OpenRaiser/Intern-Atlas huggingface dataset:
Channel Estimation for STAR-RIS-aided Wireless Communication
Venue: arXiv cs.IT 2021
Year: 2021

In this letter, we study efficient uplink channel estimation design for a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted two-user communication systems. We first consider the time switching (TS) protocol for STAR-RIS and propose an efficient scheme to separately estimate the channels of the two users with optimized training (transmission/reflection) pattern. Next, we consider the energy splitting (ES) protocol for STAR-RIS under the practical coupled phase-shift model and devise a customized scheme to simultaneously estimate the channels of both users. Although the problem of minimizing the resultant channel estimation error for the ES protocol is difficult to solve, we propose an efficient algorithm to obtain a high-quality solution by jointly designing the pilot sequences, power-splitting ratio, and training patterns. Numerical results show the effectiveness of the proposed channel estimation designs and reveal that the STAR-RIS under the TS protocol achieves a smaller channel estimation error than the ES case.
Game-Theoretic Model Based Resource Allocation During Floods
Venue: arXiv cs.GT 2021
Year: 2021

For multiple emergencies caused by natural disasters, it is crucial to allocate resources equitably to each emergency location, especially when the availability of resources is limited in quantity. This paper has developed a multi-event crisis management system using a non-cooperative, complete information, strategic form game model. In the proposed system, each emergency event is assumed to occur in different locations simultaneously. These locations are represented as the players in the game, competing with the other players for an optimal allocation of scarce resources available at different resource stations. The players incur a non-monetary cost for obtaining resource units. The objective of the proposed game is to derive optimal strategies for an effective and fair allocation of resources to the respective players.
Target Propagation via Regularized Inversion
Venue: arXiv cs.LG 2021
Year: 2021

Target Propagation (TP) algorithms compute targets instead of gradients along neural networks and propagate them backward in a way that is similar yet different than gradient back-propagation (BP). The idea was first presented as a perturbative alternative to back-propagation that may achieve greater accuracy in gradient evaluation when training multi-layer neural networks (LeCun et al., 1989). However, TP has remained more of a template algorithm with many variations than a well-identified algorithm. Revisiting insights of LeCun et al., (1989) and more recently of Lee et al. (2015), we present a simple version of target propagation based on regularized inversion of network layers, easily implementable in a differentiable programming framework. We compare its computational complexity to the one of BP and delineate the regimes in which TP can be attractive compared to BP. We show how our TP can be used to train recurrent neural networks with long sequences on various sequence modeling problems. The experimental results underscore the importance of regularization in TP in practice.
Neural Weight Step Video Compression
Venue: arXiv cs.CV 2021
Year: 2021

A variety of compression methods based on encoding images as weights of a neural network have been recently proposed. Yet, the potential of similar approaches for video compression remains unexplored. In this work, we suggest a set of experiments for testing the feasibility of compressing video using two architectural paradigms, coordinate-based MLP (CbMLP) and convolutional network. Furthermore, we propose a novel technique of neural weight stepping, where subsequent frames of a video are encoded as low-entropy parameter updates. To assess the feasibility of the considered approaches, we will test the video compression performance on several high-resolution video datasets and compare against existing conventional and neural compression techniques.
Ownership and Creativity in Generative Models
Venue: arXiv cs.CY 2021
Year: 2021

Machine learning generated content such as image artworks, textual poems and music become prominent in recent years. These tools attract much attention from the media, artists, researchers, and investors. Because these tools are data-driven, they are inherently different than the traditional creative tools which arises the question - who may own the content that is generated by these tools? In this paper we aim to address this question, we start by providing a background to this problem, raising several candidates that may own the content and arguments for each one of them. Then we propose a possible algorithmic solution in the vision-based model's regime. Finally, we discuss the broader implications of this problem.
Recognizing Scenes from Novel Viewpoints
Venue: arXiv cs.CV 2021
Year: 2021

Humans can perceive scenes in 3D from a handful of 2D views. For AI agents, the ability to recognize a scene from any viewpoint given only a few images enables them to efficiently interact with the scene and its objects. In this work, we attempt to endow machines with this ability. We propose a model which takes as input a few RGB images of a new scene and recognizes the scene from novel viewpoints by segmenting it into semantic categories. All this without access to the RGB images from those views. We pair 2D scene recognition with an implicit 3D representation and learn from multi-view 2D annotations of hundreds of scenes without any 3D supervision beyond camera poses. We experiment on challenging datasets and demonstrate our model's ability to jointly capture semantics and geometry of novel scenes with diverse layouts, object types and shapes.
Evaluator for Emotionally Consistent Chatbots
Venue: arXiv cs.CL 2021
Year: 2021

