OpenRaiser/Intern-Atlas huggingface dataset:
Incremental Object Grounding Using Scene Graphs
Venue: arXiv cs.CV 2022
Year: 2022

Object grounding tasks aim to locate the target object in an image through verbal communications. Understanding human command is an important process needed for effective human-robot communication. However, this is challenging because human commands can be ambiguous and erroneous. This paper aims to disambiguate the human's referring expressions by allowing the agent to ask relevant questions based on semantic data obtained from scene graphs. We test if our agent can use relations between objects from a scene graph to ask semantically relevant questions that can disambiguate the original user command. In this paper, we present Incremental Grounding using Scene Graphs (IGSG), a disambiguation model that uses semantic data from an image scene graph and linguistic structures from a language scene graph to ground objects based on human command. Compared to the baseline, IGSG shows promising results in complex real-world scenes where there are multiple identical target objects. IGSG can effectively disambiguate ambiguous or wrong referring expressions by asking disambiguating questions back to the user.
Network Generations and the Security Challenge in IoT Applications
Venue: arXiv cs.CR 2022
Year: 2022

Networks exist all around on the planet, and inside the brain of every living organism. On the city streets we see transport networks, inside homes and organizations we see water piping networks. And in the digital information age we see the rise of the computer networks and the mobile networks and their upgrade from generation to generation. We need networks for efficient sharing of resources and as human beings the most valuable thing that we can share along the time from age to age is our data that could represent our knowledge and information represented through text, voice, image, and video. In this paper we provide a quick overview on network generation and IoT applications. Also, we point to the security threats in these generations.
Jointly Efficient and Optimal Algorithms for Logistic Bandits
Venue: arXiv cs.LG 2022
Year: 2022

Logistic Bandits have recently undergone careful scrutiny by virtue of their combined theoretical and practical relevance. This research effort delivered statistically efficient algorithms, improving the regret of previous strategies by exponentially large factors. Such algorithms are however strikingly costly as they require $Ω(t)$ operations at each round. On the other hand, a different line of research focused on computational efficiency ($\mathcal{O}(1)$ per-round cost), but at the cost of letting go of the aforementioned exponential improvements. Obtaining the best of both world is unfortunately not a matter of marrying both approaches. Instead we introduce a new learning procedure for Logistic Bandits. It yields confidence sets which sufficient statistics can be easily maintained online without sacrificing statistical tightness. Combined with efficient planning mechanisms we design fast algorithms which regret performance still match the problem-dependent lower-bound of Abeille et al. (2021). To the best of our knowledge, those are the first Logistic Bandit algorithms that simultaneously enjoy statistical and computational efficiency.
Can Identifier Splitting Improve Open-Vocabulary Language Model of Code?
Venue: arXiv cs.SE 2022
Year: 2022

Statistical language models on source code have successfully assisted software engineering tasks. However, developers can create or pick arbitrary identifiers when writing source code. Freely chosen identifiers lead to the notorious out-of-vocabulary (OOV) problem that negatively affects model performance. Recently, Karampatsis et al. showed that using the Byte Pair Encoding (BPE) algorithm to address the OOV problem can improve the language models' predictive performance on source code. However, a drawback of BPE is that it cannot split the identifiers in a way that preserves the meaningful semantics. Prior researchers also show that splitting compound identifiers into sub-words that reflect the semantics can benefit software development tools. These two facts motivate us to explore whether identifier splitting techniques can be utilized to augment the BPE algorithm and boost the performance of open-vocabulary language models considered in Karampatsis et al.'s work. This paper proposes to split identifiers in both constructing vocabulary and processing model inputs procedures, thus exploiting three different settings of applying identifier splitting to language models for the code completion task. We contrast models' performance under these settings and find that simply inserting identifier splitting into the pipeline hurts the model performance, while a hybrid strategy combining identifier splitting and the BPE algorithm can outperform the original open-vocabulary models on predicting identifiers by 3.68% of recall and 6.32% of Mean Reciprocal Rank. The results also show that the hybrid strategy can improve the entropy of language models by 2.02%.
Skip Vectors for RDF Data: Extraction Based on the Complexity of Feature Patterns
Venue: arXiv cs.LG 2022
Year: 2022

The Resource Description Framework (RDF) is a framework for describing metadata, such as attributes and relationships of resources on the Web. Machine learning tasks for RDF graphs adopt three methods: (i) support vector machines (SVMs) with RDF graph kernels, (ii) RDF graph embeddings, and (iii) relational graph convolutional networks. In this paper, we propose a novel feature vector (called a Skip vector) that represents some features of each resource in an RDF graph by extracting various combinations of neighboring edges and nodes. In order to make the Skip vector low-dimensional, we select important features for classification tasks based on the information gain ratio of each feature. The classification tasks can be performed by applying the low-dimensional Skip vector of each resource to conventional machine learning algorithms, such as SVMs, the k-nearest neighbors method, neural networks, random forests, and AdaBoost. In our evaluation experiments with RDF data, such as Wikidata, DBpedia, and YAGO, we compare our method with RDF graph kernels in an SVM. We also compare our method with the two approaches: RDF graph embeddings such as RDF2vec and relational graph convolutional networks on the AIFB, MUTAG, BGS, and AM benchmarks.
Blizzard: a Distributed Consensus Protocol for Mobile Devices
Venue: arXiv cs.DC 2022
Year: 2022

