OpenRaiser/Intern-Atlas huggingface dataset:
An Object-Oriented Framework for Designing Reusable and Maintainable DEVS Models using Design Patterns
Venue: arXiv cs.SE 2020
Year: 2020

Design patterns are well practices to share software development experiences. These patterns allow enhancing reusability, readability and maintainability of architecture and code of software applications. As simulation applies computerized models to produce traces in order to obtain results and conclusions, designers of simulation explored design patterns to make the simulation code more reusable, more readable and easy to maintain, in addition to design complex software oriented simulation modeling. In DEVS (Discrete Event System specification), the designers have successfully designed simulations, frameworks, tools, etc. However, some issues remain still open and should be explored like how a piece of code that implements a set of states, events and transitions may be reused to design a new DEVS model? How may a DEVS model be extended to a new formalism? Etc. In this paper, we address these issues and we propose a set of patterns that may serve as guidelines to designers of DEVS models and its extensions and may contribute to the design of an operational simulation framework. These patterns are inspired partly by the available designs of DEVS community and software engineering developers.
Wikidata on MARS
Venue: arXiv cs.AI 2020
Year: 2020

Multi-attributed relational structures (MARSs) have been proposed as a formal data model for generalized property graphs, along with multi-attributed rule-based predicate logic (MARPL) as a useful rule-based logic in which to write inference rules over property graphs. Wikidata can be modelled in an extended MARS that adds the (imprecise) datatypes of Wikidata. The rules of inference for the Wikidata ontology can be modelled as a MARPL ontology, with extensions to handle the Wikidata datatypes and functions over these datatypes. Because many Wikidata qualifiers should participate in most inference rules in Wikidata a method of implicitly handling qualifier values on a per-qualifier basis is needed to make this modelling useful. The meaning of Wikidata is then the extended MARS that is the closure of running these rules on the Wikidata data model. Wikidata constraints can be modelled as multi-attributed predicate logic (MAPL) formulae, again extended with datatypes, that are evaluated over this extended MARS. The result models Wikidata in a way that fixes several of its major problems.
Evolving Deep Convolutional Neural Networks for Hyperspectral Image Denoising
Venue: arXiv cs.NE 2020
Year: 2020

Hyperspectral images (HSIs) are susceptible to various noise factors leading to the loss of information, and the noise restricts the subsequent HSIs object detection and classification tasks. In recent years, learning-based methods have demonstrated their superior strengths in denoising the HSIs. Unfortunately, most of the methods are manually designed based on the extensive expertise that is not necessarily available to the users interested. In this paper, we propose a novel algorithm to automatically build an optimal Convolutional Neural Network (CNN) to effectively denoise HSIs. Particularly, the proposed algorithm focuses on the architectures and the initialization of the connection weights of the CNN. The experiments of the proposed algorithm have been well-designed and compared against the state-of-the-art peer competitors, and the experimental results demonstrate the competitive performance of the proposed algorithm in terms of the different evaluation metrics, visual assessments, and the computational complexity.
Automatic Storage Structure Selection for hybrid Workload
Venue: arXiv cs.DB 2020
Year: 2020

In the use of database systems, the design of the storage engine and data model directly affects the performance of the database when performing queries. Therefore, the users of the database need to select the storage engine and design data model according to the workload encountered. However, in a hybrid workload, the query set of the database is dynamically changing, and the design of its optimal storage structure is also changing. Motivated by this, we propose an automatic storage structure selection system based on learning cost, which is used to dynamically select the optimal storage structure of the database under hybrid workloads. In the system, we introduce a machine learning method to build a cost model for the storage engine, and a column-oriented data layout generation algorithm. Experimental results show that the proposed system can choose the optimal combination of storage engine and data model according to the current workload, which greatly improves the performance of the default storage structure. And the system is designed to be compatible with different storage engines for easy use in practical applications.
Label-Wise Document Pre-Training for Multi-Label Text Classification
Venue: arXiv cs.CL 2020
Year: 2020

A major challenge of multi-label text classification (MLTC) is to stimulatingly exploit possible label differences and label correlations. In this paper, we tackle this challenge by developing Label-Wise Pre-Training (LW-PT) method to get a document representation with label-aware information. The basic idea is that, a multi-label document can be represented as a combination of multiple label-wise representations, and that, correlated labels always cooccur in the same or similar documents. LW-PT implements this idea by constructing label-wise document classification tasks and trains label-wise document encoders. Finally, the pre-trained label-wise encoder is fine-tuned with the downstream MLTC task. Extensive experimental results validate that the proposed method has significant advantages over the previous state-of-the-art models and is able to discover reasonable label relationship. The code is released to facilitate other researchers.
Reliable Uncertainties for Bayesian Neural Networks using Alpha-divergences
Venue: arXiv cs.LG 2020
Year: 2020