One challenge for evaluating current sequence- or dialogue-level chatbots, such as Empathetic Open-domain Conversation Models, is to determine whether the chatbot performs in an emotionally consistent way. The most recent work only evaluates on the aspects of context coherence, language fluency, response diversity, or logical self-consistency between dialogues. This work proposes training an evaluator to determine the emotional consistency of chatbots.
Label noise detection under the Noise at Random model with ensemble filters
Venue: arXiv cs.LG 2021
Year: 2021

Label noise detection has been widely studied in Machine Learning because of its importance in improving training data quality. Satisfactory noise detection has been achieved by adopting ensembles of classifiers. In this approach, an instance is assigned as mislabeled if a high proportion of members in the pool misclassifies it. Previous authors have empirically evaluated this approach; nevertheless, they mostly assumed that label noise is generated completely at random in a dataset. This is a strong assumption since other types of label noise are feasible in practice and can influence noise detection results. This work investigates the performance of ensemble noise detection under two different noise models: the Noisy at Random (NAR), in which the probability of label noise depends on the instance class, in comparison to the Noisy Completely at Random model, in which the probability of label noise is entirely independent. In this setting, we investigate the effect of class distribution on noise detection performance since it changes the total noise level observed in a dataset under the NAR assumption. Further, an evaluation of the ensemble vote threshold is conducted to contrast with the most common approaches in the literature. In many performed experiments, choosing a noise generation model over another can lead to different results when considering aspects such as class imbalance and noise level ratio among different classes.
Achieving the Capacity of a DNA Storage Channel with Linear Coding Schemes
Venue: arXiv cs.IT 2021
Year: 2021

Due to the redundant nature of DNA synthesis and sequencing technologies, a basic model for a DNA storage system is a multi-draw "shuffling-sampling" channel. In this model, a random number of noisy copies of each sequence is observed at the channel output. Recent works have characterized the capacity of such a DNA storage channel under different noise and sequencing models, relying on sophisticated typicality-based approaches for the achievability. Here, we consider a multi-draw DNA storage channel in the setting of noise corruption by a binary erasure channel. We show that, in this setting, the capacity is achieved by linear coding schemes. This leads to a considerably simpler derivation of the capacity expression of a multi-draw DNA storage channel than existing results in the literature.
Systematically reviewing the layered architectural pattern principles and their use to reconstruct software architectures
Venue: arXiv cs.SE 2021
Year: 2021

Architectural reconstruction is a reverse engineering activity aiming at recovering the missing decisions on a system. It can help identify the components, within a legacy software application, according to the application's architectural pattern. It is useful to identify architectural technical debt. We are interested in identifying layers within a layered application since the layered pattern is one of the most used patterns to structure large systems. Earlier component reconstruction work focusing on that pattern relied on generic component identification criteria, such as cohesion and coupling. Recent work has identified architectural-pattern specific criteria to identify components within that pattern. However, the architectural-pattern specific criteria that the layered pattern embodies are loosely defined. In this paper, we present a first systematic literature review (SLR) of the literature aiming at inventorying such criteria for layers within legacy applications and grouping them under four principles that embody the fundamental design principles under-lying the architectural pattern. We identify six such criteria in the form of design rules. We also perform a second systematic literature review to synthesize the literature on software architecture reconstruction in the light of these criteria. We report those principles, the rules they encompass, their representation, and their usage in software architecture reconstruction.
Differential Property Prediction: A Machine Learning Approach to Experimental Design in Advanced Manufacturing
Venue: arXiv cs.LG 2021
Year: 2021

Advanced manufacturing techniques have enabled the production of materials with state-of-the-art properties. In many cases however, the development of physics-based models of these techniques lags behind their use in the lab. This means that designing and running experiments proceeds largely via trial and error. This is sub-optimal since experiments are cost-, time-, and labor-intensive. In this work we propose a machine learning framework, differential property classification (DPC), which enables an experimenter to leverage machine learning's unparalleled pattern matching capability to pursue data-driven experimental design. DPC takes two possible experiment parameter sets and outputs a prediction of which will produce a material with a more desirable property specified by the operator. We demonstrate the success of DPC on AA7075 tube manufacturing process and mechanical property data using shear assisted processing and extrusion (ShAPE), a solid phase processing technology. We show that by focusing on the experimenter's need to choose between multiple candidate experimental parameters, we can reframe the challenging regression task of predicting material properties from processing parameters, into a classification task on which machine learning models can achieve good performance.
Emergency-braking Distance Prediction using Deep Learning
Venue: arXiv cs.RO 2021
Year: 2021

