OpenRaiser/Intern-Atlas huggingface dataset:
IRS-Aided WPCNs: A New Optimization Framework for Dynamic IRS Beamforming
Venue: arXiv cs.IT 2021
Year: 2021

In this paper, we propose a new dynamic IRS beamforming framework to boost the sum throughput of an intelligent reflecting surface (IRS) aided wireless powered communication network (WPCN). Specifically, the IRS phase-shift vectors across time and resource allocation are jointly optimized to enhance the efficiencies of both downlink wireless power transfer (DL WPT) and uplink wireless information transmission (UL WIT) between a hybrid access point (HAP) and multiple wirelessly powered devices. To this end, we first study three special cases of the dynamic IRS beamforming,namely user-adaptive IRS beamforming, UL-adaptive IRS beamforming, and static IRS beamforming,by characterizing their optimal performance relationships and proposing corresponding algorithms. Interestingly, it is rigorously proved that the latter two cases achieve the same throughput, thus helping halve the number of IRS phase shifts to be optimized and signalling overhead practically required for UL-adaptive IRS beamforming. Then, we propose a general optimization framework for dynamic IRS beamforming, which is applicable for any given number of IRS phase-shift vectors available. Despite of the non-convexity of the general problem with highly coupled optimization variables, we propose two algorithms to solve it and particularly, the low-complexity algorithm exploits the intrinsic structure of the optimal solution as well as the solutions to the cases with user-adaptive and static IRS beamforming. Simulation results validate our theoretical findings, illustrate the practical significance of IRS with dynamic beamforming for spectral and energy efficient WPCNs, and demonstrate the effectiveness of our proposed designs over various benchmark schemes.
Lemmatization of Historical Old Literary Finnish Texts in Modern Orthography
Venue: arXiv cs.CL 2021
Year: 2021

Texts written in Old Literary Finnish represent the first literary work ever written in Finnish starting from the 16th century. There have been several projects in Finland that have digitized old publications and made them available for research use. However, using modern NLP methods in such data poses great challenges. In this paper we propose an approach for simultaneously normalizing and lemmatizing Old Literary Finnish into modern spelling. Our best model reaches to 96.3\% accuracy in texts written by Agricola and 87.7\% accuracy in other contemporary out-of-domain text. Our method has been made freely available on Zenodo and Github.
Enhancing an Intelligent Digital Twin with a Self-organized Reconfiguration Management based on Adaptive Process Models
Venue: arXiv cs.AI 2021
Year: 2021

Shorter product life cycles and increasing individualization of production leads to an increased reconfiguration demand in the domain of industrial automation systems, which will be dominated by cyber-physical production systems in the future. In constantly changing systems, however, not all configuration alternatives of the almost infinite state space are fully understood. Thus, certain configurations can lead to process instability, a reduction in quality or machine failures. Therefore, this paper presents an approach that enhances an intelligent Digital Twin with a self-organized reconfiguration management based on adaptive process models in order to find optimized configurations more comprehensively.
Design an IT Policy Implementation Plan
Venue: arXiv cs.OH 2021
Year: 2021

Information technology (IT) companies implement multi-dimensional policy plans that include procedures, sub-plans, and instructions to outline their business scopes, targets, and communications. This work outlined the IT policy implementation plan designed by an imaginary company with a random name called Northcentral Cloud Consulting Firm (NCCF), containing proposed IT policies, milestones and roadmaps, control framework, stakeholder responsibilities, knowledge transfer plan, and leadership roles. As NCCF's major customers seek data-driven solutions in cloud computing, the NCCF IT policy plan provides various data policies, including security and proper usage of machine learning services. The plan offers a detailed roadmap of its financial, geographical, and reputational expansion within three years. The IT policy plan also compromises an IT risk management, contingency, and emergency communication plan, mainly for protecting data and business continuity. Stakeholder responsibilities are incorporated into the IT policy plan, as NCCF considers any engagement with its customers as a collaborative effort in which both parties have and share several responsibilities.
Building Stable Off-chain Payment Networks
Venue: arXiv cs.GT 2021
Year: 2021

Payment channel is a protocol which allows cryptocurrency users to route multiple transactions through network without committing them to the main blockchain network (mainnet). This ability makes them the most prominent solution to blockchains' scalability problem. Each modification of payment channels requires a transaction on the mainnet and therefore, big transaction fees. In this paper, we assume that a set of payment transactions are given (batch or online) and we study the problem of scheduling modificiations on payment channels to route all of the transactions with minimum modification cost. We investigate two cost models for aforementioned problem: the step cost function in which every channel modification has a constant cost and the linear cost function in which modification costs are proportional to the amount of change. For the step cost function model, we prove impossibility results for both batch and online case. Moreover, some heuristic methods for the batch case are presented and compared. For the linear cost we propose a polynomial time algorithm using linear programming for the batch case.
Evaluating Large Language Models Trained on Code
Venue: arXiv cs.LG 2021
Year: 2021