Bayesian Neural Networks (BNNs) often result uncalibrated after training, usually tending towards overconfidence. Devising effective calibration methods with low impact in terms of computational complexity is thus of central interest. In this paper we present calibration methods for BNNs based on the alpha divergences from Information Geometry. We compare the use of alpha divergence in training and in calibration, and we show how the use in calibration provides better calibrated uncertainty estimates for specific choices of alpha and is more efficient especially for complex network architectures. We empirically demonstrate the advantages of alpha calibration in regression problems involving parameter estimation and inferred correlations between output uncertainties.
We Learn Better Road Pothole Detection: from Attention Aggregation to Adversarial Domain Adaptation
Venue: arXiv cs.CV 2020
Year: 2020

Manual visual inspection performed by certified inspectors is still the main form of road pothole detection. This process is, however, not only tedious, time-consuming and costly, but also dangerous for the inspectors. Furthermore, the road pothole detection results are always subjective, because they depend entirely on the individual experience. Our recently introduced disparity (or inverse depth) transformation algorithm allows better discrimination between damaged and undamaged road areas, and it can be easily deployed to any semantic segmentation network for better road pothole detection results. To boost the performance, we propose a novel attention aggregation (AA) framework, which takes the advantages of different types of attention modules. In addition, we develop an effective training set augmentation technique based on adversarial domain adaptation, where the synthetic road RGB images and transformed road disparity (or inverse depth) images are generated to enhance the training of semantic segmentation networks. The experimental results demonstrate that, firstly, the transformed disparity (or inverse depth) images become more informative; secondly, AA-UNet and AA-RTFNet, our best performing implementations, respectively outperform all other state-of-the-art single-modal and data-fusion networks for road pothole detection; and finally, the training set augmentation technique based on adversarial domain adaptation not only improves the accuracy of the state-of-the-art semantic segmentation networks, but also accelerates their convergence.
DeVLBert: Learning Deconfounded Visio-Linguistic Representations
Venue: arXiv cs.CV 2020
Year: 2020

In this paper, we propose to investigate the problem of out-of-domain visio-linguistic pretraining, where the pretraining data distribution differs from that of downstream data on which the pretrained model will be fine-tuned. Existing methods for this problem are purely likelihood-based, leading to the spurious correlations and hurt the generalization ability when transferred to out-of-domain downstream tasks. By spurious correlation, we mean that the conditional probability of one token (object or word) given another one can be high (due to the dataset biases) without robust (causal) relationships between them. To mitigate such dataset biases, we propose a Deconfounded Visio-Linguistic Bert framework, abbreviated as DeVLBert, to perform intervention-based learning. We borrow the idea of the backdoor adjustment from the research field of causality and propose several neural-network based architectures for Bert-style out-of-domain pretraining. The quantitative results on three downstream tasks, Image Retrieval (IR), Zero-shot IR, and Visual Question Answering, show the effectiveness of DeVLBert by boosting generalization ability.
Efficient, Flexible and Secure Group Key Management Protocol for Dynamic IoT Settings
Venue: arXiv cs.NI 2020
Year: 2020

Many Internet of Things (IoT) scenarios require communication to and data acquisition from multiple devices with similar functionalities. For such scenarios, group communication in the form of multicasting and broadcasting has proven to be effective. Group Key Management (GKM) involves the handling, revocation, updation and distribution of cryptographic keys to members of various groups. Classical GKM schemes perform inefficiently in dynamic IoT environments, which are those wherein nodes frequently leave or join a network or migrate from one group to another over time. Recently, the `GroupIt' scheme has been proposed for GKM in dynamic IoT environments. However, this scheme has several limitations such as vulnerability to collusion attacks, the use of computationally expensive asymmetric encryption and threats to the backward secrecy of the system. In this paper, we present a highly efficient and secure GKM protocol for dynamic IoT settings, which maintains forward and backward secrecy at all times. Our proposed protocol uses only symmetric encryption, and is completely resistant to collusion attacks. Also, our protocol is highly flexible and can handle several new scenarios in which device or user dynamics may take place, e.g., allowing a device group to join or leave the network or creation or dissolution of a user group, which are not handled by schemes proposed in prior literature. We evaluate the performance of the proposed protocol via extensive mathematical analysis and numerical computations, and show that it outperforms the GroupIt scheme in terms of the communication and computation costs incurred by users and devices.
From Lost to Found: Discover Missing UI Design Semantics through Recovering Missing Tags
Venue: arXiv cs.HC 2020
Year: 2020