We present Blizzard, a Byzantine Fault Tolerant (BFT) distributed ledger protocol that is aimed at making mobile devices first-class citizens in the consensus process. Blizzard introduces a novel two-tier architecture by having the mobile nodes communicate through online brokers, and includes a decentralized matching scheme to ensure each node connects to a certain number of random brokers. Through mathematical analysis, we derive a guaranteed safety region (i.e. the set of ratios of malicious nodes and malicious brokers for which the safety is assured) for the Blizzard protocol. Liveness is shown as well. We analyze the performance of Blizzard in terms of its throughput, latency and message complexity. Through experiments based on a software implementation, we show that Blizzard is capable of throughput on the order of several thousand transactions per second per shard, and sub-second confirmation latency.
Sparsity-based Feature Selection for Anomalous Subgroup Discovery
Venue: arXiv cs.LG 2022
Year: 2022

Anomalous pattern detection aims to identify instances where deviation from normalcy is evident, and is widely applicable across domains. Multiple anomalous detection techniques have been proposed in the state of the art. However, there is a common lack of a principled and scalable feature selection method for efficient discovery. Existing feature selection techniques are often conducted by optimizing the performance of prediction outcomes rather than its systemic deviations from the expected. In this paper, we proposed a sparsity-based automated feature selection (SAFS) framework, which encodes systemic outcome deviations via the sparsity of feature-driven odds ratios. SAFS is a model-agnostic approach with usability across different discovery techniques. SAFS achieves more than $3\times$ reduction in computation time while maintaining detection performance when validated on publicly available critical care dataset. SAFS also results in a superior performance when compared against multiple baselines for feature selection.
Efficient Approximations for Many-Visits Multiple Traveling Salesman Problems
Venue: arXiv cs.DM 2022
Year: 2022

A fundamental variant of the classical traveling salesman problem (TSP) is the so-called multiple TSP (mTSP), where a set of $m$ salesmen jointly visit all cities from a set of $n$ cities. The mTSP models many important real-life applications, in particular for vehicle routing problems. An extensive survey by Bektas (Omega 34(3), 2006) lists a variety of heuristic and exact solution procedures for the mTSP, which quickly solve particular problem instances. In this work we consider a further generalization of mTSP, the many-visits mTSP, where each city $v$ has a request $r(v)$ of how many times it should be visited by the salesmen. This problem opens up new real-life applications such as aircraft sequencing, while at the same time it poses several computational challenges. We provide multiple efficient approximation algorithms for important variants of the many-visits mTSP, which are guaranteed to quickly compute high-quality solutions for all problem instances.
Symmetry-aware SFC Framework for 5G Networks
Venue: arXiv cs.NI 2022
Year: 2022

Network Function Virtualization (NFV), network slicing, and Software-Defined Networking (SDN) are the key enablers of the fifth generation of mobile networks (5G). Service Function Chaining (SFC) plays a critical role in delivering sophisticated service per slice and enables traffic traversal through a set of ordered Service Functions (SFs). In fully symmetric SFCs, the uplink and downlink traffic traverse the same SFs, while in asymmetric SFC, the reverse-path may not necessarily cross the same SFs in the reverse order. Proposed approaches in the literature support either full symmetry or no symmetry. In this paper, we discuss the partial symmetry concept, that enforces the reverse path to traverse the SFs only when needed. Our contribution is threefold. First, we propose a novel SFC framework with an abstraction layer that can dynamically create partial or full symmetric SFCs across multiple administrative and technological cloud/edge domains. According to the Key Performance Indicators (KPIs) and desired objectives specified at the network slice intent request, the abstraction layer would automatise different SFC operations, but specifically generating partial or full symmetric SFCs. Second, we propose an algorithm to dynamically calculate the reverse path for an SFC by including only SFs requiring symmetry. Third, we implement a prototype application to test the performance of the partial symmetry algorithm. The obtained results show the advantages of partial symmetry in reducing both the SFC delivery time and the load on VNFs.
Uncertainty Quantification Techniques for Space Weather Modeling: Thermospheric Density Application
Venue: arXiv cs.LG 2022
Year: 2022

