OpenRaiser/Intern-Atlas huggingface dataset:
Industrial Digital Twins at the Nexus of NextG Wireless Networks and Computational Intelligence: A Survey
Venue: arXiv cs.IT 2021
Year: 2021

By amalgamating recent communication and control technologies, computing and data analytics techniques, and modular manufacturing, Industry~4.0 promotes integrating cyber-physical worlds through cyber-physical systems (CPS) and digital twin (DT) for monitoring, optimization, and prognostics of industrial processes. A DT is an emerging but conceptually different construct than CPS. Like CPS, DT relies on communication to create a highly-consistent, synchronized digital mirror image of the objects or physical processes. DT, in addition, uses built-in models on this precise image to simulate, analyze, predict, and optimize their real-time operation using feedback. DT is rapidly diffusing in the industries with recent advances in the industrial Internet of things (IIoT), edge and cloud computing, machine learning, artificial intelligence, and advanced data analytics. However, the existing literature lacks in identifying and discussing the role and requirements of these technologies in DT-enabled industries from the communication and computing perspective. In this article, we first present the functional aspects, appeal, and innovative use of DT in smart industries. Then, we elaborate on this perspective by systematically reviewing and reflecting on recent research in next-generation (NextG) wireless technologies (e.g., 5G and beyond networks), various tools (e.g., age of information, federated learning, data analytics), and other promising trends in networked computing (e.g., edge and cloud computing). Moreover, we discuss the DT deployment strategies at different industrial communication layers to meet the monitoring and control requirements of industrial applications. We also outline several key reflections and future research challenges and directions to facilitate industrial DT's adoption.
SP-GAN: Sphere-Guided 3D Shape Generation and Manipulation
Venue: arXiv cs.CV 2021
Year: 2021

We present SP-GAN, a new unsupervised sphere-guided generative model for direct synthesis of 3D shapes in the form of point clouds. Compared with existing models, SP-GAN is able to synthesize diverse and high-quality shapes with fine details and promote controllability for part-aware shape generation and manipulation, yet trainable without any parts annotations. In SP-GAN, we incorporate a global prior (uniform points on a sphere) to spatially guide the generative process and attach a local prior (a random latent code) to each sphere point to provide local details. The key insight in our design is to disentangle the complex 3D shape generation task into a global shape modeling and a local structure adjustment, to ease the learning process and enhance the shape generation quality. Also, our model forms an implicit dense correspondence between the sphere points and points in every generated shape, enabling various forms of structure-aware shape manipulations such as part editing, part-wise shape interpolation, and multi-shape part composition, etc., beyond the existing generative models. Experimental results, which include both visual and quantitative evaluations, demonstrate that our model is able to synthesize diverse point clouds with fine details and less noise, as compared with the state-of-the-art models.
Decentralized Observation of Discrete-Event Systems: At Least One Can Tell
Venue: arXiv cs.FL 2021
Year: 2021

We introduce a new decentralized observation condition which we call "at least one can tell" (OCT) and which attempts to capture the idea that for any possible behavior that a system can generate, at least one decentralized observation agent can tell whether that behavior was "good" or "bad", for given formal specifications of "good" and "bad". We provide several equivalent formulations of the OCT condition, and we relate it to (and show that it is different from) previously introduced joint observability. In fact, contrary to joint observability which is undecidable, we show that the OCT condition is decidable. We also show that when the condition holds, finite-state decentralized observers exist.
Epigenetic opportunities for Evolutionary Computation
Venue: arXiv cs.NE 2021
Year: 2021

Evolutionary Computation is a group of biologically inspired algorithms used to solve complex optimisation problems. It can be split into Evolutionary Algorithms, which take inspiration from genetic inheritance, and Swarm Intelligence algorithms, that take inspiration from cultural inheritance. However, recent developments have focused on computational or mathematical adaptions, leaving their biological roots behind. This has left much of the modern evolutionary literature relatively unexplored. To understand which evolutionary mechanisms have been considered, and which have been overlooked, this paper breaks down successful bio-inspired algorithms under a contemporary biological framework based on the Extended Evolutionary Synthesis, an extension of the classical, genetics focussed, Modern Synthesis. The analysis shows that Darwinism and the Modern Synthesis have been incorporated into Evolutionary Computation but that the Extended Evolutionary Synthesis has been broadly ignored beyond:cultural inheritance, incorporated in the sub-set of Swarm Intelligence algorithms, evolvability, through CMA-ES, and multilevel selection, through Multi-Level Selection Genetic Algorithm. The framework shows a missing gap in epigenetic inheritance for Evolutionary Computation, despite being a key building block in modern interpretations of how evolution occurs. Epigenetic inheritance can explain fast adaptation, without changes in an individual's genotype, by allowing biological organisms to self-adapt quickly to environmental cues, which, increases the speed of convergence while maintaining stability in changing environments. This leaves a diverse range of biologically inspired mechanisms as low hanging fruit that should be explored further within Evolutionary Computation.
Algorithm Engineering for Cut Problems
Venue: arXiv cs.DS 2021
Year: 2021