Predicting emergency-braking distance is important for the collision avoidance related features, which are the most essential and popular safety features for vehicles. In this study, we first gathered a large data set including a three-dimensional acceleration data and the corresponding emergency-braking distance. Using this data set, we propose a deep-learning model to predict emergency-braking distance, which only requires 0.25 seconds three-dimensional vehicle acceleration data before the break as input. We consider two road surfaces, our deep learning approach is robust to both road surfaces and have accuracy within 3 feet.
Adaptive Poincaré Point to Set Distance for Few-Shot Classification
Venue: arXiv cs.CV 2021
Year: 2021

Learning and generalizing from limited examples, i,e, few-shot learning, is of core importance to many real-world vision applications. A principal way of achieving few-shot learning is to realize an embedding where samples from different classes are distinctive. Recent studies suggest that embedding via hyperbolic geometry enjoys low distortion for hierarchical and structured data, making it suitable for few-shot learning. In this paper, we propose to learn a context-aware hyperbolic metric to characterize the distance between a point and a set associated with a learned set to set distance. To this end, we formulate the metric as a weighted sum on the tangent bundle of the hyperbolic space and develop a mechanism to obtain the weights adaptively and based on the constellation of the points. This not only makes the metric local but also dependent on the task in hand, meaning that the metric will adapt depending on the samples that it compares. We empirically show that such metric yields robustness in the presence of outliers and achieves a tangible improvement over baseline models. This includes the state-of-the-art results on five popular few-shot classification benchmarks, namely mini-ImageNet, tiered-ImageNet, Caltech-UCSD Birds-200-2011 (CUB), CIFAR-FS, and FC100.
Single-Shot Black-Box Adversarial Attacks Against Malware Detectors: A Causal Language Model Approach
Venue: arXiv cs.CR 2021
Year: 2021

Deep Learning (DL)-based malware detectors are increasingly adopted for early detection of malicious behavior in cybersecurity. However, their sensitivity to adversarial malware variants has raised immense security concerns. Generating such adversarial variants by the defender is crucial to improving the resistance of DL-based malware detectors against them. This necessity has given rise to an emerging stream of machine learning research, Adversarial Malware example Generation (AMG), which aims to generate evasive adversarial malware variants that preserve the malicious functionality of a given malware. Within AMG research, black-box method has gained more attention than white-box methods. However, most black-box AMG methods require numerous interactions with the malware detectors to generate adversarial malware examples. Given that most malware detectors enforce a query limit, this could result in generating non-realistic adversarial examples that are likely to be detected in practice due to lack of stealth. In this study, we show that a novel DL-based causal language model enables single-shot evasion (i.e., with only one query to malware detector) by treating the content of the malware executable as a byte sequence and training a Generative Pre-Trained Transformer (GPT). Our proposed method, MalGPT, significantly outperformed the leading benchmark methods on a real-world malware dataset obtained from VirusTotal, achieving over 24.51\% evasion rate. MalGPT enables cybersecurity researchers to develop advanced defense capabilities by emulating large-scale realistic AMG.
BBS-KWS:The Mandarin Keyword Spotting System Won the Video Keyword Wakeup Challenge
Venue: arXiv cs.CL 2021
Year: 2021

This paper introduces the system submitted by the Yidun NISP team to the video keyword wakeup challenge. We propose a mandarin keyword spotting system (KWS) with several novel and effective improvements, including a big backbone (B) model, a keyword biasing (B) mechanism and the introduction of syllable modeling units (S). By considering this, we term the total system BBS-KWS as an abbreviation. The BBS-KWS system consists of an end-to-end automatic speech recognition (ASR) module and a KWS module. The ASR module converts speech features to text representations, which applies a big backbone network to the acoustic model and takes syllable modeling units into consideration as well. In addition, the keyword biasing mechanism is used to improve the recall rate of keywords in the ASR inference stage. The KWS module applies multiple criteria to determine the absence or presence of the keywords, such as multi-stage matching, fuzzy matching, and connectionist temporal classification (CTC) prefix score. To further improve our system, we conduct semi-supervised learning on the CN-Celeb dataset for better generalization. In the VKW task, the BBS-KWS system achieves significant gains over the baseline and won the first place in two tracks.
Given Users Recommendations Based on Reviews on Yelp
Venue: arXiv cs.IR 2021
Year: 2021