Machine learning (ML) has often been applied to space weather (SW) problems in recent years. SW originates from solar perturbations and is comprised of the resulting complex variations they cause within the systems between the Sun and Earth. These systems are tightly coupled and not well understood. This creates a need for skillful models with knowledge about the confidence of their predictions. One example of such a dynamical system is the thermosphere, the neutral region of Earth's upper atmosphere. Our inability to forecast it has severe repercussions in the context of satellite drag and collision avoidance operations for objects in low Earth orbit. Even with (assumed) perfect driver forecasts, our incomplete knowledge of the system results in often inaccurate neutral mass density predictions. Continuing efforts are being made to improve model accuracy, but density models rarely provide estimates of uncertainty. In this work, we propose two techniques to develop nonlinear ML models to predict thermospheric density while providing calibrated uncertainty estimates: Monte Carlo (MC) dropout and direct prediction of the probability distribution, both using the negative logarithm of predictive density (NLPD) loss function. We show the performance for models trained on local and global datasets. This shows that NLPD provides similar results for both techniques but the direct probability method has a much lower computational cost. For the global model regressed on the SET HASDM density database, we achieve errors of 11% on independent test data with well-calibrated uncertainty estimates. Using an in-situ CHAMP density dataset, both techniques provide test error on the order of 13%. The CHAMP models (on independent data) are within 2% of perfect calibration for all prediction intervals tested. This model can also be used to obtain global predictions with uncertainties at a given epoch.
Spectral and Energy Efficient User Pairing for RIS-assisted Uplink NOMA Systems with Imperfect Phase Compensation
Venue: arXiv cs.IT 2022
Year: 2022

Non-orthogonal multiple access (NOMA) is considered a key technology for improving the spectral efficiency of fifth-generation (5G) and beyond 5G cellular networks. NOMA is beneficial when the channel vectors of the users are in the same direction, which is not always possible in conventional wireless systems. With the help of a reconfigurable intelligent surface (RIS), the base station can control the directions of the channel vectors of the users. Thus, by combining both technologies, the RIS-assisted NOMA systems are expected to achieve greater improvements in the network throughput. However, ideal phase control at the RIS is unrealizable in practice because of the imperfections in the channel estimations and the hardware limitations. This imperfection in phase control can have a significant impact on the system performance. Motivated by this, in this paper, we consider an RIS-assisted uplink NOMA system in the presence of imperfect phase compensation. We formulate the criterion for pairing the users that achieves minimum required data rates. We propose adaptive user pairing algorithms that maximize spectral or energy efficiency. We then derive various bounds on power allocation factors for the paired users. Through extensive simulation results, we show that the proposed algorithms significantly outperform the state-of-the-art algorithms in terms of spectral and energy efficiency.
Nonlocal Kernel Network (NKN): a Stable and Resolution-Independent Deep Neural Network
Venue: arXiv cs.LG 2022
Year: 2022

Neural operators have recently become popular tools for designing solution maps between function spaces in the form of neural networks. Differently from classical scientific machine learning approaches that learn parameters of a known partial differential equation (PDE) for a single instance of the input parameters at a fixed resolution, neural operators approximate the solution map of a family of PDEs. Despite their success, the uses of neural operators are so far restricted to relatively shallow neural networks and confined to learning hidden governing laws. In this work, we propose a novel nonlocal neural operator, which we refer to as nonlocal kernel network (NKN), that is resolution independent, characterized by deep neural networks, and capable of handling a variety of tasks such as learning governing equations and classifying images. Our NKN stems from the interpretation of the neural network as a discrete nonlocal diffusion reaction equation that, in the limit of infinite layers, is equivalent to a parabolic nonlocal equation, whose stability is analyzed via nonlocal vector calculus. The resemblance with integral forms of neural operators allows NKNs to capture long-range dependencies in the feature space, while the continuous treatment of node-to-node interactions makes NKNs resolution independent. The resemblance with neural ODEs, reinterpreted in a nonlocal sense, and the stable network dynamics between layers allow for generalization of NKN's optimal parameters from shallow to deep networks. This fact enables the use of shallow-to-deep initialization techniques. Our tests show that NKNs outperform baseline methods in both learning governing equations and image classification tasks and generalize well to different resolutions and depths.
Investigating Expectation Violations in Mobile Apps
Venue: arXiv cs.SE 2022
Year: 2022