Design sharing sites provide UI designers with a platform to share their works and also an opportunity to get inspiration from others' designs. To facilitate management and search of millions of UI design images, many design sharing sites adopt collaborative tagging systems by distributing the work of categorization to the community. However, designers often do not know how to properly tag one design image with compact textual description, resulting in unclear, incomplete, and inconsistent tags for uploaded examples which impede retrieval, according to our empirical study and interview with four professional designers. Based on a deep neural network, we introduce a novel approach for encoding both the visual and textual information to recover the missing tags for existing UI examples so that they can be more easily found by text queries. We achieve 82.72% accuracy in the tag prediction. Through a simulation test of 5 queries, our system on average returns hundreds more results than the default Dribbble search, leading to better relatedness, diversity and satisfaction.
SoK: Why Johnny Can't Fix PGP Standardization
Venue: arXiv cs.CR 2020
Year: 2020

Pretty Good Privacy (PGP) has long been the primary IETF standard for encrypting email, but suffers from widespread usability and security problems that have limited its adoption. As time has marched on, the underlying cryptographic protocol has fallen out of date insofar as PGP is unauthenticated on a per message basis and compresses before encryption. There have been an increasing number of attacks on the increasingly outdated primitives and complex clients used by the PGP eco-system. However, attempts to update the OpenPGP standard have failed at the IETF except for adding modern cryptographic primitives. Outside of official standardization, Autocrypt is a "bottom-up" community attempt to fix PGP, but still falls victim to attacks on PGP involving authentication. The core reason for the inability to "fix" PGP is the lack of a simple AEAD interface which in turn requires a decentralized public key infrastructure to work with email. Yet even if standards like MLS replace PGP, the deployment of a decentralized PKI remains an open issue.
Association and Caching in Relay-Assisted mmWave Networks: From A Stochastic Geometry Perspective
Venue: arXiv cs.NI 2020
Year: 2020

Limited backhaul bandwidth and blockage effects are two main factors limiting the practical deployment of millimeter wave (mmWave) networks. To tackle these issues, we study the feasibility of relaying as well as caching in mmWave networks. A user association and relaying (UAR) criterion dependent on both caching status and maximum biased received power is proposed by considering the spatial correlation caused by the coexistence of base stations (BSs) and relay nodes (RNs). A joint UAR and caching placement problem is then formulated to maximize the backhaul offloading traffic. Using stochastic geometry tools, we decouple the joint UAR and caching placement problem by analyzing the relationship between UAR probabilities and caching placement probabilities. We then optimize the transformed caching placement problem based on polyblock outer approximation by exploiting the monotonic property in the general case and utilizing convex optimization in the noise-limited case. Accordingly, we propose a BS and RN selection algorithm where caching status at BSs and maximum biased received power are jointly considered. Experimental results demonstrate a significant enhancement of backhaul offloading using the proposed algorithms, and show that deploying more RNs and increasing cache size in mmWave networks is a more cost-effective alternative than increasing BS density to achieve similar backhaul offloading performance.
Computer-Generated Music for Tabletop Role-Playing Games
Venue: arXiv cs.SD 2020
Year: 2020

In this paper we present Bardo Composer, a system to generate background music for tabletop role-playing games. Bardo Composer uses a speech recognition system to translate player speech into text, which is classified according to a model of emotion. Bardo Composer then uses Stochastic Bi-Objective Beam Search, a variant of Stochastic Beam Search that we introduce in this paper, with a neural model to generate musical pieces conveying the desired emotion. We performed a user study with 116 participants to evaluate whether people are able to correctly identify the emotion conveyed in the pieces generated by the system. In our study we used pieces generated for Call of the Wild, a Dungeons and Dragons campaign available on YouTube. Our results show that human subjects could correctly identify the emotion of the generated music pieces as accurately as they were able to identify the emotion of pieces written by humans.
Beyond Point Estimate: Inferring Ensemble Prediction Variation from Neuron Activation Strength in Recommender Systems
Venue: arXiv cs.LG 2020
Year: 2020

Despite deep neural network (DNN)'s impressive prediction performance in various domains, it is well known now that a set of DNN models trained with the same model specification and the same data can produce very different prediction results. Ensemble method is one state-of-the-art benchmark for prediction uncertainty estimation. However, ensembles are expensive to train and serve for web-scale traffic. In this paper, we seek to advance the understanding of prediction variation estimated by the ensemble method. Through empirical experiments on two widely used benchmark datasets MovieLens and Criteo in recommender systems, we observe that prediction variations come from various randomness sources, including training data shuffling, and parameter random initialization. By introducing more randomness into model training, we notice that ensemble's mean predictions tend to be more accurate while the prediction variations tend to be higher. Moreover, we propose to infer prediction variation from neuron activation strength and demonstrate the strong prediction power from activation strength features. Our experiment results show that the average R squared on MovieLens is as high as 0.56 and on Criteo is 0.81. Our method performs especially well when detecting the lowest and highest variation buckets, with 0.92 AUC and 0.89 AUC respectively. Our approach provides a simple way for prediction variation estimation, which opens up new opportunities for future work in many interesting areas (e.g.,model-based reinforcement learning) without relying on serving expensive ensemble models.
A Deep Dive on the Impact of COVID-19 in Software Development
Venue: arXiv cs.SE 2020
Year: 2020