We introduce Codex, a GPT language model fine-tuned on publicly available code from GitHub, and study its Python code-writing capabilities. A distinct production version of Codex powers GitHub Copilot. On HumanEval, a new evaluation set we release to measure functional correctness for synthesizing programs from docstrings, our model solves 28.8% of the problems, while GPT-3 solves 0% and GPT-J solves 11.4%. Furthermore, we find that repeated sampling from the model is a surprisingly effective strategy for producing working solutions to difficult prompts. Using this method, we solve 70.2% of our problems with 100 samples per problem. Careful investigation of our model reveals its limitations, including difficulty with docstrings describing long chains of operations and with binding operations to variables. Finally, we discuss the potential broader impacts of deploying powerful code generation technologies, covering safety, security, and economics.
Differentiable Architecture Pruning for Transfer Learning
Venue: arXiv cs.LG 2021
Year: 2021

We propose a new gradient-based approach for extracting sub-architectures from a given large model. Contrarily to existing pruning methods, which are unable to disentangle the network architecture and the corresponding weights, our architecture-pruning scheme produces transferable new structures that can be successfully retrained to solve different tasks. We focus on a transfer-learning setup where architectures can be trained on a large data set but very few data points are available for fine-tuning them on new tasks. We define a new gradient-based algorithm that trains architectures of arbitrarily low complexity independently from the attached weights. Given a search space defined by an existing large neural model, we reformulate the architecture search task as a complexity-penalized subset-selection problem and solve it through a two-temperature relaxation scheme. We provide theoretical convergence guarantees and validate the proposed transfer-learning strategy on real data.
Anticipating Safety Issues in E2E Conversational AI: Framework and Tooling
Venue: arXiv cs.CL 2021
Year: 2021

Over the last several years, end-to-end neural conversational agents have vastly improved in their ability to carry a chit-chat conversation with humans. However, these models are often trained on large datasets from the internet, and as a result, may learn undesirable behaviors from this data, such as toxic or otherwise harmful language. Researchers must thus wrestle with the issue of how and when to release these models. In this paper, we survey the problem landscape for safety for end-to-end conversational AI and discuss recent and related work. We highlight tensions between values, potential positive impact and potential harms, and provide a framework for making decisions about whether and how to release these models, following the tenets of value-sensitive design. We additionally provide a suite of tools to enable researchers to make better-informed decisions about training and releasing end-to-end conversational AI models.
An Empirical Analysis of VM Startup Times in Public IaaS Clouds: An Extended Report
Venue: arXiv cs.DC 2021
Year: 2021

VM startup time is an essential factor in designing elastic cloud applications. For example, a cloud application with autoscaling can reduce under- and over-provisioning of VM instances with a precise estimation of VM startup time, and in turn, it is likely to guarantee the application's performance and improve the cost efficiency. However, VM startup time has been little studied, and available measurement results performed previously did not consider various configurations of VMs for modern cloud applications. In this work, we perform comprehensive measurements and analysis of VM startup time from two major cloud providers, namely Amazon Web Services (AWS) and Google Cloud Platform (GCP). With three months of measurements, we collected more than 300,000 data points from each provider by applying a set of configurations, including 11+ VM types, four different data center locations, four VM image sizes, two OS types, and two purchase models (e.g., spot/preemptible VMs vs. on-demand VMs). With extensive analysis, we found that VM startup time can vary significantly because of several important factors, such as VM image sizes, data center locations, VM types, and OS types. Moreover, by comparing with previous measurement results, we confirm that cloud providers (specifically AWS) made significant improvements for the VM startup times and currently have much quicker VM startup times than in the past.
Communication networks and group effectiveness: the case of English Wikipedia
Venue: arXiv cs.SI 2021
Year: 2021

A selection of intellectual goods produced by online communities - e.g. open source software or knowledge bases like Wikipedia - are in daily use by a broad audience, and thus their quality impacts the public at large. Yet, it is still unclear what contributes to the effectiveness of such online peer production systems: what conditions or social processes help them deliver quality products. Specifically, while co-contribution (i.e. bipartite networks) are often investigated in online collaboration, the role of interpersonal communication in coordination of online peer-production is much less investigated. To address this gap we have reconstructed networks of personal communication (direct messaging) between Wikipedia editors gathered in so called Wikiprojects - teams of contributors who focus on articles within specific topical areas. We found that effective projects exchange larger volume of direct messages and that their communication structure allows for complex coordination: for sharing of information locally through selective ties, and at the same time globally across the whole group. To verify how these network measures relate to the subjective perception of importance of group communication we conducted semi-structured interviews with members of selected projects. Our interviewees used direct communication for providing feedback, for maintaining close relations and for tapping on the social capital of the Wikipedia community. Our results underscore the importance of communication structure in online collaboration: online peer production communities rely on interpersonal communication to coordinate their work and to maintain high levels of engagement. Design of platforms for such communities should allow for ample group level communication as well as for one-on-one interactions.
Automated Object Behavioral Feature Extraction for Potential Risk Analysis based on Video Sensor
Venue: arXiv cs.CV 2021
Year: 2021