Information technology and software services are pervasive, occupying the centre of most aspects of contemporary societies. This has given rise to commonly expected norms and expectations around how such systems should work, appropriate penalties for violating these expectations, and more importantly, indicators of how to reduce the consequences of violations and sanctions. Evidence for expectation violations and ensuing sanctions exists in a range of portals used by individuals and groups to start new friendships, explore new ideas, and provide feedback for products and services. Therein lies insights that could lead to functional socio-technical systems, and general awareness and anticipations of human actions (and interactions) when using information technology and software services. However, limited previous work has examined such artifacts to provide these understandings. To contribute to such understandings and theoretical advancement we study expectation violations in mobile apps, considered among the most engaging socio-technical systems. We used content analysis and expectancy violation theory (EVT) and expectation confirmation theory (ECT) to explore the evidence and nature of sanctions in app reviews for a specific domain of apps. Our outcomes show that users respond to expectation violation with sanctions when their app does not work as anticipated, developers seem to target specific market niches when providing services in an app domain, and users within an app domain respond with similar sanctions. We contribute to the advancement of expectation violation theories, and we provide practical insights for the mobile app community.
Time Series Forecasting Using Fuzzy Cognitive Maps: A Survey
Venue: arXiv cs.AI 2022
Year: 2022

Among various soft computing approaches for time series forecasting, Fuzzy Cognitive Maps (FCM) have shown remarkable results as a tool to model and analyze the dynamics of complex systems. FCM have similarities to recurrent neural networks and can be classified as a neuro-fuzzy method. In other words, FCMs are a mixture of fuzzy logic, neural network, and expert system aspects, which act as a powerful tool for simulating and studying the dynamic behavior of complex systems. The most interesting features are knowledge interpretability, dynamic characteristics and learning capability. The goal of this survey paper is mainly to present an overview on the most relevant and recent FCM-based time series forecasting models proposed in the literature. In addition, this article considers an introduction on the fundamentals of FCM model and learning methodologies. Also, this survey provides some ideas for future research to enhance the capabilities of FCM in order to cover some challenges in the real-world experiments such as handling non-stationary data and scalability issues. Moreover, equipping FCMs with fast learning algorithms is one of the major concerns in this area.
Neural calibration of hidden inhomogeneous Markov chains -- Information decompression in life insurance
Venue: arXiv cs.LG 2022
Year: 2022

Markov chains play a key role in a vast number of areas, including life insurance mathematics. Standard actuarial quantities as the premium value can be interpreted as compressed, lossy information about the underlying Markov process. We introduce a method to reconstruct the underlying Markov chain given collective information of a portfolio of contracts. Our neural architecture explainably characterizes the process by explicitly providing one-step transition probabilities. Further, we provide an intrinsic, economic model validation to inspect the quality of the information decompression. Lastly, our methodology is successfully tested for a realistic data set of German term life insurance contracts.
Developing Assistive Technology to Support Reminiscence Therapy: A User-Centered Study to Identify Caregivers' Needs
Venue: arXiv cs.HC 2022
Year: 2022

Reminiscence therapy is an inexpensive non-pharmacological therapy commonly used due to its therapeutic value for PwD, as it can be used to promote independence, positive moods and behavior, and improve their quality of life. Caregivers are one of the main pillars in the adoption of digital technologies for reminiscence therapy, as they are responsible for its administration. Despite their comprehensive understanding of the needs and difficulties associated with the therapy, their perspective has not been fully taken into account in the development of existing technological solutions. To inform the design of technological solutions within dementia care, we followed a user-centered design approach through worldwide surveys, follow-up semi-structured interviews, and focus groups. Seven hundred and seven informal and 52 formal caregivers participated in our study. Our findings show that technological solutions must provide mechanisms to carry out the therapy in a simple way, reducing the amount of work for caregivers when preparing and conducting therapy sessions. They should also diversify and personalize the current session (and following ones) based on both the biographical information of the PwD and their emotional reactions. This is particularly important since the PwD often become agitated, aggressive or angry, and caregivers might not know how to properly deal with this situation (in particular, the informal ones). Additionally, formal caregivers need an easy way to manage information of the different PwD they take care of, and consult the history of sessions performed (in particular, to identify images that triggered negative emotional reactions, and consult any notes taken about them). As a result, we present a list of validated functional requirements gathered for the PwD and both formal and informal caregivers, as well as the corresponding expected primary and secondary outcomes.
Continuous-time Radar-inertial Odometry for Automotive Radars
Venue: arXiv cs.RO 2022
Year: 2022

We present an approach for radar-inertial odometry which uses a continuous-time framework to fuse measurements from multiple automotive radars and an inertial measurement unit (IMU). Adverse weather conditions do not have a significant impact on the operating performance of radar sensors unlike that of camera and LiDAR sensors. Radar's robustness in such conditions and the increasing prevalence of radars on passenger vehicles motivate us to look at the use of radar for ego-motion estimation. A continuous-time trajectory representation is applied not only as a framework to enable heterogeneous and asynchronous multi-sensor fusion, but also, to facilitate efficient optimization by being able to compute poses and their derivatives in closed-form and at any given time along the trajectory. We compare our continuous-time estimates to those from a discrete-time radar-inertial odometry approach and show that our continuous-time method outperforms the discrete-time method. To the best of our knowledge, this is the first time a continuous-time framework has been applied to radar-inertial odometry.