OpenRaiser/Intern-Atlas huggingface dataset:
Smaller Progress Measures and Separating Automata for Parity Games
Venue: arXiv cs.DS 2022
Year: 2022

Calude et al. have recently shown that parity games can be solved in quasi-polynomial time, a landmark result that has led to a number of approaches with quasi-polynomial complexity. Jurdinski and Lasic have further improved the precise complexity of parity games, especially when the number of priorities is low (logarithmic in the number of positions). Both of these algorithms belong to a class of game solving techniques now often called separating automata: deterministic automata that can be used as witness automata to decide the winner in parity games up to a given number of states and colours. We suggest a number of adjustments to the approach of Calude et al. that lead to smaller statespaces. These include and improve over those earlier introduced by Fearnley et al. We identify two of them that, together, lead to a statespace of exactly the same size Jurdzinski and Lasic's concise progress measures, which currently hold the crown as smallest statespace. The remaining improvements, hence, lead to a further reduction in the size of the statespace, making our approach the most succinct progress measures available for parity games.
Bitcoin P2P Network Measurements: A testbed study of the effect of peer selection on transaction propagation and confirmation times
Venue: arXiv cs.DC 2022
Year: 2022

Bitcoin is the first and the most extensive decentralized electronic cryptocurrency system that uses blockchain technology. It uses a peer-to-peer (P2P) network to operate without a central authority and propagate system information such as transactions or blockchain updates. The communication between participating nodes is highly relying on the underlying network infrastructure to facilitate a platform. Understanding the impact of peer formation strategies, peer list, and delay are vital on understanding node to node communication. To this aim, we performed an extensive study on the transaction characteristic of Bitcoin through a Testbed. The analysis shows that peer selection strategies affect the transactions propagation and confirmation time. Moreover, the default distance-based peer selection strategy in Bitcoin performs less when there is high arrival intensity and creates high number forks.
How does a spontaneously speaking conversational agent affect user behavior?
Venue: arXiv cs.HC 2022
Year: 2022

This study investigated the effect of synthetic voice of conversational agent trained with spontaneous speech on human interactants. Specifically, we hypothesized that humans will exhibit more social responses when interacting with conversational agent that has a synthetic voice built on spontaneous speech. Typically, speech synthesizers are built on a speech corpus where voice professionals read a set of written sentences. The synthesized speech is clear as if a newscaster were reading a news or a voice actor were playing an anime character. However, this is quite different from spontaneous speech we speak in everyday conversation. Recent advances in speech synthesis enabled us to build a speech synthesizer on a spontaneous speech corpus, and to obtain a near conversational synthesized speech with reasonable quality. By making use of these technology, we examined whether humans produce more social responses to a spontaneously speaking conversational agent. We conducted a large-scale conversation experiment with a conversational agent whose utterances were synthesized with the model trained either with spontaneous speech or read speech. The result showed that the subjects who interacted with the agent whose utterances were synthesized from spontaneous speech tended to show shorter response time and a larger number of backchannels. The result of a questionnaire showed that subjects who interacted with the agent whose utterances were synthesized from spontaneous speech tended to rate their conversation with the agent as closer to a human conversation. These results suggest that speech synthesis built on spontaneous speech is essential to realize a conversational agent as a social actor.
Superredundancy: A tool for Boolean formula minimization complexity analysis
Venue: arXiv cs.CC 2022
Year: 2022

A superredundant clause is a clause that is redundant in the resolution closure of a formula. The converse concept of superirredundancy ensures membership of the clause in all minimal CNF formulae that are equivalent to the given one. This allows for building formulae where some clauses are fixed when minimizing size. An example are proofs of complexity hardness of the problems of minimal formula size. Others are proofs of size when forgetting variables or revising a formula. Most clauses can be made superirredundant by splitting them over a new variable.
Data-driven emotional body language generation for social robotics
Venue: arXiv cs.RO 2022
Year: 2022

In social robotics, endowing humanoid robots with the ability to generate bodily expressions of affect can improve human-robot interaction and collaboration, since humans attribute, and perhaps subconsciously anticipate, such traces to perceive an agent as engaging, trustworthy, and socially present. Robotic emotional body language needs to be believable, nuanced and relevant to the context. We implemented a deep learning data-driven framework that learns from a few hand-designed robotic bodily expressions and can generate numerous new ones of similar believability and lifelikeness. The framework uses the Conditional Variational Autoencoder model and a sampling approach based on the geometric properties of the model's latent space to condition the generative process on targeted levels of valence and arousal. The evaluation study found that the anthropomorphism and animacy of the generated expressions are not perceived differently from the hand-designed ones, and the emotional conditioning was adequately differentiable between most levels except the pairs of neutral-positive valence and low-medium arousal. Furthermore, an exploratory analysis of the results reveals a possible impact of the conditioning on the perceived dominance of the robot, as well as on the participants' attention.
Sparse Compressed Spiking Neural Network Accelerator for Object Detection
Venue: arXiv cs.AR 2022
Year: 2022