Pedestrians are exposed to risk of death or serious injuries on roads, especially unsignalized crosswalks, for a variety of reasons. To date, an extensive variety of studies have reported on vision based traffic safety system. However, many studies required manual inspection of the volumes of traffic video to reliably obtain traffic related objects behavioral factors. In this paper, we propose an automated and simpler system for effectively extracting object behavioral features from video sensors deployed on the road. We conduct basic statistical analysis on these features, and show how they can be useful for monitoring the traffic behavior on the road. We confirm the feasibility of the proposed system by applying our prototype to two unsignalized crosswalks in Osan city, South Korea. To conclude, we compare behaviors of vehicles and pedestrians in those two areas by simple statistical analysis. This study demonstrates the potential for a network of connected video sensors to provide actionable data for smart cities to improve pedestrian safety in dangerous road environments.
Uncertainty-aware Human Motion Prediction
Venue: arXiv cs.CV 2021
Year: 2021

Human motion prediction is essential for tasks such as human motion analysis and human-robot interactions. Most existing approaches have been proposed to realize motion prediction. However, they ignore an important task, the evaluation of the quality of the predicted result. It is far more enough for current approaches in actual scenarios because people can't know how to interact with the machine without the evaluation of prediction, and unreliable predictions may mislead the machine to harm the human. Hence, we propose an uncertainty-aware framework for human motion prediction (UA-HMP). Concretely, we first design an uncertainty-aware predictor through Gaussian modeling to achieve the value and the uncertainty of predicted motion. Then, an uncertainty-guided learning scheme is proposed to quantitate the uncertainty and reduce the negative effect of the noisy samples during optimization for better performance. Our proposed framework is easily combined with current SOTA baselines to overcome their weakness in uncertainty modeling with slight parameters increment. Extensive experiments also show that they can achieve better performance in both short and long-term predictions in H3.6M, CMU-Mocap.
Exploiting the relationship between visual and textual features in social networks for image classification with zero-shot deep learning
Venue: arXiv cs.CV 2021
Year: 2021

One of the main issues related to unsupervised machine learning is the cost of processing and extracting useful information from large datasets. In this work, we propose a classifier ensemble based on the transferable learning capabilities of the CLIP neural network architecture in multimodal environments (image and text) from social media. For this purpose, we used the InstaNY100K dataset and proposed a validation approach based on sampling techniques. Our experiments, based on image classification tasks according to the labels of the Places dataset, are performed by first considering only the visual part, and then adding the associated texts as support. The results obtained demonstrated that trained neural networks such as CLIP can be successfully applied to image classification with little fine-tuning, and considering the associated texts to the images can help to improve the accuracy depending on the goal. The results demonstrated what seems to be a promising research direction.
Analytically Tractable Hidden-States Inference in Bayesian Neural Networks
Venue: arXiv cs.LG 2021
Year: 2021

With few exceptions, neural networks have been relying on backpropagation and gradient descent as the inference engine in order to learn the model parameters, because the closed-form Bayesian inference for neural networks has been considered to be intractable. In this paper, we show how we can leverage the tractable approximate Gaussian inference's (TAGI) capabilities to infer hidden states, rather than only using it for inferring the network's parameters. One novel aspect it allows is to infer hidden states through the imposition of constraints designed to achieve specific objectives, as illustrated through three examples: (1) the generation of adversarial-attack examples, (2) the usage of a neural network as a black-box optimization method, and (3) the application of inference on continuous-action reinforcement learning. These applications showcase how tasks that were previously reserved to gradient-based optimization approaches can now be approached with analytically tractable inference
Bounds on Eavesdropper Performance for a MIMO-NOMA Downlink Scheme
Venue: arXiv cs.IT 2021
Year: 2021

Non-Orthogonal Multiple Access (NOMA) is a multiplexing technique for future wireless, which when combined with Multiple-Input Multiple-Output (MIMO) unlocks higher capacities for systems where users have varying channel strength. NOMA utilises the channel differences to increase the throughput, while MIMO exploits the additional degrees of freedom (DoF) to enhance this. This work analyses the secrecy capacity, demonstrating the robustness of a combined MIMO-NOMA scheme at physical layer, when in the presence of a passive eavesdropper. We present bounds on the eavesdropper performance and show heuristically that, as the number of users and antennas increases, the eavesdropper's SINR becomes small, regardless of how `lucky' they may be with their channel.
Towards a Trusted Execution Environment via Reconfigurable FPGA
Venue: arXiv cs.CR 2021
Year: 2021