Graphs are a natural representation of data from various contexts, such as social connections, the web, road networks, and many more. In the last decades, many of these networks have become enormous, requiring efficient algorithms to cut networks into smaller, more readily comprehensible blocks. In this work, we aim to partition the vertices of a graph into multiple blocks while minimizing the number of edges that connect different blocks. There is a multitude of cut or partitioning problems that have been the focus of research for multiple decades. This work develops highly-efficient algorithms for the (global) minimum cut problem, the balanced graph partitioning problem and the multiterminal cut problem. All of these algorithms are efficient in practice and freely available for use.
Keep Your Stakeholders Engaged: Interactive Vision Videos in Requirements Engineering
Venue: arXiv cs.SE 2021
Year: 2021

One of the most important issues in requirements engineering (RE) is the alignment of stakeholders' mental models. Making sure that all stakeholders share the same vision of a changing system is crucial to the success of any project. Misaligned mental models of stakeholders can lead to conflicting requirements. A promising approach to this problem is the use of video showing a system vision, so-called vision videos, which help stakeholders to disclose, discuss, and align their mental models of the future system. However, videos have the drawback of allowing viewers to adopt a passive role, as has been shown in research on e-learning. In this role, viewers tend to be inactive, unfocused and bored while watching a video. In this paper, we learn and adopt findings from scientific literature in the field of e-learning on how to mitigate this passive role while watching vision videos in requirements engineering. In this way, we developed concepts that incorporate interactive elements into vision videos to help viewers stay focused. These elements include questions that are asked during the video and ways for viewers to decide what happens next in the video. In a preliminary evaluation with twelve participants, we found statistically significant differences when comparing the interactive vision videos with their traditional form. Using an interactive vision videos, viewers are noticeably more engaged and gather more information on the shown system.
Data Driven VRP: A Neural Network Model to Learn Hidden Preferences for VRP
Venue: arXiv cs.LG 2021
Year: 2021

The traditional Capacitated Vehicle Routing Problem (CVRP) minimizes the total distance of the routes under the capacity constraints of the vehicles. But more often, the objective involves multiple criteria including not only the total distance of the tour but also other factors such as travel costs, travel time, and fuel consumption.Moreover, in reality, there are numerous implicit preferences ingrained in the minds of the route planners and the drivers. Drivers, for instance, have familiarity with certain neighborhoods and knowledge of the state of roads, and often consider the best places for rest and lunch breaks. This knowledge is difficult to formulate and balance when operational routing decisions have to be made. This motivates us to learn the implicit preferences from past solutions and to incorporate these learned preferences in the optimization process. These preferences are in the form of arc probabilities, i.e., the more preferred a route is, the higher is the joint probability. The novelty of this work is the use of a neural network model to estimate the arc probabilities, which allows for additional features and automatic parameter estimation. This first requires identifying suitable features, neural architectures and loss functions, taking into account that there is typically few data available. We investigate the difference with a prior weighted Markov counting approach, and study the applicability of neural networks in this setting.
The Impact of Subspace-Based Pilot Decontamination in User-Centric Scalable Cell-Free Wireless Networks
Venue: arXiv cs.IT 2021
Year: 2021

We consider a scalable user-centric wireless network with dynamic cluster formation as defined by Björnsson and Sanguinetti. Several options for scalable uplink (UL) processing are examined including: i) cluster size and SNR threshold criterion for cluster formation; ii) UL pilot dimension; iii) local detection and global (per cluster) combining. We use a simple model for the channel vector spatial correlation, which captures the fact that the propagation between UEs and RRHs is not isotropic. In particular, we define the ideal performance based on ideal but partial CSI, i.e., the CSI that can be estimated based on the users to antenna heads cluster connectivity. In practice, CSI is estimated from UL pilots, and therefore it is affected by noise and pilot contamination. We show that a very simple subspace projection scheme is able to basically attain the same performance of perfect but partial CSI. This points out that the essential information needed to pilot decontamination reduces effectively to the dominant channel subspaces.
Relation-aware Compositional Zero-shot Learning for Attribute-Object Pair Recognition
Venue: arXiv cs.CV 2021
Year: 2021