Context: COVID-19 pandemic has impacted different business sectors around the world. Objective. This study investigates the impact of COVID-19 on software projects and software development professionals. Method: We conducted a mining software repository study based on 100 GitHub projects developed in Java using ten different metrics. Next, we surveyed 279 software development professionals for better understanding the impact of COVID-19 on daily activities and wellbeing. Results: We identified 12 observations related to productivity, code quality, and wellbeing. Conclusions: Our findings highlight that the impact of COVID-19 is not binary (reduce productivity vs. increase productivity) but rather a spectrum. For many of our observations, substantial proportions of respondents have differing opinions from each other. We believe that more research is needed to uncover specific conditions that cause certain outcomes to be more prevalent.
White blood cell classification
Venue: arXiv cs.CV 2020
Year: 2020

This paper proposes a novel automatic classification framework for the recognition of five types of white blood cells. Segmenting complete white blood cells from blood smears images and extracting advantageous features from them remain challenging tasks in the classification of white blood cells. Therefore, we present an adaptive threshold segmentation method to deal with blood smears images with non-uniform color and uneven illumination, which is designed based on color space information and threshold segmentation. Subsequently, after successfully separating the white blood cell from the blood smear image, a large number of nonlinear features including geometrical, color and texture features are extracted. Nevertheless, redundant features can affect the classification speed and efficiency, and in view of that, a feature selection algorithm based on classification and regression trees (CART) is designed. Through in-depth analysis of the nonlinear relationship between features, the irrelevant and redundant features are successfully removed from the initial nonlinear features. Afterwards, the selected prominent features are fed into particle swarm optimization support vector machine (PSO-SVM) classifier to recognize the types of the white blood cells. Finally, to evaluate the performance of the proposed white blood cell classification methodology, we build a white blood cell data set containing 500 blood smear images for experiments. By comparing with the ground truth obtained manually, the proposed segmentation method achieves an average of 95.98% and 97.57% dice similarity for segmented nucleus and cell regions respectively. Furthermore, the proposed methodology achieves 99.76% classification accuracy, which well demonstrates its effectiveness.
How to Put Users in Control of their Data in Federated Top-N Recommendation with Learning to Rank
Venue: arXiv cs.LG 2020
Year: 2020

Recommendation services are extensively adopted in several user-centered applications as a tool to alleviate the information overload problem and help users in orienteering in a vast space of possible choices. In such scenarios, data ownership is a crucial concern since users may not be willing to share their sensitive preferences (e.g., visited locations) with a central server. Unfortunately, data harvesting and collection is at the basis of modern, state-of-the-art approaches to recommendation. To address this issue, we present FPL, an architecture in which users collaborate in training a central factorization model while controlling the amount of sensitive data leaving their devices. The proposed approach implements pair-wise learning-to-rank optimization by following the Federated Learning principles, originally conceived to mitigate the privacy risks of traditional machine learning. The public implementation is available at https://split.to/sisinflab-fpl.
PAR: Personal Activity Radius Camera View for Contextual Sensing
Venue: arXiv cs.HC 2020
Year: 2020

Contextual sensing using wearable cameras has seen a variety of different camera angles proposed to capture a wide gamut of different visual scenes. In this paper, we propose a new camera view that aims to capture the same visual information as many of the camera positions and orientations combined from a single camera view point. The camera, mounted on the corner of a glasses frame is pointing downwards towards the floor, a field-of-view we named Personal Activity Radius (PAR). The PAR field-of-view captures the visual information around a wearer's personal bubble, including items they interact with, their body motion, their surrounding environment, etc. In our evaluation, we tested the PAR view's interpretability by human labelers in two different activity tracking scenarios: food related behaviors and exercise tracking. Human labelers achieved an overall high level of precision in identifying body motions in exercise tracking of 91% precision and eating/drinking motions at 96% precision. Item interaction identification reached a precision of 86% precision for labeling grocery categories. We show a high level on the device setup and contextual views we were able to capture with the device. We see that the camera wide angle captures different activities such as driving, shopping, gym exercises, walking and eating and can observe the specific interaction item of the user as well as the immediate contextual surrounding.
Privacy-Preserving Distributed Learning Framework for 6G Telecom Ecosystems
Venue: arXiv cs.NI 2020
Year: 2020

We present a privacy-preserving distributed learning framework for telecom ecosystems in the 6G-era that enables the vision of shared ownership and governance of ML models, while protecting the privacy of the data owners. We demonstrate its benefits by applying it to the use-case of Quality of Transmission (QoT) estimation in multi-domain multi-vendor optical networks, where no data of individual domains is shared with the network management system (NMS).