Trusted Execution Environments (TEEs) are used to protect sensitive data and run secure execution for security-critical applications, by providing an environment isolated from the rest of the system. However, over the last few years, TEEs have been proven weak, as either TEEs built upon security-oriented hardware extensions (e.g., Arm TrustZone) or resorting to dedicated secure elements were exploited multiple times. In this project, we introduce Trusted Execution Environments On-Demand (TEEOD), a novel TEE design that leverages the programmable logic (PL) in the heterogeneous system on chips (SoC) as the secure execution environment. Unlike other TEE designs, TEEOD can provide high-bandwidth connections and physical on-chip isolation. We implemented a proof-of-concept (PoC) implementation targeting an Ultra96-V2 platform. The conducted evaluation demonstrated TEEOD can host up to 6 simultaneous enclaves with a resource usage per enclave of 7.0%, 3.8%, and 15.3% of the total LUTs, FFs, and BRAMS, respectively. To demonstrate the practicability of TEEOD in real-world applications, we successfully run a legacy open-source Bitcoin wallet.
An Efficient Reduction of a Gammoid to a Partition Matroid
Venue: arXiv cs.DS 2021
Year: 2021

Our main contribution is a polynomial-time algorithm to reduce a $k$-colorable gammoid to a $(2k-2)$-colorable partition matroid. It is known that there are gammoids that can not be reduced to any $(2k-3)$-colorable partition matroid, so this result is tight. We then discuss how such a reduction can be used to obtain polynomial-time algorithms with better approximation ratios for various natural problems related to coloring and list coloring the intersection of matroids.
Short-term Renewable Energy Forecasting in Greece using Prophet Decomposition and Tree-based Ensembles
Venue: arXiv cs.LG 2021
Year: 2021

Energy production using renewable sources exhibits inherent uncertainties due to their intermittent nature. Nevertheless, the unified European energy market promotes the increasing penetration of renewable energy sources (RES) by the regional energy system operators. Consequently, RES forecasting can assist in the integration of these volatile energy sources, since it leads to higher reliability and reduced ancillary operational costs for power systems. This paper presents a new dataset for solar and wind energy generation forecast in Greece and introduces a feature engineering pipeline that enriches the dimensional space of the dataset. In addition, we propose a novel method that utilizes the innovative Prophet model, an end-to-end forecasting tool that considers several kinds of nonlinear trends in decomposing the energy time series before a tree-based ensemble provides short-term predictions. The performance of the system is measured through representative evaluation metrics, and by estimating the model's generalization under an industryprovided scheme of absolute error thresholds. The proposed hybrid model competes with baseline persistence models, tree-based regression ensembles, and the Prophet model, managing to outperform them, presenting both lower error rates and more favorable error distribution.
Molecular Communication with Passive Receivers in Anomalous Diffusion Channels
Venue: arXiv cs.IT 2021
Year: 2021

We consider anomalous diffusion for molecular communication with a passive receiver. We first consider the probability density function of molecules' location at a given time in a space of arbitrary dimension. The expected number of observed molecules inside a receptor space of the receiver at certain time is derived taking into account the life expectancy of the molecules. In addition, an implicit solution for the time that maximizes the expected number of observed molecules is obtained in terms of Fox's H-function. The closed-form expressions for the bit error rate of a single-bit interval transmission and a multi-bit interval transmission are derived. It is shown that lifetime limited molecules can reduce the inter-symbol interference while also enhancing the reliability of MC systems at a suitable observation time.
Remote Working Pre- and Post-COVID-19: An Analysis of New Threats and Risks to Security and Privacy
Venue: arXiv cs.CR 2021
Year: 2021

COVID-19 has radically changed society as we know it. To reduce the spread of the virus, millions across the globe have been forced to work remotely, often in make-shift home offices, and using a plethora of new, unfamiliar digital technologies. In this article, we critically analyse cyber security and privacy concerns arising due to remote working during the coronavirus pandemic. Through our work, we discover a series of security risks emerging because of the realities of this period. For instance, lack of remote-working security training, heightened stress and anxiety, rushed technology deployment, and the presence of untrusted individuals in a remote-working environment (e.g., in flatshares), can result in new cyber-risk. Simultaneously, we find that as organisations look to manage these and other risks posed by their remote workforces, employee's privacy (including personal information and activities) is often compromised. This is apparent in the significant adoption of remote workplace monitoring, management and surveillance technologies. Such technologies raise several privacy and ethical questions, and further highlight the tension between security and privacy going forward.