Spiking neural networks (SNNs), which are inspired by the human brain, have recently gained popularity due to their relatively simple and low-power hardware for transmitting binary spikes and highly sparse activation maps. However, because SNNs contain extra time dimension information, the SNN accelerator will require more buffers and take longer to infer, especially for the more difficult high-resolution object detection task. As a result, this paper proposes a sparse compressed spiking neural network accelerator that takes advantage of the high sparsity of activation maps and weights by utilizing the proposed gated one-to-all product for low power and highly parallel model execution. The experimental result of the neural network shows 71.5$\%$ mAP with mixed (1,3) time steps on the IVS 3cls dataset. The accelerator with the TSMC 28nm CMOS process can achieve 1024$\times$576@29 frames per second processing when running at 500MHz with 35.88TOPS/W energy efficiency and 1.05mJ energy consumption per frame.
VSA: Reconfigurable Vectorwise Spiking Neural Network Accelerator
Venue: arXiv cs.AR 2022
Year: 2022

Spiking neural networks (SNNs) that enable low-power design on edge devices have recently attracted significant research. However, the temporal characteristic of SNNs causes high latency, high bandwidth and high energy consumption for the hardware. In this work, we propose a binary weight spiking model with IF-based Batch Normalization for small time steps and low hardware cost when direct training with input encoding layer and spatio-temporal back propagation (STBP). In addition, we propose a vectorwise hardware accelerator that is reconfigurable for different models, inference time steps and even supports the encoding layer to receive multi-bit input. The required memory bandwidth is further reduced by two-layer fusion mechanism. The implementation result shows competitive accuracy on the MNIST and CIFAR-10 datasets with only 8 time steps, and achieves power efficiency of 25.9 TOPS/W.
Ultra-Reliable Low-Latency Millimeter-Wave Communications with Sliding Window Network Coding
Venue: arXiv cs.IT 2022
Year: 2022

Ultra-reliability and low-latency are pivotal requirements of the new 6th generation of communication systems (xURLLC). Over the past years, to increase throughput, adaptive active antennas were introduced in advanced wireless communications, specifically in the domain of millimeter-wave (mmWave). Consequently, new lower-layer techniques were proposed to cope with practical challenges of high dimensional and electronically-steerable beams. The transition from omni-directional to highly directional antennas presents a new type of wireless systems that deliver high bandwidth, but that are susceptible to high losses and high latency variation. Classical approaches cannot close the rising gap between high throughput and low delay in those advanced systems. In this work, we incorporate effective sliding window network coding solutions in mmWave communications. While legacy systems such as rateless codes improve delay, cross-layer results show that they do not provide low latency communications (LLC - below 10 ms), due to the lossy behaviour of mmWave channel and the lower-layers' retransmission mechanisms. On the other hand, fixed sliding window random linear network coding (RLNC) is able to achieve LLC, and even better, adaptive sliding window RLNC obtains ultra-reliable LLC (Ultra-Reliable and Low-Latency Communications (URLLC) - LLC with maximum delay below 10 ms with more than 99% success rate).
Rusty Links in Local Chains
Venue: arXiv cs.PL 2022
Year: 2022

Rust successfully applies ownership types to control memory allocation. This restricts the programs' topologies to the point where doubly-linked lists cannot be programmed in Safe Rust. We sketch how more flexible "local" ownership could be added to Rust, permitting multiple mutable references to objects, provided each reference is bounded by the object's lifetime. To maintain thread-safety, locally owned objects must remain thread-local; to maintain memory safety, local objects can be deallocated when their owner's lifetime expires.
Bridging Walking and Slithering -- Stokesian Locomotion
Venue: arXiv cs.RO 2022
Year: 2022

Both legged locomotion and slithering motions typically utilize periodic gaits -- repeating cycles of body shape change that produce a net motion through the world. Legged locomotion can be viewed from the perspective of piece-wise contact constraint formation and removal. Slithering and low Reynolds number swimming operate under continuous constraints of force balance, wherein dissipation removes the ability to accumulate momentum. Here we discuss how to bridge the gap between these domains of motion, thereby, among other benefits, producing models for the space of legged locomotion with slipping. The connective fabric is the use of a "Stokesian", or "local connection" model.
The Rise and Fall of Robotic World (A case study of WALL-E)
Venue: arXiv cs.RO 2022
Year: 2022