This paper proposes a novel model for recognizing images with composite attribute-object concepts, notably for composite concepts that are unseen during model training. We aim to explore the three key properties required by the task --- relation-aware, consistent, and decoupled --- to learn rich and robust features for primitive concepts that compose attribute-object pairs. To this end, we propose the Blocked Message Passing Network (BMP-Net). The model consists of two modules. The concept module generates semantically meaningful features for primitive concepts, whereas the visual module extracts visual features for attributes and objects from input images. A message passing mechanism is used in the concept module to capture the relations between primitive concepts. Furthermore, to prevent the model from being biased towards seen composite concepts and reduce the entanglement between attributes and objects, we propose a blocking mechanism that equalizes the information available to the model for both seen and unseen concepts. Extensive experiments and ablation studies on two benchmarks show the efficacy of the proposed model.
Stochastic Games with Disjunctions of Multiple Objectives (Technical Report)
Venue: arXiv cs.CC 2021
Year: 2021

Stochastic games combine controllable and adversarial non-determinism with stochastic behavior and are a common tool in control, verification and synthesis of reactive systems facing uncertainty. Multi-objective stochastic games are natural in situations where several - possibly conflicting - performance criteria like time and energy consumption are relevant. Such conjunctive combinations are the most studied multi-objective setting in the literature. In this paper, we consider the dual disjunctive problem. More concretely, we study turn-based stochastic two-player games on graphs where the winning condition is to guarantee at least one reachability or safety objective from a given set of alternatives. We present a fine-grained overview of strategy and computational complexity of such \emph{disjunctive queries} (DQs) and provide new lower and upper bounds for several variants of the problem, significantly extending previous works. We also propose a novel value iteration-style algorithm for approximating the set of Pareto optimal thresholds for a given DQ.
PyNose: A Test Smell Detector For Python
Venue: arXiv cs.SE 2021
Year: 2021

Similarly to production code, code smells also occur in test code, where they are called test smells. Test smells have a detrimental effect not only on test code but also on the production code that is being tested. To date, the majority of the research on test smells has been focusing on programming languages such as Java and Scala. However, there are no available automated tools to support the identification of test smells for Python, despite its rapid growth in popularity in recent years. In this paper, we strive to extend the research to Python, build a tool for detecting test smells in this language, and conduct an empirical analysis of test smells in Python projects. We started by gathering a list of test smells from existing research and selecting test smells that can be considered language-agnostic or have similar functionality in Python's standard Unittest framework. In total, we identified 17 diverse test smells. Additionally, we searched for Python-specific test smells by mining frequent code change patterns that can be considered as either fixing or introducing test smells. Based on these changes, we proposed our own test smell called Suboptimal assert. To detect all these test smells, we developed a tool called PyNose in the form of a plugin to PyCharm, a popular Python IDE. Finally, we conducted a large-scale empirical investigation aimed at analyzing the prevalence of test smells in Python code. Our results show that 98% of the projects and 84% of the test suites in the studied dataset contain at least one test smell. Our proposed Suboptimal assert smell was detected in as much as 70.6% of the projects, making it a valuable addition to the list.
Evaluation of Load Prediction Techniques for Distributed Stream Processing
Venue: arXiv cs.DC 2021
Year: 2021

Distributed Stream Processing (DSP) systems enable processing large streams of continuous data to produce results in near to real time. They are an essential part of many data-intensive applications and analytics platforms. The rate at which events arrive at DSP systems can vary considerably over time, which may be due to trends, cyclic, and seasonal patterns within the data streams. A priori knowledge of incoming workloads enables proactive approaches to resource management and optimization tasks such as dynamic scaling, live migration of resources, and the tuning of configuration parameters during run-times, thus leading to a potentially better Quality of Service. In this paper we conduct a comprehensive evaluation of different load prediction techniques for DSP jobs. We identify three use-cases and formulate requirements for making load predictions specific to DSP jobs. Automatically optimized classical and Deep Learning methods are being evaluated on nine different datasets from typical DSP domains, i.e. the IoT, Web 2.0, and cluster monitoring. We compare model performance with respect to overall accuracy and training duration. Our results show that the Deep Learning methods provide the most accurate load predictions for the majority of the evaluated datasets.
Falling for Phishing: An Empirical Investigation into People's Email Response Behaviors
Venue: arXiv cs.CR 2021
Year: 2021

Despite sophisticated phishing email detection systems, and training and awareness programs, humans continue to be tricked by phishing emails. In an attempt to better understand why phishing email attacks still work and how best to mitigate them, we have carried out an empirical study to investigate people's thought processes when reading their emails. We used a scenario-based role-play "think aloud" method and follow-up interviews to collect data from 19 participants. The experiment was conducted using a simulated web email client, and real phishing and legitimate emails adapted to the given scenario. The analysis of the collected data has enabled us to identify eleven factors that influence people's response decisions to both phishing and legitimate emails. Furthermore, based on the user study findings, we discuss novel insights into flaws in the general email decision-making behaviors that could make people susceptible to phishing attacks.
Examining correlation between trust and transparency with explainable artificial intelligence
Venue: arXiv cs.HC 2021
Year: 2021