In our project, we focus on NLP-based hybrid recommendation systems. Our data is from Yelp Data. For our hybrid recommendation system, we have two major components: the first part is to embed the reviews with the Bert model and word2vec model; the second part is the implementation of an item-based collaborative filtering algorithm to compute the similarity of each review under different categories of restaurants. In the end, with the help of similarity scores, we are able to recommend users the most matched restaurant based on their recorded reviews. The coding work is split into several parts: selecting samples and data cleaning, processing, embedding, computing similarity, and computing prediction and error. Due to the size of the data, each part will generate one or more JSON files as the milestone to reduce the pressure on memory and the communication between each part.
Learning Emergent Random Access Protocol for LEO Satellite Networks
Venue: arXiv cs.IT 2021
Year: 2021

A mega-constellation of low-altitude earth orbit (LEO) satellites (SATs) are envisaged to provide a global coverage SAT network in beyond fifth-generation (5G) cellular systems. LEO SAT networks exhibit extremely long link distances of many users under time-varying SAT network topology. This makes existing multiple access protocols, such as random access channel (RACH) based cellular protocol designed for fixed terrestrial network topology, ill-suited. To overcome this issue, in this paper, we propose a novel grant-free random access solution for LEO SAT networks, dubbed emergent random access channel protocol (eRACH). In stark contrast to existing model-based and standardized protocols, eRACH is a model-free approach that emerges through interaction with the non-stationary network environment, using multi-agent deep reinforcement learning (MADRL). Furthermore, by exploiting known SAT orbiting patterns, eRACH does not require central coordination or additional communication across users, while training convergence is stabilized through the regular orbiting patterns. Compared to RACH, we show from various simulations that our proposed eRACH yields 54.6% higher average network throughput with around two times lower average access delay while achieving 0.989 Jain's fairness index.
Unsupervised Low-Light Image Enhancement via Histogram Equalization Prior
Venue: arXiv cs.CV 2021
Year: 2021

Deep learning-based methods for low-light image enhancement typically require enormous paired training data, which are impractical to capture in real-world scenarios. Recently, unsupervised approaches have been explored to eliminate the reliance on paired training data. However, they perform erratically in diverse real-world scenarios due to the absence of priors. To address this issue, we propose an unsupervised low-light image enhancement method based on an effective prior termed histogram equalization prior (HEP). Our work is inspired by the interesting observation that the feature maps of histogram equalization enhanced image and the ground truth are similar. Specifically, we formulate the HEP to provide abundant texture and luminance information. Embedded into a Light Up Module (LUM), it helps to decompose the low-light images into illumination and reflectance maps, and the reflectance maps can be regarded as restored images. However, the derivation based on Retinex theory reveals that the reflectance maps are contaminated by noise. We introduce a Noise Disentanglement Module (NDM) to disentangle the noise and content in the reflectance maps with the reliable aid of unpaired clean images. Guided by the histogram equalization prior and noise disentanglement, our method can recover finer details and is more capable to suppress noise in real-world low-light scenarios. Extensive experiments demonstrate that our method performs favorably against the state-of-the-art unsupervised low-light enhancement algorithms and even matches the state-of-the-art supervised algorithms.
Prescriptive Process Monitoring: Quo Vadis?
Venue: arXiv cs.AI 2021
Year: 2021

Prescriptive process monitoring methods seek to optimize a business process by recommending interventions at runtime to prevent negative outcomes or poorly performing cases. In recent years, various prescriptive process monitoring methods have been proposed. This paper studies existing methods in this field via a Systematic Literature Review (SLR). In order to structure the field, the paper proposes a framework for characterizing prescriptive process monitoring methods according to their performance objective, performance metrics, intervention types, modeling techniques, data inputs, and intervention policies. The SLR provides insights into challenges and areas for future research that could enhance the usefulness and applicability of prescriptive process monitoring methods. The paper highlights the need to validate existing and new methods in real-world settings, to extend the types of interventions beyond those related to the temporal and cost perspectives, and to design policies that take into account causality and second-order effects.
On the Complexity of the K-way Vertex Cut Problem
Venue: arXiv cs.CC 2021
Year: 2021

The K-way vertex cut problem} consists in, given a graph G, finding a subset of vertices of a given size, whose removal partitions G into the maximum number of connected components. This problem has many applications in several areas. It has been proven to be NP-complete on general graphs, as well as on split and planar graphs. In this paper, we enrich its complexity study with two new results. First, we prove that it remains NP-complete even when restricted on the class of bipartite graphs. This is unlike what it is expected, given that the K-way vertex cut problem is a generalization of the Maximum Independent set problem which is polynomially solvable on bipartite graphs. We also provide its equivalence to the wellknown problem, namely the Critical Node Problem (CNP), On split graphs. Therefore, any solving algorithm for the CNP on split graphs is a solving algorithm for the K-way vertex cut problem and vice versa.