The current trend in technology shows that the robots will soon be seen interacting with humans and handling different tasks more efficiently in future. With advancement in artificial intelligence it can be foreseen that robots will definitely take over all of the major and minor jobs, will socially interact with humans and will minimize the burden of human significantly. With the passage of time humans will forget about their reality and the labour work they currently do by themselves. That age will be exactly the peak age of robots and we can call it age of the rise of the robots. With the rise of robots, they will start taking decision by themselves regarding the human life and the planet. Human will again start to minimize the influence of robots and take control of their life with the help of some robots. The WALL-E narrates such a story of human life dependency on robots in spaceship when earth is destroyed because of some catastrophe. The clash between human and robots occurs when humans find the earth is survivable and should go back but robots do not allow them. With the help of some social robots humans again take control and take the ship back to earth. The major part of the movie is about the a lonely robot living on planet earth, the love of two robots, their interaction. Those two robots help humans to move back to earth.
GPUNet: Searching the Deployable Convolution Neural Networks for GPUs
Venue: arXiv cs.CV 2022
Year: 2022

Customizing Convolution Neural Networks (CNN) for production use has been a challenging task for DL practitioners. This paper intends to expedite the model customization with a model hub that contains the optimized models tiered by their inference latency using Neural Architecture Search (NAS). To achieve this goal, we build a distributed NAS system to search on a novel search space that consists of prominent factors to impact latency and accuracy. Since we target GPU, we name the NAS optimized models as GPUNet, which establishes a new SOTA Pareto frontier in inference latency and accuracy. Within 1$ms$, GPUNet is 2x faster than EfficientNet-X and FBNetV3 with even better accuracy. We also validate GPUNet on detection tasks, and GPUNet consistently outperforms EfficientNet-X and FBNetV3 on COCO detection tasks in both latency and accuracy. All of these data validate that our NAS system is effective and generic to handle different design tasks. With this NAS system, we expand GPUNet to cover a wide range of latency targets such that DL practitioners can deploy our models directly in different scenarios.
A Survey on Security Issues in Modern Implantable Devices: Solutions and Future Issues
Venue: arXiv cs.CR 2022
Year: 2022

Implantable Medical Devices (IMD) is a fast pace growing medical field and continues to grow in the foreseeable future. Advancement in science and technology has led to the IMD devices offering advanced medical treatments. Modern IMDs can automatically monitor and manage different patients' health conditions without any manual intervention from medical professionals. While IMDs are also becoming more connected to enhance the delivery of care remotely and provide the means for both patients and physicians to adjust therapy at the comfort of their homes, it also increases security related concerns. Adversaries could take advantage and exploit device vulnerabilities to manipulate device settings remotely from anywhere around the world. This manuscript reviews the current threats, security goals, and proposed solutions by comparing them with their strengths and limitations. We also highlight the emerging IMD technologies and innovative ideas for new designs and implementations to improve the security of IMDs. Finally, we conclude the article with future research directions toward securing IMD systems to light the way for researchers.
On the generalization capabilities of FSL methods through domain adaptation: a case study in endoscopic kidney stone image classification
Venue: arXiv cs.CV 2022
Year: 2022

Deep learning has shown great promise in diverse areas of computer vision, such as image classification, object detection and semantic segmentation, among many others. However, as it has been repeatedly demonstrated, deep learning methods trained on a dataset do not generalize well to datasets from other domains or even to similar datasets, due to data distribution shifts. In this work, we propose the use of a meta-learning based few-shot learning approach to alleviate these problems. In order to demonstrate its efficacy, we use two datasets of kidney stones samples acquired with different endoscopes and different acquisition conditions. The results show how such methods are indeed capable of handling domain-shifts by attaining an accuracy of 74.38% and 88.52% in the 5-way 5-shot and 5-way 20-shot settings respectively. Instead, in the same dataset, traditional Deep Learning (DL) methods attain only an accuracy of 45%.
MemSeg: A semi-supervised method for image surface defect detection using differences and commonalities
Venue: arXiv cs.CV 2022
Year: 2022

Under the semi-supervised framework, we propose an end-to-end memory-based segmentation network (MemSeg) to detect surface defects on industrial products. Considering the small intra-class variance of products in the same production line, from the perspective of differences and commonalities, MemSeg introduces artificially simulated abnormal samples and memory samples to assist the learning of the network. In the training phase, MemSeg explicitly learns the potential differences between normal and simulated abnormal images to obtain a robust classification hyperplane. At the same time, inspired by the mechanism of human memory, MemSeg uses a memory pool to store the general patterns of normal samples. By comparing the similarities and differences between input samples and memory samples in the memory pool to give effective guesses about abnormal regions; In the inference phase, MemSeg directly determines the abnormal regions of the input image in an end-to-end manner. Through experimental validation, MemSeg achieves the state-of-the-art (SOTA) performance on MVTec AD datasets with AUC scores of 99.56% and 98.84% at the image-level and pixel-level, respectively. In addition, MemSeg also has a significant advantage in inference speed benefiting from the end-to-end and straightforward network structure, which better meets the real-time requirement in industrial scenarios.
Realistic soft-body tearing under 10ms in VR
Venue: arXiv cs.GR 2022
Year: 2022