Trust between humans and artificial intelligence(AI) is an issue which has implications in many fields of human computer interaction. The current issue with artificial intelligence is a lack of transparency into its decision making, and literature shows that increasing transparency increases trust. Explainable artificial intelligence has the ability to increase transparency of AI, which could potentially increase trust for humans. This paper attempts to use the task of predicting yelp review star ratings with assistance from an explainable and non explainable artificial intelligence to see if trust is increased with increased transparency. Results show that for these tasks, explainable artificial intelligence provided significant increase in trust as a measure of influence.
Improving MATLAB's isprime performance without arbitrary-precision arithmetic
Venue: arXiv cs.MS 2021
Year: 2021

MATLAB is a numerical computing platform used by scientists, engineers, mathematicians, and students which contains many mathematical functions, including isprime. MATLAB's isprime function determines which elements of an input array are prime. This research details modular arithmetic techniques, the Miller-Rabin primality test, vectorized operations, and division-minimizing strategies which harness the power of MATLAB's capabilities to improve isprime's performance. The results are typically 5 to 10 times faster for small integers and many hundreds of times faster for large integers and long arrays.
Learning Autonomous Mobility Using Real Demonstration Data
Venue: arXiv cs.RO 2021
Year: 2021

This work proposed an efficient learning-based framework to learn feedback control policies from human teleoperated demonstrations, which achieved obstacle negotiation, staircase traversal, slipping control and parcel delivery for a tracked robot. Due to uncertainties in real-world scenarios, eg obstacle and slippage, closed-loop feedback control plays an important role in improving robustness and resilience, but the control laws are difficult to program manually for achieving autonomous behaviours. We formulated an architecture based on a long-short-term-memory (LSTM) neural network, which effectively learn reactive control policies from human demonstrations. Using datasets from a few real demonstrations, our algorithm can directly learn successful policies, including obstacle-negotiation, stair-climbing and delivery, fall recovery and corrective control of slippage. We proposed decomposition of complex robot actions to reduce the difficulty of learning the long-term dependencies. Furthermore, we proposed a method to efficiently handle non-optimal demos and to learn new skills, since collecting enough demonstration can be time-consuming and sometimes very difficult on a real robotic system.
Binary Complex Neural Network Acceleration on FPGA
Venue: arXiv cs.LG 2021
Year: 2021

Being able to learn from complex data with phase information is imperative for many signal processing applications. Today' s real-valued deep neural networks (DNNs) have shown efficiency in latent information analysis but fall short when applied to the complex domain. Deep complex networks (DCN), in contrast, can learn from complex data, but have high computational costs; therefore, they cannot satisfy the instant decision-making requirements of many deployable systems dealing with short observations or short signal bursts. Recent, Binarized Complex Neural Network (BCNN), which integrates DCNs with binarized neural networks (BNN), shows great potential in classifying complex data in real-time. In this paper, we propose a structural pruning based accelerator of BCNN, which is able to provide more than 5000 frames/s inference throughput on edge devices. The high performance comes from both the algorithm and hardware sides. On the algorithm side, we conduct structural pruning to the original BCNN models and obtain 20 $\times$ pruning rates with negligible accuracy loss; on the hardware side, we propose a novel 2D convolution operation accelerator for the binary complex neural network. Experimental results show that the proposed design works with over 90% utilization and is able to achieve the inference throughput of 5882 frames/s and 4938 frames/s for complex NIN-Net and ResNet-18 using CIFAR-10 dataset and Alveo U280 Board.
Continual Learning for Grounded Instruction Generation by Observing Human Following Behavior
Venue: arXiv cs.CL 2021
Year: 2021

We study continual learning for natural language instruction generation, by observing human users' instruction execution. We focus on a collaborative scenario, where the system both acts and delegates tasks to human users using natural language. We compare user execution of generated instructions to the original system intent as an indication to the system's success communicating its intent. We show how to use this signal to improve the system's ability to generate instructions via contextual bandit learning. In interaction with real users, our system demonstrates dramatic improvements in its ability to generate language over time.
How near-duplicate detection improves editors' and authors' publishing experience
Venue: arXiv cs.DL 2021
Year: 2021

We describe a system that helps identify manuscripts submitted to multiple journals at the same time. Also, we discuss potential applications of the near-duplicate detection technology when run with manuscript text content, including identification of simultaneous submissions, prevention of duplicate published articles, and improving article transfer service.