrators to quickly discover how the total execution time is divided among its interacting threads and resources. Ultimately, the method helps detecting bottlenecks and highlighting their possible causes. Our experiments show the effectiveness of the proposed approach in several industry-level use cases. Three performance anomalies are analysed and explained using the proposed approach. Evaluating the method efficiency reveals that the imposed overhead never exceeds 10.1%, therefore making it suitable for in-production environments.
A Comparative Approach to Explainable Artificial Intelligence Methods in Application to High-Dimensional Electronic Health Records: Examining the Usability of XAI
Venue: arXiv cs.LG 2021
Year: 2021

Explainable Artificial Intelligence (XAI) is a rising field in AI. It aims to produce a demonstrative factor of trust, which for human subjects is achieved through communicative means, which Machine Learning (ML) algorithms cannot solely produce, illustrating the necessity of an extra layer producing support to the model output. When approaching the medical field, we can see challenges arise when dealing with the involvement of human-subjects, the ideology behind trusting a machine to tend towards the livelihood of a human poses an ethical conundrum - leaving trust as the basis of the human-expert in acceptance to the machines decision. The aim of this paper is to apply XAI methods to demonstrate the usability of explainable architectures as a tertiary layer for the medical domain supporting ML predictions and human-expert opinion, XAI methods produce visualization of the feature contribution towards a given models output on both a local and global level. The work in this paper uses XAI to determine feature importance towards high-dimensional data-driven questions to inform domain-experts of identifiable trends with a comparison of model-agnostic methods in application to ML algorithms. The performance metrics for a glass-box method is also provided as a comparison against black-box capability for tabular data. Future work will aim to produce a user-study using metrics to evaluate human-expert usability and opinion of the given models.
Machine Learning-based Inter-Beam Inter-Cell Interference Mitigation in mmWave
Venue: arXiv cs.NI 2021
Year: 2021

In this paper, we address inter-beam inter-cell interference mitigation in 5G networks that employ millimeter-wave (mmWave), beamforming and non-orthogonal multiple access (NOMA) techniques. Those techniques play a key role in improving network capacity and spectral efficiency by multiplexing users on both spatial and power domains. In addition, the coverage area of multiple beams from different cells can intersect, allowing more flexibility in user-cell association. However, the intersection of coverage areas also implies increased inter-beam inter-cell interference, i.e. interference among beams formed by nearby cells. Therefore, joint user-cell association and inter-beam power allocation stand as a promising solution to mitigate inter-beam, inter-cell interference. In this paper, we consider a 5G mmWave network and propose a reinforcement learning algorithm to perform joint user-cell association and inter-beam power allocation to maximize the sum rate of the network. The proposed algorithm is compared to a uniform power allocation that equally divides power among beams per cell. Simulation results present a performance enhancement of 13-30% in network's sum-rate corresponding to the lowest and highest traffic loads, respectively.
Multiparty Protocol that Usually Shuffles
Venue: arXiv cs.CR 2021
Year: 2021

Multiparty computation is raising importance because it's primary objective is to replace any trusted third party in the distributed computation. This work presents two multiparty shuffling protocols where each party, possesses a private input, agrees on a random permutation while keeping the permutation secret. The proposed shuffling protocols are based on permutation network, thereby data-oblivious. The first proposal is $n\text{-}permute$ that permutes $n$ inputs in all $n!$ possible ways. $n$-permute network consists of $2\log{n}-1$ layers, and in each layer there are $n/2$ gates. Our second protocol is $n_π$-permute shuffling that defines a permutation set $Π=\{π_1,\dots,π_N\}$ where $|Π| < n!$, and the resultant shuffling is a random permutation $π_i \in Π$. The $n_π$-permute network contains leases number of layers compare to $n$-permute network. Let $n=n_1n_2$, the $n_π$-permute network would define $2\log{n_1}-1+\log{n_2}$ layers. \par The proposed shuffling protocols are unconditionally secure against malicious adversary who can corrupt at most $t<n/3$ parties. The probability that adversary can learn the outcome of $n$-permute is upper bound by $((n-t)!)^{-1}$. Whereas, the probability that adversary can learn the outcome of $n_π$-permute is upper bounded by $\big(f_Π(n_1-θ_1)^{n_2}2^{θ_2}\big)^{-1}$, for some positive integer $θ_1, θ_2$, and a recursive definition of $f_Π$. The protocols allow the parties to build quorums, and distribute the load among the quorums.
Classification and Feature Transformation with Fuzzy Cognitive Maps
Venue: arXiv cs.LG 2021
Year: 2021

Fuzzy Cognitive Maps (FCMs) are considered a soft computing technique combining elements of fuzzy logic and recurrent neural networks. They found multiple application in such domains as modeling of system behavior, prediction of time series, decision making and process control. Less attention, however, has been turned towards using them in pattern classification. In this work we propose an FCM based classifier with a fully connected map structure. In contrast to methods that expect reaching a steady system state during reasoning, we chose to execute a few FCM iterations (steps) before collecting output labels. Weights were learned with a gradient algorithm and logloss or cross-entropy were used as the cost function. Our primary goal was to verify, whether such design would result in a descent general purpose classifier, with performance comparable to off the shelf classical methods. As the preliminary results were promising, we investigated the hypothesis that the performance of $d$-step classifier can be attributed to a fact that in previous $d-1$ steps it transforms the feature space by grouping observations belonging to a given class, so that they became more compact and separable. To verify this hypothesis we calculated three clustering scores for the transformed feature space. We also evaluated performance of pipelines built from FCM-based data transformer followed by a classification algorithm. The standard statistical analyzes confirmed both the performance of FCM based classifier and its capability to improve data. The supporting prototype software was implemented in Python using TensorFlow library.