We present a novel integration of a real-time continuous tearing algorithm for 3D meshes in VR, suitable for devices of low CPU/GPU specifications, along with a suitable particle decomposition that allows soft-body deformations on both the original and the torn model.
Revisiting Gaussian Neurons for Online Clustering with Unknown Number of Clusters
Venue: arXiv cs.LG 2022
Year: 2022

Despite the recent success of artificial neural networks, more biologically plausible learning methods may be needed to resolve the weaknesses of backpropagation trained models such as catastrophic forgetting and adversarial attacks. Although these weaknesses are not specifically addressed, a novel local learning rule is presented that performs online clustering with an upper limit on the number of clusters to be found rather than a fixed cluster count. Instead of using orthogonal weight or output activation constraints, activation sparsity is achieved by mutual repulsion of lateral Gaussian neurons ensuring that multiple neuron centers cannot occupy the same location in the input domain. An update method is also presented for adjusting the widths of the Gaussian neurons in cases where the data samples can be represented by means and variances. The algorithms were applied on the MNIST and CIFAR-10 datasets to create filters capturing the input patterns of pixel patches of various sizes. The experimental results demonstrate stability in the learned parameters across a large number of training samples.
Recording and replaying psychomotor user actions in VR
Venue: arXiv cs.GR 2022
Year: 2022

We introduce a novel method that describes the functionality and characteristics of an efficient VR recorder with replay capabilities, implemented in a modern game engine, publicly available for free.
Defending Against Advanced Persistent Threats using Game-Theory
Venue: arXiv cs.CR 2022
Year: 2022

Advanced persistent threats (APT) combine a variety of different attack forms ranging from social engineering to technical exploits. The diversity and usual stealthiness of APT turns them into a central problem of contemporary practical system security, since information on attacks, the current system status or the attacker's incentives is often vague, uncertain and in many cases even unavailable. Game theory is a natural approach to model the conflict between the attacker and the defender, and this work investigates a generalized class of matrix games as a risk mitigation tool for an APT defense. Unlike standard game and decision theory, our model is tailored to capture and handle the full uncertainty that is immanent to APT, such as disagreement among qualitative expert risk assessments, unknown adversarial incentives and uncertainty about the current system state (in terms of how deeply the attacker may have penetrated into the system's protective shells already). Practically, game-theoretic APT models can be derived straightforwardly from topological vulnerability analysis, together with risk assessments as they are done in common risk management standards like the ISO 31000 family. Theoretically, these models come with different properties than classical game theoretic models, whose technical solution presented in this work may be of independent interest.
State-of-the-art in Open-domain Conversational AI: A Survey
Venue: arXiv cs.CL 2022
Year: 2022

We survey SoTA open-domain conversational AI models with the purpose of presenting the prevailing challenges that still exist to spur future research. In addition, we provide statistics on the gender of conversational AI in order to guide the ethics discussion surrounding the issue. Open-domain conversational AI are known to have several challenges, including bland responses and performance degradation when prompted with figurative language, among others. First, we provide some background by discussing some topics of interest in conversational AI. We then discuss the method applied to the two investigations carried out that make up this study. The first investigation involves a search for recent SoTA open-domain conversational AI models while the second involves the search for 100 conversational AI to assess their gender. Results of the survey show that progress has been made with recent SoTA conversational AI, but there are still persistent challenges that need to be solved, and the female gender is more common than the male for conversational AI. One main take-away is that hybrid models of conversational AI offer more advantages than any single architecture. The key contributions of this survey are 1) the identification of prevailing challenges in SoTA open-domain conversational AI, 2) the unusual discussion about open-domain conversational AI for low-resource languages, and 3) the discussion about the ethics surrounding the gender of conversational AI.
Monocular 3D Fingerprint Reconstruction and Unwarping
Venue: arXiv cs.CV 2022
Year: 2022

Compared with contact-based fingerprint acquisition techniques, contactless acquisition has the advantages of less skin distortion, larger fingerprint area, and hygienic acquisition. However, perspective distortion is a challenge in contactless fingerprint recognition, which changes ridge orientation, frequency, and minutiae location, and thus causes degraded recognition accuracy. We propose a learning based shape from texture algorithm to reconstruct a 3D finger shape from a single image and unwarp the raw image to suppress perspective distortion. Experimental results on contactless fingerprint databases show that the proposed method has high 3D reconstruction accuracy. Matching experiments on contactless-contact and contactless-contactless matching prove that the proposed method improves matching accuracy.